Abstract:
Objective To establish a prognostic model based on endoplasmic reticulum stress-related genes for evaluating the prognosis of patients with renal cell carcinoma.
Methods This study utilized Non-negative Matrix Factorization to identify molecular subgroups based on endoplasmic reticulum stress-related genes and employed Weighted Correlation Network Analysis to determine co-expressed genes associated with these subgroups. A risk prognostic model was constructed using univariate Cox regression analysis and Lasso regression analysis. Preliminary experimental validations were conducted to elucidate the biological functions of model genes in renal cell carcinoma.
Results Two molecular subgroups with distinct survival prognoses were identified, and an intersection of related genes was used to construct a novel endoplasmic reticulum stress-related prognostic model. Patients in the high-risk group exhibited significantly poorer overall survival in both the training and validation cohorts. In vivo experiments demonstrated that PCK1, a model gene, could inhibit the proliferation, migration, and invasion of renal cell carcinoma cells.
Conclusions The risk scoring model developed in this study effectively predicts the survival probability of renal cell carcinoma patients and can serve as an independent prognostic indicator. This model offers a new direction for personalized treatment strategies in renal cell carcinoma patients.
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