ArticleJournal of thoracic disease2026
Integrating genetic and transcriptomics data to predict neoadjuvant chemoradiotherapy for patients with esophageal squamous cell carcinoma.
Article in Journal of thoracic disease, 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: Esophageal squamous cell carcinoma (ESCC) exhibits heterogeneous responses to neoadjuvant chemoradiotherapy (nCRT) and understanding the molecular features of ESCC could improve patient management. This study aimed to investigate the differences in somatic mutations and gene expression profiles among esophageal cancer patients exhibiting varying responses to nCRT. Methods: A prospective cohort of 30 patients with ESCC undergoing nCRT was enrolled. Tumor and blood samples were collected prior to the initiation of nCRT. Following transcriptome profiling and identification of differentially expressed genes, the singscore algorithm was applied to construct a response prediction model, and its predictive performance was assessed. Results: The singscore method generated a unique score for each case. The score range of patients with Tumor Regression Score (TRS) 0-1 was -0.59 to 1. The area under the receiver operating characteristic (ROC) curve was 0.888 [95% confidence interval (CI): 0.773-1], with singscore =0 identified as the optimal cutoff value of the score (sensitivity: 80.0%, specificity: 78.9%, positive predictive value: 88.2%, negative predictive value: 69.2%). Singscore score was positively correlated with better overall survival (hazard ratio: 0.14, 95% CI: 0-0.374, P=0.01). Conclusions: In this exploratory study, the singscore model demonstrated promising predictive performance for nCRT response in ESCC, with singscore =0 as the optimal cutoff in our cohort. However, these findings require validation in larger, multi-center studies before clinical application can be considered.
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