Randomized trial investigates ERO1A’s role in prognostic stratification and treatment guidance in early-stage lung adenocarcinoma, suggesting important clinical implications.
Background A considerable proportion of patients with early-stage lung adenocarcinoma (esLUAD) experience recurrence after curative resection, highlighting the need for improved biomarkers. Current biomarkers such as PD-L1 and tumor mutational burden (TMB) have limited predictive utility in early-stage disease. Through an unbiased screening approach, endoplasmic reticulum oxidoreductase 1α (ERO1A) was identified as a candidate gene for further investigation. Methods Multi-omics data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were integrated. Machine learning algorithms (least absolute shrinkage and selection operator (LASSO), random forest (RF), support vector machine (SVM)) were employed to identify core prognostic genes. Functional enrichment, immune microenvironment analysis, and drug sensitivity prediction were conducted. Key findings were validated using immunohistochemistry (IHC) in an independent institutional cohort. Results ERO1A was identified as a robust, independent predictor of poor overall and recurrence-free survival in esLUAD. It was significantly upregulated in tumors and associated with enhanced proliferative pathways. Paradoxically, high ERO1A expression correlated with an immune-activated tumor microenvironment, characterized by enriched CD8 + T cell infiltration, reduced M2 macrophages, and increased mature tertiary lymphoid structures. ERO1A-high tumors exhibited elevated TMB and PD-L1 levels, and were associated with superior response to immune checkpoint inhibitors in validation cohorts. Bioinformatic analysis suggested that ERO1A-high tumors might exhibit resistance to conventional chemotherapy while showing potential sensitivity to epidermal growth factor receptor (EGFR)-targeted therapies, though this was not confirmed in clinical validation. Conclusions ERO1A expression stratifies lung adenocarcinoma (LUAD) into distinct subsets with divergent therapeutic implications. It represents a promising biomarker for prognostic stratification and immunotherapy selection, supporting its potential clinical utility in guiding peri-operative therapy decisions.
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Chen et al. (2026) studied this question.
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