Abstract
Clear cell renal cell carcinoma (ccRCC) presents challenges in early diagnosis and effective treatment. In this study, we aimed to establish a prognostic model based on G2M checkpoint-related genes and identify associated clusters in ccRCC through clinical bioinformatic analysis and experimental validation. Utilizing a single-cell RNA dataset (GSE159115) and bulk-sequencing data from The Cancer Genome Atlas (TCGA) database, we analyzed the G2M checkpoint pathway in ccRCC. Differential expression analysis identified 45 genes associated with the G2M checkpoint, leading to the construction of a predictive model with four key genes (E2F2, GTSE1, RAD54L, and UBE2C). The model demonstrated reliable predictive ability for 1-, 3-, and 5-year overall survival, with AUC values of 0.794, 0.790, and 0.794, respectively. Patients in the high-risk group exhibited a worse prognosis, accompanied by significant differences in immune cell infiltration, immune function, TIDE and IPS scores, and drug sensitivities. Two clusters of ccRCC were identified using the "ConsensusClusterPlus" package, cluster 1 exhibited a worse survival rate and was resistant to chemotherapeutic drugs of Axitinib, Erlotinib, Pazopanib, Sunitinib, and Temsirolimus, but not Sorafenib. Targeted experiments on RAD54L, a gene involved in DNA repair processes, revealed its crucial role in inhibiting proliferation, invasion, and migration in 786-O cells. In conclusion, our study offers valuable insights into the molecular mechanisms underlying ccRCC, identifying potential prognostic genes and molecular subtypes associated with the G2M checkpoint. These findings hold promise for guiding personalized treatment strategies in the management of ccRCC.