Computational prioritization of putative herbal candidates for colorectal cancer using reverse network pharmacology and traditional Chinese medicine mapping

Colorectal cancer (CRC) is the third most prevalent malignancy and the second leading cause of cancer-related mortality worldwide. Despite significant advancements in CRC screening, diagnosis, and therapeutic strategies in recent years, the long-term prognosis of patients remains severely compromised due to the tumor’s propensity for metastasis and recurrence. In this context, traditional Chinese medicine (TCM) provides a novel research direction for improving therapeutic outcomes in CRC patients, with its unique advantages of multi-target synergistic effects, low toxicity, and integrated prevention and treatment. This study applied a disease-gene-drug reverse network pharmacology workflow integrating public microarray datasets, CRC-associated gene databases, protein-protein interaction analysis, Comparative Toxicogenomics Database (CTD)-based compound mapping, traditional Chinese medicine (TCM) reverse annotation, independent clinical-cohort validation, and molecular docking. Through systematic analysis, 28 candidate herbs, 13 active compounds, and 3 core target molecules were identified; among these, 17 herbs (including Bajiaolian, Bohe, etc.) were reported for the first time to have potential therapeutic effects against CRC. Furthermore, molecular docking was used to explore the binding affinity and interaction modes of 9 active compounds with key targets. To the best of our knowledge, this study was the first to apply reverse network pharmacology for screening TCM with therapeutic potential against CRC, providing novel insights for both clinical management and theoretical research of CRC.