ArticleTranslational cancer research2026
Integrating multiple omics and machine learning to reveal the prognostic value of endoplasmic reticulum stress gene
Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Background: Stomach adenocarcinoma (STAD) remains a leading cause of cancer-related mortality worldwide, with limited prognostic biomarkers and heterogeneous responses to immunotherapy. Endoplasmic reticulum stress (ERS) plays a critical role in tumor progression and immune modulation, yet its comprehensive prognostic value in STAD has not been systematically characterized. This study aims to identify ERS-related genes with prognostic significance and elucidate their role in the tumor microenvironment. Methods: RNA sequencing (RNA-seq) data from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) projects were integrated to identify differentially expressed ERS-related genes. Univariate Cox regression and consensus clustering were applied to define molecular subtypes. A prognostic risk model was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression and validated in independent Gene Expression Omnibus (GEO) cohorts (GSE66229, GSE14208). Immune infiltration, functional enrichment, and mutation landscapes were analyzed. Single-cell RNA sequencing (scRNA-seq) (GSE183904) and CellChat were used to explore Results: We identified 33 prognostic ERS-related genes, which classified STAD patients into two subtypes (C1 and C2) with distinct survival outcomes and immune infiltration profiles. A 14-gene risk model was constructed and stratified patients into high- and low-risk groups with significant survival differences [area under the curve (AUC) =0.702]. Risk scores correlated with age, tumor (T), metastasis (M), and overall stage. Single-cell analysis revealed Conclusions: This study establishes a robust ERS-related prognostic signature for STAD and highlights
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