Machine Learning Predictor of Immune Checkpoint Blockade Response in Gastric Cancer.

ABSTRACT: Predicting responses to immune checkpoint blockade (ICB) lacks official standards despite the discovery of several markers. Expensive drugs and different reactivities for each patient are the main disadvantages of immunotherapy. Gastric cancer is refractory and stem-like in nature and does not respond to immunotherapy. In this study, we aimed to identify a characteristic gene that predicts ICB response in gastric cancer and discover a drug target for non-responders. We built and evaluated a model using four machine learning algorithms for two cohorts of bulk and single-cell RNA seq to predict ICB response in gastric cancer patients. Through the LASSO feature selection, we discovered a marker gene signature that distinguishes responders from non-responders. VCAN, a candidate characte

SUBMITTER: Sung JY 

PROVIDER: S-EPMC9265060 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature