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

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 Genes and Genomes (KEGG) enrichment analyses on the intersecting genes, and machine learning techniques to identify hub genes. Additionally, we conducted molecular docking studies and immune infiltration analyses. Our findings revealed that narirutin targets key genes, including ABCC1, ABCG2, CA12, EPHX2, and PTGS1, which are significantly involved in CRC progression. Enrichment analyses indicated that these genes participate in crucial pathways related to drug metabolism and immune response. Molecular docking results demonstrated favorable binding affinities between narirutin and its target proteins. Furthermore, immune cell infiltration analysis showed significant differences in various immune cell types between CRC and control groups, suggesting their role in tumor microenvironment dynamics. Narirutin may play a vital role in CRC treatment by modulating key genes and pathways, underscoring its potential as a therapeutic agent. Future studies should explore its clinical applicability and mechanisms further.