Colorectal cancer (CRC) remains one of the leading causes of cancer-related mortality worldwide. Although adjuvant chemotherapy improves survival after curative resection, its efficacy varies widely among patients. The absence of reliable predictive biomarkers often leads to overtreatment or undertreatment.
This study aims to develop a machine learning-based predictive model for adjuvant chemotherapy response using tumor-derived alternative splicing signatures.
By integrating RNA-seq data, splicing isoform and clinical outcomes, this study seeks to identify molecular predictors of treatment response and recurrence risk after surgery.
Histologically confirmed stage II-III colorectal cancer (TNM classification, 8th edition)
Received standard adjuvant chemotherapy after curative resection
Availability of tumor tissue (FFPE or frozen) before chemotherapy
Sufficient clinical data for outcome analysis (recurrence, survival)
Inflammatory bowel disease
Inadequate RNA quality or lack of consent