Development and validation of a prognostic and predictive 32-gene signature for gastric cancer.

ABSTRACT: Genomic profiling can provide prognostic and predictive information to guide clinical care. Biomarkers that reliably predict patient response to chemotherapy and immune checkpoint inhibition in gastric cancer are lacking. In this retrospective analysis, we use our machine learning algorithm NTriPath to identify a gastric-cancer specific 32-gene signature. Using unsupervised clustering on expression levels of these 32 genes in tumors from 567 patients, we identify four molecular subtypes that are prognostic for survival. We then built a support vector machine with linear kernel to generate a risk score that is prognostic for five-year overall survival and validate the risk score using three independent datasets. We also find that the molecular subtypes predict response to adjuvant 5-fluorou

SUBMITTER: Cheong JH 

PROVIDER: S-EPMC8828873 | biostudies-literature | 2022 Feb

REPOSITORIES: biostudies-literature