SNPs and blood inflammatory marker featured machine learning for predicting the efficacy of fluorouracil-based chemotherapy in colorectal cancer

Abstract

Abstract Fluorouracil-based chemotherapy responses in colorectal cancer (CRC) patients vary widely, highlighting the role of pharmacogenomics in developing better predictive models. We analyzed 379 CRC patients receiving fluorouracil-based chemotherapy, collecting data on fluorouracil metabolism-related SNPs (TYMS, MTHFR, DPYD, RRM1), blood inflammatory markers, and clinical status. Six machine learning models—K-nearest neighbors, support vector machine, gradient boosting decision trees (GBDT), eXtreme Gradient Boosting (XGBoost), LightGBM, and random forest—were compared against multivariate logistic regression and a deep learning model (i.e., multilayer perceptron, MLP). Feature importance analysis highlighted seven predictors: histological grade, N and M staging, monocyte count, platelet-to-lymphocyte ratio, MTHFR rs1801131, and RRM1 rs11030918. In a five-fold cross-validation, XGBoost and GBDT exhibited superior performance, with Area Under Curve (AUC) of 0.88 ± 0.02. XGBoost excelled in identifying favorable prognosis (recall = 0.939). GBDT demonstrated balance in recognizing both categories, with a recall for favorable prognosis of 0.908 and a precision for unfavorable prognosis of 0.863. MLP had a similar AUC (0.87) with high precision for favorable prognosis (recall = 0.946). In external validation, XGBoost model achieved an accuracy of 0.79. An online prognostic tool based on XGBoost was developed, integrating metabolism-related SNPs and inflammatory markers, enhancing CRC treatment precision and supporting tailored chemotherapy.

Keywords

• Colorectal cancer
• Machine learning
• Adjuvant chemotherapy
• Fluorouracil
• Rational drug use