Establishment and validation of an endoplasmic reticulum stress-related risk model for renal cell carcinoma
10.12354/j.issn.1000-8179.2025.20241472
- VernacularTitle:肾细胞癌内质网应激相关风险模型的建立与验证
- Author:
Chen YANG
1
;
Zhu JUNMING
;
Wang ZHEN
;
Wu XIAOHUI
;
Xu NING
;
Xue XUEYI
;
Zheng QINGSHUI
Author Information
1. 福建医科大学附属第一医院泌尿外科(福州市 350005);福建医科大学附属第一医院滨海院区国家区域医疗中心泌尿外科
- Publication Type:Journal Article
- Keywords:
renal cell carcinoma;
endoplasmic reticulum stress-related genes;
prognostic model;
immune infiltration
- From:
Chinese Journal of Clinical Oncology
2025;52(3):127-133
- CountryChina
- Language:Chinese
-
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 associ-ated 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 nov-el 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,migra-tion,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 per-sonalized treatment strategies in renal cell carcinoma patients.
