Construction of a prognostic risk model for clear cell renal cell carcinomas based on centrosome...

Clear cell renal cell carcinoma (ccRCC) is the urological malignancy with the highest incidence, centrosome amplification-associated genes (CARGs) have been suggested to be associated with carcinogenesis, but their roles in ccRCC are still incompletely understood. This study utilizes bioinformatics to explore the role of CARGs in the pathogenesis of ccRCC and to establish a prognostic model for ccRCC related to CARGs. Based on publicly available ccRCC datasets, 2312 differentially expressed genes (DEGs) were identified (control vs. ccRCC). Disease samples were classified into high and low scoring groups based on CARG scores and analysed for differences to obtain 345 DEGs associated with CARG scores (S-DEGs). 137 candidate genes were obtained by taking the intersection of DEGs and S-DEGs. Six prognostic genes (PCP4, SLN, PI3, PROX1, VAT1L, and KLK2) were then screened by univariate Cox, LASSO, and multifactorial Cox regression. These genes exhibit a high degree of enrichment in ribosome-associated pathways. Both risk score and age were independent prognostic factors, and the Nomogram constructed based on them had a good predictive performance (AUC > 0.7). In addition, immunological analyses identified 6 different immune cells and 23 immune checkpoints between the high- and low-risk groups, whereas mutational analyses identified frequent VHL mutations in both high- and low-risk groups. Finally, 93 potentially sensitive drugs were identified. In conclusion, this study identified six CARGs as prognostic genes for ccRCC and established a risk model with predictive value. These findings provide insights for prognostic prediction of ccRCC, optimisation of clinical management and development of targeted therapeutic strategies.