# Integrative network pharmacology and machine learning identify potential targets of indole-3-lactic acid in colorectal cancer

## TL;DR

This study uses network pharmacology and machine learning to identify potential targets of indole-3-lactic acid in colorectal cancer, offering new insights into its anticancer mechanisms.

## Contribution

The study introduces an integrative approach combining network pharmacology, machine learning, and molecular docking to identify novel ILA targets in CRC.

## Key findings

- Four hub genes (EPHA2, HMOX1, MMP3, PARP1) were identified as potential ILA targets in colorectal cancer.
- ILA was linked to key signaling pathways like PPAR, PI3K-AKT, and IL-17, as well as metabolic and immune-related processes.
- Molecular docking and simulations confirmed stable ILA binding to HMOX1, supporting its role as a target.

## Abstract

The treatment of colorectal cancer (CRC) remains challenging due to chemotherapy resistance and genetic heterogeneity. Indole-3-lactic acid (ILA), a tryptophan metabolite derived from gut microbiota, exhibits promising anti-inflammatory and anticancer properties; however, its specific molecular targets and regulatory mechanisms in CRC remain poorly understood. In this study, we combined network pharmacology and machine learning with molecular docking to identify candidate targets and pathways for ILA in CRC. We identified 39 ILA-CRC common targets, ultimately identifying four hub genes through the intersection of machine learning models. Validation in independent GEO datasets confirmed significant differential expression of these genes in CRC tissues. Functional enrichment analyses linked these genes to the PPAR, PI3K-AKT, and IL-17 signaling pathways, and gene set enrichment analysis further implicated ascorbate and aldarate metabolism, DNA replication, and fatty acid metabolism. Immune infiltration analysis indicated associations between hub gene expression and immune cell populations, including mast cells, neutrophils, and macrophages, suggesting potential involvement in the tumor immune microenvironment. Molecular docking supported favorable binding of ILA to all four hub proteins, and 100-ns molecular dynamics simulations specifically validated the dynamic stability of the ILA-HMOX1 complex. In conclusion, these results highlight EPHA2, HMOX1, MMP3, and PARP1 as candidate targets and suggest that ILA may influence CRC-related signaling, metabolic programs, and immune contexture, providing a theoretical foundation for developing gut microbiota-derived metabolites as novel anticancer strategies.