Therefore, we explored the association between key immune checkpoints (CTLA-4, PD-1, TIGIT, LAG3, and TIM-3) and the signature

Therefore, we explored the association between key immune checkpoints (CTLA-4, PD-1, TIGIT, LAG3, and TIM-3) and the signature. developed and validated an immune-related signature to predict SU10944 the prognosis of ccRCC with VHL mutations. Methods VHL mutation status and RNA expression were analysed in the TCGA datasets and our cohort. LASSO Cox analysis was performed to develop an immune-related signature. Candidate genes for the immune-related signature were differentially expressed between VHLwt and VHLmut ccRCC patients. Results VHL mutations resulted in the downregulation of the immune response ATN1 in ccRCC. To develop an immune-related signature, LASSO Cox analysis was constructed by SU10944 immune-related genes that were differentially expressed between VHLwt (WHL wild type) and VHLmut (VHL mutation) ccRCC patients. The signature was developed and validated in the TCGA and our own cohort to classify patients into groups based on having a low or high risk of poor survival. Functional enrichment analysis showed that the immune-related pathway represented the major function and pathway. In addition, patients in the high-risk group had a positive correlation with low fractions of CD4?+?T cells and dendritic cells and presented a lower expression of CTLA-4 and PD-1 than the low-risk group. Conclusion In this study, we proposed a novel immune-related signature, which is a feasible biomarker for predicting the overall survival in VHLmut patients with ccRCC. value? ?0.05) Therefore, to comprehensively assess the relationship between immune status and VHL mutation status in patients with KIRC, we conducted the following analysis in the TCGA-KIRC cohort and validation cohort from the Jiangsu University Affiliated Wujin Hospital and the Affiliated Geriatric Hospital of Nanjing Medical University (Fig.?1b). In the TCGA-KIRC cohort, 343 KIRC patients were divided into the VHLMUT (173 patients) and VHLWT (170patients) groups according to VHL mutation status. After that, we screened for significantly differentially expressed genes (DEGs) in the RNA expression profiles of these two groups of patients, which showed that 1175 genes, including mRNAs, lncRNAs, miRNAs and pseudogenes, were significantly differentially expressed (Fig.?1c). These DEGs contained 94 downregulated genes and 1081 upregulated genes (Additional file 1: Table S2). In addition, to explore the correlation of DEGs between the VHLMUT and VHLWT groups to immune-related phenotypes, we further filtered the RNA expression profile using the ImmPort gene list. We obtained 187 immune-related DEGs by overlapping the DEGs and ImmPort genes list (Additional file 1: Table S3). The Metascape online tool was used to annotate the potential functional characteristics, which identified immune-related DEGs. We can significantly observe that several immune-related pathways are enriched (Additional file 3: Fig. S2), suggesting that we can further analyse potential immune subtypes. Construction of an immune-related risk signature To explore the predictive power of immune phenotypes for overall survival, we further analysed the correlation between 187 immune-related DEGs and overall survival. Ten genes, namely, SEMA3B, KCNH2, INHA, BPIFA2, FGF19, IL20, GDNF, ANGPTL7, MUC5AC and HLA-DQA1, were filtered using nonzero regression coefficients that have a maximum prognostic value according to LASSO Cox regression analysis (Fig.?2a , b). These 10 candidate genes are clearly involved in immune-related biological processes or directly participate in immune responses and included TGF-family members, cytokines, chemokines, antimicrobials, antigen processing and presentation (Fig.?2c, Additional file 1: Table S4). This suggests that the difference in the expression of these genes may predict the difference in tumour immune status and tumour microenvironment in patients with KIRC. Finally, a ten-gene immune-related risk score was constructed, and the risk score of each patient was calculated using the following formula:?=?0.01488135* (normalized expression of SEMA3B)?+?(0.05056229* normalized expression of KCNH2)?+?(??0.0645472* normalized expression of INHA)?+?(??0.01586218* normalized expression of BPIFA2)?+?(??0.03727866* normalized expression of FGF19)?+?(0.25913417* normalized expression of IL20)?+?(??0.04517044* normalized expression of GDNF)?+?(??0.06116952* normalized expression of ANGPTL7)?+?(??0.14067171* normalized expression of MUC5AC)?+?( ??0.01418558* normalized expression of HLA-DQA1). 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