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ACE2 Is a Prognostic Biomarker and Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma: Implication for COVID-19

ACE2 Is a Prognostic Biomarker and Associated with Immune Infiltration in Kidney Renal Clear Cell... Hindawi Journal of Oncology Volume 2021, Article ID 8847307, 15 pages https://doi.org/10.1155/2021/8847307 Research Article ACE2 Is a Prognostic Biomarker and Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma: Implication for COVID-19 Xinhao Niu, Zhe Zhu, Enming Shao, and Juan Bao Department of Urinary Surgery, e Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China Correspondence should be addressed to Juan Bao; bj901120@gmail.com Received 2 September 2020; Revised 7 January 2021; Accepted 18 January 2021; Published 30 January 2021 Academic Editor: Francesca De Felice Copyright © 2021 Xinhao Niu 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. KIRC is one of the most common cancers with a poor prognosis. ACE2 was involved in tumor angiogenesis and progression in many malignancies. +e role of ACE2 in KIRC is still ambiguous. Methods. Various bioinformatics analysis tools were investigated to evaluate the prognostic value of ACE2 and its association with immune infiltration in KIRC. Results. ACE2 was shown to be downregulated in KIRC at the mRNA and protein level. Low expression of ACE2 protein in KIRC patients was observed in subgroup analyses based on gender, age, weight, tumor grade, and cancer stage. Upregulation of ACE2 in KIRC was associated with a favorable prognosis. ACE2 mRNA expression showed a positive correlation with the abundance of immune cells (B cells, CD8+ Tcells, macrophages, neutrophils, and dendritic cells) and the level of immune markers of different immune cells in KIRC. ACE2 expression could affect, in part, the immune infiltration and the advanced cancer stage. Moreover, enrichment analysis revealed that ACE2 in KIRC were mainly involved in translation factor activity, immunoglobulin binding, metabolic pathways, transcriptional misregulation in cancerous cells, cell cycle, and ribosomal activity. Several ACE2-associated kinases, miRNA, and transcription factor targets in KIRC were also identified. Conclusion. ACE2 was downregulated in KIRC and served as a prognostic biomarker. It was also shown to be associated with immune infiltration. Immunotherapy has been suggested as the treatment for 1. Introduction metastatic KIRC [8, 9]. +erefore, clarifying the role of Kidney cancer is one of the most common malignances immune infiltration in KIRC and identifying immune- globally, accounting for about 4.5% of all newly diagnosed associated markers for the prognosis for KIRC are par- malignances [1]. It is anticipated that 73,750 people would ticularly necessary. be newly diagnosed with kidney cancer and 14,830 pa- Angiotensin converting enzyme 2 (ACE2) is a member of tients are likely to die because of the disease in the USA in the renin angiotensin system (RAS) whose open reading 2020 [2]. +e most common subtype of renal cancer is framework encodes an 805-amino-acid polypeptide [10]. In- kidney renal clear cell carcinoma (KIRC), which makes up creasing evidence indicates a significant function of ACE2 in over 70% of kidney cancers [3]. Surgery excision remains the tumor angiogenesis and its progression in many cancers, the primary therapy for KIRC due to the growing resis- such as thyroid carcinoma, breast carcinoma, and lung ade- tance to radiotherapy and chemotherapy [4]. Much worse, nocarcinoma [11–13]. ACE2 has also been suggested as a the prognosis of KIRC patients tends to be poor, especially biomarker for many diseases, including squamous cell/ade- for patients in an advanced stage. +e five-year overall nosquamous carcinoma, endometrial carcinoma, and hyper- survival rate of stage IV patients is less than 10% [5]. tension [10, 14, 15]. However, limited studies have clarified the Previous studies have revealed that immune infiltration is function of ACE2 in immune infiltration and its role in the significantly linked to the survival of KIRC patients. [6, 7]. prognosis in KIRC. 2 Journal of Oncology Coronavirus disease 2019 (COVID-19), caused by the biomarkers were excluded because they have already been novel coronavirus severe acute respiratory syndrome described in previous studies [26–28]. coronavirus 2 (SARS-CoV-2), was initially found in Wuhan of China since December 2019 [16, 17]. It is well known that 2.4. cBioPortal for Genetic Alteration Analysis. cBioPortal the functional host receptor of SARS-CoV-2 is ACE2 (http://www.cbioportal.org) is a TCGA visual tool used to [18, 19]. Over 10 million peoples were diagnosed with perform genome analysis [29]. We analyzed ACE2 genetic COVID-19 and over 520000 peoples died of this disease alteration in KIRC with the threshold as ±2.0 in mRNA globally until July 1, 2020. As we have seen, the prognosis of expression z-scores (RNASeq V2 RSEM) and protein ex- COVID-19 patients with KIRC remains ambiguous. pression z-scores (RPPA). +erefore, our study was performed to detect ACE2 levels and the prognostic value in patients with KIRC. +e function of ACE2 in immune infiltration in KIRC was also 2.5. LinkedOmics for Enrichment Analysis. In order to verify clarified. Our results may provide additional evidence re- the ACE2-associated functions in KIRC, LinkedOmics garding the role of ACE2 and immune infiltration in patients (http://www.linkedomics.org/), a comprehensive tool for with KIRC. multiomics analysis, was used [30]. A Pearson correlation test was used to explore genes that are linked to ACE2 in 2. Materials and Methods KIRC, while gene set enrichment analysis (GSEA) was performed for the enrichment analyses (GO and KEGG 2.1. ACE2 Expression Analysis in the Oncomine , UALCAN, pathways), and ACE2-associated targets (kinase, miRNA, and Human Protein Atlas. ACE2 expression in KIRC was and transcription factor) were obtained with GSEA. +ese identified in the Oncomine (https://www.oncomine.org/), analyses were carried out using the TCGA KIRC dataset, UALCAN (http://ualcan.path.uab.edu/cgi-bin/ualcan-res. with a P value < 0.05. pl), and Human Protein Atlas (https://www.proteinatlas. org/). ACE2 mRNA levels in various malignances, includ- 3. Results ing KIRC, were determined with the Oncomine database and the threshold was set to the P value � 0.05 and fold 3.1. e Expression of ACE2 in KIRC. We initially detected change (FC) � 2, as well as gene ranking � top 10% [20]. In the mRNA and protein expression of ACE2 in KIRC in order to further detect the ACE2 protein expression in Oncomine, UALCAN, and Human Protein Atlas. According KIRC, we then used UALCAN and Human Protein Atlas. to the data from Oncomine, ACE2 mRNA expression was Based on data from Clinical Proteomic Tumor Analysis dramatically reduced in KIRC when compared with normal Consortium (CPTAC), UALCAN could be also used to kidney tissues (Figures 1(a)–1(c)). A gene expression profile detect the ACE2 protein expression in various subtribes of also revealed that ACE2 mRNA expression was reduced in patients with KIRC [21]. +e Human Protein Atlas is a KIRC when compared with normal kidney tissues, with an program designed to map all of the human proteins in the FC of −2.843 as well as a P value of 0.01 (Figure 1(b)) [31]. cells, tissues, and organs [22]. Immunohistochemical Another study indicated that ACE2 mRNA is expressed staining of ACE2 in KIRC was obtained from the Human 5.131 times more in renal tissues than in KIRC tissues Protein Atlas. (Figure 1(c), P � 1.50E − 10) [32]. In order to further verify these results, we decided to use the CPTAC dataset to ob- serve ACE2 protein expression. As expected, the results 2.2. Prognosis Analysis in GEPIA and Kaplan–Meier (KM) demonstrated a downwards regulation of ACE2 protein Plotter. In order to evaluate the significance of ACE2 level in expression in KIRC when compared with normal kidney the prognosis of KIRC, GEPIA (http://gepia.cancer-pku.cn/) tissues (Figure 1(d)). ACE2 protein expression was detected [23], OSkirc (http://bioinfo.henu.edu.cn/KIRC/KIRCList. with staining and the expression data from the Human jsp) [24], and KM plotter databases (https://kmplot.com/) Protein Atlas. Interestingly, the immunohistochemical were applied separately. +e median value of ACE2 ex- staining map suggested a low protein expression of ACE2 in pression was utilized to identify high/low ACE2 expression KIRC tissues with a high protein expression of ACE2 in patients and the P value was set as 0.05. In Meier plotter, normal kidney tissues (Figure 1(e)). subgroup prognosis analysis based on different clinico- However, we evaluated ACE2 protein expression in pathologic features and immune cells in KIRC was per- various subtribes of patients with KIRC. +e results are formed using TCGA KIRC dataset. shown in Figure 2. +is indicates a low expression of the ACE2 protein in KIRC patients in the subtribes analyses 2.3. TIMER for Immune Infiltrates Analysis. TIMER (https:// based on gender, age, weight, tumor grade, and cancer stage. cistrome.shinyapps.io/timer/) is a comprehensive tool pro- +erefore, ACE2 was downregulated in KIRC and may be viding immune infiltrates analysis across TCGA tumors [25]. involved in tumor progression. Immune cell infiltration and immune biomarker expression were correlated with ACE2 and were evaluated with Spear- man’s correlation analysis using the TCGA KIRC dataset. +e 3.2. ACE2 Could Serve as a Prognostic Biomarker in KIRC. immune cells included were B cells, CD4+ Tcells, CD8+ Tcells, A Kaplan–Meier curve was applied using TCGA KIRC and neutrophils, macrophages, and dendritic cells. Immune GSE29609 datasets for prognosis analysis. KIRC patients Journal of Oncology 3 mRNA level of ACE2 in KIRC in cutcliffe dataset Cancer 0.0 vs. normal –0.5 Analysis type by cancer –1.0 –1.5 1 5 10 10 51 ACE2 –2.0 –2.5 Bladder cancer –3.0 –3.5 Brain and CNS cancer –4.0 Breast cancer 1 3 –4.5 Cervical cancer –5.0 P = 0.01 –5.5 Colorectal cancer 2 Esophageal cancer 2 2 Normal renal tissues KIRC tissues Gastric cancer 1 Head and neck cancer Kidney cancer 7 Leukemia Liver cancer 1 Lung cancer 1 Lymphoma Melanoma Myeloma Other cancer 1 8 Ovarian cancer Pancreatic cancer Prostate cancer Sarcoma 6 25 Significant unique analyses Total unique analyses (a) (b) mRNA level of ACE2 in KIRC in jones dataset Protein expression of ACE2 in clear cell RCC 2.0 1.5 1.0 0.5 0.0 –0.5 –1.0 –1.5 –2.0 –1 –2.5 P = 1.50E – 10 P = 9.72E – 09 –3.0 –2 Normal Primary tumor Normal renal tissues KIRC tumor (n = 84) (n = 110) (c) (d) Antibody HPA000288 Staining: low Staining: high KIRC tissues Normal renal tissues (e) Figure 1: +e level of ACE2 in KIRC. (a) Upregulation or downregulation of ACE2 in different types of cancers, including KIRC, compared to the different types of normal tissues. (Oncomine). ((b), (c)) Plot showing ACE2 mRNA expression in KIRC and normal tissues in the dataset from Oncomine. (d) Plot showing ACE2 protein expression in KIRC and normal tissues in the dataset from UALCAN. (e) Immunohistochemical staining showing the protein level of ACE2 in KIRC and normal tissue (the Human Protein Atlas). log2 median–centered intensity log2 median-centered intensity Z-value 4 Journal of Oncology Protein expression of ACE2 in KIRC by gender Protein expression of ACE2 in KIRC by age ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ –1 –1 –2 –2 Normal Male Female Normal 21 – 40 yrs 41 – 60 yrs 61 – 80 yrs 81 – 100 yrs (n = 84) (n = 80) (n = 30) (n = 84) (n = 6) (n = 49) (n = 50) (n = 5) CPTAC samples CPTAC samples (a) (b) Protein expression of ACE2 in KIRC by weight Protein expression of ACE2 in KIRC by tumor grade 4 3 ∗∗ ∗∗∗ ∗∗∗ ∗∗ 2 ∗∗ ∗∗ ∗∗ –1 –1 –2 –2 Normal Normal weight Normal weight Obese Extreme obese Normal Grade 1 Grade 2 Grade 3 Grade 4 (n = 84) (n = 17) (n = 39) (n = 35) (n = 15) (n = 84) (n = 7) (n = 53) (n = 41) (n = 9) CPTAC samples CPTAC samples (c) (d) Protein expression of ACE2 in KIRC by stage ∗∗∗ ∗∗∗ –1 –2 Normal Stage 1 Stage 2 Stage 3 Stage 4 (n = 84) (n = 52) (n = 13) (n = 33) (n = 12) CPTAC samples (e) Figure 2: +e protein expression of ACE2 in subgroups of patients with KIRC (UALCAN). (a) ACE2 protein expression in normal and KIRC (male or female) samples. (b) ACE2 protein expression in normal and KIRC (21–40, 41–60, 61–80, or 81–100 years old) samples. (c) ACE2 protein expression in normal and KIRC (normal weight, extreme weight, obese, or extreme obese) samples. (d) ACE2 protein expression in normal and KIRC (grade 1, 2, 3, or 4) samples. (e) ACE2 protein expression in normal and KIRC (Stage 1, 2, 3, or 4) samples. Data are mean± SE. ∗P< 0.05; ∗∗ P< 0.01; ∗∗ ∗ P< 0.001. Z-value Z-value Z-value Z-value Z-value Journal of Oncology 5 that the expression levels of ACE2 were associated with with a high level of ACE2 expression were strongly corre- lated with better overall survival (OS) (Figure 3(a), logrank favorable prognoses and immune infiltration in patients with KIRC. A prognostic analysis was performed to verify if P � 1.1e − 05) and disease-free survival (DFS) rates (Figure 3(b), logrank P � 0.000034). +us, ACE2 could the expression of ACE2 affects prognosis and immune in- potentially serve as a prognostic biomarker in KIRC patients. filtration in KIRC. +is was based on immune cells using the +e correlation between ACE2 expression and clinical Kaplan–Meier plotter. As we could see in Figure 5, high characteristics of KIRC patients in the Kaplan–Meier plot expression of ACE2 in KIRC from the cohorts of enriched/ was also explored to see how ACE2 expression affects the decreased basophils (Figure 5(a)), enriched/decreased B cells prognosis of patients with KIRC. As shown in Table 1, (Figure 5(b)), enriched/decreased CD4+ memory T cells increasing levels of ACE2 were linked to better prognosis in (Figure 5(c)), enriched/decreased CD8+ Tcells (Figure 5(d)), male and female patients and high/low mutation burden and enriched/decreased eosinophils (Figure 5(e)) were as- sociated with favorable prognosis. Similarly, the high ex- patients (all P< 0.05). Moreover, an increased expression level of ACE2 was linked to better prognosis in tumor grades pression of ACE2 in KIRC from the cohorts of enriched/ decreased mesenchymal stem cells (Figure 6(a)), enriched/ 2 to 4 of KIRC patients. However, there is not enough data about KIRC patients in tumor grade 1 to perform the same decreased natural killer T cells (Figure 6(b)), enriched/de- analysis. Specifically, the increasing level of ACE2 was linked creased regulatory T cells (Figure 6(c)), and enriched/de- to better prognosis in cancers in stages 2 to 4 of KIRC creased type 2 T-helper cells (Figure 6(e)) were also linked to patients (All P< 0.05) but was not linked to better prognosis a better prognosis. However, the high expression of ACE2 in in cancer stage 1 patients (HR � 0.57, P � 0.069, Table 1). KIRC from the cohorts of enriched macrophages +ese data demonstrate that ACE2 expression could affect (Figure 6(f)) and decreased type 1 T-helper cells (Figure 6(d)) were associated with a favorable prognosis. the prognosis of KIRC patients with advanced cancer stage. However, no correlation was observed between the high expression of ACE2 and the prognosis of KIRC in decreased 3.3. ACE2 Was Associated with Tumor Immune Infiltration in macrophages (Figure 6(f)) and enriched type 1 T-helper cell KIRC. Previous studies have highlighted the significance of cohorts (Figure 6(d)). +erefore, ACE2 may affect the the tumor immune infiltration in the prognosis of renal prognosis of patients with KIRC, in part, due to immune cancer [6, 33]. +erefore, we evaluated the correlation be- infiltration. tween ACE2 mRNA expression and immune infiltration in KIRC using the TIMER database. Interestingly, ACE2 mRNA expression showed a positive link to the abundance 3.5. Genetic Alteration of ACE2 in KIRC. Genomic mutations are known to be significantly linked to tumorigenesis. In our of B cells (P � 9.78e − 07), CD8+ T cells (P � 0.00395), macrophages (P � 0.0275), neutrophils (P � 0.00742), and study, genetic alteration analysis of ACE2 in KIRC patient dendritic cells (P � 0.0116) (Figure 4(a)). Conversely, the datasets revealed that a total of 9% of genetic alterations in copy number alteration of ACE2 could inhibit immune ACE2 in KIRC and the genetic alteration form contained infiltration (Figure 4(b)). missense mutations, truncating mutations, deep deletions, We further investigated if the expression of ACE2 was and low mRNA (Figure 7(a)). Moreover, ACE2 mutations associated with immune markers of different immune cells could lead to protein change, including E489∗ and I21 V (Figure 7(b)). Interestingly, we found that ACE2 alterations in KIRC. As expected, a significant correlation was obtained between the expression of ACE2, and most of the immune in KIRC predicted a worse overall survival rate (P � 0.00121, Figure 7(c)). +ese findings suggest that an ACE2 genetic markers in KIRC after tumor purity modulation were performed (Table 2). Specifically, ACE2 was strongly linked alteration may regulate tumorigenesis and its progression to KIRC, thus impacting the patients’ prognosis. to CD8A and CD8B (CD8+ T cell), CD19 and CD79A (B cell), CD86, and CD115 (monocyte), as well as CCL2 and CD68 (TAM). ACE2 was also positively linked to all markers 3.6. Enrichment Analysis of ACE2 in KIRC. +e TCGA KIRC of M1 macrophage (INOS, IRF5, and PTGS2). Moreover, dataset was analyzed with LinkedOmics. Figure 8(a) shows ACE2 levels showed a positive association with most that 3792 genes were positively linked to ACE2, and 6892 markers of natural killer cell (KIR2DL1, KIR2DL3, genes were negatively linked to ACE2 (false discovery rate KIR2DL4, KIR3DL1, and KIR3DL2), Dendritic cell (HLA- <0.01). +e top 50 significant genes that showed a positive DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1 C, and and negative correlation with ACE2 were also obtained NRP1), and +2 (GATA3, STAT6, and STAT5A). Similarly, (Figures 8(b) and 8(c)). GSEA was performed to analyze GO ACE2 in KIRC showed a positive correlation with STAT3 in enrichment analysis, which revealed that ACE2 in KIRC +17, FOXP3, STAT5B, and TGFB1 in Treg, as well as TIM- were mainly involved in extracellular structure organization, 3 in T cell exhaustion (Table 2). Taken together, ACE2 was small molecule catabolic processes, cellular amino acid associated with tumor immune infiltration in KIRC, and metabolic processes, translation factor activity, structural ACE2 may play a vital role in immune escape in the KIRC constituent of ribosomes, immunoglobulin binding, cyto- microenvironment. kine receptor binding, and RNA binding (Figure 8(d)–8(f), P< 0.05). Moreover, the KEGG pathway items indicate 3.4. Prognostic Analysis of ACE2 Expression in KIRC Based on that ACE2 in KIRC was mainly in charge of metabolic Immune Cell Analysis. +e abovementioned results found pathways, pathways in cancer, focal adhesion, transcriptional 6 Journal of Oncology Overall survival Disease free survival 1.0 1.0 Logrank p = 1.1e – 05 Logrank p = 0.00034 HR (high) = 0.5 HR (high) = 0.52 p (HR) = 1.6e – 05 p (HR) = 0.00043 0.8 0.8 n (high) = 258 n (high) = 258 n (low) = 258 n (low) = 258 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 50 100 150 0 204060 80 100 120 140 Months Months Low ACE2 TPM Low ACE2 TPM High ACE2 TPM High ACE2 TPM (a) (b) Figure 3: ACE2 served as a biomarker in KIRC. (a) High ACE2 expression in KIRC was associated with a favorable overall survival (GEPIA). (b) High ACE2 expression in KIRC was associated with a favorable disease-free survival (GEPIA). +e median value of ACE2 expression was utilized to identify high/low ACE2 expression patients. kinase, miRNA, and transcription factor targets in KIRC Table 1: Correlation of ACE2 expression and the overall survival of using GSEA in LinkedOmics. As a result, the top five most KIRC with different clinicopathological factors (Kaplan–Meier significant ACE2-associated kinase targets in KIRC were plotter). Kinase_LCK, Kinase_LYN, Kinase_SYK, Kinase_JAK3, and Kinase_HCK (Table 3, all P< 0.05), and the top five ACE2- Case Pathological parameters Hazard radio P value number associated miRNA targets were MIR-96 (GTGCCAA), MIR-519C, MIR-519B and MIR-519A (TGCACTT), MIR- Stage status 1 398 0.57 (0.31–1.05) 0.069 148A, MIR-152, and MIR-148B (TGCACTG), MIR-506 2 184 0.29 (0.1–0.89) 0.021 (GTGCCTT), and MIR-374 (TATTATA) (Table 3, all 3 332 0.34 (0.19–0.6) 0.00011 P< 0.05). In the transcription factor target analysis, the results −7 4 188 0.26 (0.15–0.45) 2.5e demonstrated V$IRF_Q6, V$NFKB_Q6_01, V$ELF1_Q6, Gender V$PEA3_Q6, and V$PU1_Q6 as the ACE2-associated targets Female 284 0.41 (0.25–0.68) 0.00038 in KIRC (Table 3, all P< 0.05). −8 Male 948 0.35 (0.24–0.52) 2e −11 White 690 0.36 (0.26–0.5) 3.2e Asian 8 NA NA 4. Discussion Black/African- 2.81 111 0.18 American (0.59–13.37) ACE2, a novel identified component of RAS, could regulate Tumor grade the tumorigenesis and progression in cancers and serve as a 1 14 NA NA biomarker for many diseases [34–36]. Moreover, increasing 2 340 0.5 (0.28–0.92) 0.022 evidence highlights the association between ACE2, tumor −5 3 585 0.38 (0.24–0.61) 2.2e microenvironment, and immune infiltration [10, 37]. 4 174 0.42 (0.23–0.77) 0.0039 However, there were limited studies that clarified the Mutation burden function of ACE2 in immune infiltration and the prognosis high 246 0.43 (0.25–0.76) 0.0027 of KIRC. +erefore, our study was undertaken. low 437 0.34 (0.16–0.75) 0.0051 +e expression analysis revealed that ACE2 was downregulated in KIRC patients at the mRNA and protein misregulation in cancer cells, cell cycle, and ribosomes level, and a low expression of ACE2 protein in KIRC patients (Figure 8(g), P< 0.05). was obtained in the subgroup analysis. +ese results indicate that ACE2 may play a significant role in KIRC. Further prognosis analysis indicated that high ACE2 level in KIRC 3.7. ACE2-Associated Targets in KIRC. To further clarify the patients was linked to a favorable prognosis in both the underlining mechanisms of how ACE2 affects tumorigenesis TCGA and GEO cohorts, suggesting ACE2 could be a novel and the progression of KIRC, we explored ACE2-associated prognostic biomarker for KIRC and the prediction of a Percent survival Percent survival Journal of Oncology 7 Macrophage B cell CD8 + T cell CD4 + T cell Neutrophil Dendritic cell Partial.cor = 0.226 Partial.cor = 0.137 Partial.cor = –0.012 Partial.cor = 0.104 Partial.cor = 0.125 Partial.cor = 0.118 p = 9.78e – 07 p = 3.95e – 03 p = 7.99e – 01 p = 2.75e – 02 p = 7.42e – 03 p = 1.16e – 02 7.5 5.0 2.5 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.0 0.4 0.8 1.2 Infiltration level (a) KIRC 2.0 1.5 1.0 0.5 0.0 B cell CD8 + T cell CD4 + T cell Macrophage Neutrophil Dendritic cell Copy number Deep deletion Diploid/normal Arm-level deletion Arm-level gain (b) Figure 4: +e correlation between ACE2 and immune infiltration (TIMER). (a) +e correlation between ACE2 expression, the abundance of CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. (b) +e correlation between SCNA of ACE2 and immune-cell infiltration. SCNA, somatic copy number alterations; ∗P< 0.05; ∗∗ P< 0.01; ∗∗ ∗ P< 0.001. Table 2: Correlation analysis between ACE2 and gene biomarkers of immune cells in KIRC (TIMER). None Purity Description Biomarkers Cor. P value Cor. P value CD8A 0.0.119 ∗∗ 0.107 ∗ CD8+ T cell CD8B 0.115 ∗∗∗ 0.099 ∗ CD3D 0.045 0.298 0.027 0.565 T cell (general) CD3E 0.064 0.14 0.048 0.302 CD2 0.09 ∗ 0.077 0.0985 CD19 −0.176 ∗∗∗ −0.186 ∗∗∗ B cell CD79A −0.12 ∗∗ −0.144 ∗∗ CD86 0.126 ∗∗ 0.117 ∗ Monocyte CD115 (CSF1R) 0.096 ∗ 0.092 ∗ CCL2 0.0.266 ∗∗∗ 0.28 ∗∗∗ TAM CD68 0.146 ∗∗∗ 0.097 ∗ IL10 0.006 0.885 −0.016 0.733 INOS (NOS2) 0.2 ∗∗∗ 0.18 ∗∗∗ M1 macrophage IRF5 0.229 ∗∗∗ 0.208 ∗∗∗ COX2 (PTGS2) −0.219 ∗∗∗ −0.205 ∗∗∗ CD163 0.066 0.128 0.04 0.396 M2 macrophage VSIG4 −0.027 0.538 −0.061 0.188 MS4A4A 0.019 0.668 0.002 0.973 CD66b (CEACAM8) 0.071 0.0996 0.053 0.255 Neutrophil CD11b (ITGAM) 0.159 ∗∗∗ 0.147 ∗∗∗ CCR7 −0.004 0.932 −0.039 0.403 ACE2 expression level (log2 TPM) KIRC Infiltration level 8 Journal of Oncology Table 2: Continued. None Purity Description Biomarkers Cor. P value Cor. P value KIR2DL1 0.17 ∗∗∗ 0.136 ∗∗ KIR2DL3 0.139 ∗∗ 0.106 ∗ KIR2DL4 0.024 0.584 0.002 0.964 Natural killer cell KIR3DL1 0.206 ∗∗∗ 0.168 ∗∗∗ KIR3DL2 0.138 ∗∗ 0.106 ∗ KIR3DL3 0.029 0.508 0.017 0.712 KIR2DS4 0.08 0.0634 0.058 0.217 HLA-DPB1 0.221 ∗∗∗ 0.193 ∗∗∗ HLA-DQB1 0.208 ∗∗∗ 0.185 ∗∗∗ HLA-DRA 0.257 ∗∗∗ 0.23 ∗∗∗ Dendritic cell HLA-DPA1 0.264 ∗∗∗ 0.25 ∗∗∗ BDCA-1 (CD1C) 0.185 ∗∗∗ 0.175 ∗∗∗ BDCA-4 (NRP1) 0.218 ∗∗∗ 0.207 ∗∗∗ CD11c (ITGAX) 0.038 0.385 0.025 0.591 T-bet (TBX21) 0.093 ∗ 0.064 0.167 STAT4 −0.002 0.956 −0.04 0.886 +1 STAT1 0.219 ∗∗∗ 0.208 ∗∗∗ IFN-g (IFNG) 0.053 0.22 0.034 0.461 TNF-a (TNF) 0.062 0.152 0.043 0.358 GATA3 −0.212 ∗∗∗ −0.165 ∗∗∗ STAT6 0.323 ∗∗∗ 0.31 ∗∗∗ STAT5A 0.139 ∗∗ 0.16 ∗∗∗ IL13 −0.059 0.17 −0.088 0.0588 BCL6 −0.076 0.0793 −0.089 0.0570 Tfh IL21 −0.072 0.0972 −0.09 0.0546 STAT3 0.237 ∗∗∗ 0.239 ∗∗∗ IL17A −0.031 0.472 −0.029 0.532 FOXP3 −0.127 −0.143 ∗∗ ∗∗ CCR8 0.04 0.361 0.028 0.552 Treg STAT5B 0.433 ∗∗∗ 0.417 ∗∗∗ TGFb (TGFB1) −0.253 ∗∗∗ −0.271 ∗∗∗ PD-1 (PDCD1) 0.036 0.404 0.016 0.733 CTLA4 0.012 0.78 −0.004 0.925 T cell exhaustion LAG3 0.017 0.694 0.009 0.855 TIM-3 (HAVCR2) 0.355 ∗∗∗ 0.32 ∗∗∗ GZMB −0.029 0.507 −0.066 0.157 ∗ ∗∗ ∗∗∗ P < 0.05; P < 0.01; P < 0.001. favorable outcome. ACE2 has also been suggested as a and reduced response to treatment [40]. Moreover, CD39+ CD8+ T cells were shown to act as prognostic biomarkers in biomarker for other diseases or cancers. In thyroid carci- noma, ACE2 was employed as a biomarker and was also patients with KIRC and were also used to indicate poor found to regulate tumor progression [13]. Moreover, ACE2 prognosis [41]. In our study, we also found that ACE2 may acted as a biomarker in chronic kidney disease and asso- affect the prognosis of KIRC patients, in part, due to immune ciated with higher risk for silent atherosclerosis [36]. infiltration. However, previous studies have suggested that A steady accumulation of data suggests that immune-cell T-regulatory cells are correlated with the poor outcomes of infiltration could regulate tumor progression and metastasis, patients with KIRC [6]. However, we found that ACE2 thus affecting the patients’ prognosis [38, 39]. In our study, positively associated with the abundance of several immune we also clarified the correlation between ACE2 and immune cells. +us, all correlations of ACE2 and infiltrating immune infiltration. We found ACE2 to be positively associated with cells in KIRC may not be favorable. Further study should be the abundance of immune cells, including B cells, CD8+ performed to verify these results and observations. T cells, macrophages, neutrophils, and dendritic cells. +e enrichment analysis suggested that ACE2 in KIRC Moreover, a strong correlation between the expression of were primarily involved in translation factor activity, im- ACE2 and most of the immune biomarker sets were ana- munoglobulin binding, metabolic pathways, transcriptional lyzed. +ese immune cells or biomarkers were known to be misregulation in cancer cells, cell cycle, and ribosomes. involved in tumor progression and as a biomarker for +ese findings are consistent with the previous study where prognosis or therapy of KIRC. Ying et al. found that tumor ACE2 was associated with cell cycle transcription [42]. microenvironment B cells were associated with poor survival Misregulation of ribosome functions and the cell cycle has Journal of Oncology 9 Enriched basophils Decreased basophils Enriched B cells Decreased B cells 1.0 1.0 1.0 1.0 HR = 0.46 (0.28 – 0.73) HR = 0.62 (0.41 – 0.94) HR = 0.41 (0.26 – 0.67) HR = 0.66 (0.44 – 1) logrank P = 0.00082 logrank P = 0.022 logrank P = 0.046 logrank P = 0.00021 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 50 100 150 0 20406080 100 120 140 020 40 60 80 100 120 0 50 100 150 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 96 32 8 0 Low 167 119 87 47 22 11 4 0 Low 121 82 60 32 19 11 4 Low 142 53 9 0 High 97 42 6 1 High 167 127 87 54 29 15 5 0 High 121 93 56 35 17 6 4 High 143 62 14 1 Expression Expression Expression Expression Low Low Low Low High High High High (a) (b) Enriched CD4 + memory T cells Decreased CD4 + memory T cells Enriched CD8 + T cells Decreased CD8 + T cells 1.0 1.0 1.0 1.0 HR = 0.63 (0.44 – 0.91) HR = 0.4 (0.23 – 0.72) HR = 0.52 (0.37 – 0.74) HR = 0.46 (0.24 – 0.9) logrank P = 0.013 logrank P = 0.0016 logrank P = 0.00023 logrank P = 0.02 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 50 100 150 0 20406080 100 120 140 0 50 100 150 0 20406080 100 120 140 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 174 67 14 0 Low 90 61 42 19 6 5 1 0 Low 192 65 13 0 Low 72 52 41 22 10 6 1 0 High 174 75 16 1 High 89 64 49 24 13 5 2 0 High 192 83 15 1 High 71 51 38 21 14 6 3 0 Expression Expression Expression Expression Low Low Low Low High High High High (c) (d) Enriched eosinophils Decreased eosinophils Enriched macrophages Decreased macrophages 1.0 1.0 1.0 1.0 HR = 0.49 (0.31 – 0.77) HR = 0.57 (0.37 – 0.86) HR = 0.53 (0.38 – 0.75) HR = 0.48 (0.22 – 1.04) logrank P = 0.0017 logrank P = 0.0075 logrank P = 0.00024 logrank P = 0.056 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 50 100 150 020 40 60 80 100 120 140 0 50 100 150 0 20406080 100 120 140 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 153 57 12 0 Low 110 75 52 26 11 6 3 0 Low 217 79 18 0 Low 46 33 22 11 3 2 0 0 High 153 65 10 1 High 111 84 57 37 23 12 4 0 High 217 88 15 1 High 47 36 28 19 10 5 2 0 Expression Expression Expression Expression Low Low Low Low High High High High (e) (f) Figure 5: Prognostic value of ACE2 in KIRC based on immune-cell subgroups (Kaplan–Meier plotter). Enriched mesenchymal stem cells Diseased mesenchymal stem cells Enriched natural killer T cells Diseased natural killer T cells 1.0 1.0 1.0 1.0 HR = 0.3 (0.13 – 0.72) HR = 0.54 (0.37 – 0.8) HR = 0.55 (0.39 – 0.77) HR = 0.54 (0.32 – 89) logrank P = 0.004 logrank P = 0.0017 logrank P = 0.00034 logrank P = 0.014 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 20406080 100 120 0 50 100 150 020 40 60 80 100 120 140 0 50 100 150 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 18 7 4 2 0 0 0 Low 246 93 19 0 Low 63 41 26 13 5 2 1 0 Low 200 78 16 0 High 18 12 6 5 3 2 1 High 245 105 19 1 High 63 43 28 18 8 4 3 0 High 201 85 18 1 Expression Expression Expression Expression Low Low Low Low High High High High (a) (b) Figure 6: Continued. Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability 10 Journal of Oncology Enriched regulatory T cells Diseased regulatory T cells Enriched type 1 T-helper cells Diseased type 1 T-helper cells 1.0 1.0 1.0 1.0 HR = 0.46 (0.26 – 0.83) HR = 0.5 (0.34 – 0.72) HR = 0.67 (0.24 – 1.88) HR = 0.55 (0.4 – 0.76) logrank P = 0.0076 logrank P = 0.00019 logrank P = 0.44 logrank P = 0.00021 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 20406080 100 120 0 50 100 150 020 40 60 80 100 120 0 50 100 150 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 99 70 46 23 15 6 0 Low 164 60 10 0 Low 12 8 6 3 1 1 0 Low 252 93 19 0 High 99 70 44 27 14 5 3 High 165 76 19 1 High 12 9 4 3 2 2 2 High 251 106 18 1 Expression Expression Expression Expression Low Low Low Low High High High High (c) (d) Enriched type 2 T-helper cells Diseased type 2 T-helper cells 1.0 1.0 HR = 0.56 (0.4 – 0.79) HR = 0.27 (0.12 – 0.64) logrank P = 0.00066 logrank P = 0.0014 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 20406080 100 120 0 50 100 150 Time (months) Time (months) Number at risk Number at risk Low 238 88 17 0 Low 26 12 7 3 1 1 0 High 25 16 10 8 5 2 1 High 238 103 20 1 Expression Expression Low Low High High (e) Figure 6: Prognostic value of ACE2 in KIRC based on immune-cells subgroup (Kaplan–Meier plotter). ACE2 9% Genetic alteration Missense mutation (unknown significance) mRNA low Truncating mutation (unknown significance) No alterations Deep deletion (a) E498 Peptidase_M2 0 200 400 600 805aa Sample ID Protein change Annotation ▾ Mutation type Copy # COSMIC # Mut in sample TCGA-B0-5709-01 E498 ○ Nonsense Diploid 1 60 TCGA-B4-5835-01 I21V Missense ○ Diploid 1 77 (b) Figure 7: Continued. Probability # ACE2 mutations Probability Probability Probability Probability Probability Journal of Oncology 11 P = 0.00121 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Months overall Overall Altered group Unaltered group (c) Figure 7: Genetic alteration of ACE2 in KIRC patients (cBioPortal). (a) OncoPrint visual summary of alteration on ACE2 in KIRC. (b) +e missense mutation of ACE2 amino acids was analyzed. Color images are available online. (c) +e overall survival in cases with/without ACE2 alterations. ACE2 association result ACE2 TMEM27 GLYATL1 CLCN5 LRP2 120 SLC13A1 PHYHIPL SLC3A1 GBA3 CUBN FUT6 SLC27A2 HAO2 PKLR AFTPH PDZK1 Z-score GLYAT AGXT2 SLC16A9 > 3 SLCO4C1 TMEM174 FMO1 TINAG 0 DDC –1 SLC22A11 NAT8 < –3 SLC1A1 ACMSD RAB3IP LRRC19 group SATB2 SLC5A10 EHHADH BBOX1 SLC7A9 0 SLC17A3 SLC22A24 –1 SLC5A1 GIPC2 –2 C11or154 –3 MIOX NAT8B LOC15332B GJB1 –1 012 CYP4A11 AVPR1B TAL2 Spearman's rho statistic (spearman test) SLC22A13 USH1C SLC22A12 (a) (b) Figure 8: Continued. –log10 (pvalue) Overall (%) 12 Journal of Oncology BP BDKRB1 C1or121 Extracellular structure organization TPST2 MINA Small molecule catabolic process TRNP1 GPRC5A TMEM158 Coenzyme metabolic process COL6A1 PCOLCE Organic anion transport SMARCD3 GNAS Cellular amino acid metabolic process GLT8D1 BALAP2L1 KCNG1 Peroxisome organization CD55 ETV4 Z-score Peroxisomal transport DBN1 PPM1J Cellular aldehyde metabolic process ST5 > 3 GAD1 PSD3 1 Tricarboxylic acid metabolic process PODNL1 SNX21 0 Modified amino acid transport MAGED4 CACNB3 –1 IMPDH1 < –3 0 20 40 60 80 100 120 140 160 SIX4 PCBP3 GPSM1 LMO1 Group MAGED4B TUBB3 MPP2 CREB3L1 LEF1 ITPKA PORCN –1 DAPK2 –2 B4GALNT1 ARTN –3 PTPN1 SEC13 PSD BDKR82 ACCN2 MARCH10 IRS2 FNDC4 ZBTB47 GGN (c) (d) CC MF Extracellular matrix Translation factor activity, RNA binding Mitochondrial matrix Structural constituent of ribosome Ribosome Immunoglobulin binding Apical part of cell Collagen biding Microbody Cytokine receptor binding Cluster of actin-based cell projections Nucleotide receptor activity Polysome Cytokine binding Collagen trimer Protein transporter activity Intraciliary transport particle rRNA binding Endoplasmic reticulum exit site snoRNA binding 0 20 40 60 80 100 120 140 160 180 200 0 10 20 30 40 50 60 70 80 90 100 (e) (f) KEGG Metabolic pathways Pathways in cancer Human papillomavirus infection Focal adhesion Transcriptional misregulation in cancer Ribosome Cell cycle ECM-receptor interaction Drug metabolism Retinol metabolism 0 200 400 600 800 1000 1200 1400 (g) Figure 8: Enrichment analysis of ACE2 in KIRC (LinkedOmics). (a) +e differentially expressed genes significantly correlated with ACE2 in KIRC. ((b), (c)) Heat maps showing the top 50 genes positively and negatively correlated with ACE2 in KIRC. (d) BP analysis. (e) CC analysis. (f) MF analysis. (g) KEGG pathway analysis. +e analysis was performed by GSEA. Table 3: +e kinase, miRNA, and transcription factor target networks of ACE2 in KIRC (LinkedOmics). Enriched category Gene set Leading edge number P value Kinase_LCK 29 0 Kinase_LYN 30 0 Kinase target Kinase_SYK 16 0 Kinase_JAK3 8 0 Kinase_HCK 17 0 GTGCCAA, MIR-96 91 0 TGCACTT, MIR-519 C, MIR-519 B, MIR-519A 140 0 miRNA target TGCACTG, MIR-148A, MIR-152, MIR-148B 97 0 GTGCCTT, MIR-506 213 0 TATTATA, MIR-374 105 0.002 V$IRF_Q6 104 0 V$NFKB_Q6_01 57 0 Transcription factor target V$ELF1_Q6 84 0 V$PEA3_Q6 81 0 V$PU1_Q6 150 0 Journal of Oncology 13 been linked to many diseases, in particular cancers [43]. Our the final manuscript. 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ACE2 Is a Prognostic Biomarker and Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma: Implication for COVID-19

Niu, Xinhao; Zhu, Zhe; Shao, Enming; Bao, Juan
Journal of Oncology , Volume 2021 – Jan 30, 2021
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Copyright © 2021 Xinhao Niu 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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1687-8450
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10.1155/2021/8847307
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Abstract

Hindawi Journal of Oncology Volume 2021, Article ID 8847307, 15 pages https://doi.org/10.1155/2021/8847307 Research Article ACE2 Is a Prognostic Biomarker and Associated with Immune Infiltration in Kidney Renal Clear Cell Carcinoma: Implication for COVID-19 Xinhao Niu, Zhe Zhu, Enming Shao, and Juan Bao Department of Urinary Surgery, e Shanghai Public Health Clinical Center, Fudan University, Shanghai 201508, China Correspondence should be addressed to Juan Bao; bj901120@gmail.com Received 2 September 2020; Revised 7 January 2021; Accepted 18 January 2021; Published 30 January 2021 Academic Editor: Francesca De Felice Copyright © 2021 Xinhao Niu 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. KIRC is one of the most common cancers with a poor prognosis. ACE2 was involved in tumor angiogenesis and progression in many malignancies. +e role of ACE2 in KIRC is still ambiguous. Methods. Various bioinformatics analysis tools were investigated to evaluate the prognostic value of ACE2 and its association with immune infiltration in KIRC. Results. ACE2 was shown to be downregulated in KIRC at the mRNA and protein level. Low expression of ACE2 protein in KIRC patients was observed in subgroup analyses based on gender, age, weight, tumor grade, and cancer stage. Upregulation of ACE2 in KIRC was associated with a favorable prognosis. ACE2 mRNA expression showed a positive correlation with the abundance of immune cells (B cells, CD8+ Tcells, macrophages, neutrophils, and dendritic cells) and the level of immune markers of different immune cells in KIRC. ACE2 expression could affect, in part, the immune infiltration and the advanced cancer stage. Moreover, enrichment analysis revealed that ACE2 in KIRC were mainly involved in translation factor activity, immunoglobulin binding, metabolic pathways, transcriptional misregulation in cancerous cells, cell cycle, and ribosomal activity. Several ACE2-associated kinases, miRNA, and transcription factor targets in KIRC were also identified. Conclusion. ACE2 was downregulated in KIRC and served as a prognostic biomarker. It was also shown to be associated with immune infiltration. Immunotherapy has been suggested as the treatment for 1. Introduction metastatic KIRC [8, 9]. +erefore, clarifying the role of Kidney cancer is one of the most common malignances immune infiltration in KIRC and identifying immune- globally, accounting for about 4.5% of all newly diagnosed associated markers for the prognosis for KIRC are par- malignances [1]. It is anticipated that 73,750 people would ticularly necessary. be newly diagnosed with kidney cancer and 14,830 pa- Angiotensin converting enzyme 2 (ACE2) is a member of tients are likely to die because of the disease in the USA in the renin angiotensin system (RAS) whose open reading 2020 [2]. +e most common subtype of renal cancer is framework encodes an 805-amino-acid polypeptide [10]. In- kidney renal clear cell carcinoma (KIRC), which makes up creasing evidence indicates a significant function of ACE2 in over 70% of kidney cancers [3]. Surgery excision remains the tumor angiogenesis and its progression in many cancers, the primary therapy for KIRC due to the growing resis- such as thyroid carcinoma, breast carcinoma, and lung ade- tance to radiotherapy and chemotherapy [4]. Much worse, nocarcinoma [11–13]. ACE2 has also been suggested as a the prognosis of KIRC patients tends to be poor, especially biomarker for many diseases, including squamous cell/ade- for patients in an advanced stage. +e five-year overall nosquamous carcinoma, endometrial carcinoma, and hyper- survival rate of stage IV patients is less than 10% [5]. tension [10, 14, 15]. However, limited studies have clarified the Previous studies have revealed that immune infiltration is function of ACE2 in immune infiltration and its role in the significantly linked to the survival of KIRC patients. [6, 7]. prognosis in KIRC. 2 Journal of Oncology Coronavirus disease 2019 (COVID-19), caused by the biomarkers were excluded because they have already been novel coronavirus severe acute respiratory syndrome described in previous studies [26–28]. coronavirus 2 (SARS-CoV-2), was initially found in Wuhan of China since December 2019 [16, 17]. It is well known that 2.4. cBioPortal for Genetic Alteration Analysis. cBioPortal the functional host receptor of SARS-CoV-2 is ACE2 (http://www.cbioportal.org) is a TCGA visual tool used to [18, 19]. Over 10 million peoples were diagnosed with perform genome analysis [29]. We analyzed ACE2 genetic COVID-19 and over 520000 peoples died of this disease alteration in KIRC with the threshold as ±2.0 in mRNA globally until July 1, 2020. As we have seen, the prognosis of expression z-scores (RNASeq V2 RSEM) and protein ex- COVID-19 patients with KIRC remains ambiguous. pression z-scores (RPPA). +erefore, our study was performed to detect ACE2 levels and the prognostic value in patients with KIRC. +e function of ACE2 in immune infiltration in KIRC was also 2.5. LinkedOmics for Enrichment Analysis. In order to verify clarified. Our results may provide additional evidence re- the ACE2-associated functions in KIRC, LinkedOmics garding the role of ACE2 and immune infiltration in patients (http://www.linkedomics.org/), a comprehensive tool for with KIRC. multiomics analysis, was used [30]. A Pearson correlation test was used to explore genes that are linked to ACE2 in 2. Materials and Methods KIRC, while gene set enrichment analysis (GSEA) was performed for the enrichment analyses (GO and KEGG 2.1. ACE2 Expression Analysis in the Oncomine , UALCAN, pathways), and ACE2-associated targets (kinase, miRNA, and Human Protein Atlas. ACE2 expression in KIRC was and transcription factor) were obtained with GSEA. +ese identified in the Oncomine (https://www.oncomine.org/), analyses were carried out using the TCGA KIRC dataset, UALCAN (http://ualcan.path.uab.edu/cgi-bin/ualcan-res. with a P value < 0.05. pl), and Human Protein Atlas (https://www.proteinatlas. org/). ACE2 mRNA levels in various malignances, includ- 3. Results ing KIRC, were determined with the Oncomine database and the threshold was set to the P value � 0.05 and fold 3.1. e Expression of ACE2 in KIRC. We initially detected change (FC) � 2, as well as gene ranking � top 10% [20]. In the mRNA and protein expression of ACE2 in KIRC in order to further detect the ACE2 protein expression in Oncomine, UALCAN, and Human Protein Atlas. According KIRC, we then used UALCAN and Human Protein Atlas. to the data from Oncomine, ACE2 mRNA expression was Based on data from Clinical Proteomic Tumor Analysis dramatically reduced in KIRC when compared with normal Consortium (CPTAC), UALCAN could be also used to kidney tissues (Figures 1(a)–1(c)). A gene expression profile detect the ACE2 protein expression in various subtribes of also revealed that ACE2 mRNA expression was reduced in patients with KIRC [21]. +e Human Protein Atlas is a KIRC when compared with normal kidney tissues, with an program designed to map all of the human proteins in the FC of −2.843 as well as a P value of 0.01 (Figure 1(b)) [31]. cells, tissues, and organs [22]. Immunohistochemical Another study indicated that ACE2 mRNA is expressed staining of ACE2 in KIRC was obtained from the Human 5.131 times more in renal tissues than in KIRC tissues Protein Atlas. (Figure 1(c), P � 1.50E − 10) [32]. In order to further verify these results, we decided to use the CPTAC dataset to ob- serve ACE2 protein expression. As expected, the results 2.2. Prognosis Analysis in GEPIA and Kaplan–Meier (KM) demonstrated a downwards regulation of ACE2 protein Plotter. In order to evaluate the significance of ACE2 level in expression in KIRC when compared with normal kidney the prognosis of KIRC, GEPIA (http://gepia.cancer-pku.cn/) tissues (Figure 1(d)). ACE2 protein expression was detected [23], OSkirc (http://bioinfo.henu.edu.cn/KIRC/KIRCList. with staining and the expression data from the Human jsp) [24], and KM plotter databases (https://kmplot.com/) Protein Atlas. Interestingly, the immunohistochemical were applied separately. +e median value of ACE2 ex- staining map suggested a low protein expression of ACE2 in pression was utilized to identify high/low ACE2 expression KIRC tissues with a high protein expression of ACE2 in patients and the P value was set as 0.05. In Meier plotter, normal kidney tissues (Figure 1(e)). subgroup prognosis analysis based on different clinico- However, we evaluated ACE2 protein expression in pathologic features and immune cells in KIRC was per- various subtribes of patients with KIRC. +e results are formed using TCGA KIRC dataset. shown in Figure 2. +is indicates a low expression of the ACE2 protein in KIRC patients in the subtribes analyses 2.3. TIMER for Immune Infiltrates Analysis. TIMER (https:// based on gender, age, weight, tumor grade, and cancer stage. cistrome.shinyapps.io/timer/) is a comprehensive tool pro- +erefore, ACE2 was downregulated in KIRC and may be viding immune infiltrates analysis across TCGA tumors [25]. involved in tumor progression. Immune cell infiltration and immune biomarker expression were correlated with ACE2 and were evaluated with Spear- man’s correlation analysis using the TCGA KIRC dataset. +e 3.2. ACE2 Could Serve as a Prognostic Biomarker in KIRC. immune cells included were B cells, CD4+ Tcells, CD8+ Tcells, A Kaplan–Meier curve was applied using TCGA KIRC and neutrophils, macrophages, and dendritic cells. Immune GSE29609 datasets for prognosis analysis. KIRC patients Journal of Oncology 3 mRNA level of ACE2 in KIRC in cutcliffe dataset Cancer 0.0 vs. normal –0.5 Analysis type by cancer –1.0 –1.5 1 5 10 10 51 ACE2 –2.0 –2.5 Bladder cancer –3.0 –3.5 Brain and CNS cancer –4.0 Breast cancer 1 3 –4.5 Cervical cancer –5.0 P = 0.01 –5.5 Colorectal cancer 2 Esophageal cancer 2 2 Normal renal tissues KIRC tissues Gastric cancer 1 Head and neck cancer Kidney cancer 7 Leukemia Liver cancer 1 Lung cancer 1 Lymphoma Melanoma Myeloma Other cancer 1 8 Ovarian cancer Pancreatic cancer Prostate cancer Sarcoma 6 25 Significant unique analyses Total unique analyses (a) (b) mRNA level of ACE2 in KIRC in jones dataset Protein expression of ACE2 in clear cell RCC 2.0 1.5 1.0 0.5 0.0 –0.5 –1.0 –1.5 –2.0 –1 –2.5 P = 1.50E – 10 P = 9.72E – 09 –3.0 –2 Normal Primary tumor Normal renal tissues KIRC tumor (n = 84) (n = 110) (c) (d) Antibody HPA000288 Staining: low Staining: high KIRC tissues Normal renal tissues (e) Figure 1: +e level of ACE2 in KIRC. (a) Upregulation or downregulation of ACE2 in different types of cancers, including KIRC, compared to the different types of normal tissues. (Oncomine). ((b), (c)) Plot showing ACE2 mRNA expression in KIRC and normal tissues in the dataset from Oncomine. (d) Plot showing ACE2 protein expression in KIRC and normal tissues in the dataset from UALCAN. (e) Immunohistochemical staining showing the protein level of ACE2 in KIRC and normal tissue (the Human Protein Atlas). log2 median–centered intensity log2 median-centered intensity Z-value 4 Journal of Oncology Protein expression of ACE2 in KIRC by gender Protein expression of ACE2 in KIRC by age ∗∗∗ ∗∗∗ ∗∗∗ ∗∗∗ –1 –1 –2 –2 Normal Male Female Normal 21 – 40 yrs 41 – 60 yrs 61 – 80 yrs 81 – 100 yrs (n = 84) (n = 80) (n = 30) (n = 84) (n = 6) (n = 49) (n = 50) (n = 5) CPTAC samples CPTAC samples (a) (b) Protein expression of ACE2 in KIRC by weight Protein expression of ACE2 in KIRC by tumor grade 4 3 ∗∗ ∗∗∗ ∗∗∗ ∗∗ 2 ∗∗ ∗∗ ∗∗ –1 –1 –2 –2 Normal Normal weight Normal weight Obese Extreme obese Normal Grade 1 Grade 2 Grade 3 Grade 4 (n = 84) (n = 17) (n = 39) (n = 35) (n = 15) (n = 84) (n = 7) (n = 53) (n = 41) (n = 9) CPTAC samples CPTAC samples (c) (d) Protein expression of ACE2 in KIRC by stage ∗∗∗ ∗∗∗ –1 –2 Normal Stage 1 Stage 2 Stage 3 Stage 4 (n = 84) (n = 52) (n = 13) (n = 33) (n = 12) CPTAC samples (e) Figure 2: +e protein expression of ACE2 in subgroups of patients with KIRC (UALCAN). (a) ACE2 protein expression in normal and KIRC (male or female) samples. (b) ACE2 protein expression in normal and KIRC (21–40, 41–60, 61–80, or 81–100 years old) samples. (c) ACE2 protein expression in normal and KIRC (normal weight, extreme weight, obese, or extreme obese) samples. (d) ACE2 protein expression in normal and KIRC (grade 1, 2, 3, or 4) samples. (e) ACE2 protein expression in normal and KIRC (Stage 1, 2, 3, or 4) samples. Data are mean± SE. ∗P< 0.05; ∗∗ P< 0.01; ∗∗ ∗ P< 0.001. Z-value Z-value Z-value Z-value Z-value Journal of Oncology 5 that the expression levels of ACE2 were associated with with a high level of ACE2 expression were strongly corre- lated with better overall survival (OS) (Figure 3(a), logrank favorable prognoses and immune infiltration in patients with KIRC. A prognostic analysis was performed to verify if P � 1.1e − 05) and disease-free survival (DFS) rates (Figure 3(b), logrank P � 0.000034). +us, ACE2 could the expression of ACE2 affects prognosis and immune in- potentially serve as a prognostic biomarker in KIRC patients. filtration in KIRC. +is was based on immune cells using the +e correlation between ACE2 expression and clinical Kaplan–Meier plotter. As we could see in Figure 5, high characteristics of KIRC patients in the Kaplan–Meier plot expression of ACE2 in KIRC from the cohorts of enriched/ was also explored to see how ACE2 expression affects the decreased basophils (Figure 5(a)), enriched/decreased B cells prognosis of patients with KIRC. As shown in Table 1, (Figure 5(b)), enriched/decreased CD4+ memory T cells increasing levels of ACE2 were linked to better prognosis in (Figure 5(c)), enriched/decreased CD8+ Tcells (Figure 5(d)), male and female patients and high/low mutation burden and enriched/decreased eosinophils (Figure 5(e)) were as- sociated with favorable prognosis. Similarly, the high ex- patients (all P< 0.05). Moreover, an increased expression level of ACE2 was linked to better prognosis in tumor grades pression of ACE2 in KIRC from the cohorts of enriched/ decreased mesenchymal stem cells (Figure 6(a)), enriched/ 2 to 4 of KIRC patients. However, there is not enough data about KIRC patients in tumor grade 1 to perform the same decreased natural killer T cells (Figure 6(b)), enriched/de- analysis. Specifically, the increasing level of ACE2 was linked creased regulatory T cells (Figure 6(c)), and enriched/de- to better prognosis in cancers in stages 2 to 4 of KIRC creased type 2 T-helper cells (Figure 6(e)) were also linked to patients (All P< 0.05) but was not linked to better prognosis a better prognosis. However, the high expression of ACE2 in in cancer stage 1 patients (HR � 0.57, P � 0.069, Table 1). KIRC from the cohorts of enriched macrophages +ese data demonstrate that ACE2 expression could affect (Figure 6(f)) and decreased type 1 T-helper cells (Figure 6(d)) were associated with a favorable prognosis. the prognosis of KIRC patients with advanced cancer stage. However, no correlation was observed between the high expression of ACE2 and the prognosis of KIRC in decreased 3.3. ACE2 Was Associated with Tumor Immune Infiltration in macrophages (Figure 6(f)) and enriched type 1 T-helper cell KIRC. Previous studies have highlighted the significance of cohorts (Figure 6(d)). +erefore, ACE2 may affect the the tumor immune infiltration in the prognosis of renal prognosis of patients with KIRC, in part, due to immune cancer [6, 33]. +erefore, we evaluated the correlation be- infiltration. tween ACE2 mRNA expression and immune infiltration in KIRC using the TIMER database. Interestingly, ACE2 mRNA expression showed a positive link to the abundance 3.5. Genetic Alteration of ACE2 in KIRC. Genomic mutations are known to be significantly linked to tumorigenesis. In our of B cells (P � 9.78e − 07), CD8+ T cells (P � 0.00395), macrophages (P � 0.0275), neutrophils (P � 0.00742), and study, genetic alteration analysis of ACE2 in KIRC patient dendritic cells (P � 0.0116) (Figure 4(a)). Conversely, the datasets revealed that a total of 9% of genetic alterations in copy number alteration of ACE2 could inhibit immune ACE2 in KIRC and the genetic alteration form contained infiltration (Figure 4(b)). missense mutations, truncating mutations, deep deletions, We further investigated if the expression of ACE2 was and low mRNA (Figure 7(a)). Moreover, ACE2 mutations associated with immune markers of different immune cells could lead to protein change, including E489∗ and I21 V (Figure 7(b)). Interestingly, we found that ACE2 alterations in KIRC. As expected, a significant correlation was obtained between the expression of ACE2, and most of the immune in KIRC predicted a worse overall survival rate (P � 0.00121, Figure 7(c)). +ese findings suggest that an ACE2 genetic markers in KIRC after tumor purity modulation were performed (Table 2). Specifically, ACE2 was strongly linked alteration may regulate tumorigenesis and its progression to KIRC, thus impacting the patients’ prognosis. to CD8A and CD8B (CD8+ T cell), CD19 and CD79A (B cell), CD86, and CD115 (monocyte), as well as CCL2 and CD68 (TAM). ACE2 was also positively linked to all markers 3.6. Enrichment Analysis of ACE2 in KIRC. +e TCGA KIRC of M1 macrophage (INOS, IRF5, and PTGS2). Moreover, dataset was analyzed with LinkedOmics. Figure 8(a) shows ACE2 levels showed a positive association with most that 3792 genes were positively linked to ACE2, and 6892 markers of natural killer cell (KIR2DL1, KIR2DL3, genes were negatively linked to ACE2 (false discovery rate KIR2DL4, KIR3DL1, and KIR3DL2), Dendritic cell (HLA- <0.01). +e top 50 significant genes that showed a positive DPB1, HLA-DQB1, HLA-DRA, HLA-DPA1, CD1 C, and and negative correlation with ACE2 were also obtained NRP1), and +2 (GATA3, STAT6, and STAT5A). Similarly, (Figures 8(b) and 8(c)). GSEA was performed to analyze GO ACE2 in KIRC showed a positive correlation with STAT3 in enrichment analysis, which revealed that ACE2 in KIRC +17, FOXP3, STAT5B, and TGFB1 in Treg, as well as TIM- were mainly involved in extracellular structure organization, 3 in T cell exhaustion (Table 2). Taken together, ACE2 was small molecule catabolic processes, cellular amino acid associated with tumor immune infiltration in KIRC, and metabolic processes, translation factor activity, structural ACE2 may play a vital role in immune escape in the KIRC constituent of ribosomes, immunoglobulin binding, cyto- microenvironment. kine receptor binding, and RNA binding (Figure 8(d)–8(f), P< 0.05). Moreover, the KEGG pathway items indicate 3.4. Prognostic Analysis of ACE2 Expression in KIRC Based on that ACE2 in KIRC was mainly in charge of metabolic Immune Cell Analysis. +e abovementioned results found pathways, pathways in cancer, focal adhesion, transcriptional 6 Journal of Oncology Overall survival Disease free survival 1.0 1.0 Logrank p = 1.1e – 05 Logrank p = 0.00034 HR (high) = 0.5 HR (high) = 0.52 p (HR) = 1.6e – 05 p (HR) = 0.00043 0.8 0.8 n (high) = 258 n (high) = 258 n (low) = 258 n (low) = 258 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 50 100 150 0 204060 80 100 120 140 Months Months Low ACE2 TPM Low ACE2 TPM High ACE2 TPM High ACE2 TPM (a) (b) Figure 3: ACE2 served as a biomarker in KIRC. (a) High ACE2 expression in KIRC was associated with a favorable overall survival (GEPIA). (b) High ACE2 expression in KIRC was associated with a favorable disease-free survival (GEPIA). +e median value of ACE2 expression was utilized to identify high/low ACE2 expression patients. kinase, miRNA, and transcription factor targets in KIRC Table 1: Correlation of ACE2 expression and the overall survival of using GSEA in LinkedOmics. As a result, the top five most KIRC with different clinicopathological factors (Kaplan–Meier significant ACE2-associated kinase targets in KIRC were plotter). Kinase_LCK, Kinase_LYN, Kinase_SYK, Kinase_JAK3, and Kinase_HCK (Table 3, all P< 0.05), and the top five ACE2- Case Pathological parameters Hazard radio P value number associated miRNA targets were MIR-96 (GTGCCAA), MIR-519C, MIR-519B and MIR-519A (TGCACTT), MIR- Stage status 1 398 0.57 (0.31–1.05) 0.069 148A, MIR-152, and MIR-148B (TGCACTG), MIR-506 2 184 0.29 (0.1–0.89) 0.021 (GTGCCTT), and MIR-374 (TATTATA) (Table 3, all 3 332 0.34 (0.19–0.6) 0.00011 P< 0.05). In the transcription factor target analysis, the results −7 4 188 0.26 (0.15–0.45) 2.5e demonstrated V$IRF_Q6, V$NFKB_Q6_01, V$ELF1_Q6, Gender V$PEA3_Q6, and V$PU1_Q6 as the ACE2-associated targets Female 284 0.41 (0.25–0.68) 0.00038 in KIRC (Table 3, all P< 0.05). −8 Male 948 0.35 (0.24–0.52) 2e −11 White 690 0.36 (0.26–0.5) 3.2e Asian 8 NA NA 4. Discussion Black/African- 2.81 111 0.18 American (0.59–13.37) ACE2, a novel identified component of RAS, could regulate Tumor grade the tumorigenesis and progression in cancers and serve as a 1 14 NA NA biomarker for many diseases [34–36]. Moreover, increasing 2 340 0.5 (0.28–0.92) 0.022 evidence highlights the association between ACE2, tumor −5 3 585 0.38 (0.24–0.61) 2.2e microenvironment, and immune infiltration [10, 37]. 4 174 0.42 (0.23–0.77) 0.0039 However, there were limited studies that clarified the Mutation burden function of ACE2 in immune infiltration and the prognosis high 246 0.43 (0.25–0.76) 0.0027 of KIRC. +erefore, our study was undertaken. low 437 0.34 (0.16–0.75) 0.0051 +e expression analysis revealed that ACE2 was downregulated in KIRC patients at the mRNA and protein misregulation in cancer cells, cell cycle, and ribosomes level, and a low expression of ACE2 protein in KIRC patients (Figure 8(g), P< 0.05). was obtained in the subgroup analysis. +ese results indicate that ACE2 may play a significant role in KIRC. Further prognosis analysis indicated that high ACE2 level in KIRC 3.7. ACE2-Associated Targets in KIRC. To further clarify the patients was linked to a favorable prognosis in both the underlining mechanisms of how ACE2 affects tumorigenesis TCGA and GEO cohorts, suggesting ACE2 could be a novel and the progression of KIRC, we explored ACE2-associated prognostic biomarker for KIRC and the prediction of a Percent survival Percent survival Journal of Oncology 7 Macrophage B cell CD8 + T cell CD4 + T cell Neutrophil Dendritic cell Partial.cor = 0.226 Partial.cor = 0.137 Partial.cor = –0.012 Partial.cor = 0.104 Partial.cor = 0.125 Partial.cor = 0.118 p = 9.78e – 07 p = 3.95e – 03 p = 7.99e – 01 p = 2.75e – 02 p = 7.42e – 03 p = 1.16e – 02 7.5 5.0 2.5 0.0 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.2 0.4 0.6 0.0 0.1 0.2 0.3 0.4 0.5 0.0 0.1 0.2 0.3 0.4 0.0 0.1 0.2 0.3 0.0 0.4 0.8 1.2 Infiltration level (a) KIRC 2.0 1.5 1.0 0.5 0.0 B cell CD8 + T cell CD4 + T cell Macrophage Neutrophil Dendritic cell Copy number Deep deletion Diploid/normal Arm-level deletion Arm-level gain (b) Figure 4: +e correlation between ACE2 and immune infiltration (TIMER). (a) +e correlation between ACE2 expression, the abundance of CD8+ T cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells. (b) +e correlation between SCNA of ACE2 and immune-cell infiltration. SCNA, somatic copy number alterations; ∗P< 0.05; ∗∗ P< 0.01; ∗∗ ∗ P< 0.001. Table 2: Correlation analysis between ACE2 and gene biomarkers of immune cells in KIRC (TIMER). None Purity Description Biomarkers Cor. P value Cor. P value CD8A 0.0.119 ∗∗ 0.107 ∗ CD8+ T cell CD8B 0.115 ∗∗∗ 0.099 ∗ CD3D 0.045 0.298 0.027 0.565 T cell (general) CD3E 0.064 0.14 0.048 0.302 CD2 0.09 ∗ 0.077 0.0985 CD19 −0.176 ∗∗∗ −0.186 ∗∗∗ B cell CD79A −0.12 ∗∗ −0.144 ∗∗ CD86 0.126 ∗∗ 0.117 ∗ Monocyte CD115 (CSF1R) 0.096 ∗ 0.092 ∗ CCL2 0.0.266 ∗∗∗ 0.28 ∗∗∗ TAM CD68 0.146 ∗∗∗ 0.097 ∗ IL10 0.006 0.885 −0.016 0.733 INOS (NOS2) 0.2 ∗∗∗ 0.18 ∗∗∗ M1 macrophage IRF5 0.229 ∗∗∗ 0.208 ∗∗∗ COX2 (PTGS2) −0.219 ∗∗∗ −0.205 ∗∗∗ CD163 0.066 0.128 0.04 0.396 M2 macrophage VSIG4 −0.027 0.538 −0.061 0.188 MS4A4A 0.019 0.668 0.002 0.973 CD66b (CEACAM8) 0.071 0.0996 0.053 0.255 Neutrophil CD11b (ITGAM) 0.159 ∗∗∗ 0.147 ∗∗∗ CCR7 −0.004 0.932 −0.039 0.403 ACE2 expression level (log2 TPM) KIRC Infiltration level 8 Journal of Oncology Table 2: Continued. None Purity Description Biomarkers Cor. P value Cor. P value KIR2DL1 0.17 ∗∗∗ 0.136 ∗∗ KIR2DL3 0.139 ∗∗ 0.106 ∗ KIR2DL4 0.024 0.584 0.002 0.964 Natural killer cell KIR3DL1 0.206 ∗∗∗ 0.168 ∗∗∗ KIR3DL2 0.138 ∗∗ 0.106 ∗ KIR3DL3 0.029 0.508 0.017 0.712 KIR2DS4 0.08 0.0634 0.058 0.217 HLA-DPB1 0.221 ∗∗∗ 0.193 ∗∗∗ HLA-DQB1 0.208 ∗∗∗ 0.185 ∗∗∗ HLA-DRA 0.257 ∗∗∗ 0.23 ∗∗∗ Dendritic cell HLA-DPA1 0.264 ∗∗∗ 0.25 ∗∗∗ BDCA-1 (CD1C) 0.185 ∗∗∗ 0.175 ∗∗∗ BDCA-4 (NRP1) 0.218 ∗∗∗ 0.207 ∗∗∗ CD11c (ITGAX) 0.038 0.385 0.025 0.591 T-bet (TBX21) 0.093 ∗ 0.064 0.167 STAT4 −0.002 0.956 −0.04 0.886 +1 STAT1 0.219 ∗∗∗ 0.208 ∗∗∗ IFN-g (IFNG) 0.053 0.22 0.034 0.461 TNF-a (TNF) 0.062 0.152 0.043 0.358 GATA3 −0.212 ∗∗∗ −0.165 ∗∗∗ STAT6 0.323 ∗∗∗ 0.31 ∗∗∗ STAT5A 0.139 ∗∗ 0.16 ∗∗∗ IL13 −0.059 0.17 −0.088 0.0588 BCL6 −0.076 0.0793 −0.089 0.0570 Tfh IL21 −0.072 0.0972 −0.09 0.0546 STAT3 0.237 ∗∗∗ 0.239 ∗∗∗ IL17A −0.031 0.472 −0.029 0.532 FOXP3 −0.127 −0.143 ∗∗ ∗∗ CCR8 0.04 0.361 0.028 0.552 Treg STAT5B 0.433 ∗∗∗ 0.417 ∗∗∗ TGFb (TGFB1) −0.253 ∗∗∗ −0.271 ∗∗∗ PD-1 (PDCD1) 0.036 0.404 0.016 0.733 CTLA4 0.012 0.78 −0.004 0.925 T cell exhaustion LAG3 0.017 0.694 0.009 0.855 TIM-3 (HAVCR2) 0.355 ∗∗∗ 0.32 ∗∗∗ GZMB −0.029 0.507 −0.066 0.157 ∗ ∗∗ ∗∗∗ P < 0.05; P < 0.01; P < 0.001. favorable outcome. ACE2 has also been suggested as a and reduced response to treatment [40]. Moreover, CD39+ CD8+ T cells were shown to act as prognostic biomarkers in biomarker for other diseases or cancers. In thyroid carci- noma, ACE2 was employed as a biomarker and was also patients with KIRC and were also used to indicate poor found to regulate tumor progression [13]. Moreover, ACE2 prognosis [41]. In our study, we also found that ACE2 may acted as a biomarker in chronic kidney disease and asso- affect the prognosis of KIRC patients, in part, due to immune ciated with higher risk for silent atherosclerosis [36]. infiltration. However, previous studies have suggested that A steady accumulation of data suggests that immune-cell T-regulatory cells are correlated with the poor outcomes of infiltration could regulate tumor progression and metastasis, patients with KIRC [6]. However, we found that ACE2 thus affecting the patients’ prognosis [38, 39]. In our study, positively associated with the abundance of several immune we also clarified the correlation between ACE2 and immune cells. +us, all correlations of ACE2 and infiltrating immune infiltration. We found ACE2 to be positively associated with cells in KIRC may not be favorable. Further study should be the abundance of immune cells, including B cells, CD8+ performed to verify these results and observations. T cells, macrophages, neutrophils, and dendritic cells. +e enrichment analysis suggested that ACE2 in KIRC Moreover, a strong correlation between the expression of were primarily involved in translation factor activity, im- ACE2 and most of the immune biomarker sets were ana- munoglobulin binding, metabolic pathways, transcriptional lyzed. +ese immune cells or biomarkers were known to be misregulation in cancer cells, cell cycle, and ribosomes. involved in tumor progression and as a biomarker for +ese findings are consistent with the previous study where prognosis or therapy of KIRC. Ying et al. found that tumor ACE2 was associated with cell cycle transcription [42]. microenvironment B cells were associated with poor survival Misregulation of ribosome functions and the cell cycle has Journal of Oncology 9 Enriched basophils Decreased basophils Enriched B cells Decreased B cells 1.0 1.0 1.0 1.0 HR = 0.46 (0.28 – 0.73) HR = 0.62 (0.41 – 0.94) HR = 0.41 (0.26 – 0.67) HR = 0.66 (0.44 – 1) logrank P = 0.00082 logrank P = 0.022 logrank P = 0.046 logrank P = 0.00021 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 50 100 150 0 20406080 100 120 140 020 40 60 80 100 120 0 50 100 150 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 96 32 8 0 Low 167 119 87 47 22 11 4 0 Low 121 82 60 32 19 11 4 Low 142 53 9 0 High 97 42 6 1 High 167 127 87 54 29 15 5 0 High 121 93 56 35 17 6 4 High 143 62 14 1 Expression Expression Expression Expression Low Low Low Low High High High High (a) (b) Enriched CD4 + memory T cells Decreased CD4 + memory T cells Enriched CD8 + T cells Decreased CD8 + T cells 1.0 1.0 1.0 1.0 HR = 0.63 (0.44 – 0.91) HR = 0.4 (0.23 – 0.72) HR = 0.52 (0.37 – 0.74) HR = 0.46 (0.24 – 0.9) logrank P = 0.013 logrank P = 0.0016 logrank P = 0.00023 logrank P = 0.02 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 50 100 150 0 20406080 100 120 140 0 50 100 150 0 20406080 100 120 140 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 174 67 14 0 Low 90 61 42 19 6 5 1 0 Low 192 65 13 0 Low 72 52 41 22 10 6 1 0 High 174 75 16 1 High 89 64 49 24 13 5 2 0 High 192 83 15 1 High 71 51 38 21 14 6 3 0 Expression Expression Expression Expression Low Low Low Low High High High High (c) (d) Enriched eosinophils Decreased eosinophils Enriched macrophages Decreased macrophages 1.0 1.0 1.0 1.0 HR = 0.49 (0.31 – 0.77) HR = 0.57 (0.37 – 0.86) HR = 0.53 (0.38 – 0.75) HR = 0.48 (0.22 – 1.04) logrank P = 0.0017 logrank P = 0.0075 logrank P = 0.00024 logrank P = 0.056 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 50 100 150 020 40 60 80 100 120 140 0 50 100 150 0 20406080 100 120 140 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 153 57 12 0 Low 110 75 52 26 11 6 3 0 Low 217 79 18 0 Low 46 33 22 11 3 2 0 0 High 153 65 10 1 High 111 84 57 37 23 12 4 0 High 217 88 15 1 High 47 36 28 19 10 5 2 0 Expression Expression Expression Expression Low Low Low Low High High High High (e) (f) Figure 5: Prognostic value of ACE2 in KIRC based on immune-cell subgroups (Kaplan–Meier plotter). Enriched mesenchymal stem cells Diseased mesenchymal stem cells Enriched natural killer T cells Diseased natural killer T cells 1.0 1.0 1.0 1.0 HR = 0.3 (0.13 – 0.72) HR = 0.54 (0.37 – 0.8) HR = 0.55 (0.39 – 0.77) HR = 0.54 (0.32 – 89) logrank P = 0.004 logrank P = 0.0017 logrank P = 0.00034 logrank P = 0.014 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 20406080 100 120 0 50 100 150 020 40 60 80 100 120 140 0 50 100 150 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 18 7 4 2 0 0 0 Low 246 93 19 0 Low 63 41 26 13 5 2 1 0 Low 200 78 16 0 High 18 12 6 5 3 2 1 High 245 105 19 1 High 63 43 28 18 8 4 3 0 High 201 85 18 1 Expression Expression Expression Expression Low Low Low Low High High High High (a) (b) Figure 6: Continued. Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability Probability 10 Journal of Oncology Enriched regulatory T cells Diseased regulatory T cells Enriched type 1 T-helper cells Diseased type 1 T-helper cells 1.0 1.0 1.0 1.0 HR = 0.46 (0.26 – 0.83) HR = 0.5 (0.34 – 0.72) HR = 0.67 (0.24 – 1.88) HR = 0.55 (0.4 – 0.76) logrank P = 0.0076 logrank P = 0.00019 logrank P = 0.44 logrank P = 0.00021 0.8 0.8 0.8 0.8 0.6 0.6 0.6 0.6 0.4 0.4 0.4 0.4 0.2 0.2 0.2 0.2 0.0 0.0 0.0 0.0 0 20406080 100 120 0 50 100 150 020 40 60 80 100 120 0 50 100 150 Time (months) Time (months) Time (months) Time (months) Number at risk Number at risk Number at risk Number at risk Low 99 70 46 23 15 6 0 Low 164 60 10 0 Low 12 8 6 3 1 1 0 Low 252 93 19 0 High 99 70 44 27 14 5 3 High 165 76 19 1 High 12 9 4 3 2 2 2 High 251 106 18 1 Expression Expression Expression Expression Low Low Low Low High High High High (c) (d) Enriched type 2 T-helper cells Diseased type 2 T-helper cells 1.0 1.0 HR = 0.56 (0.4 – 0.79) HR = 0.27 (0.12 – 0.64) logrank P = 0.00066 logrank P = 0.0014 0.8 0.8 0.6 0.6 0.4 0.4 0.2 0.2 0.0 0.0 0 20406080 100 120 0 50 100 150 Time (months) Time (months) Number at risk Number at risk Low 238 88 17 0 Low 26 12 7 3 1 1 0 High 25 16 10 8 5 2 1 High 238 103 20 1 Expression Expression Low Low High High (e) Figure 6: Prognostic value of ACE2 in KIRC based on immune-cells subgroup (Kaplan–Meier plotter). ACE2 9% Genetic alteration Missense mutation (unknown significance) mRNA low Truncating mutation (unknown significance) No alterations Deep deletion (a) E498 Peptidase_M2 0 200 400 600 805aa Sample ID Protein change Annotation ▾ Mutation type Copy # COSMIC # Mut in sample TCGA-B0-5709-01 E498 ○ Nonsense Diploid 1 60 TCGA-B4-5835-01 I21V Missense ○ Diploid 1 77 (b) Figure 7: Continued. Probability # ACE2 mutations Probability Probability Probability Probability Probability Journal of Oncology 11 P = 0.00121 0 10 20 30 40 50 60 70 80 90 100 110 120 130 140 150 Months overall Overall Altered group Unaltered group (c) Figure 7: Genetic alteration of ACE2 in KIRC patients (cBioPortal). (a) OncoPrint visual summary of alteration on ACE2 in KIRC. (b) +e missense mutation of ACE2 amino acids was analyzed. Color images are available online. (c) +e overall survival in cases with/without ACE2 alterations. ACE2 association result ACE2 TMEM27 GLYATL1 CLCN5 LRP2 120 SLC13A1 PHYHIPL SLC3A1 GBA3 CUBN FUT6 SLC27A2 HAO2 PKLR AFTPH PDZK1 Z-score GLYAT AGXT2 SLC16A9 > 3 SLCO4C1 TMEM174 FMO1 TINAG 0 DDC –1 SLC22A11 NAT8 < –3 SLC1A1 ACMSD RAB3IP LRRC19 group SATB2 SLC5A10 EHHADH BBOX1 SLC7A9 0 SLC17A3 SLC22A24 –1 SLC5A1 GIPC2 –2 C11or154 –3 MIOX NAT8B LOC15332B GJB1 –1 012 CYP4A11 AVPR1B TAL2 Spearman's rho statistic (spearman test) SLC22A13 USH1C SLC22A12 (a) (b) Figure 8: Continued. –log10 (pvalue) Overall (%) 12 Journal of Oncology BP BDKRB1 C1or121 Extracellular structure organization TPST2 MINA Small molecule catabolic process TRNP1 GPRC5A TMEM158 Coenzyme metabolic process COL6A1 PCOLCE Organic anion transport SMARCD3 GNAS Cellular amino acid metabolic process GLT8D1 BALAP2L1 KCNG1 Peroxisome organization CD55 ETV4 Z-score Peroxisomal transport DBN1 PPM1J Cellular aldehyde metabolic process ST5 > 3 GAD1 PSD3 1 Tricarboxylic acid metabolic process PODNL1 SNX21 0 Modified amino acid transport MAGED4 CACNB3 –1 IMPDH1 < –3 0 20 40 60 80 100 120 140 160 SIX4 PCBP3 GPSM1 LMO1 Group MAGED4B TUBB3 MPP2 CREB3L1 LEF1 ITPKA PORCN –1 DAPK2 –2 B4GALNT1 ARTN –3 PTPN1 SEC13 PSD BDKR82 ACCN2 MARCH10 IRS2 FNDC4 ZBTB47 GGN (c) (d) CC MF Extracellular matrix Translation factor activity, RNA binding Mitochondrial matrix Structural constituent of ribosome Ribosome Immunoglobulin binding Apical part of cell Collagen biding Microbody Cytokine receptor binding Cluster of actin-based cell projections Nucleotide receptor activity Polysome Cytokine binding Collagen trimer Protein transporter activity Intraciliary transport particle rRNA binding Endoplasmic reticulum exit site snoRNA binding 0 20 40 60 80 100 120 140 160 180 200 0 10 20 30 40 50 60 70 80 90 100 (e) (f) KEGG Metabolic pathways Pathways in cancer Human papillomavirus infection Focal adhesion Transcriptional misregulation in cancer Ribosome Cell cycle ECM-receptor interaction Drug metabolism Retinol metabolism 0 200 400 600 800 1000 1200 1400 (g) Figure 8: Enrichment analysis of ACE2 in KIRC (LinkedOmics). (a) +e differentially expressed genes significantly correlated with ACE2 in KIRC. ((b), (c)) Heat maps showing the top 50 genes positively and negatively correlated with ACE2 in KIRC. (d) BP analysis. (e) CC analysis. (f) MF analysis. (g) KEGG pathway analysis. +e analysis was performed by GSEA. Table 3: +e kinase, miRNA, and transcription factor target networks of ACE2 in KIRC (LinkedOmics). Enriched category Gene set Leading edge number P value Kinase_LCK 29 0 Kinase_LYN 30 0 Kinase target Kinase_SYK 16 0 Kinase_JAK3 8 0 Kinase_HCK 17 0 GTGCCAA, MIR-96 91 0 TGCACTT, MIR-519 C, MIR-519 B, MIR-519A 140 0 miRNA target TGCACTG, MIR-148A, MIR-152, MIR-148B 97 0 GTGCCTT, MIR-506 213 0 TATTATA, MIR-374 105 0.002 V$IRF_Q6 104 0 V$NFKB_Q6_01 57 0 Transcription factor target V$ELF1_Q6 84 0 V$PEA3_Q6 81 0 V$PU1_Q6 150 0 Journal of Oncology 13 been linked to many diseases, in particular cancers [43]. Our the final manuscript. 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