Variant‐to‐Biomarker Integration and Mechanistic Validation Identify CES1 as a Copy Number‐Linked Predictor of Radiotherapy Response in Rectal Cancer

Background Radiotherapy is a fundamental component of rectal cancer treatment, yet patient responses remain highly heterogeneous due to the lack of reliable biomarkers supported by genomic variation evidence. Integrating multi‐cohort transcriptomic data with machine‐learning approaches enables systematic identification of genes with both predictive and therapeutic relevance. This study aimed to develop a robust model for predicting radiotherapy response and to functionally characterize CES1 as a key regulator of radiosensitivity and a potential therapeutic target. Methods Three independent GEO cohorts were standardized and integrated, followed by a comprehensive machine‐learning pipeline incorporating LASSO, Elastic Net, Random Forest, XGBoost, and information gain. A consensus‐ranked five‐gene model was constructed using nested cross‐validation. CES1, identified as the top‐ranked contributor to model performance, was selected for biological validation. To connect transcriptomic findings with genetic variation, copy number alteration (GISTIC2) and somatic mutation analyses were performed in TCGA‐READ. Functional assays—including quantitative PCR, CCK‐8 viability assays, colony formation, wound‐healing migration assays, Annexin V/PI flow cytometry, and rescue by CES1 overexpression—were performed in HT‐29 and SW480 cells to evaluate its mechanistic role in radiotherapy response. Results The machine‐learning model demonstrated high discriminative accuracy across datasets and consistently highlighted CES1 as a dominant contributor to radiosensitivity prediction. CES1 expression increased after clinical chemoradiotherapy and showed dose‐dependent induction following irradiation in vitro. CES1 knockdown significantly reduced radiation‐induced apoptosis, enhanced clonogenic survival, and promoted migratory capacity, collectively indicating a radioresistant and more aggressive phenotype. Restoration of CES1 expression in CES1‐silenced cells reversed radioresistance and re‐established irradiation sensitivity. Genomic analysis in TCGA‐READ further demonstrated that CES1 expression was positively associated with copy number status, whereas coding‐sequence mutations in CES1 were infrequent, suggesting dysregulation primarily through copy‐number and transcriptional mechanisms. Conclusion This integrative computational and experimental study identifies CES1 as a predictive biomarker and copy number‐linked regulator of radiosensitivity in rectal cancer. Modulation of CES1 directly alters cellular responses to irradiation, supporting its role as a mechanistically interpretable biomarker for response stratification. These findings align with the emerging concept that integrating genetic variation profiling with functional validation can accelerate variant‐to‐biomarker translation in precision oncology.