Identification and validation of a genetic risk signature associated with prognosis in clear-cell renal cell carcinoma patients

Abstract

Clear-cell renal cell carcinoma (ccRCC) is the most common subtype of renal cell carcinoma (RCC), which exhibits great variability in the prognosis of patients. Endoplasmic reticulum stress (ERS) is a persistent state triggered by disruption of endoplasmic reticulum (ER) homeostasis, which has been shown to control multiple pro-tumor-associated pathways in malignant cells while dynamically reprogramming immune cell function. This study aimed to identify ERS-related genetic risk signatures (ERSGRS) to ameliorate survival prediction in ccRCC patients. In this study, we adopted differentially expressed genes (DEGs) from the Cancer Genome Atlas (TCGA) and constructed ERSGRS with independent prognostic significance by least absolute shrinkage and selection operator (LASSO) regression. After separation of patients based on risk score, survival analysis showed that low-risk patients had longer overall survival (OS) than high-risk patients, and receiver operating characteristic (ROC) curve analysis confirmed the strong predictive ability of ERSGRS. Meanwhile, the tumor microenvironment (TME) of the high-risk group demonstrated an immunosuppressive phenotype, with more infiltration of regulatory T cells (Tregs) and macrophages. The TME in the low-risk group had a stronger potential for anti-tumor immunity. Overall, the ERSGRS could be a valuable predictive tool for ccRCC prognosis.

Mathematical and Machine Learning Models of Renal Cell Carcinoma: A Review

“…Recognizing the strains imposed by clinical trials on patients' well-being, researchers have diligently sought optimized treatments and an expanded understanding of RCC through diverse models, including in vitro or in vivo models such as mice, cell lines, machine learning, and mathematical frameworks [49][50][51][52][53][54][55][56][57][58][59][60][61]. Moreover, researchers have attempted to predict patient outcomes and improve treatment options for patients using various models and findings [21,36,40,45,47,[62][63][64][65][66][67][68][69][70][71][72][73].…”