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

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-lymp

SUBMITTER: Li J 

PROVIDER: S-EPMC11557704 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature