Integrative machine learning and molecular docking reveal Narirutin targets ABCC1/ABCG2 for immune microenvironment remodeling in colorectal cancer.

ABSTRACT: Colorectal cancer (CRC) is one of the most prevalent and deadly cancers worldwide, with late-stage diagnosis often associated with poor prognosis. The natural compound narirutin has shown various biological activities, including anti-inflammatory and anti-cancer effects, indicating its potential clinical application. This study aims to identify the key targets and potential mechanisms of narirutin treatment in colorectal cancer (CRC) through comprehensive bioinformatics analysis and machine learning methods, emphasizing its importance as a potential therapeutic agent. We utilized SwissTargetPrediction to identify 23 target genes of narirutin, followed by differential expression analysis of 3,338 genes from the TCGA-COAD dataset. We performed Gene Ontology (GO) and Kyoto Encyclopedia of Gen

SUBMITTER: Bian R 

PROVIDER: S-EPMC12936098 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature