Endoplasmic reticulum stress-related signatures: a game-changer in prognostic stratification for hepatocellular carcinoma

Abstract

Background Hepatocellular carcinoma (HCC) has limited therapeutic options and a poor prognosis. The endoplasmic reticulum (ER) plays a crucial role in tumor progression and response to stress, making it a promising target for HCC stratification. This study aimed to develop a risk stratification model using ER stress-related signatures. Methods We utilized transcriptome data from The Cancer Genome Atlas and Gene Expression Omnibus, which encompass whole-genome expression profiles and clinical annotations. Machine learning algorithms, including the least absolute shrinkage and selection operator, random forest, and support vector machine recursive feature elimination, were applied to the key genes associated with HCC prognosis. A prognostic system was developed using univariate Cox hazard analysis and least absolute shrinkage and selection operator Cox regression, followed by validation using Kaplan–Meier analysis and receiver operating characteristic curves. Tumor immune dysfunction and exclusion tools were used to predict immunotherapy responsiveness. Results Two distinct clusters associated with ER stress were identified in HCC, each exhibiting unique clinical and biological features. Using a computational approach, a prognostic risk model, namely the ER stress-related signature, was formulated, demonstrating enhanced predictive accuracy compared with that of existing prognostic models. An effective clinical nomogram was established by integrating the risk model with clinicopathological factors. Patients with lower risk scores exhibited improved responsiveness to various chemotherapeutic, targeted, and immunotherapeutic agents. Conclusion The critical role of ER stress in HCC is highlighted. The ER stress-related signature developed in this study is a powerful tool to assess the risk and clinical treatment of HCC.