In this study, bioinformatics methods were performed to screen the candidate prognosis-related genes of clear cell renal cell carcinoma (ccRCC) by analyzing the tumor microenvironment (TME).
Gene expression and clinical data of ccRCC patients were accessed from TCGA, and R package ESTIMATE was applied to calculate immune, stromal, and ESTIMATE scores of the patients. Survival analysis was conducted per median of these three scores. Based on the scoring results, differentially expressed genes (DEGs) were screened. Regression algorithms were utilized to screen prognostic genes and establish a risk model. Finally, pathway activity differences were analyzed through GSEA.
Patients with the unfavorable prognosis had high immune scores. 619 DEGs (499 up-regulated and 120 down-regulated) were screened based on the differences in gene expression of the patients with high and low immune scores. These genes mainly participated in immune-related signaling pathways. A prognostic risk model for ccRCC patients was constructed and 7 immune-related signature genes (RORB, TNFSF14, UCN2, USP2, TOX3, KLRC2, SLAMF9) were obtained through regression analysis. The constructed prognostic risk model could be used for determining prognoses of patients with ccRCC.
We unraveled the association between TME and prognosis of ccRCC patients and established a prognostic risk model based on the differentially expressed genes. These results contributed to understanding of TME that affected patients’ prognosis and progression of ccRCC and conduced to finding potential biomarkers of ccRCC.
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