ArticleJournal of gastrointestinal oncology2026
Prognostic significance of DNA damage response-related markers in esophageal squamous cell carcinoma using machine learning approaches.
Article in Journal of gastrointestinal oncology, 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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8 authors.
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Abstract
Background: Esophageal squamous cell carcinoma (ESCC) lacks reliable prognostic biomarkers. Homologous recombination deficiency (HRD) has been implicated in genomic instability across multiple cancers, but its prognostic significance in ESCC remains unexplored. This study aimed to evaluate HRD score as a prognostic biomarker and develop a machine learning-based predictive model for ESCC. Methods: Transcriptomic and clinical data from 78 ESCC patients were obtained from The Cancer Genome Atlas (TCGA) and randomly split into training (70%) and test (30%) cohorts. Prognostic models were constructed using 112 machine learning algorithm combinations based on DNA damage response (DDR)-related genes. Gene set enrichment analysis (GSEA), somatic mutation profiling, and immune cell infiltration estimation via CIBERSORT were performed to characterize HRD-associated molecular features. Results: High HRD scores were significantly associated with poorer overall survival (P<0.05). Among 112 algorithm combinations, the survival support vector machine (Survival-SVM) model demonstrated optimal performance [training concordance index (C-index): 0.741; test C-index: 0.708], identifying six hub genes: Conclusions: We developed a novel HRD-based prognostic model incorporating six DDR-related genes that demonstrates robust predictive performance in ESCC. HRD score is identified as an independent prognostic factor associated with genomic instability, immune microenvironment alterations, and clinical outcomes. These findings provide a theoretical basis for personalized treatment strategies, including potential applications of PARP inhibitors and immunotherapy in ESCC.
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