Combinatorial Chemistry & High Throughput ScreeningCombinatorial Chemistry & High Throughput ScreeningCombinatorial Chemistry & High Throughput Screening1386-20731875-5402Bentham Science64464810.2174/1386207326666230511153724ChemistryResearch ArticleThe Expression and Prognostic Value of Co-stimulatory Molecules in Clear Cell Renal Cell Carcinoma (CcRcc)WuChengjianginfo@benthamscience.netCaiXiaojieinfo@benthamscience.netHeChunyaninfo@benthamscience.netDepartment of Clinical Laboratory, The Second Affiliated Hospital of Soochow UniversityDepartment of Radiology, Affiliated Changshu Hospital of Soochow University, First People’s Hospital of Changshu CityDepartment of Clinical Laboratory,, Kunshan Hospital of Chinese Medicine Kunshan1501202427233534507012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://rjpbr.com/1386-2073/article/view/644648

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.

Methods: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.

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