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Immunotherapeutic Value of Transcription Factor 19 (TCF19) Associated with Renal Clear Cell Carcinoma: A Comprehensive Analysis of 33 Human Cancer Cases

Immunotherapeutic Value of Transcription Factor 19 (TCF19) Associated with Renal Clear Cell... Hindawi Journal of Oncology Volume 2022, Article ID 1488165, 15 pages https://doi.org/10.1155/2022/1488165 Research Article Immunotherapeutic Value of Transcription Factor 19 (TCF19) Associated with Renal Clear Cell Carcinoma: A Comprehensive Analysis of 33 Human Cancer Cases Xiaobao Cheng , Jian Hou, Xiangyang Wen, Runan Dong, Zhenquan Lu, Yi Jiang, Guoqing Wu, and Yuan Yuan Department of Urology, e University of Hongkong-Shenzhen Hospital, Shenzhen, China Correspondence should be addressed to Xiaobao Cheng; chengxb@hku-szh.org Received 19 June 2022; Revised 30 June 2022; Accepted 6 July 2022; Published 6 September 2022 Academic Editor: Recep Liman Copyright © 2022 Xiaobao Cheng et al. �is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. We aimed to study the relationship between transcription factor 19 (TCF19) and cancer immunotherapy in the 33 types of human cancers. Methods. �e Cancer Genome Atlas database was analyzed to obtain the gene expression data and clinical characteristics for the cases of 33 types of cancers. GSE67501, GSE78220, and IMvigor 210 were included in the immunotherapy cohorts. Relevant data were obtained by analyzing the gene expression database. �e prognostic value of TCF19 was determined by analyzing various clinical parameters, such as survival duration, age, the stage of the tumor, and sex of the patients. �e single- sample gene set enrichment analysis method was used to determine the activity of TCF19 and the method was also used to assess the di•erences between the TCF19 transcriptome and protein levels. �e correlation between TCF19 and various immune processes and elements such as immunosuppressants, stimulants, and major histocompatibility complexes were analyzed to gain insights into the role of TCF19. �e coherent paths associated with the process of TCF19 signal transduction and the in—uence of TCF19 on immunotherapy biomarkers have also been discussed herein. Finally, three independent immunotherapy methods were used to understand the relationship between TCF19 and immunotherapy response. Results. It was observed that TCF19 was not signi˜cantly in—uenced by the age (5/33), sex (3/33), or tumor stage (3/21) of cancer patients. But the results revealed that TCF19 exhibited a potential prognostic value and could predict the survival rate of the patients. In some cases of this study, the activity and expression of TCF19 were taken at the same level (7/33). Conclusion. TCF19 is strongly related to immune cell in˜ltration, immunomodulators, and immunotherapy markers. Our study demonstrated that high expression levels of TCF19 are strongly linked with the immune-related pathways. Nevertheless, it is noteworthy that TCF19 is not signi˜cantly associated with im- munotherapy response. characterized as an aggressive tumor and approximately 1. Introduction one-third of the patients su•ering from ccRCC were di- �e renal tumor is one of the most common tumors in agnosed while tumor metastasis already occurred [3]. Cel- urology. Results from the statistical analysis conducted with lular molecular-targeted therapy is the most e•ective the data associated with cancer revealed that renal tumors method of treating metastatic ccRCC as patients su•ering ranked second in terms of incidence of urinary system from kidney cancer do not respond to radiotherapy and malignant tumors in China [1]. Clear cell renal cell carci- chemotherapy. �e European Urology Association (EUA) noma (ccRCC) is the major pathological type of renal cancer, and the United States National Comprehensive Cancer which accounts for 70–80% of the cancers in urology. �e Network (NCCN) recommended the molecular-targeted annual percentage of increase in the rate of incidence is 3% drugs as the ˜rst and second-line medicine for metastatic in Europe and in the United States [2]. CcRCC is ccRCC [4, 5]. �e prognostic factors of ccRCC include 2 Journal of Oncology microenvironment. Also, we studied the microsatellite in- histological factors, tumor anatomical factors, molecular factors, and clinical factors. Among these, currently known stability (MSI) and tumor mutation burden (TMB) in ccRCC. Moreover, the association of the expression level of molecular markers such as carbonic anhydrase 9, CRP [6, 7] , and cabozantinib [8] are not of high prognostic value and TCF19 with immune checkpoint blocking therapy was also accuracy, and these have not been recommended for clinical investigated. In brief, this research provides data that help application. At present, there are no universally accepted understand the immunotherapeutic role of TCF19 in ccRCC and reliable standard predictors for the diagnosis and which may potentially help design various functional prognosis of ccRCC at an early stage. *e exploration of experiments. abnormally expressed genes in ccRCC tissues can potentially help identify new molecular biomarkers for the diagnosis 2. Methods and prognosis of ccRCC. (See Figure 1) shows the flowchart of this research. Transcription Factor 19 (TCF19) is a protein-coding gene that encodes a protein with a PHD-type zinc finger domain that is involved in transcriptional regulations [9]. At 2.1. Data Collection. *e TCGA database (https://portal.gdc. first, TCF19 was isolated from human, mouse, and hamster cancer.gov/), a robust database, provides information on cells and it acts as a growth regulatory molecule [10]. TCF19 cancer genes. *e database includes information on gene is associated with cell growth and regulation by affecting the expression profiles, copy number variation (CNV), and G1S phase of the cell cycle. *e genetic coding region of single nucleotide polymorphism (SNP). We downloaded the TCF19 is located on the short arm 6P21.3 of autoch- mRNA expression and SNP data of 33 tumors for this study. romosome 6, with a total length of 5.60 KB [11]. TCF19 is Also, we downloaded the data from the GTEX database present in almost all human tissues, and its levels of ex- (https://commonfund.nih.gov/GTEx). Following the merg- pression are high in various tumor tissues [12–15]. Although ing with the TCGA data and correction, we identified the current studies indicate that TCF19 may be associated with differential expressions for various types of cancers. the progression of various tumors, few mechanisms have Moreover, we downloaded the corresponding tumor cell been reported for the role of TCF19 in carcinogenesis and lines data from the CCLE database (https://portals. immune regulation. broadinstitute.org/ccle/), and we investigated the expres- *e processes of carcinogenesis and immune regulation sion level of the gene in these tumor tissues. Furthermore, we are significantly affected by the physiological effects of TCF19 investigated the significant correlation of this gene with the activation. Since TCF19 is chronically activated, it is highly stages of tumor progression. expressed in various solid tumors [12–15] and chronic in- flammatory tissues [16–18]. *e presence of highly expressed TCF19 has been found not only in invasive tumor tissues but 2.2. Association of TCF19 Expression with Clinical Charac- also in malignant tumor cell lines. *is potentially indicates teristics of 33 Cancers. We downloaded the progression-free that TCF19 is correlated to the responses of inflammation and survival (PFS) and overall survival (OS) TCGA data of cell cycle progression [11, 16]. *e genes associated with the patients from the Xena database to evaluate the association TCF family regulate innate immunity and adaptive immunity of this gene with the prognosis of the patients. We utilized [19, 20]. It has been previously reported that TCF1 helps the Kaplan–Meier (K-M) method to analyze the survival achieve a balance between the CD8+ T cells by regulating the curve (P< 0.05) for every cancer type. We employed “sur- internal IL-10 signaling pathway which in turn influences vival” and “SurvMiner” R packages for the survival analysis. immunotherapy [21]. Macrophages, a substantial component Also, we used “survival” and “forest-plot”R packages for the of the innate immune system, are related to the antitumor Cox analysis to evaluate the interrelation of gene expression immune response in various cancers. It was stated that the with the magnitude of survival of the patients. M2 tumor-associated macrophages (TAMs) promote the processes of tumor progression, recurrence, and distal me- 2.3. TCF19 Enrichment Analysis tastasis [22]. Macrophages are polarized by the stimulation of transcription factors in the tumor microenvironment by 2.3.1. Gene Set Variation Analysis (GSVA) Enrichment controlling their antitumor activity and by affecting their Analysis. GSVA, a package for the R program, was used to immunotherapy [23, 24]. Our previous study also confirmed identify the enrichment of transcriptomic gene sets. GSVA that changes in macrophage polarization play substantial identifies the changes from the level of the gene to the level of activities to regulate the inflammatory traumatic urethral the pathway. *is is achieved by using the specific gene sets of stricture [25] and resistance to chemotherapy and endocrine biological function. We utilized the Molecular Signatures Da- therapy in advanced prostate cancer [26]. In general, TCF tabase (v7.0) for downloading the gene sets. GSVA algorithm family genes significantly influence the immune system and identified the score of each gene set to determine the ability of the state of tumor tissue. Nevertheless, the immunothera- changes in biological function within the different samples. peutic value of TCF19 in the cases of human cancer has been rarely studied. Herein, we described the expression profile of TCF19 in 2.3.2. Gene Set Enrichment Analysis (GSEA) Enrichment 33 different cancers and studied the potential regulatory Analysis. In the GSEA analysis, we used predefined gene sets roles of TCF19 for controlling the ccRCC immune and sequencing gene sets (based on the differential Journal of Oncology 3 Age Clinical correlation Stage in 33 human cancers Gender Tomor and normal Survival MHC molecules Estimate score Immune cells infilatration based on CIBERSORT Immune mechanism Immune inhibitors in Renal Clear cell carcinoma Microsatellite instability TCF19 in Renal Tumor mutation burden Immune stimulatore Renal Clear cell carcinoma Relevant signaling pathways (GSVA and GSEA) IMvigor210 cohort Immunotherapeutic response GSE67501 GSE78220 Drug sensitivity correlation in renal clear cell carcinoma Figure 1: *e flowchart of the study. Firstly, the expression of TCF19 is investigated within the different ages, stages, genders, and tissues, then the GSEA is utilized to explore the relevant immune signaling pathways based on the expression level of TCF19. Secondly, we apply the univariate Cox regression model and the Wilcoxon test between the nonresponder and responder groups of the immunotherapeutic response cohort to identify the survival association. Finally, we perform the drug sensitivity correlation with TCF19 expression in renal clear cell carcinoma. expression level between the two types of samples). *is the widely used database is the NCI-60 cell line with a broad method identifies whether the predefined gene sets were range of cancer cell samples and it is used to investigate the anticancer drugs. In our study, we downloaded the NCI-60 significantly enriched in the sequencing table. *e “cluster profiler” and the “enrich-Plot” packages were used for the drug sensitivity data and the RNA-seq gene expression data to evaluate the relations of gene expression with the sen- GSEA analysis and for exploring the imaginable mechanisms at the molecular level for the differential prognosis of different sitivity of antitumor drugs. *e correlation analysis method patients with different tumors. *e differences in the signaling was utilized to achieve the results. We considered a Pvalue pathways associated with the high and low gene expression <0.05 for the statistical threshold. groups were studied, and the findings were compared. We analyzed the immunotherapeutic response accord- ing to the previous method [2]. We used three independent immunotherapeutic cohorts in our present study. Usually, 2.3.3. e Expression Level of TCF19 Is Correlated with immunotherapeutic ways provided four outcomes, in- Immune-Related Factors. RNA-seq data from patients with cluding complete response (CR), partial response (PR), different subgroups of 33 cancers were analyzed by using the progressive disease (PD), and stable disease (SD). We di- CIBERSORT algorithm to understand the content of in- vided the patients into responders and nonresponders. filtrating immune cells. *is method also identifies the re- Patients who had CR or PR signs were categorized as re- lation of gene expression with the content of immune cells. sponders compared to the nonresponders, who had signs of Moreover, we used the TISIDB website to identify the re- SD or PD. We utilized the Wilcoxon rank-sum test to in- lation of gene expression with various immune factors, vestigate the expression differences of TCF19 between the including chemokines, immune-stimulators, immune- responder and the nonresponder groups. suppressants, and MHC molecules. 2.3.6. Statistical Analyses. R (version 4.0) was used for all 2.3.4. Correlation Analysis of TCF19 Expression and Tumor statistical analyses. We calculated the hazard ratios (HRs) Mutation. *e total number of mutations, including base and 95% confidence intervals followed by applying the substitutions, deletions, and insertions in tumor cells is univariate survival analysis model. We applied the K-M called TMB. *e frequency and number of variation/exon survival analysis to investigate patient survival time. We lengths were calculated for every sample tumor, and TMB divided the patients into the high gene expression level and was calculated by dividing the nonsynonymous mutation the low gene expression level to arrive at the appropriate sites by the total length of the protein-coding region. *e results. *e statistical tests were bilateral, and we considered MSI of every TCGA sample was obtained from the data a Pvalue<0.05 for the statistical threshold. presented in previously published reports [27]. 3. Results 2.3.5. Correlation Analysis of TCF19 Expression with Drug Sensitivity and Immunotherapy Response. *e National 3.1. Results of the Analysis of TCF19 Expression and Clinical Cancer Institute (NCI) listed the Cellminer database which Correlation in 33 Cancers. We analyzed the expression level contains the information on 60 cancer cells [1]. At present, of TCF19 in 33 types of human cancers using the data 4 Journal of Oncology Table 1: 33 types of human cancer studied in this research. presented in the TCGA and GTEX datasets. Table 1 pre- sented the full names of the 33 cancer types utilized in this Abbreviation Full name comprehensive study. *e high levels of expression of the ACC Adrenocortical carcinoma gene were observed in 27 types of carcinomas, including BLCA Bladder urothelial carcinoma ACC, BLCA, BRCA, CHOL, CESC, COAD, ESCA, GBM, BRCA Breast invasive carcinoma HNSC, KIRC, LAML, LGG, LIHC, LUAD, LUSC, OV, Cervical squamous cell carcinoma and CESC PCPG, PAAD, PRAD, READ, SARC, SKCM, STAD, endocervical adenocarcinoma TCGT, THCA, UCEC, and UCS (Figure 2(a)). TCF19 CHOL Cholangiocarcinoma expression levels in most normal tissues were lower than COAD Colon adenocarcinoma DLBC Lymphoid neoplasm diffuse large B-cell lymphoma that in cancer cells. In the CCLE expression profile of ESCA Esophageal carcinoma various cell lines, the expression level of TCF19 is illus- GBM Glioblastoma multiforme trated in figure 2(b). Moreover, we found that TCF19 HNSC Head and neck squamous cell carcinoma expression was related to the stages of various tumors, such KICH Kidney chromophobe as ACC, BRCA, TGCT, KICH, KIRC, and LIHC (Figure 3). KIPAN Pan-kidney cohort (KICH + KIRC + KIRP) *is work studied the correlation between the expression KIRC Kidney renal clear cell carcinoma levels of TCF19 and survival prognosis in patients suffering KIRP Kidney renal papillary cell carcinoma from cancer. We found that the expression level of TCF19 LAML Acute myeloid leukemia was closely associated with the OS of patients in 14 different LGG Brain lower grade glioma types of cancers (such as KIRC, ACC, KICH, KIRP, LAML, LIHC Liver hepatocellular carcinoma THYM, LGG, HNSC, LIHC, MESO, PRAD, SKCM, UVM, LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma and PAAD; Figure 4(a)). In addition, the results from the MESO Mesothelioma KM-curve survival analysis suggested that the highly OV Ovarian serous cystadenocarcinoma expressed TCF19 was correlated with poor OS in 13 types of PAAD Pancreatic adenocarcinoma malignant cancers, including ACC, BRCA, KICH, LIHC, PCPG Pheochromocytoma and paraganglioma GBM, SKCM, KIRC, KIRP, LGG, LUAD, PAAD, PCPG, PRAD Prostate adenocarcinoma and MESO (Supplementary Figure 1). *e expression level READ Rectum adenocarcinoma of TCF19 was closely linked with PFI in 12 cancer types, SARC Sarcoma including PAAD, ACC, MESO, KICH, LIHC, PCPG, STAD Stomach adenocarcinoma PRAD, LGG, SARC, THCA, KIRC, UCEC, and other tu- SKCM Skin cutaneous melanoma mors (Figure 4(b)). *e K-M curve analysis for survival STES Stomach and esophageal carcinoma TGCT Testicular germ cell tumors prognosis suggested that a highly expressed group of THCA *yroid carcinoma TCF19 was associated with a shorter PFI in 10 kinds of THYM *ymoma malignant cancers (such as UCEC, ACC, KICH, PAAD, UCEC Uterine corpus endometrial carcinoma KIRC, LGG, LIHC, PCPG, PRAD, and THCA; Supple- UCS Uterine carcinosarcoma mentary Figure 2). UVM Uveal melanoma A nomogram prediction model was constructed using the TCF19 expression level and the clinical features. *e results obtained from regression analysis were displayed in kinds of cancers, the TCF19 expression level was signifi- the form of alignment charts. Variables such as gender, age, cantly related to the follicular helper cells, and in the other 14 tumor stage, and grade were analyzed, and the results were kinds of cancers the TCF19 expression level were correlated presented. *e gene correlation column diagram model of significantly with the macrophages M1 cell (Figure 6). TCF19 of the constructed TCGA-KIRC sample is shown in Further analysis of the tumor microenvironment in kidney Figure 5(a). Correction curves corresponding to the two carcinoma (KIRC) revealed that TCF19 expression level was periods were generated in the fifth and seventh years. *e significantly related to the various gene set scores including model effect was quite consistent (Figure 5(b)). the CD_8_T effector, TME score A, TME score, DNA damage response, base excision repair, immune checkpoint, antigen processing machinery, mismatch repair, nucleotide 3.2. e TCF19 Expression Is Potentially Associated with excision repair, DNA replication, Pan F TBRs, EMT1, and Immune-Associated Factors. Tumor-associated fibroblasts, EMT2 in kidney carcinoma (). extracellular matrix, immune cells, various growth factors, inflammatory factors (characterized by special physico- chemical characteristics), cancer cells, etc., are present in the 3.3. GSVA/GSEA Correlation Analysis of TCF19. *e GSVA tumor microenvironment. *e microenvironment signifi- scores were determined for all tumors to elucidate the cantly affects the diagnosis of tumors, survival outcome, and molecular mechanism associated with the TCF19 gene as- degree of the response generated toward clinical treatment. sociated with pan-cancer. We divided the tumor samples into two groups based on the higher expression level and the Our findings indicated that the TCF19 expression level was substantially correlated with the infiltration of immune lower expression levels. *e median value of the gene ex- factors. TCF19 expression level was significantly related to pression level in each tumor was utilized for comparison. It the CD4 memory-activated cells in 14 kinds of cancers. In 15 was observed that in the case of kidney carcinoma, highly Journal of Oncology 5 ns ns ns **** **** **** **** **** **** **** **** **** **** **** **** **** **** **** **** **** ** **** *** * **** **** **** **** **** **** Tissue Normal Tumor (a) CancerType Kruskal-Wallis, p < 2.2e16 Bile Duct Cancer Kidney Cancer Eye Cancer Cervical Cancer Head and Neck Cancer Fibroblast Neuroblastoma Skin Cancer Esophageal Cancer Sarcoma Colon/Colorectal Cancer Prostate Cancer Endometrial/Uterine Cancer Bladder Cancer Gallbladder Cancer Breast Cancer Liposarcoma Leukemia Gastric Cancer Lung Cancer Rhabdoid yroid Cancer Liver Cancer Bone Cancer Pancreatic Cancer Myeloma Ovarian Cancer Lymphoma Engineered Brain Cancer Expression of TCF19 (b) Figure 2: *e expression of TCF19. (a) *e TCF19 expression level in 33 human cancers using the TCGA combined with GTEx datasets and (b) the CCLE expression profile revealed that TCF19 is expressed in different tumor cell lines. expressed TCF19 genes were primarily associated with some maximum correlation was observed for the ME green yellow module (COR � 0.35, P � (5E-19)) (Supplementary Fig- specific pathways such as interferon alpha response, E2F targets, allograft rejection, IL-6-JAK-STAT3 signaling, in- ure 3). *e coexpression analysis method was further used to explore the relationship between the level of TCF19 ex- terferon gamma response, and G2M checkpoint (Figure 8(a)–8(c)). Results from the GSEA analyses of pression and 33 tumor immune-related genes. *e analyzed TCF19 and kidney carcinoma are presented in Figures 8(d)– genes included genes associated with MHC, immune acti- 8(f). vator, chemokine receptor proteins, immunosuppressor, and chemokine. It was observed that TCF19 was signifi- 3.4. Correlation Analysis of TCF19 Expression with Tumor cantly associated with most of the immune-related genes Mutations and Gene Regulation. *e study further con- (Supplementary Figure 4). Moreover, TCF19 was signifi- structed the WGCNA net based on the KIRC expression cantly associated with the crucial tumor-related marker profile for exploring the coexpression network linked with genes that controlled the various biological processes, in- cluding the TGF beta signaling pathway, TNFA signaling, TCF19 in pan-cancer. *e clustering chart of patients is shown in Supplementary Figure 3. We utilized the “soft hypoxia, coking death, repair of DNA, autophagy, and power Estimate” function in the WGCNA package to ferroptosis (Supplementary Figure 5). identify the soft threshold β value and the value of β is set to *e immunotherapy response was crucially associated 12. We detected 17 gene modules using the Tom matrix. with some biomarkers, including TMB and MSI. We in- *ese are black (298), blue (519), brown (446), cyan (357), vestigated the relation of TCF19 expression level with TMB green (354), green yellow (489), grey (3788), grey60 (82), in this study. We revealed that the TCF19 expression level light cyan (129), light green (74), light yellow (57), night blue was significantly correlated with TMB in all tumors, in- (155), pink (449), purple (230), red (308), turquoise (1822), cluding P ACC, CPG, UCEC, SKCM, COAD, PRAD, STAD, and yellow (443) (Supplementary Figure 3). *e modules KICH, LIHC, LUAD, and THCA (Figure 9(a)). A significant and traits were further analyzed, and it was found that the difference was observed for MSI in various cancers, Module eigengenes Bile Duct Cancer Eye Cancer symbolACC Head and Neck Cancer Neuroblastoma symbolBLCA Esophageal Cancer symbolBRCA Colon/Colorectal Cancer symbolCESC Endometrial/Uterine Cancer Gallbladder Cancer symbolCHOL Liposarcoma symbolCOAD Gastric Cancer symbolDLBC Rhabdoid Liver Cancer symbolESCA Pancreatic Cancer symbolGBM Ovarian Cancer symbolHNSC Engineered Kidney Cancer symbolKICH Cervical Cancer symbolKIRC Fibroblast symbolKIRP Skin Cancer symbolLAML Sarcoma Prostate Cancer symbolLGG Bladder Cancer symbolLIHC Breast Cancer symbolLUAD Leukemia Lung Cancer symbolLUSC yroid Cancer symbolMESO Bone Cancer symbolOV Myeloma Lymphoma symbolPAAD Brain Cancer symbolPCPG symbolPRAD symbolREAD symbolSARC symbolSKCM symbolSTAD symbolTGCT symbolTHCA symbolTHYM symbolUCEC symbolUCS symbolUVM 6 Journal of Oncology Kruskal-Wallis, p = 0.031 0.012 0.0048 Kruskal-Wallis, p = 7.4e07 8.3e06 0.005 0.021 0.48 0.0087 3.4e07 0.88 0.027 0.42 0.45 Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV Stage I Stage III Stage I Stage III Stage II Stage IV Stage II Stage IV (a) (b) 0.057 Kruskal-Wallis, p = 0.0035 Kruskal-Wallis, p = 0.016 0.1 0.41 0.27 0.0015 0.028 0 0 Stage I Stage II Stage III Stage I Stage II Stage III Stage IV Stage I Stage III Stage I Stage II Stage IV Stage II Stage III (c) (d) 0.027 0.7 Kruskal-Wallis, p = 0.0065 Kruskal-Wallis, p = 0.38 0.00099 0.29 0.16 0.39 0.018 0.18 0.52 0.16 0.25 0.7 0 0 Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV Stage I Stage III Stage I Stage III Stage II Stage IV Stage II Stage IV (e) (f) Figure 3: *e correlation analysis of TCF19 with the stage of multiple tumors. TCF19 Expression TCF19 Expression TCF19 Expression TCF19 Expression TCF19 Expression TCF19 Expression Journal of Oncology 7 pvalue Hazard ratio pvalue Hazard ratio ACC <0.001 1.180 (1.0931.273) ACC <0.001 1.131 (1.0591.208) BLCA 0.532 0.997 (0.9861.00 ) 0.965 1.000 (0.9901.010) BLCA BRCA 0.712 0.997 (0.9811.013) 0.388 0.992 (0.9741.010) BRCA CESC 0.334 0.991 (0.9741.009) 1.002 (0.9861.018) CESC 0.817 CHOL 0.138 1.088 (0.9731.215) 0.175 1.077 (0.9681.198) CHOL COAD 0.792 0.994 (0.9491.041) 0.401 1.017 (0.9771.059) COAD DLBC 0.356 0.962 (0.8861.044) 0.649 1.010 (0.9671.056) DLBC ESCA 0.916 1.003 (0.9531.055) ESCA 0.615 1.012 (0.9661.059) 1.020 (0.9941.046) GBM 0.137 0.706 0.995 (0.9691.022) GBM HNSC 0.044 0.983 (0.9660.999) 0.810 0.998 (0.9831.013) HNSC KICH 0.002 1.156 (1.0551.268) 1.194 (1.0841.315) KICH <0.001 KIRC 0.020 1.016 (1.0021.029) KIRC <0.001 1.022 (1.0101.035) KIRP <0.001 1.204 (1.0961.323) <0.056 1.097 (0.9981.20 ) KIRP LAML 0.004 1.063 (1.0201.109) <0.001 1.061 (1.0311.091) LGG LGG <0.001 1.085 (1.0511.120) 1.025 (1.0051.046) LIHC 0.014 LIHC 0.015 1.029 (1.0051.053) 0.733 0.996 (0.9761.01 ) LUAD 0.502 1.007 (0.9871.028) LUAD 0.395 1.010 (0.9881.032) LUSC 0.733 0.997 (0.9781.016) LUSC 0.008 1.104 (1.0261.188) MESO <0.001 1.139 (1.0661.21 ) MESO 0.993 (0.9761.010) OV 0.142 0.986 (1.9661.005) OV <0.410 PAAD 0.005 1.114 (1.0341.200) 0.037 1.081 (1.0051.163) PAAD PCPG 0.055 1.242 (0.9961.550) 0.036 1.191 (1.0111.402) PCPG PRAD 0.003 1.378 (1.1131. 06) 1.246 (1.1471.355) PRAD <0.001 READ 0.145 0.924 (0.8311.028) 1.025 (0.9541.101) READ 0.502 SARC 1.005 (0.9941.01 ) 0.367 0.010 1.012 (1.0031.021) SARC SKCM 0.022 1.022 (1.0031.040) 0.533 1.006 (0.9881.024) SKCM STAD 0.215 0.976 (0.9401.014) 0.972 (0.9331.013) STAD 0.183 TGCT 0.356 0.884 (0.6801.149) 0.396 1.024 (0.9691.083) TGCT THCA 0.133 0.686 (0.4201.121) 0.022 1.236 (1.0311.481) THCA THYM 0.003 0.795 (0.6850.923) 0.249 0.962 (0.9001.028) THYM UCEC 0.096 1.018 (0.9971.041) 1.021 (1.0031.040) UCEC 0.024 UCS 0.612 0.985 (0.9311.043) 0.846 1.005 (0.9581.054) UCS UVM 0.017 0.695 (0.5160.93 ) 0.536 0.930 (0.7391.1 0) UVM 0.35 0.50 0.71 1.0 1.41 2.0 0.71 1.0 1.41 Hazard ratio Hazard ratio (a) (b) Figure 4: *e association between TCF19 expression and prognosis of patients with multiple cancers. (a) *e univariate regression model identifies the association of TCF19 expression with the overall survival (OS) rate in multiple cancer patients and (b) the univariate regression model identifies the association of TCF19 expression with the progression-free interval (PFI) of patients with multiple cancers. including UCEC, KIRC, GBM, COAD, BRCA, STAD, 4. Discussion PRAD, and DLBC (Figure 9(b)). In China, kidney carcinoma is the second-highest malignant rd tumor in urology [1]. Approximately 1/3 of the patients 3.5. Correlation Analysis of TCF19 Expression with Drug developed metastatic carcinoma before diagnosis [5]. Ad- Sensitivity and Immunotherapeutic Response. *e effect of vanced renal clear cell carcinoma showed resistance to the surgery and chemotherapy on the conditions of early-stage treatment strategies including radiotherapy and chemo- tumors had been widely explored. We investigated the cell therapy. Hence, the cellular and molecular-targeted treat- miner database to identify the association of TCF19 ex- ment method is widely used to treat ccRCC. Multiple pression level with IC50 values of antitumor drugs. We guidelines recommend molecular-targeted therapy as the revealed that the higher expression level of TCF19 was first and second choice of treatment for metastatic ccRCC correlated with the tolerance level of multiple antitumor [6, 7]. *erefore, it is important to explore new therapeutic drugs (Supplementary Figure 6). It was observed that TCF19 targets for advanced ccRCC. correlated positively with fludarabine, 6-mercaptopurine, At the beginning of the research, we identified the dexamethasone decadron, nelarabine, and fenretinide. *e expression differences of TCF19 in tumor tissues relative to gene negatively correlated with AFP464, trametinib, ami- the normal samples. *e results helped identify the po- noflavone, cobimetinib (isomer 1), palbociclib, and lificguat. tential immunotherapeutic value of TCF19. TCF19 is *e dataset corresponding to IMvigor 210 tumor im- a gene that is associated with cell growth regulation which munotherapy was downloaded and 348 patients subjected to primarily regulates the cell cycle and the process of apo- the conditions of PD-L1 therapy (and presenting complete ptosis. TCF19 was first isolated from mouse, human, and survival information) were enrolled. *e K-M survival hamster cells. *e previous report indicated that the TCF19 analysis was used for the studies, and the results revealed that expression level was higher in various cancerous tissues, high TCF19 expression levels reflected the poor prognosis of including the liver, colon, rectum, head and neck, lung, and patients (figure 5(c)). gastrointestinal tract [12–15]. In this work, TCF19 was 8 Journal of Oncology 0 10 20 30 40 50 60 70 80 90 100 Points age 25 30 35 40 45 50 55 60 65 70 75 80 85 90 gender 2 4 stage 2 4 grade TCF19 0 30 70 Total Points 0 20 40 60 80 100 120 140 160 180 200 220 240 Linear Predictor 2.5 2 1.5 1 0.5 0 0.5 1 1.5 2 2.5 5-year survival Probability 0.95 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 7-year survival Probability 0.95 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 (a) 1.00 0.75 1.0 0.50 0.8 0.25 0.6 p = 0.0044 0.00 0.4 05 10 15 20 25 Time (Years) 0.2 High 171 112 88 74 41 0 177 111 Low 68 45 25 0 0.0 05 10 15 20 25 0.0 0.2 0.4 0.6 0.8 1.0 Time (Years) Nomogram-predicted OS (%) n = 919 d = 327 p = 5, 130 subjects per group x-resampling optimism added, B = 1000 Gray: Ideal Based on observed-predicted TCF19 5-year High 7-year Low (b) (c) Figure 5: *e TCF19 expression level is associated with the risk and prognosis of patients. (a) It shows the gene correlation column line graph model for TCF1, (b) it shows the correction curves plotted for two periods of five and seven years, and (c) it shows the Kaplan–Meier survival analysis plots of TCF19 expression versus patients treated with PD-L1. highly expressed in ACC, BLCA, KIRC, PRAD, TCGT, and suggest that TCF19 is crucially linked with a shorter other urinary system tumors which were under previous prognosis of multiple tumors. findings. In addition, the results from the K-M survival Since TCF19 significantly affects the tumor immune investigation suggested that a higher expression level of microenvironment, more studies need to be conducted on TCF19 is significantly associated with a shorter prognosis the immune cells, tumor microenvironment, immuno- of various tumors in both OS and PFI. *ese studies might modulators, and immunotherapy responses to gain in-depth Observed OS (%) TCF19 Survival probability Journal of Oncology 9 KICH KIRP ns ns ns** ? ns ns ns ns ns ns ns ns ns* ns ns ns ns ns ns ns ns ns ns ? ns ns ns ns ns ns** ns **** ns *** ns ns **** ns ns 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 variable variable Group Group Hexp Hexp Lexp Lexp (a) (b) KIRC KIRP ns ns ns ns ns ns ns 20 ns ns ** ns ns **** **** **** **** *** ** ns ns * ns * * * **** ? * *** * ******** * *** **** ** * 0.4 0.3 0.2 10 0.1 0.0 20 variable Signature Group TMEcluster Hexp Hexp Lexp Lexp (c) (d) KICH KIRC 20 ** ns ns ns ns **** *** *** **** *** ns ns ns ns ns **** **** **** **** **** **** ** *** **** ****n * * nss 10 10 10 10 20 Signature Signature TMEcluster TMEcluster Hexp Hexp Lexp Lexp (e) (f) Figure 6: Continued. Signature_score value value B cells naive B cells naive TMEscore B cells memory B cells memory Plasma cells CD_8_T_effector Plasma cells T cells CD8 T cells CD8 Immune_Checkpoint T cells CD4 naive T cells CD4 naive Antigen_processing_machinery T cells CD4 memory resting T cells CD4 memory resting T cells CD4 memory activated T cells CD4 memory activated TMEscoreA T cells follicular helper T cells follicular helper Mismatch_Repair T cells regulatory (Tregs) T cells regulatory (Tregs) T cells gamma delta T cells gamma delta Nucleotide_excision_repair NK cells resting NK cells resting DNA_damage_response NK cells activated NK cells activated Monocytes DNA_replication Monocytes Macrophages M0 Macrophages M0 Base_excision_repair Macrophages M1 Macrophages M1 Macrophages M2 Pan_F_TBRs Macrophages M2 Dendritic cells resting Dendritic cells resting EMT1 Dendritic cells activated Dendritic cells activated EMT2 Mast cells resting Mast cells resting Mast cells activated Mast cells activated EMT3 Eosinophils Eosinophils TMEscoreB Neutrophils Neutrophils Signature_score Signature_score value B cells naive TMEscore TMEscore B cells memory CD_8_T_effector CD_8_T_effector Plasma cells T cells CD8 Immune_Checkpoint Immune_Checkpoint T cells CD4 naive Antigen_processing_machinery Antigen_processing_machinery T cells CD4 memory resting TMEscoreA T cells CD4 memory activated TMEscoreA T cells follicular helper Mismatch_Repair Mismatch_Repair T cells regulatory (Tregs) Nucleotide_excision_repair T cells gamma delta Nucleotide_excision_repair NK cells resting DNA_damage_response DNA_damage_response NK cells activated DNA_replication DNA_replication Monocytes Macrophages M0 Base_excision_repair Base_excision_repair Macrophages M1 Pan_F_TBRs Pan_F_TBRs Macrophages M2 Dendritic cells resting EMT1 EMT1 Dendritic cells activated EMT2 EMT2 Mast cells resting EMT3 Mast cells activated EMT3 Eosinophils TMEscoreB TMEscoreB Neutrophils 10 Journal of Oncology T cells follicular helper Macrophages M1 T cells CD4 memory activated T cells CD8 Macrophages M0 T cells CD4 naive Mast cells resting NK cells activated Pearson Dendritic cells resting correlation T cells regulatory (Tregs) 1.0 Dendritic cells activated 0.5 NK cells resting 0.0 Plasma cells 0.5 Macrophages M2 1.0 B cells memory T cells CD4 memory resting Mast cells activated T cells gamma delta B cells naive Monocytes Eosinophils Neutrophils CancerType (g) Figure 6: *e TCF19 is correlated with the immune infiltration in pan-cancer. (a–f) *e expression level of TCF19 is significantly correlated with the infiltration of immune cells in multiple cancers and (g) it indicates the correlation analysis of TCF19 expression with multiple tumors. knowledge. *is study aimed to gain insights into the un- associated with ccRCC could potentially help obtain results derlying mechanisms associated with the TCF19 gene that that can help improve the therapeutic techniques. was associated with immune-related factors. 33 types of Conventional surgical treatment and radiotherapy and human cancers were studied to obtain relevant information. chemotherapy cannot be effective to treat patients suffering *is work also aimed to explore the immune-related from late-stage ccRCC. Maybe more research should be mechanisms associated with urinary tumors. *e expres- conducted on the gene targets and immune checkpoint sion of TCF19 and clinical characteristics was analyzed, and inhibitors associated with pan-cancer as the results can potentially help predict the prognosis of antitumor immu- the results obtained from COX regression analysis revealed that TCF19 was a prognostic factor of ccRCC. Correction notherapy. *is research studied the relation of TCF19 with the process of immune cell infiltration for further in- curves were generated for the ccRCC patients in the fifth and seventh years and the consistent model effects were ob- vestigating the crucial immunotherapeutic potential of served. Daniela Ruggiero reported the increased level of TCF19. *e results revealed that the expression level of expression of the TCF19 gene in two major histological TCF19 significantly correlated with the infiltration of the subtypes (squamous cell carcinoma (SCC) and lung ade- immune cells, including CD4 memory T cells, T follicular nocarcinoma) and revealed that TCF19 promoted the helper cells, and M1 macrophages. Analysis of the re- progression of the cell cycle in NSCLC cells. *is validated lationship between tumor microenvironment and KIRC the fact that TCF19 was a therapeutic target [28]. Du WB revealed that KIRC was significantly correlated with some reported that TCF19 was significantly upregulated in co- scores such as TMEscoreA, TMEscore, mismatch repair, CD8 lorectal cancer and TCF19 was closely related to the pro- T effector, immune checkpoint, antigen processing gression of malignancy, distant metastasis, and poor machinery, nucleotide excision repair, and DNA damage. prognosis of colorectal cancer. So, he speculated that TCF19 *e scores of the responses, Pan F TBRs, DNA replication, could aggravate the malignant progression of CRC [29]. Ji, base excision repair, EMT1, and EMT2 significantly corre- Xu, and Miao further reported that TCF19 was highly lated with KIRC. And this study further investigated the expressed in cancer cells associated with head and neck SCC, relations of TCF19 with the immune-related genes, in- liver cancer, and gastric cancer. *ey reported that TCF19 cluding genes associated with MHC, immune activator, could be potentially correlated with tumor prognosis by immuno-suppressive markers, chemokine, and their re- conducting gene assays, K–M survival analysis, and western- ceptor protein. Interestingly, we found that immune- blot tests [12, 13, 15]. It is worth noting that the results of our associated factors were significantly correlated with the research reflected the association of the gene with a sub- expression level of the TCF19 gene. Our previous study stantial prognosis of these tumors and confirmed the re- reported that several immune-prognostic genes influenced liability of the analytical results obtained. Moreover, the the process of immunotherapy associated with urinary correlation between TCF19 and the prognosis of ccRCC was bladder cancer [30]. Besides, it has been reported that the also reported. But now the mechanism involving TCF19 in regulation of macrophage polarization attenuated the in- the occurrence of ccRCC has not been clearly described. We flammatory traumatic urethral stricture in New Zealand may infer that the modulation of the TCF19 activity rabbits [25]. Another study recently reported that M2- TCF19 ACC UVM THYM KICH UCS PAAD GBM DLBC LGG TGCT KIRP PCPG BRCA KIRC LIHC CHOL MESO CESC UCEC PRAD THCA LAML LUSC ESCA STAD LUAD SARC SKCM READ BLCA COAD OV HNSC Journal of Oncology 11 progression of pan-cancer were also affected [16–18]. It has been reported recently that TCF19 influences the effect of immunotherapy in lung cancer through nanotechnology by regulating the polarity of the tumor-associated macrophages NA ACC (n=79) [31]. *ose results revealed that TCF19 might influence the BLCA (n=408) BRCA-Basal (n=191) BRCA-Her2 (n=82) process of immunotherapy by regulating the immune- BRCA-LumA (n=568) BRCA-LumB (n=219) related genes and the inflammatory cells such as macro- BRCA (n=1100) CESC (n=306) CHOL (n=36) phages associated with tumor cell immunotherapy. COAD (n=458) DLBC (n=48) Furthermore, we observed that two immunotherapy ESCA (n=185) GBM (n=153) HNSC-HPV- (n=422) biomarkers (TMB and MSI) were associated with TCF19 in HNSC (n=522) KICH (n=66) various tumors. In general, as the number of somatic mu- KIRC (n=533) KIRP (n=290) LGG (n=516) tations in a tumor increase, the ability to generate neo- LIHC (n=371) LUAD (n=515) LUSC (n=501) antigens increases. It was also observed that the tumor MESO (n=87) OV (n=303) neoantigen load could be efficiently determined by analyzing PAAD (n=179) PCPG (n=181) PRAD (n=498) the TMB [32]. MSI is a robust mutant factor phenotype, the READ (n=166) SARC (n=260) generation of which can be attributed to the presence of SKCM-Metastasis (n=368) SKCM-Primary (n=103) SKCM (n=471) defects in mismatch repairing of DNA. MSI is a crucial STAD (n=415) TGCT (n=150) predictor for immunotherapy responses [33]. *is study THCA (n=509) THYM (n=120) UCEC (n=545) showed that TMB and MSI were significantly associated with UCS (n=57) UVM (n=80) the TCF19 expression level in various tumors. However, the NA ACC (n=79) BLCA (n=408) TCF19 expression level was not significantly associated with BRCA-Basal (n=191) BRCA-Her2 (n=82) immunotherapy responses. Despite all 3 cohorts responded BRCA-LumA (n=568) BRCA-LumB (n=219) BRCA (n=1100) to antiPD1 therapy. We hypothesized that TCF19 might CESC (n=306) CHOL (n=36) influence the extent of the response generated toward im- COAD (n=458) DLBC (n=48) ESCA (n=185) munotherapy by targeting the various immune checkpoints. GBM (n=153) HNSC-HPV- (n=422) Also, our study only analyzed 3 relevant cohorts, which HNSC (n=522) KICH (n=66) KIRC (n=533) makes it difficult to elucidate the actual immunotherapy KIRP (n=290) LGG (n=516) rho response of TCF19. More relevant immunotherapy cohort LIHC (n=371) 0.25 LUAD (n=515) 0.00 LUSC (n=501) studies should be conducted in the future. 0.25 MESO (n=87) OV (n=303) PAAD (n=179) And finally, we followed the gene enrichment analysis to PCPG (n=181) PRAD (n=498) arrive at the result which revealed that the highly expressed READ (n=166) SARC (n=260) SKCM-Metastasis (n=368) TCF19 gene was primarily associated with specific pathways SKCM-Primary (n=103) SKCM (n=471) such as E2F, IL6, and G2M. *e E2F and IL6 families are STAD (n=415) TGCT (n=150) THCA (n=509) classical tumor signaling pathways. It has been reported that THYM (n=120) UCEC (n=545) they exhibit unique and overlapping properties during the UCS (n=57) UVM (n=80) processes of transcription, proliferation, and apoptosis of tumor cells [34, 35]. *e results might indicate that TCF19 potentially affects the extent of proliferation, infiltration, and metastasis realized by regulating multiple classical signaling pathways.Also, this specific mechanism associated with the pvalue p<0.05 processes needs to be explored further. *e Cellminer p = 0.05 database was analyzed to determine the relationship be- Figure 7: *e analysis of TCF19 expression and the tumor mi- tween TCF19 and IC to explore the correlation between croenvironment in the ccRCC. TCF19 and antitumor drug sensitivity. *e results revealed that the high level of expression of TCF19 reflected the tumor-associated macrophages (TAMs) were able to pro- tolerance level toward multiple antitumor drugs. *e mote the process of bone metastasis and were able to in- factors and mechanisms affecting the sensitivity of anti- fluence the chemotherapy and drug resistance ability of the tumor drugs are complex and diverse but results from the cells of prostate cancer. *e regulation of the process of analysis of the K-M survival plot revealed that the higher macrophage polarization can influence the effect of im- expression group of TCF19 was significantly linked with a shorter prognosis for cancer patients. It was also observed munotherapy in patients suffering from prostate cancer [26]. Sen, Yang GH, and Mondal reported that TCF19, a novel that TCF19 negatively correlated with the effect of im- pancreatic islet regulator, regulated the processes of energy munotherapy. *e results indicated that TCF19 can be used metabolism and stress adaptation associated with the tumor as a potential indicator of the extent of the response cells by regulating gluconeogenesis. It was associated with generated toward renal cancer immunotherapy. Cancer the inflammatory responses in the beta cells of the pancreas immunotherapy based on TCF19 can also be explored and and the DNA damage response network. *e occurrence and the results can potentially open a new avenue for the B cell memory_CIBERSORT Mast cell activated_CIBERSORT Mast cell activated_CIBERSORT-ABS B cell memory_CIBERSORT-ABS Mast cell resting_CIBERSORT B cell memory_XCELL Mast cell resting_CIBERSORT-ABS B cell aive_CIBERSORT Mast cell_XCELL B cell naive_CIBERSORT-ABS MDSC_TIDE B cell naive_XCELL B cell plasma_CIBERSORT Macrophage/Monocyte_MCPCOUNTER B cell plasma__CIBERSORT-ABS Monocyte_CIBERSORT B cell plasma_XCELL Monocyte_CIBERSORT-ABS B-cell_EPIC Monocyte_MCPCOUNTER B-cell_MCPCOUNTER Monocyte_QUANTISEQ Monocyte_XCELL B cell_QUANTISEQ B cell_TIMER Neutrophil_CIBERSORT B cell_XCELL Neutrophil_CIBERSORT-ABS Class-switched memory B cell_XCELL Neutrophil_MCPCOUNTER Neutrophil_QUANTISEQ Cancer associated fibroblast_EPIC Neutrophil-TIMER Neutrophil_XCELL Cancer associated fibroblast_MCPCOUNTER Cancer associated fibroblast_TIDE Cancer associated fibroblast_XCELL NK cell activated_CIBERSORT NK cell activated_CIBERSORT-ABS NK cell resting_CIBERSORT Common lymphoid progenitor_XCELL NK cell resting_CIBERSORT-ABS NK cell_EPIC Common myeloid progenitor _XCELL NK cell_MCPCOUNTER NK cell_QUANTISQ Myeloid dendritic cell activated_CIBERSORT NK cell_XCELL Myeloid dendritic cell activated_CIBERSORT-ABS T cell CD4+ (non-regulatory)_QUANTISEQ Myeloid dendritic cell activated_XCELL T cell CD4+ (non-regulatory)_XCELL Myeloid dendritic cell resting CIBERSORT T cell CD4+ central memory_XCELL Myeloid dendritic cell resting_CIBERSORT-ABS T cell CD4+ effector memory_XCELL Myeloid dendritic cell_MCPCOUNTER T cell CD4+ memory activated_CIBERSORT Myeloid dendritic cell_QUANTISEQ T cell CD4+ memory activated_CIBERSORT-ABS Myeloid dendritic cell_TIMER T cell CD4+ memory resting_CIBERSORT T cell CD4+ memory resting_CIBERSORT-ABS Myeloid dendritic cell_XCELL T cell CD4+ memory _XCELL Plasmacytoid dendritic cell_XCELL T cell CD4+ naive_CIBERSORT T cell CD4+ naive_CIBERSORT-ABS Endothelial cell_EPIC T cell CD4+ naive_XCELL T cell CD4+ 1_XCELL Endothelial cell_MCPCOUNTER T cell CD4+ 2_XCELL Endothelial cell_XCELL T cell CD4+_EPIC T cell CD4+_TIMER Eosinophil_CIBERSORT Eosinophil_CIBERSORT-ABS T cell CD8+ central memory_XCELL Eosinophil_XCELL T cell CD8+ effector memory_XCELL T cell CD8+ naive _XCELL Granulocyte-monocyte progenitor_XCELL T cell CD8+_CIBERSORT T cell CD8+_CIBERSORT-ABS T cell CD8+_EPIC Hematopoietic stem cell_XCELL T cell CD8+_MCPCOUNTER T cell CD8+_QUANTISEQ Macrophage MO_CIBERSORT T cell CD8+_TIMER Macrophage MO_CIBERSORT-ABS T cell CD8+_XCELL Macrophage M1_CIBERSORT Macrophage M1_CIBERSORT-ABS T cell follicular helper_CIBERSORT Macrophage M1_QUANTISEQ T cell follicular helper_CIBERSORT-ABS Macrophage M1_XCELL T cell gamma delta_CIBERSORT Macrophage M2_CIBERSORT T cell gamma delta_CIBERSORT-ABS Macrophage M2_CIBERSORT-ABS T cell gamma delta_XCELL Macrophage M2 QUANTISEQ Macrophage M2_TIDE T cell NK_XCELL Macrophage M2 XCELL Macrophage/Monocyte_MCPCOUNTER T cell regulatory(Tregs)_CIBERSORT Macrophage_EPIC T cell regulatory(Tregs)_CIBERSORT-ABS T cell regulatory(Tregs)_QUANTISEQ Macrophage_TIMER T cell regulatory(Tregs)_XCELL Macrophage_XCELL 12 Journal of Oncology E2F_TARGETS E2F_TARGETS G2M_CHECKPOINT INTERFERON_GAMMA_RESPONSE UV_RESPONSE_UP INTERFERON_ALPHA_RESPONSE DNA_REPAIR ALLOGRAFT_REJECTION GLYCOLYSIS IL6_JAK_STAT3_SIGNALING UNFOLDED_PROTEIN_RESPONSE G2M_CHECKPOINT MITOTIC_SPINDLE INFLAMMATORY_RESPONSE PEROXISOME COMPLEMENT MTORC1_SIGNALING APOPTOSIS SPERMATOGENESIS CHOLESTEROL_HOMEOSTASIS MYC_TARGETS_V1 SPERMATOGENESIS MYC_TARGETS_V2 IL2_STAT5_SIGNALING NOTCH_SIGNALING GLYCOLYSIS PI3K_AKT_MTOR_SIGNALING UNFOLDED_PROTEIN_RESPONSE UV_RESPONSE_DN DNA_REPAIR ADIPOGENESIS MTORC1_SIGNALING TGF_BETA_SIGNALING MITOTIC_SPINDLE P53_PATHWAY KRAS_SIGNALING_UP PROTEIN_SECRETION WNT_BETA_CATENIN_SIGNALING XENOBIOTIC_METABOLISM REACTIVE_OXYGEN_SPECIES_PATHWAY HEME_METABOLISM UV_RESPONSE_UP OXIDATIVE_PHOSPHORYLATION APICAL_JUNCTION CHOLESTEROL_HOMEOSTASIS TNFA_SIGNALING_VIA_NFKB EPITHELIAL_MESENCHYMAL_TRANSITION MYC_TARGETS_V1 WNT_BETA_CATENIN_SIGNALING P53_PATHWAY APOPTOSIS HEDGEHOG_SIGNALING APICAL_JUNCTION EPITHELIAL_MESENCHYMAL_TRANSITION COAGULATION PANCREAS_BETA_CELLS FATTY_ACID_METABOLISM KRAS_SIGNALING_DN ANGIOGENESIS MYC_TARGETS_V2 ANDROGEN_RESPONSE PEROXISOME ESTROGEN_RESPONSE_EARLY PI3K_AKT_MTOR_SIGNALING HEDGEHOG_SIGNALING NOTCH_SIGNALING IL6_JAK_STAT3_SIGNALING UV_RESPONSE_DN KRAS_SIGNALING_UP XENOBIOTIC_METABOLISM REACTIVE_OXYGEN_SPECIES_PATHWAY ESTROGEN_RESPONSE_EARLY ESTROGEN_RESPONSE_LATE HYPOXIA HYPOXIA TGF_BETA_SIGNALING COMPLEMENT PROTEIN_SECRETION KRAS_SIGNALING_DN COAGULATION INTERFERON_ALPHA_RESPONSE ANDROGEN_RESPONSE IL2_STAT5_SIGNALING HEME_METABOLISM BILE_ACID_METABOLISM BILE_ACID_METABOLISM MYOGENESIS APICAL_SURFACE APICAL_SURFACE ADIPOGENESIS ALLOGRAFT_REJECTION ESTROGEN_RESPONSE_LATE INTERFERON_GAMMA_RESPONSE OXIDATIVE_PHOSPHORYLATION PANCREAS_BETA_CELLS FATTY_ACID_METABOLISM INFLAMMATORY_RESPONSE ANGIOGENESIS TNFA_SIGNALING_VIA_NFKB MYOGENESIS 5 0 5 10 5 0 5 10 t value of GSVA score t value of GSVA score HExp vs LExp group of KICH HExp vs LExp group of KIRC (a) (b) E2F_TARGETS G2M_CHECKPOINT TGF_BETA_SIGNALING MITOTIC_SPINDLE UV_RESPONSE_DN WNT_BETA_CATENIN_SIGNALING APICAL_JUNCTION INTERFERON_ALPHA_RESPONSE SPERMATOGENESIS APICAL_SURFACE UNFOLDED_PROTEIN_RESPONSE IL6_JAK_STAT3_SIGNALING NOTCH_SIGNALING KICH PROTEIN_SECRETION DNA_REPAIR KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM UV_RESPONSE_UP ANDROGEN_RESPONSE KEGG_BASAL_CELL_CARCINOMA PI3K_AKT_MTOR_SIGNALING KRAS_SIGNALING_UP KEGG_CELL_CYCLE COMPLEMENT KEGG_CITRATE_CYCLE_TCA_CYCLE INTERFERON_GAMMA_RESPONSE HEME_METABOLISM KEGG_COMPLEMENT_AND_COAGULATION_CASCADES APOPTOSIS P53_PATHWAY KEGG_DNA_REPLICATION HYPOXIA EPITHELIAL_MESENCHYMAL_TRANSITION KEGG_ECM_RECEPTOR_INTERACTION MYC_TARGETS_V1 IL2_STAT5_SIGNALING KEGG_FOCAL_ADHESION PEROXISOME ESTROGEN_RESPONSE_EARLY KEGG_GYLCOLYSIS_GLUCONEOGENSIS MTORC1_SIGNALING KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM ESTROGEN_RESPONSE_LATE CHOLESTEROL_HOMEOSTASIS KEGG_MISMATCH_REPAIR ANGIOGENESIS TNFA_SIGNALING_VIA_NFKB KEGG_NUCLEOTIDE_EXCISION_REPAIR INFLAMMATORY_RESPONSE GLYCOLYSIS KEGG_ONE_CARBON_POOL_BY_FOLATE KRAS_SIGNALING_DN KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION MYC_TARGETS_V2 PANCREAS_BETA_CELLS KEGG_PATHWAYS_IN_CANCER HEDGEHOG_SIGNALING ALLOGRAFT_REJECTION KEGG_PROPANOATE_METABOLISM MYOGENESIS ADIPOGENESIS KEGG_PURINE_METABOLISM REACTIVE_OXYGEN_SPECIES_PATHWAY KEGG_PYRIIMIDINE_METABOLISM BILE_ACID_METABOLISM COAGULATION KEGG_PYRUVATE_METABOLISM FATTY_ACID_METABOLISM XENOBIOTIC_METABOLISM KEGG_SMALL_CELL_LUNG_CANCER OXIDATIVE_PHOSPHORYLATION 0.2 0.4 0.6 0.8 10 5 0 5 10 t value of GSVA score HExp vs LExp group of KIRP (c) (d) KIRP KIRC KEGG_ADHERENS_JUNCTION KEGG_ALLOGRAFT_REJECTION KEGG_AXON_GUIDANCE KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION KEGG_CELL_ADHESION_MOLECULES_CAMS KEGG_AUTOIMMUNE_THYROID_DISEASE KEGG_CELL_CYCLE KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION KEGG_CELL_ADHESION_MOLECULES_CAMS KEGG_DNA_REPLICATION KEGG_CELL_CYCLE KEGG_DORSO_VENTRAL_AXIS_FORMATION KEGG_CHEMOKINE_SIGNALING_PATHWAY KEGG_ECM_RECEPTOR_INTERACTION KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION KEGG_FOCAL_ADHESION KEGG_CYTOSOLIC_DNA_SENSING_PATHWAY KEGG_JAK_STAT_SIGNALING_PATHWAY KEGG_DNA_REPLICATION KEGG_MELANOMA KEGG_HEMATOPOIETIC_CELL_LINEAGE KEGG_MISMATCH_REPAIR KEGG_INTESTINAL_IMMUE_NETWORK_FOR_IGA_PRODUCTION KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION KEGG_LEISHMANIA_INFECTION KEGG_PANCREATIC_CANCER KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY KEGG_PATHWAYS_IN_CANCER KEGG_PRIMARY_IMMUNODEFICIENCY KEGG_PRIMARY_IMMUNODEFICIENCY KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS KEGG_PROSTATE_CANCER KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY KEGG_RENAL_CELL_CARCINOMA KEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY KEGG_SMALL_CELL_LUNG_CANCER KEGG_TYPE_I_DIABETES_MELLITUS KEGG_TGF_BETA_SIGNALING_PATHWAY KEGG_VIRAL_MYOCARDITIS 0.00 0.25 0.50 0.75 0.2 0.4 0.6 0.8 (e) (f) Figure 8: *e results of GSVA analysis of TCF19. (a–c) It shows the GSVA analysis of TCF19 in KIRC, KIRP, and KICH, and (d–f) represents the GSEA analysis of TCF19 in KIRC, KIRP, and KICH. development of tumor immunotherapy strategies. For subjected to conditions of radiotherapy and immuno- example, Han [36] predicted the clinical outcome when therapy by analyzing the genetic characteristics of the patients suffering from lung adenocarcinoma were B cells. Dai [37] constructed an immune-related gene Journal of Oncology 13 Correlation of NEO Correlation of TMB 0.1 0.0 0.1 0.2 0.3 0.0 0.2 0.4 0.6 0.8 KICH*** THYM CESC PRAD*** ACC*** CESC LUAD*** UCS UCEC*** GBM DLBC ESCA READ PRAD*** LUSC BRCA*** LAML SKCM*** HNSC STAD*** STAD* THCA CHOL TGCT KIRP PCPG* LUAD* OV LIHC** UVM UCEC** SARC BLCA HNSC THCA* PAAD COAD* LUSC KIRP KIRC BLCA GBM BRCA SKCM LGG OV LIHC LGG MESO KIRC (a) (b) Correlation of MSI 0.2 0.0 0.2 DLBC* GBM*** CESC KICH THYM PRAD*** UCEC*** ESCA STAD*** TGCT KIRC* HNSC BLCA MESO SARC LGG PCPG UVM COAD* LUSC ACC LUAD BRCA* SKCM LIHC PAAD CHOL THCA OV UCS KIRP LAML READ (c) Figure 9: *e relationship of TMB and MSI with the TCF19 expression in cancers. (a) Shows the relationship between TCF19 expression and TMB, (b) indicates the relations of TCF19 expression with MSI, and (c) represents the correlations of TCF19 expression with Neoantigen. prognostic index (IRGPI) based on 11 immune-related underlying mechanisms associated with TCF19 and the genes, which can accurately forecast the immune cell in- immune system. Although the correlation between tumor filtrations in the tumor microenvironment of hepatocel- immune microenvironment and TCF19 cannot be applied to lular carcinoma and the response generated toward all kinds of tumors, our work revealed the immune effects of immunotherapy. Feng Xu [38] studied lung adenocarci- TCF19 on the microenvironment of specific cancer cells noma cases and reported that immune-related genes were which may potentially help improve the processes of independently predicting the poor survival rate of patients. TCRCC targeting therapy. However, preliminary results have been reported using various bioinformatics methods. As per we know, there is a minor number of relevant researches currently available to explain the functions of *erefore, further research should be conducted to un- TCF19 in ccRCC. *is study provided valuable information derstand how TCF19 influences cancer immunotherapy. In on how the TCF19 gene participated in cancer immuno- our next step, we need to extend the existing analysis da- therapy. *e results also revealed the relationship between tabase and mutually authenticate with the existing database. TCF19 and various immune indicators (such as the in- Authentication should be realized at the molecular, cyto- filtration process of immune cells, immune-modulatory logical, and animal levels by conducting experiments to factors, and the biomarkers of the immune system). *e investigate the relationship between the prognosis of the obtained data can potentially help understand the patients and the properties of the clinical tumor tissue 14 Journal of Oncology samples. We believe that the results can potentially help for Supplementary Figure 3: the WGCNA analysis of TCF19 in improving the efficiency of diagnosis, treatment methods, pan-cancer. Supplementary figure 4(a–f): the relationship and survival prognosis of cancer patients. between TCF19 expression and 33 tumor immune-related genes (genes analyzed include MHC, immune activators, immune suppressors, chemokines, and chemokine receptor 5. Conclusion proteins). Supplementary Figures 5(a–g): the association *is is one of the few studies that focus on the immuno- between TCF19 and common tumor-associated regulatory therapeutic value of TCF19 associated with ccRCC. We genes (such as TGF beta signaling, TNFA signaling, hypoxia, believe that the results reported herein can potentially help scorch death, DNA repair, autophagy genes, and iron death- design functional experiments that can help develop the field related genes). Supplementary figure 6: the analysis of the of clinical treatment. relationship between TCF19 and the sensitivity of common antitumor drugs. (Supplementary Materials) Abbreviations References BCa: Bladder cancer TCGA: *e cancer genome atlas [1] R. L. Siegel, K. D. Miller, and A. Jemal, “Cancer statistics, diff- Differentially expressed LncRNAs 2018,” CA: a Cancer Journal for Clinicians, vol. 68, no. 1, LncRNAs: pp. 7–30, 2018. DEGs: Differentially expressed genes [2] S. Chevrier, J. H. Levine, V. R. T. 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Immunotherapeutic Value of Transcription Factor 19 (TCF19) Associated with Renal Clear Cell Carcinoma: A Comprehensive Analysis of 33 Human Cancer Cases

Immunotherapeutic Value of Transcription Factor 19 (TCF19) Associated with Renal Clear Cell Carcinoma: A Comprehensive Analysis of 33 Human Cancer Cases

Abstract

<i>Background</i>. We aimed to study the relationship between transcription factor 19 (TCF19) and cancer immunotherapy in the 33 types of human cancers. <i>Methods</i>. The Cancer Genome Atlas database was analyzed to obtain the gene expression data and clinical characteristics for the cases of 33 types of cancers. GSE67501, GSE78220, and IMvigor 210 were included in the immunotherapy cohorts. Relevant data were obtained by analyzing the gene expression database. The prognostic value of TCF19 was determined by analyzing various clinical parameters, such as survival duration, age, the stage of the tumor, and sex of the patients. The single-sample gene set enrichment analysis method was used to determine the activity of TCF19 and the method was also used to assess the differences between the TCF19 transcriptome and protein levels. The correlation between TCF19 and various immune processes and elements such as immunosuppressants, stimulants, and major histocompatibility complexes were analyzed to gain insights into the role of TCF19. The coherent paths associated with the process of TCF19 signal transduction and the influence of TCF19 on immunotherapy biomarkers have also been discussed herein. Finally, three independent immunotherapy methods were used to understand the relationship between TCF19 and immunotherapy response. <i>Results</i>. It was observed that TCF19 was not significantly influenced by the age (5/33), sex (3/33), or tumor stage (3/21) of cancer patients. But the results revealed that TCF19 exhibited a potential prognostic value and could predict the survival rate of the patients. In some cases of this study, the activity and expression of TCF19 were taken at the same level (7/33). <i>Conclusion</i>. TCF19 is strongly related to immune cell infiltration, immunomodulators, and immunotherapy markers. Our study demonstrated that high expression levels of TCF19 are strongly linked with the immune-related pathways. Nevertheless, it is noteworthy that TCF19 is not significantly associated with immunotherapy response.

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Abstract

Hindawi Journal of Oncology Volume 2022, Article ID 1488165, 15 pages https://doi.org/10.1155/2022/1488165 Research Article Immunotherapeutic Value of Transcription Factor 19 (TCF19) Associated with Renal Clear Cell Carcinoma: A Comprehensive Analysis of 33 Human Cancer Cases Xiaobao Cheng , Jian Hou, Xiangyang Wen, Runan Dong, Zhenquan Lu, Yi Jiang, Guoqing Wu, and Yuan Yuan Department of Urology, e University of Hongkong-Shenzhen Hospital, Shenzhen, China Correspondence should be addressed to Xiaobao Cheng; chengxb@hku-szh.org Received 19 June 2022; Revised 30 June 2022; Accepted 6 July 2022; Published 6 September 2022 Academic Editor: Recep Liman Copyright © 2022 Xiaobao Cheng et al. �is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Background. We aimed to study the relationship between transcription factor 19 (TCF19) and cancer immunotherapy in the 33 types of human cancers. Methods. �e Cancer Genome Atlas database was analyzed to obtain the gene expression data and clinical characteristics for the cases of 33 types of cancers. GSE67501, GSE78220, and IMvigor 210 were included in the immunotherapy cohorts. Relevant data were obtained by analyzing the gene expression database. �e prognostic value of TCF19 was determined by analyzing various clinical parameters, such as survival duration, age, the stage of the tumor, and sex of the patients. �e single- sample gene set enrichment analysis method was used to determine the activity of TCF19 and the method was also used to assess the di•erences between the TCF19 transcriptome and protein levels. �e correlation between TCF19 and various immune processes and elements such as immunosuppressants, stimulants, and major histocompatibility complexes were analyzed to gain insights into the role of TCF19. �e coherent paths associated with the process of TCF19 signal transduction and the in—uence of TCF19 on immunotherapy biomarkers have also been discussed herein. Finally, three independent immunotherapy methods were used to understand the relationship between TCF19 and immunotherapy response. Results. It was observed that TCF19 was not signi˜cantly in—uenced by the age (5/33), sex (3/33), or tumor stage (3/21) of cancer patients. But the results revealed that TCF19 exhibited a potential prognostic value and could predict the survival rate of the patients. In some cases of this study, the activity and expression of TCF19 were taken at the same level (7/33). Conclusion. TCF19 is strongly related to immune cell in˜ltration, immunomodulators, and immunotherapy markers. Our study demonstrated that high expression levels of TCF19 are strongly linked with the immune-related pathways. Nevertheless, it is noteworthy that TCF19 is not signi˜cantly associated with im- munotherapy response. characterized as an aggressive tumor and approximately 1. Introduction one-third of the patients su•ering from ccRCC were di- �e renal tumor is one of the most common tumors in agnosed while tumor metastasis already occurred [3]. Cel- urology. Results from the statistical analysis conducted with lular molecular-targeted therapy is the most e•ective the data associated with cancer revealed that renal tumors method of treating metastatic ccRCC as patients su•ering ranked second in terms of incidence of urinary system from kidney cancer do not respond to radiotherapy and malignant tumors in China [1]. Clear cell renal cell carci- chemotherapy. �e European Urology Association (EUA) noma (ccRCC) is the major pathological type of renal cancer, and the United States National Comprehensive Cancer which accounts for 70–80% of the cancers in urology. �e Network (NCCN) recommended the molecular-targeted annual percentage of increase in the rate of incidence is 3% drugs as the ˜rst and second-line medicine for metastatic in Europe and in the United States [2]. CcRCC is ccRCC [4, 5]. �e prognostic factors of ccRCC include 2 Journal of Oncology microenvironment. Also, we studied the microsatellite in- histological factors, tumor anatomical factors, molecular factors, and clinical factors. Among these, currently known stability (MSI) and tumor mutation burden (TMB) in ccRCC. Moreover, the association of the expression level of molecular markers such as carbonic anhydrase 9, CRP [6, 7] , and cabozantinib [8] are not of high prognostic value and TCF19 with immune checkpoint blocking therapy was also accuracy, and these have not been recommended for clinical investigated. In brief, this research provides data that help application. At present, there are no universally accepted understand the immunotherapeutic role of TCF19 in ccRCC and reliable standard predictors for the diagnosis and which may potentially help design various functional prognosis of ccRCC at an early stage. *e exploration of experiments. abnormally expressed genes in ccRCC tissues can potentially help identify new molecular biomarkers for the diagnosis 2. Methods and prognosis of ccRCC. (See Figure 1) shows the flowchart of this research. Transcription Factor 19 (TCF19) is a protein-coding gene that encodes a protein with a PHD-type zinc finger domain that is involved in transcriptional regulations [9]. At 2.1. Data Collection. *e TCGA database (https://portal.gdc. first, TCF19 was isolated from human, mouse, and hamster cancer.gov/), a robust database, provides information on cells and it acts as a growth regulatory molecule [10]. TCF19 cancer genes. *e database includes information on gene is associated with cell growth and regulation by affecting the expression profiles, copy number variation (CNV), and G1S phase of the cell cycle. *e genetic coding region of single nucleotide polymorphism (SNP). We downloaded the TCF19 is located on the short arm 6P21.3 of autoch- mRNA expression and SNP data of 33 tumors for this study. romosome 6, with a total length of 5.60 KB [11]. TCF19 is Also, we downloaded the data from the GTEX database present in almost all human tissues, and its levels of ex- (https://commonfund.nih.gov/GTEx). Following the merg- pression are high in various tumor tissues [12–15]. Although ing with the TCGA data and correction, we identified the current studies indicate that TCF19 may be associated with differential expressions for various types of cancers. the progression of various tumors, few mechanisms have Moreover, we downloaded the corresponding tumor cell been reported for the role of TCF19 in carcinogenesis and lines data from the CCLE database (https://portals. immune regulation. broadinstitute.org/ccle/), and we investigated the expres- *e processes of carcinogenesis and immune regulation sion level of the gene in these tumor tissues. Furthermore, we are significantly affected by the physiological effects of TCF19 investigated the significant correlation of this gene with the activation. Since TCF19 is chronically activated, it is highly stages of tumor progression. expressed in various solid tumors [12–15] and chronic in- flammatory tissues [16–18]. *e presence of highly expressed TCF19 has been found not only in invasive tumor tissues but 2.2. Association of TCF19 Expression with Clinical Charac- also in malignant tumor cell lines. *is potentially indicates teristics of 33 Cancers. We downloaded the progression-free that TCF19 is correlated to the responses of inflammation and survival (PFS) and overall survival (OS) TCGA data of cell cycle progression [11, 16]. *e genes associated with the patients from the Xena database to evaluate the association TCF family regulate innate immunity and adaptive immunity of this gene with the prognosis of the patients. We utilized [19, 20]. It has been previously reported that TCF1 helps the Kaplan–Meier (K-M) method to analyze the survival achieve a balance between the CD8+ T cells by regulating the curve (P< 0.05) for every cancer type. We employed “sur- internal IL-10 signaling pathway which in turn influences vival” and “SurvMiner” R packages for the survival analysis. immunotherapy [21]. Macrophages, a substantial component Also, we used “survival” and “forest-plot”R packages for the of the innate immune system, are related to the antitumor Cox analysis to evaluate the interrelation of gene expression immune response in various cancers. It was stated that the with the magnitude of survival of the patients. M2 tumor-associated macrophages (TAMs) promote the processes of tumor progression, recurrence, and distal me- 2.3. TCF19 Enrichment Analysis tastasis [22]. Macrophages are polarized by the stimulation of transcription factors in the tumor microenvironment by 2.3.1. Gene Set Variation Analysis (GSVA) Enrichment controlling their antitumor activity and by affecting their Analysis. GSVA, a package for the R program, was used to immunotherapy [23, 24]. Our previous study also confirmed identify the enrichment of transcriptomic gene sets. GSVA that changes in macrophage polarization play substantial identifies the changes from the level of the gene to the level of activities to regulate the inflammatory traumatic urethral the pathway. *is is achieved by using the specific gene sets of stricture [25] and resistance to chemotherapy and endocrine biological function. We utilized the Molecular Signatures Da- therapy in advanced prostate cancer [26]. In general, TCF tabase (v7.0) for downloading the gene sets. GSVA algorithm family genes significantly influence the immune system and identified the score of each gene set to determine the ability of the state of tumor tissue. Nevertheless, the immunothera- changes in biological function within the different samples. peutic value of TCF19 in the cases of human cancer has been rarely studied. Herein, we described the expression profile of TCF19 in 2.3.2. Gene Set Enrichment Analysis (GSEA) Enrichment 33 different cancers and studied the potential regulatory Analysis. In the GSEA analysis, we used predefined gene sets roles of TCF19 for controlling the ccRCC immune and sequencing gene sets (based on the differential Journal of Oncology 3 Age Clinical correlation Stage in 33 human cancers Gender Tomor and normal Survival MHC molecules Estimate score Immune cells infilatration based on CIBERSORT Immune mechanism Immune inhibitors in Renal Clear cell carcinoma Microsatellite instability TCF19 in Renal Tumor mutation burden Immune stimulatore Renal Clear cell carcinoma Relevant signaling pathways (GSVA and GSEA) IMvigor210 cohort Immunotherapeutic response GSE67501 GSE78220 Drug sensitivity correlation in renal clear cell carcinoma Figure 1: *e flowchart of the study. Firstly, the expression of TCF19 is investigated within the different ages, stages, genders, and tissues, then the GSEA is utilized to explore the relevant immune signaling pathways based on the expression level of TCF19. Secondly, we apply the univariate Cox regression model and the Wilcoxon test between the nonresponder and responder groups of the immunotherapeutic response cohort to identify the survival association. Finally, we perform the drug sensitivity correlation with TCF19 expression in renal clear cell carcinoma. expression level between the two types of samples). *is the widely used database is the NCI-60 cell line with a broad method identifies whether the predefined gene sets were range of cancer cell samples and it is used to investigate the anticancer drugs. In our study, we downloaded the NCI-60 significantly enriched in the sequencing table. *e “cluster profiler” and the “enrich-Plot” packages were used for the drug sensitivity data and the RNA-seq gene expression data to evaluate the relations of gene expression with the sen- GSEA analysis and for exploring the imaginable mechanisms at the molecular level for the differential prognosis of different sitivity of antitumor drugs. *e correlation analysis method patients with different tumors. *e differences in the signaling was utilized to achieve the results. We considered a Pvalue pathways associated with the high and low gene expression <0.05 for the statistical threshold. groups were studied, and the findings were compared. We analyzed the immunotherapeutic response accord- ing to the previous method [2]. We used three independent immunotherapeutic cohorts in our present study. Usually, 2.3.3. e Expression Level of TCF19 Is Correlated with immunotherapeutic ways provided four outcomes, in- Immune-Related Factors. RNA-seq data from patients with cluding complete response (CR), partial response (PR), different subgroups of 33 cancers were analyzed by using the progressive disease (PD), and stable disease (SD). We di- CIBERSORT algorithm to understand the content of in- vided the patients into responders and nonresponders. filtrating immune cells. *is method also identifies the re- Patients who had CR or PR signs were categorized as re- lation of gene expression with the content of immune cells. sponders compared to the nonresponders, who had signs of Moreover, we used the TISIDB website to identify the re- SD or PD. We utilized the Wilcoxon rank-sum test to in- lation of gene expression with various immune factors, vestigate the expression differences of TCF19 between the including chemokines, immune-stimulators, immune- responder and the nonresponder groups. suppressants, and MHC molecules. 2.3.6. Statistical Analyses. R (version 4.0) was used for all 2.3.4. Correlation Analysis of TCF19 Expression and Tumor statistical analyses. We calculated the hazard ratios (HRs) Mutation. *e total number of mutations, including base and 95% confidence intervals followed by applying the substitutions, deletions, and insertions in tumor cells is univariate survival analysis model. We applied the K-M called TMB. *e frequency and number of variation/exon survival analysis to investigate patient survival time. We lengths were calculated for every sample tumor, and TMB divided the patients into the high gene expression level and was calculated by dividing the nonsynonymous mutation the low gene expression level to arrive at the appropriate sites by the total length of the protein-coding region. *e results. *e statistical tests were bilateral, and we considered MSI of every TCGA sample was obtained from the data a Pvalue<0.05 for the statistical threshold. presented in previously published reports [27]. 3. Results 2.3.5. Correlation Analysis of TCF19 Expression with Drug Sensitivity and Immunotherapy Response. *e National 3.1. Results of the Analysis of TCF19 Expression and Clinical Cancer Institute (NCI) listed the Cellminer database which Correlation in 33 Cancers. We analyzed the expression level contains the information on 60 cancer cells [1]. At present, of TCF19 in 33 types of human cancers using the data 4 Journal of Oncology Table 1: 33 types of human cancer studied in this research. presented in the TCGA and GTEX datasets. Table 1 pre- sented the full names of the 33 cancer types utilized in this Abbreviation Full name comprehensive study. *e high levels of expression of the ACC Adrenocortical carcinoma gene were observed in 27 types of carcinomas, including BLCA Bladder urothelial carcinoma ACC, BLCA, BRCA, CHOL, CESC, COAD, ESCA, GBM, BRCA Breast invasive carcinoma HNSC, KIRC, LAML, LGG, LIHC, LUAD, LUSC, OV, Cervical squamous cell carcinoma and CESC PCPG, PAAD, PRAD, READ, SARC, SKCM, STAD, endocervical adenocarcinoma TCGT, THCA, UCEC, and UCS (Figure 2(a)). TCF19 CHOL Cholangiocarcinoma expression levels in most normal tissues were lower than COAD Colon adenocarcinoma DLBC Lymphoid neoplasm diffuse large B-cell lymphoma that in cancer cells. In the CCLE expression profile of ESCA Esophageal carcinoma various cell lines, the expression level of TCF19 is illus- GBM Glioblastoma multiforme trated in figure 2(b). Moreover, we found that TCF19 HNSC Head and neck squamous cell carcinoma expression was related to the stages of various tumors, such KICH Kidney chromophobe as ACC, BRCA, TGCT, KICH, KIRC, and LIHC (Figure 3). KIPAN Pan-kidney cohort (KICH + KIRC + KIRP) *is work studied the correlation between the expression KIRC Kidney renal clear cell carcinoma levels of TCF19 and survival prognosis in patients suffering KIRP Kidney renal papillary cell carcinoma from cancer. We found that the expression level of TCF19 LAML Acute myeloid leukemia was closely associated with the OS of patients in 14 different LGG Brain lower grade glioma types of cancers (such as KIRC, ACC, KICH, KIRP, LAML, LIHC Liver hepatocellular carcinoma THYM, LGG, HNSC, LIHC, MESO, PRAD, SKCM, UVM, LUAD Lung adenocarcinoma LUSC Lung squamous cell carcinoma and PAAD; Figure 4(a)). In addition, the results from the MESO Mesothelioma KM-curve survival analysis suggested that the highly OV Ovarian serous cystadenocarcinoma expressed TCF19 was correlated with poor OS in 13 types of PAAD Pancreatic adenocarcinoma malignant cancers, including ACC, BRCA, KICH, LIHC, PCPG Pheochromocytoma and paraganglioma GBM, SKCM, KIRC, KIRP, LGG, LUAD, PAAD, PCPG, PRAD Prostate adenocarcinoma and MESO (Supplementary Figure 1). *e expression level READ Rectum adenocarcinoma of TCF19 was closely linked with PFI in 12 cancer types, SARC Sarcoma including PAAD, ACC, MESO, KICH, LIHC, PCPG, STAD Stomach adenocarcinoma PRAD, LGG, SARC, THCA, KIRC, UCEC, and other tu- SKCM Skin cutaneous melanoma mors (Figure 4(b)). *e K-M curve analysis for survival STES Stomach and esophageal carcinoma TGCT Testicular germ cell tumors prognosis suggested that a highly expressed group of THCA *yroid carcinoma TCF19 was associated with a shorter PFI in 10 kinds of THYM *ymoma malignant cancers (such as UCEC, ACC, KICH, PAAD, UCEC Uterine corpus endometrial carcinoma KIRC, LGG, LIHC, PCPG, PRAD, and THCA; Supple- UCS Uterine carcinosarcoma mentary Figure 2). UVM Uveal melanoma A nomogram prediction model was constructed using the TCF19 expression level and the clinical features. *e results obtained from regression analysis were displayed in kinds of cancers, the TCF19 expression level was signifi- the form of alignment charts. Variables such as gender, age, cantly related to the follicular helper cells, and in the other 14 tumor stage, and grade were analyzed, and the results were kinds of cancers the TCF19 expression level were correlated presented. *e gene correlation column diagram model of significantly with the macrophages M1 cell (Figure 6). TCF19 of the constructed TCGA-KIRC sample is shown in Further analysis of the tumor microenvironment in kidney Figure 5(a). Correction curves corresponding to the two carcinoma (KIRC) revealed that TCF19 expression level was periods were generated in the fifth and seventh years. *e significantly related to the various gene set scores including model effect was quite consistent (Figure 5(b)). the CD_8_T effector, TME score A, TME score, DNA damage response, base excision repair, immune checkpoint, antigen processing machinery, mismatch repair, nucleotide 3.2. e TCF19 Expression Is Potentially Associated with excision repair, DNA replication, Pan F TBRs, EMT1, and Immune-Associated Factors. Tumor-associated fibroblasts, EMT2 in kidney carcinoma (). extracellular matrix, immune cells, various growth factors, inflammatory factors (characterized by special physico- chemical characteristics), cancer cells, etc., are present in the 3.3. GSVA/GSEA Correlation Analysis of TCF19. *e GSVA tumor microenvironment. *e microenvironment signifi- scores were determined for all tumors to elucidate the cantly affects the diagnosis of tumors, survival outcome, and molecular mechanism associated with the TCF19 gene as- degree of the response generated toward clinical treatment. sociated with pan-cancer. We divided the tumor samples into two groups based on the higher expression level and the Our findings indicated that the TCF19 expression level was substantially correlated with the infiltration of immune lower expression levels. *e median value of the gene ex- factors. TCF19 expression level was significantly related to pression level in each tumor was utilized for comparison. It the CD4 memory-activated cells in 14 kinds of cancers. In 15 was observed that in the case of kidney carcinoma, highly Journal of Oncology 5 ns ns ns **** **** **** **** **** **** **** **** **** **** **** **** **** **** **** **** **** ** **** *** * **** **** **** **** **** **** Tissue Normal Tumor (a) CancerType Kruskal-Wallis, p < 2.2e16 Bile Duct Cancer Kidney Cancer Eye Cancer Cervical Cancer Head and Neck Cancer Fibroblast Neuroblastoma Skin Cancer Esophageal Cancer Sarcoma Colon/Colorectal Cancer Prostate Cancer Endometrial/Uterine Cancer Bladder Cancer Gallbladder Cancer Breast Cancer Liposarcoma Leukemia Gastric Cancer Lung Cancer Rhabdoid yroid Cancer Liver Cancer Bone Cancer Pancreatic Cancer Myeloma Ovarian Cancer Lymphoma Engineered Brain Cancer Expression of TCF19 (b) Figure 2: *e expression of TCF19. (a) *e TCF19 expression level in 33 human cancers using the TCGA combined with GTEx datasets and (b) the CCLE expression profile revealed that TCF19 is expressed in different tumor cell lines. expressed TCF19 genes were primarily associated with some maximum correlation was observed for the ME green yellow module (COR � 0.35, P � (5E-19)) (Supplementary Fig- specific pathways such as interferon alpha response, E2F targets, allograft rejection, IL-6-JAK-STAT3 signaling, in- ure 3). *e coexpression analysis method was further used to explore the relationship between the level of TCF19 ex- terferon gamma response, and G2M checkpoint (Figure 8(a)–8(c)). Results from the GSEA analyses of pression and 33 tumor immune-related genes. *e analyzed TCF19 and kidney carcinoma are presented in Figures 8(d)– genes included genes associated with MHC, immune acti- 8(f). vator, chemokine receptor proteins, immunosuppressor, and chemokine. It was observed that TCF19 was signifi- 3.4. Correlation Analysis of TCF19 Expression with Tumor cantly associated with most of the immune-related genes Mutations and Gene Regulation. *e study further con- (Supplementary Figure 4). Moreover, TCF19 was signifi- structed the WGCNA net based on the KIRC expression cantly associated with the crucial tumor-related marker profile for exploring the coexpression network linked with genes that controlled the various biological processes, in- cluding the TGF beta signaling pathway, TNFA signaling, TCF19 in pan-cancer. *e clustering chart of patients is shown in Supplementary Figure 3. We utilized the “soft hypoxia, coking death, repair of DNA, autophagy, and power Estimate” function in the WGCNA package to ferroptosis (Supplementary Figure 5). identify the soft threshold β value and the value of β is set to *e immunotherapy response was crucially associated 12. We detected 17 gene modules using the Tom matrix. with some biomarkers, including TMB and MSI. We in- *ese are black (298), blue (519), brown (446), cyan (357), vestigated the relation of TCF19 expression level with TMB green (354), green yellow (489), grey (3788), grey60 (82), in this study. We revealed that the TCF19 expression level light cyan (129), light green (74), light yellow (57), night blue was significantly correlated with TMB in all tumors, in- (155), pink (449), purple (230), red (308), turquoise (1822), cluding P ACC, CPG, UCEC, SKCM, COAD, PRAD, STAD, and yellow (443) (Supplementary Figure 3). *e modules KICH, LIHC, LUAD, and THCA (Figure 9(a)). A significant and traits were further analyzed, and it was found that the difference was observed for MSI in various cancers, Module eigengenes Bile Duct Cancer Eye Cancer symbolACC Head and Neck Cancer Neuroblastoma symbolBLCA Esophageal Cancer symbolBRCA Colon/Colorectal Cancer symbolCESC Endometrial/Uterine Cancer Gallbladder Cancer symbolCHOL Liposarcoma symbolCOAD Gastric Cancer symbolDLBC Rhabdoid Liver Cancer symbolESCA Pancreatic Cancer symbolGBM Ovarian Cancer symbolHNSC Engineered Kidney Cancer symbolKICH Cervical Cancer symbolKIRC Fibroblast symbolKIRP Skin Cancer symbolLAML Sarcoma Prostate Cancer symbolLGG Bladder Cancer symbolLIHC Breast Cancer symbolLUAD Leukemia Lung Cancer symbolLUSC yroid Cancer symbolMESO Bone Cancer symbolOV Myeloma Lymphoma symbolPAAD Brain Cancer symbolPCPG symbolPRAD symbolREAD symbolSARC symbolSKCM symbolSTAD symbolTGCT symbolTHCA symbolTHYM symbolUCEC symbolUCS symbolUVM 6 Journal of Oncology Kruskal-Wallis, p = 0.031 0.012 0.0048 Kruskal-Wallis, p = 7.4e07 8.3e06 0.005 0.021 0.48 0.0087 3.4e07 0.88 0.027 0.42 0.45 Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV Stage I Stage III Stage I Stage III Stage II Stage IV Stage II Stage IV (a) (b) 0.057 Kruskal-Wallis, p = 0.0035 Kruskal-Wallis, p = 0.016 0.1 0.41 0.27 0.0015 0.028 0 0 Stage I Stage II Stage III Stage I Stage II Stage III Stage IV Stage I Stage III Stage I Stage II Stage IV Stage II Stage III (c) (d) 0.027 0.7 Kruskal-Wallis, p = 0.0065 Kruskal-Wallis, p = 0.38 0.00099 0.29 0.16 0.39 0.018 0.18 0.52 0.16 0.25 0.7 0 0 Stage I Stage II Stage III Stage IV Stage I Stage II Stage III Stage IV Stage I Stage III Stage I Stage III Stage II Stage IV Stage II Stage IV (e) (f) Figure 3: *e correlation analysis of TCF19 with the stage of multiple tumors. TCF19 Expression TCF19 Expression TCF19 Expression TCF19 Expression TCF19 Expression TCF19 Expression Journal of Oncology 7 pvalue Hazard ratio pvalue Hazard ratio ACC <0.001 1.180 (1.0931.273) ACC <0.001 1.131 (1.0591.208) BLCA 0.532 0.997 (0.9861.00 ) 0.965 1.000 (0.9901.010) BLCA BRCA 0.712 0.997 (0.9811.013) 0.388 0.992 (0.9741.010) BRCA CESC 0.334 0.991 (0.9741.009) 1.002 (0.9861.018) CESC 0.817 CHOL 0.138 1.088 (0.9731.215) 0.175 1.077 (0.9681.198) CHOL COAD 0.792 0.994 (0.9491.041) 0.401 1.017 (0.9771.059) COAD DLBC 0.356 0.962 (0.8861.044) 0.649 1.010 (0.9671.056) DLBC ESCA 0.916 1.003 (0.9531.055) ESCA 0.615 1.012 (0.9661.059) 1.020 (0.9941.046) GBM 0.137 0.706 0.995 (0.9691.022) GBM HNSC 0.044 0.983 (0.9660.999) 0.810 0.998 (0.9831.013) HNSC KICH 0.002 1.156 (1.0551.268) 1.194 (1.0841.315) KICH <0.001 KIRC 0.020 1.016 (1.0021.029) KIRC <0.001 1.022 (1.0101.035) KIRP <0.001 1.204 (1.0961.323) <0.056 1.097 (0.9981.20 ) KIRP LAML 0.004 1.063 (1.0201.109) <0.001 1.061 (1.0311.091) LGG LGG <0.001 1.085 (1.0511.120) 1.025 (1.0051.046) LIHC 0.014 LIHC 0.015 1.029 (1.0051.053) 0.733 0.996 (0.9761.01 ) LUAD 0.502 1.007 (0.9871.028) LUAD 0.395 1.010 (0.9881.032) LUSC 0.733 0.997 (0.9781.016) LUSC 0.008 1.104 (1.0261.188) MESO <0.001 1.139 (1.0661.21 ) MESO 0.993 (0.9761.010) OV 0.142 0.986 (1.9661.005) OV <0.410 PAAD 0.005 1.114 (1.0341.200) 0.037 1.081 (1.0051.163) PAAD PCPG 0.055 1.242 (0.9961.550) 0.036 1.191 (1.0111.402) PCPG PRAD 0.003 1.378 (1.1131. 06) 1.246 (1.1471.355) PRAD <0.001 READ 0.145 0.924 (0.8311.028) 1.025 (0.9541.101) READ 0.502 SARC 1.005 (0.9941.01 ) 0.367 0.010 1.012 (1.0031.021) SARC SKCM 0.022 1.022 (1.0031.040) 0.533 1.006 (0.9881.024) SKCM STAD 0.215 0.976 (0.9401.014) 0.972 (0.9331.013) STAD 0.183 TGCT 0.356 0.884 (0.6801.149) 0.396 1.024 (0.9691.083) TGCT THCA 0.133 0.686 (0.4201.121) 0.022 1.236 (1.0311.481) THCA THYM 0.003 0.795 (0.6850.923) 0.249 0.962 (0.9001.028) THYM UCEC 0.096 1.018 (0.9971.041) 1.021 (1.0031.040) UCEC 0.024 UCS 0.612 0.985 (0.9311.043) 0.846 1.005 (0.9581.054) UCS UVM 0.017 0.695 (0.5160.93 ) 0.536 0.930 (0.7391.1 0) UVM 0.35 0.50 0.71 1.0 1.41 2.0 0.71 1.0 1.41 Hazard ratio Hazard ratio (a) (b) Figure 4: *e association between TCF19 expression and prognosis of patients with multiple cancers. (a) *e univariate regression model identifies the association of TCF19 expression with the overall survival (OS) rate in multiple cancer patients and (b) the univariate regression model identifies the association of TCF19 expression with the progression-free interval (PFI) of patients with multiple cancers. including UCEC, KIRC, GBM, COAD, BRCA, STAD, 4. Discussion PRAD, and DLBC (Figure 9(b)). In China, kidney carcinoma is the second-highest malignant rd tumor in urology [1]. Approximately 1/3 of the patients 3.5. Correlation Analysis of TCF19 Expression with Drug developed metastatic carcinoma before diagnosis [5]. Ad- Sensitivity and Immunotherapeutic Response. *e effect of vanced renal clear cell carcinoma showed resistance to the surgery and chemotherapy on the conditions of early-stage treatment strategies including radiotherapy and chemo- tumors had been widely explored. We investigated the cell therapy. Hence, the cellular and molecular-targeted treat- miner database to identify the association of TCF19 ex- ment method is widely used to treat ccRCC. Multiple pression level with IC50 values of antitumor drugs. We guidelines recommend molecular-targeted therapy as the revealed that the higher expression level of TCF19 was first and second choice of treatment for metastatic ccRCC correlated with the tolerance level of multiple antitumor [6, 7]. *erefore, it is important to explore new therapeutic drugs (Supplementary Figure 6). It was observed that TCF19 targets for advanced ccRCC. correlated positively with fludarabine, 6-mercaptopurine, At the beginning of the research, we identified the dexamethasone decadron, nelarabine, and fenretinide. *e expression differences of TCF19 in tumor tissues relative to gene negatively correlated with AFP464, trametinib, ami- the normal samples. *e results helped identify the po- noflavone, cobimetinib (isomer 1), palbociclib, and lificguat. tential immunotherapeutic value of TCF19. TCF19 is *e dataset corresponding to IMvigor 210 tumor im- a gene that is associated with cell growth regulation which munotherapy was downloaded and 348 patients subjected to primarily regulates the cell cycle and the process of apo- the conditions of PD-L1 therapy (and presenting complete ptosis. TCF19 was first isolated from mouse, human, and survival information) were enrolled. *e K-M survival hamster cells. *e previous report indicated that the TCF19 analysis was used for the studies, and the results revealed that expression level was higher in various cancerous tissues, high TCF19 expression levels reflected the poor prognosis of including the liver, colon, rectum, head and neck, lung, and patients (figure 5(c)). gastrointestinal tract [12–15]. In this work, TCF19 was 8 Journal of Oncology 0 10 20 30 40 50 60 70 80 90 100 Points age 25 30 35 40 45 50 55 60 65 70 75 80 85 90 gender 2 4 stage 2 4 grade TCF19 0 30 70 Total Points 0 20 40 60 80 100 120 140 160 180 200 220 240 Linear Predictor 2.5 2 1.5 1 0.5 0 0.5 1 1.5 2 2.5 5-year survival Probability 0.95 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 7-year survival Probability 0.95 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 (a) 1.00 0.75 1.0 0.50 0.8 0.25 0.6 p = 0.0044 0.00 0.4 05 10 15 20 25 Time (Years) 0.2 High 171 112 88 74 41 0 177 111 Low 68 45 25 0 0.0 05 10 15 20 25 0.0 0.2 0.4 0.6 0.8 1.0 Time (Years) Nomogram-predicted OS (%) n = 919 d = 327 p = 5, 130 subjects per group x-resampling optimism added, B = 1000 Gray: Ideal Based on observed-predicted TCF19 5-year High 7-year Low (b) (c) Figure 5: *e TCF19 expression level is associated with the risk and prognosis of patients. (a) It shows the gene correlation column line graph model for TCF1, (b) it shows the correction curves plotted for two periods of five and seven years, and (c) it shows the Kaplan–Meier survival analysis plots of TCF19 expression versus patients treated with PD-L1. highly expressed in ACC, BLCA, KIRC, PRAD, TCGT, and suggest that TCF19 is crucially linked with a shorter other urinary system tumors which were under previous prognosis of multiple tumors. findings. In addition, the results from the K-M survival Since TCF19 significantly affects the tumor immune investigation suggested that a higher expression level of microenvironment, more studies need to be conducted on TCF19 is significantly associated with a shorter prognosis the immune cells, tumor microenvironment, immuno- of various tumors in both OS and PFI. *ese studies might modulators, and immunotherapy responses to gain in-depth Observed OS (%) TCF19 Survival probability Journal of Oncology 9 KICH KIRP ns ns ns** ? ns ns ns ns ns ns ns ns ns* ns ns ns ns ns ns ns ns ns ns ? ns ns ns ns ns ns** ns **** ns *** ns ns **** ns ns 0.4 0.4 0.3 0.3 0.2 0.2 0.1 0.1 0.0 0.0 variable variable Group Group Hexp Hexp Lexp Lexp (a) (b) KIRC KIRP ns ns ns ns ns ns ns 20 ns ns ** ns ns **** **** **** **** *** ** ns ns * ns * * * **** ? * *** * ******** * *** **** ** * 0.4 0.3 0.2 10 0.1 0.0 20 variable Signature Group TMEcluster Hexp Hexp Lexp Lexp (c) (d) KICH KIRC 20 ** ns ns ns ns **** *** *** **** *** ns ns ns ns ns **** **** **** **** **** **** ** *** **** ****n * * nss 10 10 10 10 20 Signature Signature TMEcluster TMEcluster Hexp Hexp Lexp Lexp (e) (f) Figure 6: Continued. Signature_score value value B cells naive B cells naive TMEscore B cells memory B cells memory Plasma cells CD_8_T_effector Plasma cells T cells CD8 T cells CD8 Immune_Checkpoint T cells CD4 naive T cells CD4 naive Antigen_processing_machinery T cells CD4 memory resting T cells CD4 memory resting T cells CD4 memory activated T cells CD4 memory activated TMEscoreA T cells follicular helper T cells follicular helper Mismatch_Repair T cells regulatory (Tregs) T cells regulatory (Tregs) T cells gamma delta T cells gamma delta Nucleotide_excision_repair NK cells resting NK cells resting DNA_damage_response NK cells activated NK cells activated Monocytes DNA_replication Monocytes Macrophages M0 Macrophages M0 Base_excision_repair Macrophages M1 Macrophages M1 Macrophages M2 Pan_F_TBRs Macrophages M2 Dendritic cells resting Dendritic cells resting EMT1 Dendritic cells activated Dendritic cells activated EMT2 Mast cells resting Mast cells resting Mast cells activated Mast cells activated EMT3 Eosinophils Eosinophils TMEscoreB Neutrophils Neutrophils Signature_score Signature_score value B cells naive TMEscore TMEscore B cells memory CD_8_T_effector CD_8_T_effector Plasma cells T cells CD8 Immune_Checkpoint Immune_Checkpoint T cells CD4 naive Antigen_processing_machinery Antigen_processing_machinery T cells CD4 memory resting TMEscoreA T cells CD4 memory activated TMEscoreA T cells follicular helper Mismatch_Repair Mismatch_Repair T cells regulatory (Tregs) Nucleotide_excision_repair T cells gamma delta Nucleotide_excision_repair NK cells resting DNA_damage_response DNA_damage_response NK cells activated DNA_replication DNA_replication Monocytes Macrophages M0 Base_excision_repair Base_excision_repair Macrophages M1 Pan_F_TBRs Pan_F_TBRs Macrophages M2 Dendritic cells resting EMT1 EMT1 Dendritic cells activated EMT2 EMT2 Mast cells resting EMT3 Mast cells activated EMT3 Eosinophils TMEscoreB TMEscoreB Neutrophils 10 Journal of Oncology T cells follicular helper Macrophages M1 T cells CD4 memory activated T cells CD8 Macrophages M0 T cells CD4 naive Mast cells resting NK cells activated Pearson Dendritic cells resting correlation T cells regulatory (Tregs) 1.0 Dendritic cells activated 0.5 NK cells resting 0.0 Plasma cells 0.5 Macrophages M2 1.0 B cells memory T cells CD4 memory resting Mast cells activated T cells gamma delta B cells naive Monocytes Eosinophils Neutrophils CancerType (g) Figure 6: *e TCF19 is correlated with the immune infiltration in pan-cancer. (a–f) *e expression level of TCF19 is significantly correlated with the infiltration of immune cells in multiple cancers and (g) it indicates the correlation analysis of TCF19 expression with multiple tumors. knowledge. *is study aimed to gain insights into the un- associated with ccRCC could potentially help obtain results derlying mechanisms associated with the TCF19 gene that that can help improve the therapeutic techniques. was associated with immune-related factors. 33 types of Conventional surgical treatment and radiotherapy and human cancers were studied to obtain relevant information. chemotherapy cannot be effective to treat patients suffering *is work also aimed to explore the immune-related from late-stage ccRCC. Maybe more research should be mechanisms associated with urinary tumors. *e expres- conducted on the gene targets and immune checkpoint sion of TCF19 and clinical characteristics was analyzed, and inhibitors associated with pan-cancer as the results can potentially help predict the prognosis of antitumor immu- the results obtained from COX regression analysis revealed that TCF19 was a prognostic factor of ccRCC. Correction notherapy. *is research studied the relation of TCF19 with the process of immune cell infiltration for further in- curves were generated for the ccRCC patients in the fifth and seventh years and the consistent model effects were ob- vestigating the crucial immunotherapeutic potential of served. Daniela Ruggiero reported the increased level of TCF19. *e results revealed that the expression level of expression of the TCF19 gene in two major histological TCF19 significantly correlated with the infiltration of the subtypes (squamous cell carcinoma (SCC) and lung ade- immune cells, including CD4 memory T cells, T follicular nocarcinoma) and revealed that TCF19 promoted the helper cells, and M1 macrophages. Analysis of the re- progression of the cell cycle in NSCLC cells. *is validated lationship between tumor microenvironment and KIRC the fact that TCF19 was a therapeutic target [28]. Du WB revealed that KIRC was significantly correlated with some reported that TCF19 was significantly upregulated in co- scores such as TMEscoreA, TMEscore, mismatch repair, CD8 lorectal cancer and TCF19 was closely related to the pro- T effector, immune checkpoint, antigen processing gression of malignancy, distant metastasis, and poor machinery, nucleotide excision repair, and DNA damage. prognosis of colorectal cancer. So, he speculated that TCF19 *e scores of the responses, Pan F TBRs, DNA replication, could aggravate the malignant progression of CRC [29]. Ji, base excision repair, EMT1, and EMT2 significantly corre- Xu, and Miao further reported that TCF19 was highly lated with KIRC. And this study further investigated the expressed in cancer cells associated with head and neck SCC, relations of TCF19 with the immune-related genes, in- liver cancer, and gastric cancer. *ey reported that TCF19 cluding genes associated with MHC, immune activator, could be potentially correlated with tumor prognosis by immuno-suppressive markers, chemokine, and their re- conducting gene assays, K–M survival analysis, and western- ceptor protein. Interestingly, we found that immune- blot tests [12, 13, 15]. It is worth noting that the results of our associated factors were significantly correlated with the research reflected the association of the gene with a sub- expression level of the TCF19 gene. Our previous study stantial prognosis of these tumors and confirmed the re- reported that several immune-prognostic genes influenced liability of the analytical results obtained. Moreover, the the process of immunotherapy associated with urinary correlation between TCF19 and the prognosis of ccRCC was bladder cancer [30]. Besides, it has been reported that the also reported. But now the mechanism involving TCF19 in regulation of macrophage polarization attenuated the in- the occurrence of ccRCC has not been clearly described. We flammatory traumatic urethral stricture in New Zealand may infer that the modulation of the TCF19 activity rabbits [25]. Another study recently reported that M2- TCF19 ACC UVM THYM KICH UCS PAAD GBM DLBC LGG TGCT KIRP PCPG BRCA KIRC LIHC CHOL MESO CESC UCEC PRAD THCA LAML LUSC ESCA STAD LUAD SARC SKCM READ BLCA COAD OV HNSC Journal of Oncology 11 progression of pan-cancer were also affected [16–18]. It has been reported recently that TCF19 influences the effect of immunotherapy in lung cancer through nanotechnology by regulating the polarity of the tumor-associated macrophages NA ACC (n=79) [31]. *ose results revealed that TCF19 might influence the BLCA (n=408) BRCA-Basal (n=191) BRCA-Her2 (n=82) process of immunotherapy by regulating the immune- BRCA-LumA (n=568) BRCA-LumB (n=219) related genes and the inflammatory cells such as macro- BRCA (n=1100) CESC (n=306) CHOL (n=36) phages associated with tumor cell immunotherapy. COAD (n=458) DLBC (n=48) Furthermore, we observed that two immunotherapy ESCA (n=185) GBM (n=153) HNSC-HPV- (n=422) biomarkers (TMB and MSI) were associated with TCF19 in HNSC (n=522) KICH (n=66) various tumors. In general, as the number of somatic mu- KIRC (n=533) KIRP (n=290) LGG (n=516) tations in a tumor increase, the ability to generate neo- LIHC (n=371) LUAD (n=515) LUSC (n=501) antigens increases. It was also observed that the tumor MESO (n=87) OV (n=303) neoantigen load could be efficiently determined by analyzing PAAD (n=179) PCPG (n=181) PRAD (n=498) the TMB [32]. MSI is a robust mutant factor phenotype, the READ (n=166) SARC (n=260) generation of which can be attributed to the presence of SKCM-Metastasis (n=368) SKCM-Primary (n=103) SKCM (n=471) defects in mismatch repairing of DNA. MSI is a crucial STAD (n=415) TGCT (n=150) predictor for immunotherapy responses [33]. *is study THCA (n=509) THYM (n=120) UCEC (n=545) showed that TMB and MSI were significantly associated with UCS (n=57) UVM (n=80) the TCF19 expression level in various tumors. However, the NA ACC (n=79) BLCA (n=408) TCF19 expression level was not significantly associated with BRCA-Basal (n=191) BRCA-Her2 (n=82) immunotherapy responses. Despite all 3 cohorts responded BRCA-LumA (n=568) BRCA-LumB (n=219) BRCA (n=1100) to antiPD1 therapy. We hypothesized that TCF19 might CESC (n=306) CHOL (n=36) influence the extent of the response generated toward im- COAD (n=458) DLBC (n=48) ESCA (n=185) munotherapy by targeting the various immune checkpoints. GBM (n=153) HNSC-HPV- (n=422) Also, our study only analyzed 3 relevant cohorts, which HNSC (n=522) KICH (n=66) KIRC (n=533) makes it difficult to elucidate the actual immunotherapy KIRP (n=290) LGG (n=516) rho response of TCF19. More relevant immunotherapy cohort LIHC (n=371) 0.25 LUAD (n=515) 0.00 LUSC (n=501) studies should be conducted in the future. 0.25 MESO (n=87) OV (n=303) PAAD (n=179) And finally, we followed the gene enrichment analysis to PCPG (n=181) PRAD (n=498) arrive at the result which revealed that the highly expressed READ (n=166) SARC (n=260) SKCM-Metastasis (n=368) TCF19 gene was primarily associated with specific pathways SKCM-Primary (n=103) SKCM (n=471) such as E2F, IL6, and G2M. *e E2F and IL6 families are STAD (n=415) TGCT (n=150) THCA (n=509) classical tumor signaling pathways. It has been reported that THYM (n=120) UCEC (n=545) they exhibit unique and overlapping properties during the UCS (n=57) UVM (n=80) processes of transcription, proliferation, and apoptosis of tumor cells [34, 35]. *e results might indicate that TCF19 potentially affects the extent of proliferation, infiltration, and metastasis realized by regulating multiple classical signaling pathways.Also, this specific mechanism associated with the pvalue p<0.05 processes needs to be explored further. *e Cellminer p = 0.05 database was analyzed to determine the relationship be- Figure 7: *e analysis of TCF19 expression and the tumor mi- tween TCF19 and IC to explore the correlation between croenvironment in the ccRCC. TCF19 and antitumor drug sensitivity. *e results revealed that the high level of expression of TCF19 reflected the tumor-associated macrophages (TAMs) were able to pro- tolerance level toward multiple antitumor drugs. *e mote the process of bone metastasis and were able to in- factors and mechanisms affecting the sensitivity of anti- fluence the chemotherapy and drug resistance ability of the tumor drugs are complex and diverse but results from the cells of prostate cancer. *e regulation of the process of analysis of the K-M survival plot revealed that the higher macrophage polarization can influence the effect of im- expression group of TCF19 was significantly linked with a shorter prognosis for cancer patients. It was also observed munotherapy in patients suffering from prostate cancer [26]. Sen, Yang GH, and Mondal reported that TCF19, a novel that TCF19 negatively correlated with the effect of im- pancreatic islet regulator, regulated the processes of energy munotherapy. *e results indicated that TCF19 can be used metabolism and stress adaptation associated with the tumor as a potential indicator of the extent of the response cells by regulating gluconeogenesis. It was associated with generated toward renal cancer immunotherapy. Cancer the inflammatory responses in the beta cells of the pancreas immunotherapy based on TCF19 can also be explored and and the DNA damage response network. *e occurrence and the results can potentially open a new avenue for the B cell memory_CIBERSORT Mast cell activated_CIBERSORT Mast cell activated_CIBERSORT-ABS B cell memory_CIBERSORT-ABS Mast cell resting_CIBERSORT B cell memory_XCELL Mast cell resting_CIBERSORT-ABS B cell aive_CIBERSORT Mast cell_XCELL B cell naive_CIBERSORT-ABS MDSC_TIDE B cell naive_XCELL B cell plasma_CIBERSORT Macrophage/Monocyte_MCPCOUNTER B cell plasma__CIBERSORT-ABS Monocyte_CIBERSORT B cell plasma_XCELL Monocyte_CIBERSORT-ABS B-cell_EPIC Monocyte_MCPCOUNTER B-cell_MCPCOUNTER Monocyte_QUANTISEQ Monocyte_XCELL B cell_QUANTISEQ B cell_TIMER Neutrophil_CIBERSORT B cell_XCELL Neutrophil_CIBERSORT-ABS Class-switched memory B cell_XCELL Neutrophil_MCPCOUNTER Neutrophil_QUANTISEQ Cancer associated fibroblast_EPIC Neutrophil-TIMER Neutrophil_XCELL Cancer associated fibroblast_MCPCOUNTER Cancer associated fibroblast_TIDE Cancer associated fibroblast_XCELL NK cell activated_CIBERSORT NK cell activated_CIBERSORT-ABS NK cell resting_CIBERSORT Common lymphoid progenitor_XCELL NK cell resting_CIBERSORT-ABS NK cell_EPIC Common myeloid progenitor _XCELL NK cell_MCPCOUNTER NK cell_QUANTISQ Myeloid dendritic cell activated_CIBERSORT NK cell_XCELL Myeloid dendritic cell activated_CIBERSORT-ABS T cell CD4+ (non-regulatory)_QUANTISEQ Myeloid dendritic cell activated_XCELL T cell CD4+ (non-regulatory)_XCELL Myeloid dendritic cell resting CIBERSORT T cell CD4+ central memory_XCELL Myeloid dendritic cell resting_CIBERSORT-ABS T cell CD4+ effector memory_XCELL Myeloid dendritic cell_MCPCOUNTER T cell CD4+ memory activated_CIBERSORT Myeloid dendritic cell_QUANTISEQ T cell CD4+ memory activated_CIBERSORT-ABS Myeloid dendritic cell_TIMER T cell CD4+ memory resting_CIBERSORT T cell CD4+ memory resting_CIBERSORT-ABS Myeloid dendritic cell_XCELL T cell CD4+ memory _XCELL Plasmacytoid dendritic cell_XCELL T cell CD4+ naive_CIBERSORT T cell CD4+ naive_CIBERSORT-ABS Endothelial cell_EPIC T cell CD4+ naive_XCELL T cell CD4+ 1_XCELL Endothelial cell_MCPCOUNTER T cell CD4+ 2_XCELL Endothelial cell_XCELL T cell CD4+_EPIC T cell CD4+_TIMER Eosinophil_CIBERSORT Eosinophil_CIBERSORT-ABS T cell CD8+ central memory_XCELL Eosinophil_XCELL T cell CD8+ effector memory_XCELL T cell CD8+ naive _XCELL Granulocyte-monocyte progenitor_XCELL T cell CD8+_CIBERSORT T cell CD8+_CIBERSORT-ABS T cell CD8+_EPIC Hematopoietic stem cell_XCELL T cell CD8+_MCPCOUNTER T cell CD8+_QUANTISEQ Macrophage MO_CIBERSORT T cell CD8+_TIMER Macrophage MO_CIBERSORT-ABS T cell CD8+_XCELL Macrophage M1_CIBERSORT Macrophage M1_CIBERSORT-ABS T cell follicular helper_CIBERSORT Macrophage M1_QUANTISEQ T cell follicular helper_CIBERSORT-ABS Macrophage M1_XCELL T cell gamma delta_CIBERSORT Macrophage M2_CIBERSORT T cell gamma delta_CIBERSORT-ABS Macrophage M2_CIBERSORT-ABS T cell gamma delta_XCELL Macrophage M2 QUANTISEQ Macrophage M2_TIDE T cell NK_XCELL Macrophage M2 XCELL Macrophage/Monocyte_MCPCOUNTER T cell regulatory(Tregs)_CIBERSORT Macrophage_EPIC T cell regulatory(Tregs)_CIBERSORT-ABS T cell regulatory(Tregs)_QUANTISEQ Macrophage_TIMER T cell regulatory(Tregs)_XCELL Macrophage_XCELL 12 Journal of Oncology E2F_TARGETS E2F_TARGETS G2M_CHECKPOINT INTERFERON_GAMMA_RESPONSE UV_RESPONSE_UP INTERFERON_ALPHA_RESPONSE DNA_REPAIR ALLOGRAFT_REJECTION GLYCOLYSIS IL6_JAK_STAT3_SIGNALING UNFOLDED_PROTEIN_RESPONSE G2M_CHECKPOINT MITOTIC_SPINDLE INFLAMMATORY_RESPONSE PEROXISOME COMPLEMENT MTORC1_SIGNALING APOPTOSIS SPERMATOGENESIS CHOLESTEROL_HOMEOSTASIS MYC_TARGETS_V1 SPERMATOGENESIS MYC_TARGETS_V2 IL2_STAT5_SIGNALING NOTCH_SIGNALING GLYCOLYSIS PI3K_AKT_MTOR_SIGNALING UNFOLDED_PROTEIN_RESPONSE UV_RESPONSE_DN DNA_REPAIR ADIPOGENESIS MTORC1_SIGNALING TGF_BETA_SIGNALING MITOTIC_SPINDLE P53_PATHWAY KRAS_SIGNALING_UP PROTEIN_SECRETION WNT_BETA_CATENIN_SIGNALING XENOBIOTIC_METABOLISM REACTIVE_OXYGEN_SPECIES_PATHWAY HEME_METABOLISM UV_RESPONSE_UP OXIDATIVE_PHOSPHORYLATION APICAL_JUNCTION CHOLESTEROL_HOMEOSTASIS TNFA_SIGNALING_VIA_NFKB EPITHELIAL_MESENCHYMAL_TRANSITION MYC_TARGETS_V1 WNT_BETA_CATENIN_SIGNALING P53_PATHWAY APOPTOSIS HEDGEHOG_SIGNALING APICAL_JUNCTION EPITHELIAL_MESENCHYMAL_TRANSITION COAGULATION PANCREAS_BETA_CELLS FATTY_ACID_METABOLISM KRAS_SIGNALING_DN ANGIOGENESIS MYC_TARGETS_V2 ANDROGEN_RESPONSE PEROXISOME ESTROGEN_RESPONSE_EARLY PI3K_AKT_MTOR_SIGNALING HEDGEHOG_SIGNALING NOTCH_SIGNALING IL6_JAK_STAT3_SIGNALING UV_RESPONSE_DN KRAS_SIGNALING_UP XENOBIOTIC_METABOLISM REACTIVE_OXYGEN_SPECIES_PATHWAY ESTROGEN_RESPONSE_EARLY ESTROGEN_RESPONSE_LATE HYPOXIA HYPOXIA TGF_BETA_SIGNALING COMPLEMENT PROTEIN_SECRETION KRAS_SIGNALING_DN COAGULATION INTERFERON_ALPHA_RESPONSE ANDROGEN_RESPONSE IL2_STAT5_SIGNALING HEME_METABOLISM BILE_ACID_METABOLISM BILE_ACID_METABOLISM MYOGENESIS APICAL_SURFACE APICAL_SURFACE ADIPOGENESIS ALLOGRAFT_REJECTION ESTROGEN_RESPONSE_LATE INTERFERON_GAMMA_RESPONSE OXIDATIVE_PHOSPHORYLATION PANCREAS_BETA_CELLS FATTY_ACID_METABOLISM INFLAMMATORY_RESPONSE ANGIOGENESIS TNFA_SIGNALING_VIA_NFKB MYOGENESIS 5 0 5 10 5 0 5 10 t value of GSVA score t value of GSVA score HExp vs LExp group of KICH HExp vs LExp group of KIRC (a) (b) E2F_TARGETS G2M_CHECKPOINT TGF_BETA_SIGNALING MITOTIC_SPINDLE UV_RESPONSE_DN WNT_BETA_CATENIN_SIGNALING APICAL_JUNCTION INTERFERON_ALPHA_RESPONSE SPERMATOGENESIS APICAL_SURFACE UNFOLDED_PROTEIN_RESPONSE IL6_JAK_STAT3_SIGNALING NOTCH_SIGNALING KICH PROTEIN_SECRETION DNA_REPAIR KEGG_ALANINE_ASPARTATE_AND_GLUTAMATE_METABOLISM UV_RESPONSE_UP ANDROGEN_RESPONSE KEGG_BASAL_CELL_CARCINOMA PI3K_AKT_MTOR_SIGNALING KRAS_SIGNALING_UP KEGG_CELL_CYCLE COMPLEMENT KEGG_CITRATE_CYCLE_TCA_CYCLE INTERFERON_GAMMA_RESPONSE HEME_METABOLISM KEGG_COMPLEMENT_AND_COAGULATION_CASCADES APOPTOSIS P53_PATHWAY KEGG_DNA_REPLICATION HYPOXIA EPITHELIAL_MESENCHYMAL_TRANSITION KEGG_ECM_RECEPTOR_INTERACTION MYC_TARGETS_V1 IL2_STAT5_SIGNALING KEGG_FOCAL_ADHESION PEROXISOME ESTROGEN_RESPONSE_EARLY KEGG_GYLCOLYSIS_GLUCONEOGENSIS MTORC1_SIGNALING KEGG_HYPERTROPHIC_CARDIOMYOPATHY_HCM ESTROGEN_RESPONSE_LATE CHOLESTEROL_HOMEOSTASIS KEGG_MISMATCH_REPAIR ANGIOGENESIS TNFA_SIGNALING_VIA_NFKB KEGG_NUCLEOTIDE_EXCISION_REPAIR INFLAMMATORY_RESPONSE GLYCOLYSIS KEGG_ONE_CARBON_POOL_BY_FOLATE KRAS_SIGNALING_DN KEGG_PATHOGENIC_ESCHERICHIA_COLI_INFECTION MYC_TARGETS_V2 PANCREAS_BETA_CELLS KEGG_PATHWAYS_IN_CANCER HEDGEHOG_SIGNALING ALLOGRAFT_REJECTION KEGG_PROPANOATE_METABOLISM MYOGENESIS ADIPOGENESIS KEGG_PURINE_METABOLISM REACTIVE_OXYGEN_SPECIES_PATHWAY KEGG_PYRIIMIDINE_METABOLISM BILE_ACID_METABOLISM COAGULATION KEGG_PYRUVATE_METABOLISM FATTY_ACID_METABOLISM XENOBIOTIC_METABOLISM KEGG_SMALL_CELL_LUNG_CANCER OXIDATIVE_PHOSPHORYLATION 0.2 0.4 0.6 0.8 10 5 0 5 10 t value of GSVA score HExp vs LExp group of KIRP (c) (d) KIRP KIRC KEGG_ADHERENS_JUNCTION KEGG_ALLOGRAFT_REJECTION KEGG_AXON_GUIDANCE KEGG_ANTIGEN_PROCESSING_AND_PRESENTATION KEGG_CELL_ADHESION_MOLECULES_CAMS KEGG_AUTOIMMUNE_THYROID_DISEASE KEGG_CELL_CYCLE KEGG_B_CELL_RECEPTOR_SIGNALING_PATHWAY KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION KEGG_CELL_ADHESION_MOLECULES_CAMS KEGG_DNA_REPLICATION KEGG_CELL_CYCLE KEGG_DORSO_VENTRAL_AXIS_FORMATION KEGG_CHEMOKINE_SIGNALING_PATHWAY KEGG_ECM_RECEPTOR_INTERACTION KEGG_CYTOKINE_CYTOKINE_RECEPTOR_INTERACTION KEGG_FOCAL_ADHESION KEGG_CYTOSOLIC_DNA_SENSING_PATHWAY KEGG_JAK_STAT_SIGNALING_PATHWAY KEGG_DNA_REPLICATION KEGG_MELANOMA KEGG_HEMATOPOIETIC_CELL_LINEAGE KEGG_MISMATCH_REPAIR KEGG_INTESTINAL_IMMUE_NETWORK_FOR_IGA_PRODUCTION KEGG_NEUROACTIVE_LIGAND_RECEPTOR_INTERACTION KEGG_LEISHMANIA_INFECTION KEGG_PANCREATIC_CANCER KEGG_NATURAL_KILLER_CELL_MEDIATED_CYTOTOXICITY KEGG_PATHWAYS_IN_CANCER KEGG_PRIMARY_IMMUNODEFICIENCY KEGG_PRIMARY_IMMUNODEFICIENCY KEGG_SYSTEMIC_LUPUS_ERYTHEMATOSUS KEGG_PROSTATE_CANCER KEGG_T_CELL_RECEPTOR_SIGNALING_PATHWAY KEGG_RENAL_CELL_CARCINOMA KEGG_TOLL_LIKE_RECEPTOR_SIGNALING_PATHWAY KEGG_SMALL_CELL_LUNG_CANCER KEGG_TYPE_I_DIABETES_MELLITUS KEGG_TGF_BETA_SIGNALING_PATHWAY KEGG_VIRAL_MYOCARDITIS 0.00 0.25 0.50 0.75 0.2 0.4 0.6 0.8 (e) (f) Figure 8: *e results of GSVA analysis of TCF19. (a–c) It shows the GSVA analysis of TCF19 in KIRC, KIRP, and KICH, and (d–f) represents the GSEA analysis of TCF19 in KIRC, KIRP, and KICH. development of tumor immunotherapy strategies. For subjected to conditions of radiotherapy and immuno- example, Han [36] predicted the clinical outcome when therapy by analyzing the genetic characteristics of the patients suffering from lung adenocarcinoma were B cells. Dai [37] constructed an immune-related gene Journal of Oncology 13 Correlation of NEO Correlation of TMB 0.1 0.0 0.1 0.2 0.3 0.0 0.2 0.4 0.6 0.8 KICH*** THYM CESC PRAD*** ACC*** CESC LUAD*** UCS UCEC*** GBM DLBC ESCA READ PRAD*** LUSC BRCA*** LAML SKCM*** HNSC STAD*** STAD* THCA CHOL TGCT KIRP PCPG* LUAD* OV LIHC** UVM UCEC** SARC BLCA HNSC THCA* PAAD COAD* LUSC KIRP KIRC BLCA GBM BRCA SKCM LGG OV LIHC LGG MESO KIRC (a) (b) Correlation of MSI 0.2 0.0 0.2 DLBC* GBM*** CESC KICH THYM PRAD*** UCEC*** ESCA STAD*** TGCT KIRC* HNSC BLCA MESO SARC LGG PCPG UVM COAD* LUSC ACC LUAD BRCA* SKCM LIHC PAAD CHOL THCA OV UCS KIRP LAML READ (c) Figure 9: *e relationship of TMB and MSI with the TCF19 expression in cancers. (a) Shows the relationship between TCF19 expression and TMB, (b) indicates the relations of TCF19 expression with MSI, and (c) represents the correlations of TCF19 expression with Neoantigen. prognostic index (IRGPI) based on 11 immune-related underlying mechanisms associated with TCF19 and the genes, which can accurately forecast the immune cell in- immune system. Although the correlation between tumor filtrations in the tumor microenvironment of hepatocel- immune microenvironment and TCF19 cannot be applied to lular carcinoma and the response generated toward all kinds of tumors, our work revealed the immune effects of immunotherapy. Feng Xu [38] studied lung adenocarci- TCF19 on the microenvironment of specific cancer cells noma cases and reported that immune-related genes were which may potentially help improve the processes of independently predicting the poor survival rate of patients. TCRCC targeting therapy. However, preliminary results have been reported using various bioinformatics methods. As per we know, there is a minor number of relevant researches currently available to explain the functions of *erefore, further research should be conducted to un- TCF19 in ccRCC. *is study provided valuable information derstand how TCF19 influences cancer immunotherapy. In on how the TCF19 gene participated in cancer immuno- our next step, we need to extend the existing analysis da- therapy. *e results also revealed the relationship between tabase and mutually authenticate with the existing database. TCF19 and various immune indicators (such as the in- Authentication should be realized at the molecular, cyto- filtration process of immune cells, immune-modulatory logical, and animal levels by conducting experiments to factors, and the biomarkers of the immune system). *e investigate the relationship between the prognosis of the obtained data can potentially help understand the patients and the properties of the clinical tumor tissue 14 Journal of Oncology samples. We believe that the results can potentially help for Supplementary Figure 3: the WGCNA analysis of TCF19 in improving the efficiency of diagnosis, treatment methods, pan-cancer. Supplementary figure 4(a–f): the relationship and survival prognosis of cancer patients. between TCF19 expression and 33 tumor immune-related genes (genes analyzed include MHC, immune activators, immune suppressors, chemokines, and chemokine receptor 5. Conclusion proteins). 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Published: Sep 6, 2022

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