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Construction of an Immune-Autophagy Prognostic Model Based on ssGSEA Immune Scoring Algorithm Analysis and Prognostic Value Exploration of the Immune-Autophagy Gene in Endometrial Carcinoma (EC) Based on Bioinformatics

Construction of an Immune-Autophagy Prognostic Model Based on ssGSEA Immune Scoring Algorithm... Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 7832618, 11 pages https://doi.org/10.1155/2022/7832618 Research Article Construction of an Immune-Autophagy Prognostic Model Based on ssGSEA Immune Scoring Algorithm Analysis and Prognostic Value Exploration of the Immune-Autophagy Gene in Endometrial Carcinoma (EC) Based on Bioinformatics 1 2 3 1 Xiaomin Xu, Fang Lu, Cheng Fang, and Shumin Liu Heilongjiang University of Chinese Medicine, Harbin, China School of Continuing Education, Heilongjiang University of Traditional Chinese Medicine, Harbin, China Drug Safety Evaluation Center of Heilongjiang University of Traditional Chinese Medicine, Harbin, China Correspondence should be addressed to Shumin Liu; xxm17344990810@163.com Received 28 November 2021; Revised 7 January 2022; Accepted 15 January 2022; Published 22 February 2022 Academic Editor: Chinmay Chakraborty Copyright © 2022 Xiaomin Xu 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. Endometrial carcinoma (EC) is a malignant cancer spreading worldwide and in the fourth position among all other types of cancer in women. .e purpose of this paper is to explore the prognostic value of the immune-autophagy gene in endometrial carcinoma (EC) based on bioinformatics, construct an immune-autophagy prognostic model of endometrial carcinoma, search for independent prognostic markers, and finally predict the potential therapeutic drugs of TCGA subgroup. Methods. .e Cancer Genome Atlas (TCGA) database was used to extract transcriptome sequencing data of patients suffering from EC; 28 kinds of immune cells were scored by ssGSEA, and the immune subtypes were grouped by consistency cluster analysis. .e accuracy and effectiveness of the grouping were verified by the analysis of differential gene expression and survival rate of immune checkpoints in the two groups to provide the premise and basis for the establishment of independent prognostic factors. .e expression of different genes in high and low immune groups was analyzed. .e analysis of various genes’ expression in immune groups (high and low) has been performed. Go function annotation and KEGG pathway enrichment analysis were used to evaluate the difference of immune infiltration between high and low immune groups. .e immune and autophagy genes were crossed, the key (hub) genes were selected, the risk was scored, the prognosis model was constructed, and the independent prognostic markers were established. CAMP and CTRP 2.0 were used to test the drug sensitivity. Results. According to the level of immune cell enrichment, the results have been subcategorized into two immune subtypes: high immunity group_ H and low immunity group_ L. Two immune subtypes, CD274, PDCD1, and CTLA4, were detected in the immune system_ H and immunity_L. A significant difference was detected between these two groups in the expression and survival rate. Few more differences were also detected between the two groups through the evaluation of immune infiltration, which proved the grouping’s accuracy and effectiveness. Differential gene expression analysis showed that there were 721 DEGs and 3 hub genes. DEGs are mainly involved in lymphocyte activation, proliferation, differentiation, leukocyte proliferation, and other biological processes, mediate chemokines’ activities, chemokine receptor binding, and other molecular functions, and are enriched in the outer plasma membrane, endoplasmic reticulum, and T cell receptor complex. .e enriched pathways are allograft, complex, inflammatory, interferon-alpha, interferon-gamma, E2F, G2M, mitotic, etc. Conclusion. .rough bioinformatics analysis, we successfully constructed the immuno-autophagy prognosis model of endometrial cancer and identified three high-risk immunoautophagy genes, including VEGFA, CCL2, and Ifng. Four potential therapeutic drugs were predicted as sildenafil, sunitinib, TPCA-1, and etoposide. 2 Journal of Healthcare Engineering model based on immune-autophagy has the following ad- 1. Introduction vantages: (1) supplementing the EC research at the immune Endometrial carcinoma (EC) is a tumour of epithelial cells cell level; (2) making the prognosis model more stable and that arises from the endometrium, which is the most effective; (3) compensating for the limitations of single prevalent gynaecological malignant tumour and on the autophagy; and (4) providing the corresponding foundation fourth position among all other types of cancers in women for exploring the relationship between tumour cell auto- worldwide [1]. According to available statistics data, about phagy and cell immunity to lay the foundation for the 140000 women worldwide are diagnosed with endometrial further study of EC. cancer per year, with an estimated 40000 women dying due to this disease. .e standard endometrial cancer age curve 2. Materials and Methods indicates that most cases are discovered after menopause, 2.1. Identification of EC Subtypes Based on Immunocyte with the largest prevalence rate occurring in the seventh ten Transcriptome. .e TCGA knowledge base was used to years of life [2]. download the gene expression data of 536 patients having In the last decades, the incidence rate of EC has been EC. Each EC data set has been classified using 28 immune increasing worldwide. In recent times, based on clinical cell gene sets. .en, mRNAseq of normalized RSEM/RPKM manifestations, timely diagnosis of EC patients can be made; value with log2 was transformed as the input RNAseq data these manifestations include postmenopausal bleeding and for the clustering. RSEM is used to estimate gene and tumour markers’ abnormal levels [3]. At the same time, transcript abundances and these values are normalized to a about 15% of EC occurs in women with no vaginal bleeding fixed upper quartile value of 1000 for gene and 300 for [4]. For example, various serologic markers in EC diagnosis, transcript level estimates. RPKM for a given GeneX is cal- a carbohydrate antigen 19-9, and carbohydrate antigen-125 ∗ ∗ culated by (raw read counts 10^9)/(total reads length of were identified, but only in 20%, −30% of EC patients were GeneX). Total reads are the lane yield after removing poor under control [5]. Because of late diagnosis, EC patients quality reads and the length of GeneX is defined as the cannot be adequately treated, resulting in more risk of median length of all transcripts associated with GeneX. .e metastatic cancer and poor prognosis [6, 7]. Despite the R language GSVA, Lima, GSEABase software package was progress of treatment methods, the prognosis of advanced used to perform analysis known as ssGSEA [19], and 28 endometrial cancer is still a big challenge. .erefore, this kinds of immune cells were scored [20, 21] to quantify the study aims to build a prognostic model of EC and find more enrichment level of gene set in each EC sample. According to reliable and accurate prognostic biomarkers so that the EC the enrichment degree of immune cells, they have been patient’s survival rate can be improved. classified into high immunity group and low immunity Much evidence shows that tumour immune cell infil- group. To quantify the gene set in each EC sample, the tration is very much similar to the occurrence and devel- opment of cancer [8–10]. In tumours, the type and software package Consensus Cluster Plus was used for the consistency cluster analysis of the ssGSEA score. .e esti- proportion of immune cell infiltration are closely related to mate package was used to draw the heat map and predict the clinical results, which have predictive value for patients’ purity of the tumour. .e optimal clustering number is survival and can affect the therapeutic effect of the tumour, determined by the clustering score of the CDF curve. so it is expected to become a drug target and clinical bio- marker [11, 12]. .e neutrophils associated with tumours are the main types of immune cells, which can eliminate the 2.2. Immune Checkpoints in Different EC Subtypes in growth of pathogens and prevent host from microbial in- Immunotherapy. PDCD1, CTLA4, and CD274, three fection and are associated with breast cancer and gastric immune checkpoints, are closely related to multiple types cancer prognosis [13–15]. Besides, tumour-associated of tumour prognosis [22–24]. At the same time, the high macrophages are involved in EC’s invasive progression expression of immune checkpoints PDCD1, CTLA-4, and [16, 17]. Simultaneously, some studies have shown that the CD274 is associated with the prolongation of the overall induction of autophagy is very much similar to the poor survival of tumour patients. .erefore, we studied the prognosis of endometrial cancer [18]. Autophagy involves PDCD1, CTLA4, and CD274 expression in each subtype. the survival, differentiation, metabolism, immunity, and Subsequently, the survival rate difference analysis of other physiological functions of normal cells and tumour immune checkpoint inhibitor treatment was used to cells. .e relationship between autophagy and cellular im- verify. munity has attracted more and more attention. .ere are still many problems in EC autophagy studies: first, most of the current research limitations and stromal and epithelial cells; 2.3. Survival Verification and Difference Analysis. .e sur- very little research on immune cells and endometrial stem vival, surviving software package is used to analyze the cells. Secondly, in many studies, the sample size is small. .e difference in survival rate. .e differences have been de- detection of autophagy pairs is not comprehensive enough scribed using the Kaplan–Meier curve in EC patients and to be limited to detecting partial autophagy-related protein their survival rates in multiple classified immune cell sub- or RNA levels that do not represent dynamically varying types datasets. .e survival outcomes of EC patients com- autophagy levels. .us, this study will construct the prog- pared to detect the difference in survival time was significant. nostic model based on immune-autophagy. .e prognostic .e differences between the two subtypes were analyzed. Journal of Healthcare Engineering 3 2.5. Analysis of Results DEGs meet the requirements of P< 0.05 and FC <1.5 and draw the related volcanic map and thermal map to visually 2.5.1. Identification of EC Subtypes Based on Immune Cell show DEGs’ differential expression. Genome. Each sample of tumour was divided into K (k � 2–10) subtypes using the Consensus Cluster Plus software package. PAC algorithm verifies that when k � 2, the CDF 2.4. Enrichment Analysis of DEGs Gene and Evaluation of curve provides the best segmentation, as shown in Figure 1(b). Immune Infiltration Degree In addition, the analysis results represented that the ssGSEA 2.4.1. Functional Enrichment Analysis of DEGs Gene. scores based on 28 immune cell gene sets were divided into two After processing the TCGA data, it is divided into two subtypes, as shown in Figures 1(a)–1(f). .ey were defined as subtypes using consistent clustering analysis. .e two high immunity group and low immunity group, divided into subtypes of immunity_H and immunity_L are analyzed for 264 immunity_ H and 272 immunity_ L. At the same time, the related differences, and Limma is used for differential ex- comparison of matrix content showed the same trend (im- pression analysis and output DEGs and to draw the cor- munity_ H>immunity_ L _ H<immunity_ 50), as shown in responding volcano map and heat map [25]. .e functional Figure 1(g). .erefore, it can be verified that the classification of enrichment analysis of immunological differentially immune subtypes is accurate and reasonable. expressed genes is performed. GO functional annotation covers all biological processes. To analyze the function of 2.5.2. 3e 3erapeutic Effect of Immune Checkpoint Inhibi- DEGs, the cluster profile package of R software was used to tors in Different EC Subtypes. .e immunotherapy was annotate the go function of DEGs and analyze the enrich- observed by screening CD274, PDCD1, CTLA4, and other ment of the KEGG pathway. GSEA analyzed KEGG; the cut- genes. .e immunotherapy of PDCD1, CTLA-4, and CD274 off standard was set as FDR <0.05 and P< 1.5, and the was observed in immunity_ H, and immunity_ L expression analysis results were visualized. was analyzed, as shown in Figures 1(d)–1(f). It was found that it was all in the immunity_. .e high expression of h was 2.4.2. Immune Infiltration Evaluation. .e CIBERSORT significant (P< 0.05). It is suggested that the above genes have [26] was used to analyze the immune infiltration of 22 kinds an immunotherapeutic effect and sensitivity to the treatment of immune cells. .rough the analysis, it can still be con- of immunosuppressants. At the same time, its differential cluded that there are differences in immune infiltration expression in the immunity_H and immunity_L groups also between groups. verified the reliability and stability of the grouping. 2.4.3. Screening of Key (Hub) Genes and Construction of 2.5.3. Survival Verification and Difference Analysis. .e Prognosis Model. .e list of related genes was downloaded survival rate difference analysis of immunity_H and immu- from the autophagy database and immune database. Im- nity_L showed that P � 0.05 was significant, as shown in mune genes (import), autophagy genes (HADB), and Figure 2(a). .e difference between the two groups of immune differentially expressed genes (DEGs) were drawn by Venn genes was analyzed, DEGs met P< 0.05 and FC <1.5, and map to get the essential genes. .e obtained hub was an- related volcano maps and heat maps were drawn, as shown in alyzed by single factor Cox regression analysis. Forest map Figures 2(b) and 2(c). Among them, there were 721 GEGs was drawn by the R package to show its expression in genes, 633 genes upregulated, and 88 genes downregulated. different subtypes and then screen the genes related to the prognosis of endometrial cancer. At the same time, the 2.5.4. DEGs Gene Enrichment Analysis and Immune ggrisk package was used to group test the hub further to Infiltration. .e GO and KEGG signalling pathways of the evaluate the rationality and accuracy of the prognosis 721 differentially expressed genes were analysed. .e upre- model. gulated DEG genes were mainly related to the allograft (al- lograft inflammatory factors), complement (complement system), inflammatory (inflammatory mediators), interferon- 2.4.4. Drug Sensitivity Test. .e drug sensitivity data of CCLE are derived from the cancer therapeutics response alpha (INF-α), and interferon-gamma (IFN-c) pathways; the portal and PRISM replicas datasets. Both datasets provide downregulated DEG genes were mainly related to E2F, G2/M, the area under the dose-response curve as a measure of drug mitotic (spindle mitosis), and other pathways, as shown in sensitivity. .e lower the AUC value, the higher the sen- Figures 3(a)–3(b). .ey were mainly involved in the biological sitivity to treatment [27]. .e Camp database [28] uses gene processes of lymphocyte activation, proliferation, differentia- expression characteristics to predict small molecular com- tion, and leukocyte proliferation, and they mediated chemo- pounds for specific diseases. .e up- and downregulation kine activities and the binding of chemokine receptors. .ey genes were uploaded to the query page in Camp, and small were enriched in the plasma membrane's outer part, endo- plasmic reticulum, and T cell receptor complex, as shown in molecule drugs that might treat EC were searched. .e range from −1-1 scores represents the correlation between the Figure 3(c). Meanwhile, the immunoinfiltration analysis showed that there was a difference in immune infiltration drug and the DEG. A drug with more negative correlations indicates a more significant correlation with uploaded DEG. among the groups. .e immune score of the high immune 4 Journal of Healthcare Engineering consensus matrix k=2 consensus CDF Delta area 1.0 0.5 0.8 0.4 0.6 0.3 0.4 0.2 0.2 0.1 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 2468 10 1 consensus index k 2 5 7 9 3 6 8 10 (a) (b) (c) Anova, p < 2.2e−16 Anova, p < 2.2e−16 Anova, p < 2.2e−16 Immunity_H Immunity_L Immunity_H Immunity_L Immunity_H Immunity_L Subtype Subtype Subtype Subtype Subtype Subtype Immunity_H Immunity_H Immunity_H Immunity_L Immunity_L Immunity_L (d) (e) (f) TumorPurity TumorPurity ESTIMATEScore 0.9 ImmuneScore StromalScore Subtype 0.4 Memory B cell Type 2 T helper cell Activated CD4 T cell Effector memeory CD4 T cell ESTIMATEScore CD56bright natural killer cell Immature dendritic cell Plasmacytoid dendritic cell Eosinophil Mast cell −3000 Central memory CD4 T cell Natural killer cell Activated B cell Activated CD8 T cell ImmuneScore Effector memeory CD8 T cell Type 1 T helper cell Immature B cell MDSC −2 Activated dendritic cell −1000 T follicular helper cell Macrophage Gamma delta T cell Natural killer T cell −4 Central memory CD8 T cell StromalScore Regulatory T cell Type 17 T helper cell Neutrophil CD56dim natural killer cell −6 −2000 Monocyte Subtype Immunity_H Immunity_L (g) Figure 1: Development and validation of two immune cell subtypes in EC TCGA cohort. (a) When k � 2, the consensus score matrix of the EC sample is higher. .e higher consensus score between the two samples indicates that they are more likely to be assigned to the same cluster in different iterations; (b) EC described the real random variables of its probability distribution, based on the consensus scores of different subtype numbers (k � 2–10); (c) the trigonometric curve of all samples is k � 2; (d) CTLA-4 was differentially expressed in the two subtypes; (e) the differential expression of CD274 in the two subtypes was observed; (f) the expression of PDCD1 was different between the two subtypes; (g) ssGSEA fractional thermogram of 28 kinds of immune cells. CTLA4 expression CD274 expression CDF relative change in area under CDF curve PDCD1 expression Journal of Healthcare Engineering 5 1.00 Type 0.75 0.50 0.25 p = 0.005 0.00 0 5 10 15 20 Time Number at risk −1 cluster=Immunity_H 272 47 3 1 0 cluster=Immunity_L 264 62 4 1 0 0 5 10 15 20 Time Strata −2 Type cluster=Immunity_H High cluster=Immunity_L Low (a) (b) CD3D CXCL9 IGLL5 JCHAIN CXCL13 FREM2 TMSB15A DLX5 0 CRABP1 LGR5 −1 0123 Log2 (fold change) direction Down NS Up (c) Figure 2: Difference analysis of DEGs genes. (a) Immunity_H and Immunity_L Kaplan–Meier curve; (b) differential distribution heat map of 721 DEGs genes; (c) volcano map of differential gene expression; red represents upregulation; green represents downregulation. group was higher than that of the low immune group. M1 immport.org/) to download the list of related genes, draw macrophages, M0 macrophages, CD8 Tcells, M2 macrophages, Venn map of immune genes, autophagy genes, and 721 dif- dormant memory CD4 T cells, activated NK cells, monocytes, ferentially expressed genes and find that there are three dormant dendritic cells, and follicular helper T cells were overlapping genes, such as (Figure 4(a)) VEGFA, CCL2, and significantly higher in the high immune group than those in the Ifng, in which VEGFA is upregulated and CCL2 and Ifng are low immune group, as shown in Figure 3(d). downregulated. Based on the single factor Cox regression analysis, the prognosis-related immune and autophagy genes were determined, and the prognosis models of immunity and 2.5.5. Selection of Hub Gene, Construction, and Evaluation of autophagy were constructed. A risk curve was drawn for the Prognosis Model. By using the Human Autophagy Database grouping (Figure 4(b)) to further evaluate the prognosis (HADB) and immune database (import, https:0//www. model’s predictive ability. .e risk curve represented that the Strata Survival probability −log10 (p−value) 6 Journal of Healthcare Engineering 0.0 0.75 −0.2 0.50 −0.4 0.25 −0.6 0.00 5000 10000 15000 5000 10000 15000 HALLMARK_E2F_TARGETS HALLMARK_ALLOGRAFT_REJECTION HALLMARK_G2M_CHECKPOINT HALLMARK_COMPLEMENT HALLMARK_MITOTIC_SPINDLE HALLMARK_INFLAMMATORY_RESPONSE HALLMARK_MYC_TARGETS_V1 HALLMARK_INTERFERON_ALPHA_RESPONSE HALLMARK_MYC_TARGETS_V2 HALLMARK_INTERFERON_GAMMA_RESPONSE (a) (b) p.adjust Subtype 0.8 regulation of lymphocyte activation Plasma.cells lymphocyte proliferation mononuclear cell proliferation Macrophages.M1 leukocyte proliferation antigen receptor−mediated signaling pathway lymphocyte differentiation T.cells.CD8 regulation of cell−cell adhesion mediated by integrin 0.01 cell−cell adhesion mediated by integrin 0.6 T.cells.follicular.helper positive regulation of cell adhesion mediated by integrin regulation of cell adhesion mediated by integrin T.cells.regulatory..Tregs. external side of plasma membrane immunoglobulin complex Macrophages.M0 plasma membrane signaling receptor complex immunoglobulin complex, circulating 0.02 clathrin−coated vesicle membrane T.cells.CD4.memory.resting 0.4 T cell receptor complex coated vesicle membrane Monocytes clathrin−coated vesicle endoplasmic reticulum chaperone complex Macrophages.M2 chemokine activity 0.03 chemokine receptor binding Dendritic.cells.resting antigen binding 0.2 cytokine activity B.cells.naive glycosaminoglycan binding G protein−coupled receptor binding cytokine receptor binding NK.cells.activated CXCR chemokine receptor binding 0.04 CCR chemokine receptor binding protein self−association Mast.cells.resting 0.1 0.2 0.3 0.4 0.5 Subtype GeneRatio Immunity_H Count Immunity_L 1 3 2 4 6 (c) (d) Figure 3: Go and KEGG enrichment analysis and immune infiltration analysis; (a) KEGG enrichment analysis of upregulated DEGs; (b) KEGG enrichment analysis of downregulated DEGs; (c) Go enrichment analysis of DEGs; (d) heat map of immune infiltration correlation. independent prognosis analysis of Cox was performed, and the sensitivity database data of CTRP 2.0, as shown in forest map is drawn in Figure 3(d). Figure 5(a); Spearman correlation analysis and differential .e results showed that in the single factor independent drug response analysis were performed for 19 compounds, prognosis analysis, the risk score P< 0.05, indicating that the as shown in Figures 5(b) and 5(c). .e lower the value on the risk score could be used as an independent prognostic Y-axis of the box graph, the higher the drug sensitivity. molecule; VEGF, CCL2, and IFN were all P< 0.05, indicating Results: four kinds of susceptible drugs sildenafil, sunitinib, that the above genes could be used as independent prognostic TPCA-1, and etoposide were obtained. Relevant studies have shown that sildenafil, a phosphodiesterase 5 (PDE5) in- factors. Also, the three genes of VEGF, CCL2, and IFN were tested by ggrisk, such as Figure 4(d), and we found that the hibitor, can activate cGMP signal transduction in mouse three genes had a better classification effect, indicating that colonic mucosa, resist barrier dysfunction induced by DSS the constructed prognosis model was accurate. (dextran sodium sulfate), reduce bone marrow cell infil- tration, and reduce the expression of iNOS, IFN-c and IL-6, thus effectively inhibiting inflammation-driven colorectal 2.5.6. Drug Sensitivity Test. .e 150 upregulated genes and cancer in mice [29]. Sunitinib, a tyrosine kinase inhibitor 88 downregulated genes were selected and imported into (TKI), can inhibit the migration and invasion of RCC cells CMAP to obtain the drug table; Venn intersected two drug by reducing the expression of mir-452-5p [30, 31]. Running Enrichment Score MF CC BP Running Enrichment Score Journal of Healthcare Engineering 7 Intersection 1.5 IMM_Gene AUT_Gene 1.0 0.5 1305 187 0.0 cutoff: −0.05 −0.5 −1.0 174 5 Risk Group Status High Immunity_H Low Immunity_L (a) (b) pvalue Hazard ratio Expression IFNG VEGFA <0.001 0.764 (0.677−0.861) CCL2 CCL2 <0.001 1.198 (1.089−1.317) VEGFA −2 Risk Group High Low IFNG <0.001 1.260 (1.159−1.370) Hazard ratio (c) (d) Figure 4: Construction and evaluation of prognostic model; (a) Venn diagram map of DEGs, autophagy, and immune genes; (b) risk curve of endometrial cancer patients; (c) univariate Cox independent prognostic analysis; (d) risk classification test heat map. Topoisomerase II inhibitor etoposide has been successfully pathways, while downregulated DEGs were primarily and widely used to treat various types of cancer in children enriched in E2F, mitotic, G2M, and different signalling and adults [32]. pathways. .e key genes were VEGFA, CCL2, and IFN genes. In univariate independent prognostic analysis, the risk score was P< 0.05, indicating that the risk score can be 3. Discussion used as an independent prognostic molecule. .e results suggest that VEGFA, CCL2, and IFN genes may be the In this study, the bioinformatics analysis method was used to search for the relevant data of endometrial cancer in the critical gene targets in endometrial carcinoma. TCGA database, divided into high immunity and low im- .ere are six secretion subtypes in the VEGF family: munity groups. A total of 721 differentially expressed genes VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGF-E, and pla- were screened, including 633 upregulated genes and 88 cental growth factor [33]. VEGF-A is an endothelial cell- downregulated genes. According to go and KEGG analysis of specific growth factor regulating angiogenesis. It is an ef- differentially expressed genes, upregulated DEGs were fective stimulator in angiogenesis and is involved in multiple mainly enriched in the allograft, complement system, in- tumour types, including endometrial carcinoma [34]. flammatory mediators, IFN-α, IFN-c, and other signalling lncRNA-TDRG1 may promote endometrial cancer’s 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Survival Time Risk Score 8 Journal of Healthcare Engineering bexarotene CTRP BRD−K63431240 SNX−2112 AZD7762 birinapant cabozantinib lovastatin tamatinib MGCD−265 CMAP TPCA−1 sunitinib ruxolitinib sildenafil CHIR−99021 etoposide BRD−K33199242 bosutinib fumonisin B1 momelotinib 0.0 −0.3 Correlation coefficient −log (P−value) 12 14 16 13 15 17 (a) (b) *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 0.5 0.4 0.3 0.2 Immunity_H Immunity_L (c) Figure 5: Drug sensitivity test: (a) drug Venn diagram; (b-c) Spearman correlation analysis and drug response difference analysis results. Estimated AUC value AZD7762 bexarotene birinapant bosutinib BRD−K33199242 BRD−K63431240 cabozantinib CHIR−99021 etoposide fumonisin B1 lovastatin MGCD−265 momelotinib ruxolitinib sildenafil SNX−2112 sunitinib tamatinib TPCA−1 Journal of Healthcare Engineering 9 cyclooxygenase-2 in bladder cancer cells by inhibiting the occurrence and regulate VEGF-A downstream protein expression [35, 36]. Mir-140-5p can reduce ovarian tpl2/NF-κB pathway. IFN-α also inhibits the COX-2 ex- pression by inhibiting cAMP signal transduction of PDE4D cancer angiogenesis and inhibit cancer progression by downregulating VEGFA expression. .e mir-140-5p can activity mediated by tpl2-erk. PDE4D can enhance the reduce ovarian cancer angiogenesis and inhibit cancer antitumour effect of IFN-α on bladder cancer [46]. Mean- progression by downregulating VEGFA expression. More while, studies found that differential expressed genes may be than 50% of tumours overexpress VEGF-A in endome- one of the reasons for the different drug sensitivity in pa- trial carcinoma and have a poor prognosis [37]. .e tients with the related disease. .erefore, this study pre- high expression of VEGF and VEGFR in preoperative dicted four potential therapeutic drugs sildenafil, sunitinib, TPCA-1, and etoposide to provide certain drugs support for serum is closely related to angiogenesis and malignant phenotype and is a prognostic factor of endometrial clinical treatments of endometrial cancer, which may work through differential expression of genes [47]. In cancer [38, 39]. Inflammatory factors generally involve inflammatory addition, autophagy is an intracellular self-degradative process providing elimination of damaged or dysfunc- chemokines, especially CCL2 is related to tumour pro- gression. CC chemokine ligand 2 (CCL2) belongs to the tional organelles under stressful conditions such as nu- chemokine CC family, can raise tumour-related macro- trient deficiency, hypoxia, or chemotherapy. Interestingly, phages, promote tumour angiogenesis, and regulate the the signalling pathways that are involved in cancer-as- immune response. .e expression level of ccl2mrna in sociated inflammation may regulate autophagy as well breast cancer tissue is 13.18 times higher than in adjacent [48, 49]. tissues [40]. A high level of CCL2 is positively correlated with TNM stage and lymph node metastasis of gastric 3.1. Limitations. .is study is based on bioinformatics cancer [41]. CCL2 may be involved in the invasive growth methods and uses various tools and software to process of gastric cancer. .e high level of CCL2 expression in and analyze a large number of data. However, there are gastric cancer indicates a poor prognosis of gastric cancer. still shortcomings: (1) first, the predicted prognostic genes In the study of endometrial cancer, the inactivation of should be further verified to observe their specific role in LKB1 leads to the abnormal expression of inflammatory vitro in EC. (2) Secondly, experimental data should be cytokine chemokine in the tumour, leading to the increase used to verify the stability and accuracy of the prognosis of macrophage recruitment with significant tumour- model. (3) Finally, experimental evidence should be used promoting activity [42]. .e study showed that CCL2 to study further the effects of potential drugs predicted by expression is an important prognostic factor of cancer Camp and CTRP 2.0 on EC treatment. In future research, [43]. we hope to collect our own experimental and clinical data, IFNs (interferons) are a group of signalling proteins further explore the mechanism of molecular biology level, synthesized and released by host cells in response to build a more reliable and stable prognosis prediction pathogens. Under normal circumstances, the virus-in- model, and apply this model to clinical, which can better fected cells will release interferon, making the sur- serve patients. rounding cells improve their antivirus defence ability. Based on the receptor type, human interferon can be divided into three types: type I interferon, including IFN- 4. Conclusion α, IFN-β, type II interferon (known as IFN-c in humans), In summary, this study constructed a prognostic model of and type III interferon. In many tumour studies, IFN-α endometrial cancer based on 22 immune-related genes by and IFN-β can promote and inhibit tumour cells, which mining TCGA and HADB databases. Finally, it identified may be an important prognostic factor of cancer. .e three high-risk genes as prognostic genes of endometrial connection between IL-18 and its receptor activates the cancer, including VEGFA, CCL2, and IFN. .e identifica- MyD88 signalling pathway, inducing IFN-c production. tion of these genes will also provide new possibilities for the In terms of the tumour, tumour-infiltrating lymphocytes treatment and intervention of endometrial cancer. At the (TIL) are the primary source of IFN-c, which has shown same time, the drug sensitivity test showed that four po- special significance in tumour immune monitoring [44]. tential therapeutic drugs were sildenafil, sunitinib, TPCA-1, Studies have shown that IFN-c has dual effects on tumour and etoposide, to provide certain drugs support for the cells. treatment of endometrial cancer. On the one hand, IFN-c can inhibit the growth of human melanoma cells in vitro. On the other hand, it can increase HLA-DR expression and other tumour markers in advanced Data Availability melanoma. It indicates that IFN-c may promote the de- .e data used to support this study are available from the velopment of more aggressive phenotypes in cancer cells. Relevant studies have shown that IFN-c can promote tu- corresponding author upon request. mour occurrence and then promote the change of tumour cell phenotype to improve the growth adaptability of the Conflicts of Interest immuno-competent host [45]. Studies have shown that interferon-α (IFN-α) downregulates the expression of .e authors declare that they have no conflicts of interest. 10 Journal of Healthcare Engineering by M2 polarized macrophages in endometrial cancer,” Journal References of Immunology Research, vol. 2018, Article ID 6156757, [1] M. M. Braun, E. A. Overbeek-Wager, and R. J. Grumbo, 7 pages, 2018. “Diagnosis and management of Endometrial Cancer,” [18] L. Deng, R. R. Broaddus, A. 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Construction of an Immune-Autophagy Prognostic Model Based on ssGSEA Immune Scoring Algorithm Analysis and Prognostic Value Exploration of the Immune-Autophagy Gene in Endometrial Carcinoma (EC) Based on Bioinformatics

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Hindawi Publishing Corporation
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Copyright © 2022 Xiaomin Xu et al. This 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.
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2040-2295
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10.1155/2022/7832618
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Abstract

Hindawi Journal of Healthcare Engineering Volume 2022, Article ID 7832618, 11 pages https://doi.org/10.1155/2022/7832618 Research Article Construction of an Immune-Autophagy Prognostic Model Based on ssGSEA Immune Scoring Algorithm Analysis and Prognostic Value Exploration of the Immune-Autophagy Gene in Endometrial Carcinoma (EC) Based on Bioinformatics 1 2 3 1 Xiaomin Xu, Fang Lu, Cheng Fang, and Shumin Liu Heilongjiang University of Chinese Medicine, Harbin, China School of Continuing Education, Heilongjiang University of Traditional Chinese Medicine, Harbin, China Drug Safety Evaluation Center of Heilongjiang University of Traditional Chinese Medicine, Harbin, China Correspondence should be addressed to Shumin Liu; xxm17344990810@163.com Received 28 November 2021; Revised 7 January 2022; Accepted 15 January 2022; Published 22 February 2022 Academic Editor: Chinmay Chakraborty Copyright © 2022 Xiaomin Xu 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. Endometrial carcinoma (EC) is a malignant cancer spreading worldwide and in the fourth position among all other types of cancer in women. .e purpose of this paper is to explore the prognostic value of the immune-autophagy gene in endometrial carcinoma (EC) based on bioinformatics, construct an immune-autophagy prognostic model of endometrial carcinoma, search for independent prognostic markers, and finally predict the potential therapeutic drugs of TCGA subgroup. Methods. .e Cancer Genome Atlas (TCGA) database was used to extract transcriptome sequencing data of patients suffering from EC; 28 kinds of immune cells were scored by ssGSEA, and the immune subtypes were grouped by consistency cluster analysis. .e accuracy and effectiveness of the grouping were verified by the analysis of differential gene expression and survival rate of immune checkpoints in the two groups to provide the premise and basis for the establishment of independent prognostic factors. .e expression of different genes in high and low immune groups was analyzed. .e analysis of various genes’ expression in immune groups (high and low) has been performed. Go function annotation and KEGG pathway enrichment analysis were used to evaluate the difference of immune infiltration between high and low immune groups. .e immune and autophagy genes were crossed, the key (hub) genes were selected, the risk was scored, the prognosis model was constructed, and the independent prognostic markers were established. CAMP and CTRP 2.0 were used to test the drug sensitivity. Results. According to the level of immune cell enrichment, the results have been subcategorized into two immune subtypes: high immunity group_ H and low immunity group_ L. Two immune subtypes, CD274, PDCD1, and CTLA4, were detected in the immune system_ H and immunity_L. A significant difference was detected between these two groups in the expression and survival rate. Few more differences were also detected between the two groups through the evaluation of immune infiltration, which proved the grouping’s accuracy and effectiveness. Differential gene expression analysis showed that there were 721 DEGs and 3 hub genes. DEGs are mainly involved in lymphocyte activation, proliferation, differentiation, leukocyte proliferation, and other biological processes, mediate chemokines’ activities, chemokine receptor binding, and other molecular functions, and are enriched in the outer plasma membrane, endoplasmic reticulum, and T cell receptor complex. .e enriched pathways are allograft, complex, inflammatory, interferon-alpha, interferon-gamma, E2F, G2M, mitotic, etc. Conclusion. .rough bioinformatics analysis, we successfully constructed the immuno-autophagy prognosis model of endometrial cancer and identified three high-risk immunoautophagy genes, including VEGFA, CCL2, and Ifng. Four potential therapeutic drugs were predicted as sildenafil, sunitinib, TPCA-1, and etoposide. 2 Journal of Healthcare Engineering model based on immune-autophagy has the following ad- 1. Introduction vantages: (1) supplementing the EC research at the immune Endometrial carcinoma (EC) is a tumour of epithelial cells cell level; (2) making the prognosis model more stable and that arises from the endometrium, which is the most effective; (3) compensating for the limitations of single prevalent gynaecological malignant tumour and on the autophagy; and (4) providing the corresponding foundation fourth position among all other types of cancers in women for exploring the relationship between tumour cell auto- worldwide [1]. According to available statistics data, about phagy and cell immunity to lay the foundation for the 140000 women worldwide are diagnosed with endometrial further study of EC. cancer per year, with an estimated 40000 women dying due to this disease. .e standard endometrial cancer age curve 2. Materials and Methods indicates that most cases are discovered after menopause, 2.1. Identification of EC Subtypes Based on Immunocyte with the largest prevalence rate occurring in the seventh ten Transcriptome. .e TCGA knowledge base was used to years of life [2]. download the gene expression data of 536 patients having In the last decades, the incidence rate of EC has been EC. Each EC data set has been classified using 28 immune increasing worldwide. In recent times, based on clinical cell gene sets. .en, mRNAseq of normalized RSEM/RPKM manifestations, timely diagnosis of EC patients can be made; value with log2 was transformed as the input RNAseq data these manifestations include postmenopausal bleeding and for the clustering. RSEM is used to estimate gene and tumour markers’ abnormal levels [3]. At the same time, transcript abundances and these values are normalized to a about 15% of EC occurs in women with no vaginal bleeding fixed upper quartile value of 1000 for gene and 300 for [4]. For example, various serologic markers in EC diagnosis, transcript level estimates. RPKM for a given GeneX is cal- a carbohydrate antigen 19-9, and carbohydrate antigen-125 ∗ ∗ culated by (raw read counts 10^9)/(total reads length of were identified, but only in 20%, −30% of EC patients were GeneX). Total reads are the lane yield after removing poor under control [5]. Because of late diagnosis, EC patients quality reads and the length of GeneX is defined as the cannot be adequately treated, resulting in more risk of median length of all transcripts associated with GeneX. .e metastatic cancer and poor prognosis [6, 7]. Despite the R language GSVA, Lima, GSEABase software package was progress of treatment methods, the prognosis of advanced used to perform analysis known as ssGSEA [19], and 28 endometrial cancer is still a big challenge. .erefore, this kinds of immune cells were scored [20, 21] to quantify the study aims to build a prognostic model of EC and find more enrichment level of gene set in each EC sample. According to reliable and accurate prognostic biomarkers so that the EC the enrichment degree of immune cells, they have been patient’s survival rate can be improved. classified into high immunity group and low immunity Much evidence shows that tumour immune cell infil- group. To quantify the gene set in each EC sample, the tration is very much similar to the occurrence and devel- opment of cancer [8–10]. In tumours, the type and software package Consensus Cluster Plus was used for the consistency cluster analysis of the ssGSEA score. .e esti- proportion of immune cell infiltration are closely related to mate package was used to draw the heat map and predict the clinical results, which have predictive value for patients’ purity of the tumour. .e optimal clustering number is survival and can affect the therapeutic effect of the tumour, determined by the clustering score of the CDF curve. so it is expected to become a drug target and clinical bio- marker [11, 12]. .e neutrophils associated with tumours are the main types of immune cells, which can eliminate the 2.2. Immune Checkpoints in Different EC Subtypes in growth of pathogens and prevent host from microbial in- Immunotherapy. PDCD1, CTLA4, and CD274, three fection and are associated with breast cancer and gastric immune checkpoints, are closely related to multiple types cancer prognosis [13–15]. Besides, tumour-associated of tumour prognosis [22–24]. At the same time, the high macrophages are involved in EC’s invasive progression expression of immune checkpoints PDCD1, CTLA-4, and [16, 17]. Simultaneously, some studies have shown that the CD274 is associated with the prolongation of the overall induction of autophagy is very much similar to the poor survival of tumour patients. .erefore, we studied the prognosis of endometrial cancer [18]. Autophagy involves PDCD1, CTLA4, and CD274 expression in each subtype. the survival, differentiation, metabolism, immunity, and Subsequently, the survival rate difference analysis of other physiological functions of normal cells and tumour immune checkpoint inhibitor treatment was used to cells. .e relationship between autophagy and cellular im- verify. munity has attracted more and more attention. .ere are still many problems in EC autophagy studies: first, most of the current research limitations and stromal and epithelial cells; 2.3. Survival Verification and Difference Analysis. .e sur- very little research on immune cells and endometrial stem vival, surviving software package is used to analyze the cells. Secondly, in many studies, the sample size is small. .e difference in survival rate. .e differences have been de- detection of autophagy pairs is not comprehensive enough scribed using the Kaplan–Meier curve in EC patients and to be limited to detecting partial autophagy-related protein their survival rates in multiple classified immune cell sub- or RNA levels that do not represent dynamically varying types datasets. .e survival outcomes of EC patients com- autophagy levels. .us, this study will construct the prog- pared to detect the difference in survival time was significant. nostic model based on immune-autophagy. .e prognostic .e differences between the two subtypes were analyzed. Journal of Healthcare Engineering 3 2.5. Analysis of Results DEGs meet the requirements of P< 0.05 and FC <1.5 and draw the related volcanic map and thermal map to visually 2.5.1. Identification of EC Subtypes Based on Immune Cell show DEGs’ differential expression. Genome. Each sample of tumour was divided into K (k � 2–10) subtypes using the Consensus Cluster Plus software package. PAC algorithm verifies that when k � 2, the CDF 2.4. Enrichment Analysis of DEGs Gene and Evaluation of curve provides the best segmentation, as shown in Figure 1(b). Immune Infiltration Degree In addition, the analysis results represented that the ssGSEA 2.4.1. Functional Enrichment Analysis of DEGs Gene. scores based on 28 immune cell gene sets were divided into two After processing the TCGA data, it is divided into two subtypes, as shown in Figures 1(a)–1(f). .ey were defined as subtypes using consistent clustering analysis. .e two high immunity group and low immunity group, divided into subtypes of immunity_H and immunity_L are analyzed for 264 immunity_ H and 272 immunity_ L. At the same time, the related differences, and Limma is used for differential ex- comparison of matrix content showed the same trend (im- pression analysis and output DEGs and to draw the cor- munity_ H>immunity_ L _ H<immunity_ 50), as shown in responding volcano map and heat map [25]. .e functional Figure 1(g). .erefore, it can be verified that the classification of enrichment analysis of immunological differentially immune subtypes is accurate and reasonable. expressed genes is performed. GO functional annotation covers all biological processes. To analyze the function of 2.5.2. 3e 3erapeutic Effect of Immune Checkpoint Inhibi- DEGs, the cluster profile package of R software was used to tors in Different EC Subtypes. .e immunotherapy was annotate the go function of DEGs and analyze the enrich- observed by screening CD274, PDCD1, CTLA4, and other ment of the KEGG pathway. GSEA analyzed KEGG; the cut- genes. .e immunotherapy of PDCD1, CTLA-4, and CD274 off standard was set as FDR <0.05 and P< 1.5, and the was observed in immunity_ H, and immunity_ L expression analysis results were visualized. was analyzed, as shown in Figures 1(d)–1(f). It was found that it was all in the immunity_. .e high expression of h was 2.4.2. Immune Infiltration Evaluation. .e CIBERSORT significant (P< 0.05). It is suggested that the above genes have [26] was used to analyze the immune infiltration of 22 kinds an immunotherapeutic effect and sensitivity to the treatment of immune cells. .rough the analysis, it can still be con- of immunosuppressants. At the same time, its differential cluded that there are differences in immune infiltration expression in the immunity_H and immunity_L groups also between groups. verified the reliability and stability of the grouping. 2.4.3. Screening of Key (Hub) Genes and Construction of 2.5.3. Survival Verification and Difference Analysis. .e Prognosis Model. .e list of related genes was downloaded survival rate difference analysis of immunity_H and immu- from the autophagy database and immune database. Im- nity_L showed that P � 0.05 was significant, as shown in mune genes (import), autophagy genes (HADB), and Figure 2(a). .e difference between the two groups of immune differentially expressed genes (DEGs) were drawn by Venn genes was analyzed, DEGs met P< 0.05 and FC <1.5, and map to get the essential genes. .e obtained hub was an- related volcano maps and heat maps were drawn, as shown in alyzed by single factor Cox regression analysis. Forest map Figures 2(b) and 2(c). Among them, there were 721 GEGs was drawn by the R package to show its expression in genes, 633 genes upregulated, and 88 genes downregulated. different subtypes and then screen the genes related to the prognosis of endometrial cancer. At the same time, the 2.5.4. DEGs Gene Enrichment Analysis and Immune ggrisk package was used to group test the hub further to Infiltration. .e GO and KEGG signalling pathways of the evaluate the rationality and accuracy of the prognosis 721 differentially expressed genes were analysed. .e upre- model. gulated DEG genes were mainly related to the allograft (al- lograft inflammatory factors), complement (complement system), inflammatory (inflammatory mediators), interferon- 2.4.4. Drug Sensitivity Test. .e drug sensitivity data of CCLE are derived from the cancer therapeutics response alpha (INF-α), and interferon-gamma (IFN-c) pathways; the portal and PRISM replicas datasets. Both datasets provide downregulated DEG genes were mainly related to E2F, G2/M, the area under the dose-response curve as a measure of drug mitotic (spindle mitosis), and other pathways, as shown in sensitivity. .e lower the AUC value, the higher the sen- Figures 3(a)–3(b). .ey were mainly involved in the biological sitivity to treatment [27]. .e Camp database [28] uses gene processes of lymphocyte activation, proliferation, differentia- expression characteristics to predict small molecular com- tion, and leukocyte proliferation, and they mediated chemo- pounds for specific diseases. .e up- and downregulation kine activities and the binding of chemokine receptors. .ey genes were uploaded to the query page in Camp, and small were enriched in the plasma membrane's outer part, endo- plasmic reticulum, and T cell receptor complex, as shown in molecule drugs that might treat EC were searched. .e range from −1-1 scores represents the correlation between the Figure 3(c). Meanwhile, the immunoinfiltration analysis showed that there was a difference in immune infiltration drug and the DEG. A drug with more negative correlations indicates a more significant correlation with uploaded DEG. among the groups. .e immune score of the high immune 4 Journal of Healthcare Engineering consensus matrix k=2 consensus CDF Delta area 1.0 0.5 0.8 0.4 0.6 0.3 0.4 0.2 0.2 0.1 0.0 0.0 0.0 0.2 0.4 0.6 0.8 1.0 2468 10 1 consensus index k 2 5 7 9 3 6 8 10 (a) (b) (c) Anova, p < 2.2e−16 Anova, p < 2.2e−16 Anova, p < 2.2e−16 Immunity_H Immunity_L Immunity_H Immunity_L Immunity_H Immunity_L Subtype Subtype Subtype Subtype Subtype Subtype Immunity_H Immunity_H Immunity_H Immunity_L Immunity_L Immunity_L (d) (e) (f) TumorPurity TumorPurity ESTIMATEScore 0.9 ImmuneScore StromalScore Subtype 0.4 Memory B cell Type 2 T helper cell Activated CD4 T cell Effector memeory CD4 T cell ESTIMATEScore CD56bright natural killer cell Immature dendritic cell Plasmacytoid dendritic cell Eosinophil Mast cell −3000 Central memory CD4 T cell Natural killer cell Activated B cell Activated CD8 T cell ImmuneScore Effector memeory CD8 T cell Type 1 T helper cell Immature B cell MDSC −2 Activated dendritic cell −1000 T follicular helper cell Macrophage Gamma delta T cell Natural killer T cell −4 Central memory CD8 T cell StromalScore Regulatory T cell Type 17 T helper cell Neutrophil CD56dim natural killer cell −6 −2000 Monocyte Subtype Immunity_H Immunity_L (g) Figure 1: Development and validation of two immune cell subtypes in EC TCGA cohort. (a) When k � 2, the consensus score matrix of the EC sample is higher. .e higher consensus score between the two samples indicates that they are more likely to be assigned to the same cluster in different iterations; (b) EC described the real random variables of its probability distribution, based on the consensus scores of different subtype numbers (k � 2–10); (c) the trigonometric curve of all samples is k � 2; (d) CTLA-4 was differentially expressed in the two subtypes; (e) the differential expression of CD274 in the two subtypes was observed; (f) the expression of PDCD1 was different between the two subtypes; (g) ssGSEA fractional thermogram of 28 kinds of immune cells. CTLA4 expression CD274 expression CDF relative change in area under CDF curve PDCD1 expression Journal of Healthcare Engineering 5 1.00 Type 0.75 0.50 0.25 p = 0.005 0.00 0 5 10 15 20 Time Number at risk −1 cluster=Immunity_H 272 47 3 1 0 cluster=Immunity_L 264 62 4 1 0 0 5 10 15 20 Time Strata −2 Type cluster=Immunity_H High cluster=Immunity_L Low (a) (b) CD3D CXCL9 IGLL5 JCHAIN CXCL13 FREM2 TMSB15A DLX5 0 CRABP1 LGR5 −1 0123 Log2 (fold change) direction Down NS Up (c) Figure 2: Difference analysis of DEGs genes. (a) Immunity_H and Immunity_L Kaplan–Meier curve; (b) differential distribution heat map of 721 DEGs genes; (c) volcano map of differential gene expression; red represents upregulation; green represents downregulation. group was higher than that of the low immune group. M1 immport.org/) to download the list of related genes, draw macrophages, M0 macrophages, CD8 Tcells, M2 macrophages, Venn map of immune genes, autophagy genes, and 721 dif- dormant memory CD4 T cells, activated NK cells, monocytes, ferentially expressed genes and find that there are three dormant dendritic cells, and follicular helper T cells were overlapping genes, such as (Figure 4(a)) VEGFA, CCL2, and significantly higher in the high immune group than those in the Ifng, in which VEGFA is upregulated and CCL2 and Ifng are low immune group, as shown in Figure 3(d). downregulated. Based on the single factor Cox regression analysis, the prognosis-related immune and autophagy genes were determined, and the prognosis models of immunity and 2.5.5. Selection of Hub Gene, Construction, and Evaluation of autophagy were constructed. A risk curve was drawn for the Prognosis Model. By using the Human Autophagy Database grouping (Figure 4(b)) to further evaluate the prognosis (HADB) and immune database (import, https:0//www. model’s predictive ability. .e risk curve represented that the Strata Survival probability −log10 (p−value) 6 Journal of Healthcare Engineering 0.0 0.75 −0.2 0.50 −0.4 0.25 −0.6 0.00 5000 10000 15000 5000 10000 15000 HALLMARK_E2F_TARGETS HALLMARK_ALLOGRAFT_REJECTION HALLMARK_G2M_CHECKPOINT HALLMARK_COMPLEMENT HALLMARK_MITOTIC_SPINDLE HALLMARK_INFLAMMATORY_RESPONSE HALLMARK_MYC_TARGETS_V1 HALLMARK_INTERFERON_ALPHA_RESPONSE HALLMARK_MYC_TARGETS_V2 HALLMARK_INTERFERON_GAMMA_RESPONSE (a) (b) p.adjust Subtype 0.8 regulation of lymphocyte activation Plasma.cells lymphocyte proliferation mononuclear cell proliferation Macrophages.M1 leukocyte proliferation antigen receptor−mediated signaling pathway lymphocyte differentiation T.cells.CD8 regulation of cell−cell adhesion mediated by integrin 0.01 cell−cell adhesion mediated by integrin 0.6 T.cells.follicular.helper positive regulation of cell adhesion mediated by integrin regulation of cell adhesion mediated by integrin T.cells.regulatory..Tregs. external side of plasma membrane immunoglobulin complex Macrophages.M0 plasma membrane signaling receptor complex immunoglobulin complex, circulating 0.02 clathrin−coated vesicle membrane T.cells.CD4.memory.resting 0.4 T cell receptor complex coated vesicle membrane Monocytes clathrin−coated vesicle endoplasmic reticulum chaperone complex Macrophages.M2 chemokine activity 0.03 chemokine receptor binding Dendritic.cells.resting antigen binding 0.2 cytokine activity B.cells.naive glycosaminoglycan binding G protein−coupled receptor binding cytokine receptor binding NK.cells.activated CXCR chemokine receptor binding 0.04 CCR chemokine receptor binding protein self−association Mast.cells.resting 0.1 0.2 0.3 0.4 0.5 Subtype GeneRatio Immunity_H Count Immunity_L 1 3 2 4 6 (c) (d) Figure 3: Go and KEGG enrichment analysis and immune infiltration analysis; (a) KEGG enrichment analysis of upregulated DEGs; (b) KEGG enrichment analysis of downregulated DEGs; (c) Go enrichment analysis of DEGs; (d) heat map of immune infiltration correlation. independent prognosis analysis of Cox was performed, and the sensitivity database data of CTRP 2.0, as shown in forest map is drawn in Figure 3(d). Figure 5(a); Spearman correlation analysis and differential .e results showed that in the single factor independent drug response analysis were performed for 19 compounds, prognosis analysis, the risk score P< 0.05, indicating that the as shown in Figures 5(b) and 5(c). .e lower the value on the risk score could be used as an independent prognostic Y-axis of the box graph, the higher the drug sensitivity. molecule; VEGF, CCL2, and IFN were all P< 0.05, indicating Results: four kinds of susceptible drugs sildenafil, sunitinib, that the above genes could be used as independent prognostic TPCA-1, and etoposide were obtained. Relevant studies have shown that sildenafil, a phosphodiesterase 5 (PDE5) in- factors. Also, the three genes of VEGF, CCL2, and IFN were tested by ggrisk, such as Figure 4(d), and we found that the hibitor, can activate cGMP signal transduction in mouse three genes had a better classification effect, indicating that colonic mucosa, resist barrier dysfunction induced by DSS the constructed prognosis model was accurate. (dextran sodium sulfate), reduce bone marrow cell infil- tration, and reduce the expression of iNOS, IFN-c and IL-6, thus effectively inhibiting inflammation-driven colorectal 2.5.6. Drug Sensitivity Test. .e 150 upregulated genes and cancer in mice [29]. Sunitinib, a tyrosine kinase inhibitor 88 downregulated genes were selected and imported into (TKI), can inhibit the migration and invasion of RCC cells CMAP to obtain the drug table; Venn intersected two drug by reducing the expression of mir-452-5p [30, 31]. Running Enrichment Score MF CC BP Running Enrichment Score Journal of Healthcare Engineering 7 Intersection 1.5 IMM_Gene AUT_Gene 1.0 0.5 1305 187 0.0 cutoff: −0.05 −0.5 −1.0 174 5 Risk Group Status High Immunity_H Low Immunity_L (a) (b) pvalue Hazard ratio Expression IFNG VEGFA <0.001 0.764 (0.677−0.861) CCL2 CCL2 <0.001 1.198 (1.089−1.317) VEGFA −2 Risk Group High Low IFNG <0.001 1.260 (1.159−1.370) Hazard ratio (c) (d) Figure 4: Construction and evaluation of prognostic model; (a) Venn diagram map of DEGs, autophagy, and immune genes; (b) risk curve of endometrial cancer patients; (c) univariate Cox independent prognostic analysis; (d) risk classification test heat map. Topoisomerase II inhibitor etoposide has been successfully pathways, while downregulated DEGs were primarily and widely used to treat various types of cancer in children enriched in E2F, mitotic, G2M, and different signalling and adults [32]. pathways. .e key genes were VEGFA, CCL2, and IFN genes. In univariate independent prognostic analysis, the risk score was P< 0.05, indicating that the risk score can be 3. Discussion used as an independent prognostic molecule. .e results suggest that VEGFA, CCL2, and IFN genes may be the In this study, the bioinformatics analysis method was used to search for the relevant data of endometrial cancer in the critical gene targets in endometrial carcinoma. TCGA database, divided into high immunity and low im- .ere are six secretion subtypes in the VEGF family: munity groups. A total of 721 differentially expressed genes VEGF-A, VEGF-B, VEGF-C, VEGF-D, VEGF-E, and pla- were screened, including 633 upregulated genes and 88 cental growth factor [33]. VEGF-A is an endothelial cell- downregulated genes. According to go and KEGG analysis of specific growth factor regulating angiogenesis. It is an ef- differentially expressed genes, upregulated DEGs were fective stimulator in angiogenesis and is involved in multiple mainly enriched in the allograft, complement system, in- tumour types, including endometrial carcinoma [34]. flammatory mediators, IFN-α, IFN-c, and other signalling lncRNA-TDRG1 may promote endometrial cancer’s 0.0 0.2 0.4 0.6 0.8 1.0 1.2 Survival Time Risk Score 8 Journal of Healthcare Engineering bexarotene CTRP BRD−K63431240 SNX−2112 AZD7762 birinapant cabozantinib lovastatin tamatinib MGCD−265 CMAP TPCA−1 sunitinib ruxolitinib sildenafil CHIR−99021 etoposide BRD−K33199242 bosutinib fumonisin B1 momelotinib 0.0 −0.3 Correlation coefficient −log (P−value) 12 14 16 13 15 17 (a) (b) *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** *** 0.5 0.4 0.3 0.2 Immunity_H Immunity_L (c) Figure 5: Drug sensitivity test: (a) drug Venn diagram; (b-c) Spearman correlation analysis and drug response difference analysis results. Estimated AUC value AZD7762 bexarotene birinapant bosutinib BRD−K33199242 BRD−K63431240 cabozantinib CHIR−99021 etoposide fumonisin B1 lovastatin MGCD−265 momelotinib ruxolitinib sildenafil SNX−2112 sunitinib tamatinib TPCA−1 Journal of Healthcare Engineering 9 cyclooxygenase-2 in bladder cancer cells by inhibiting the occurrence and regulate VEGF-A downstream protein expression [35, 36]. Mir-140-5p can reduce ovarian tpl2/NF-κB pathway. IFN-α also inhibits the COX-2 ex- pression by inhibiting cAMP signal transduction of PDE4D cancer angiogenesis and inhibit cancer progression by downregulating VEGFA expression. .e mir-140-5p can activity mediated by tpl2-erk. PDE4D can enhance the reduce ovarian cancer angiogenesis and inhibit cancer antitumour effect of IFN-α on bladder cancer [46]. Mean- progression by downregulating VEGFA expression. More while, studies found that differential expressed genes may be than 50% of tumours overexpress VEGF-A in endome- one of the reasons for the different drug sensitivity in pa- trial carcinoma and have a poor prognosis [37]. .e tients with the related disease. .erefore, this study pre- high expression of VEGF and VEGFR in preoperative dicted four potential therapeutic drugs sildenafil, sunitinib, TPCA-1, and etoposide to provide certain drugs support for serum is closely related to angiogenesis and malignant phenotype and is a prognostic factor of endometrial clinical treatments of endometrial cancer, which may work through differential expression of genes [47]. In cancer [38, 39]. Inflammatory factors generally involve inflammatory addition, autophagy is an intracellular self-degradative process providing elimination of damaged or dysfunc- chemokines, especially CCL2 is related to tumour pro- gression. CC chemokine ligand 2 (CCL2) belongs to the tional organelles under stressful conditions such as nu- chemokine CC family, can raise tumour-related macro- trient deficiency, hypoxia, or chemotherapy. Interestingly, phages, promote tumour angiogenesis, and regulate the the signalling pathways that are involved in cancer-as- immune response. .e expression level of ccl2mrna in sociated inflammation may regulate autophagy as well breast cancer tissue is 13.18 times higher than in adjacent [48, 49]. tissues [40]. A high level of CCL2 is positively correlated with TNM stage and lymph node metastasis of gastric 3.1. Limitations. .is study is based on bioinformatics cancer [41]. CCL2 may be involved in the invasive growth methods and uses various tools and software to process of gastric cancer. .e high level of CCL2 expression in and analyze a large number of data. However, there are gastric cancer indicates a poor prognosis of gastric cancer. still shortcomings: (1) first, the predicted prognostic genes In the study of endometrial cancer, the inactivation of should be further verified to observe their specific role in LKB1 leads to the abnormal expression of inflammatory vitro in EC. (2) Secondly, experimental data should be cytokine chemokine in the tumour, leading to the increase used to verify the stability and accuracy of the prognosis of macrophage recruitment with significant tumour- model. (3) Finally, experimental evidence should be used promoting activity [42]. .e study showed that CCL2 to study further the effects of potential drugs predicted by expression is an important prognostic factor of cancer Camp and CTRP 2.0 on EC treatment. In future research, [43]. we hope to collect our own experimental and clinical data, IFNs (interferons) are a group of signalling proteins further explore the mechanism of molecular biology level, synthesized and released by host cells in response to build a more reliable and stable prognosis prediction pathogens. Under normal circumstances, the virus-in- model, and apply this model to clinical, which can better fected cells will release interferon, making the sur- serve patients. rounding cells improve their antivirus defence ability. Based on the receptor type, human interferon can be divided into three types: type I interferon, including IFN- 4. Conclusion α, IFN-β, type II interferon (known as IFN-c in humans), In summary, this study constructed a prognostic model of and type III interferon. In many tumour studies, IFN-α endometrial cancer based on 22 immune-related genes by and IFN-β can promote and inhibit tumour cells, which mining TCGA and HADB databases. Finally, it identified may be an important prognostic factor of cancer. .e three high-risk genes as prognostic genes of endometrial connection between IL-18 and its receptor activates the cancer, including VEGFA, CCL2, and IFN. .e identifica- MyD88 signalling pathway, inducing IFN-c production. tion of these genes will also provide new possibilities for the In terms of the tumour, tumour-infiltrating lymphocytes treatment and intervention of endometrial cancer. At the (TIL) are the primary source of IFN-c, which has shown same time, the drug sensitivity test showed that four po- special significance in tumour immune monitoring [44]. tential therapeutic drugs were sildenafil, sunitinib, TPCA-1, Studies have shown that IFN-c has dual effects on tumour and etoposide, to provide certain drugs support for the cells. treatment of endometrial cancer. On the one hand, IFN-c can inhibit the growth of human melanoma cells in vitro. On the other hand, it can increase HLA-DR expression and other tumour markers in advanced Data Availability melanoma. It indicates that IFN-c may promote the de- .e data used to support this study are available from the velopment of more aggressive phenotypes in cancer cells. Relevant studies have shown that IFN-c can promote tu- corresponding author upon request. mour occurrence and then promote the change of tumour cell phenotype to improve the growth adaptability of the Conflicts of Interest immuno-competent host [45]. Studies have shown that interferon-α (IFN-α) downregulates the expression of .e authors declare that they have no conflicts of interest. 10 Journal of Healthcare Engineering by M2 polarized macrophages in endometrial cancer,” Journal References of Immunology Research, vol. 2018, Article ID 6156757, [1] M. M. Braun, E. A. Overbeek-Wager, and R. J. Grumbo, 7 pages, 2018. “Diagnosis and management of Endometrial Cancer,” [18] L. Deng, R. R. Broaddus, A. 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Journal of Healthcare EngineeringHindawi Publishing Corporation

Published: Feb 22, 2022

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