The expression and prognostic value of co-stimulatory molecules in clear cell renal cell carcinoma (ccRCC).

BACKGROUND: Renal cell carcinoma (RCC) was one of the most common malignant cancers in the urinary system. Clear cell carcinoma (ccRCC) is the most common pathological type, accounting for approximately 80% of RCC. The lack of accurate and effective prognosis prediction methods has been a weak link in ccRCC treatment. Co-stimulatory molecules played the main role in increasing anti-tumor immune response, which determined the prognosis of patients. Therefore, the main objective of the present study was to explore the prognostic value of Co-stimulatory molecules genes in ccRCC patients. METHOD: The TCGA database was used to get gene expression and clinical characteristics of patients with ccRCC. A total of 60 Co-stimulatory molecule genes were also obtained from TCGA-ccRCC, including 13 genes of the B7/ CD28 Co-stimulatory molecules family and 47 genes of the TNF family. In the TCGA cohort, the least absolute shrinkage and selection operator (LASSO) Cox regression model was used to generate a multigene signature. R and Perl programming languages were used for data processing and drawing. Real-time PCR was used to verify the expression of differentially expressed genes. RESULTS: The study's initial dataset included 539 ccRCC samples and 72 normal samples. The 13 samples have been eliminated. According to FDR<0.05, there were differences in the expression of 55 Co-stimulatory molecule genes in ccRCC and normal tissues. LASSO Cox regression analysis results indicated that 13 risk genes were optimally used to construct a prognostic model of ccRCC. The patients were divided into a high-risk group and a low-risk group. Those in the high-risk group had significantly lower OS (Overall Survival rate) than patients in the low-risk group. Receiver operating characteristic (ROC) curve analysis confirmed the predictive value of the prognosis model of ccRCC (AUC>0.7). There are substantial differences in immune cell infiltration between high and low-risk groups. Functional analysis revealed that immune-related pathways were enriched, and immune status was different between the two risk groups. Real-time PCR results for genes were consistent with TCGA DEGs. CONCLUSION: By stratifying patients with all independent risk factors, the prognostic score model developed in this study may improve the accuracy of prognosis prediction for patients with ccRCC.