Clear cell renal cell carcinoma (ccRCC) is a highly heterogeneous malignancy with complex molecular features, posing significant challenges for accurate prognosis prediction and treatment stratification. Lactylation, an emerging epigenetic regulatory mechanism, plays a pivotal role in tumor progression and immune microenvironment remodeling. This study aims to develop a lactylation-related gene signature (LRGS) and systematically evaluate its prognostic value and potential for guiding clinical interventions in ccRCC. We developed a machine learning-derived lactylation signature using lactylation-related genes (LRGs) selected through bulk and single-cell RNA sequencing data. The model’s performance was subsequently validated using internal (TCGA-KIRC) and external (E-MTAB-1980) cohorts. The associations of LRGS with clinical characteristics, immune cell infiltration, and drug response were further analyzed. The functions of the key risk gene SHC1 were investigated using in vitro experiments. Ultimately, an 11-gene LRGS was identified that effectively predicts the prognosis of ccRCC patients. This model demonstrated robust predictive capability in an external dataset (AUC > 0.7). Univariate and multivariate analyses indicated that the model’s risk score was an independent predictor of clinical outcomes. The high-risk group exhibited significant infiltration of Tregs and a higher tumor immune dysfunction score, indicating a poorer responsiveness to immunotherapy. Finally, cell experiments confirmed that inhibiting SHC1 expression suppressed the proliferation and migration of ccRCC cells. In conclusion, we successfully constructed a novel LRGS, which provides a new perspective for risk stratification and personalized treatment of ccRCC.
Zhang et al. (Thu,) studied this question.