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<p>Aflatoxin B1 (AFB1) contributes to colorectal cancer development through multiple molecular pathways. This study aims to investigate the molecular mechanisms underlying Colorectal cancer (CRC) induced by AFB1. Using integrative transcriptomic differential expression data, we enumerated candidate genes for AFB1-related colorectal cancer based on the list of published AFB1 protein targets. The molecular relationship between AFB1 and its candidates was verified by machine learning, network-based toxicology and molecular docking algorithms. The study screened 55 AFB1-related CRC genes. Through machine learning algorithm, three key genes, ABCG2, PDE3A, and CCND1, were selected. Down-regulation of ABCG2 and PDE3A but upregulation of CCND1 were observed under expression (<i>P</i> &lt; 0.05). Molecular docking indicated that the binding was stable between AFB1 and target proteins. Our analysis determines three key regulatory genes that play a key role in the colorectal cancer induction induced by AFB1. We show that AFB1 directly interacts with the transcripts of these genes, their protein products may disturb the network function. This study provides systems-level, multi-algorithm evidence suggesting that AFB1 may promote CRC pathogenesis by targeting specific genes and signaling pathways. Machine learning identified three core regulatory genes, and molecular docking suggested AFB1's high binding affinity with key targets. These findings provide critical insights for further mechanistic exploration of AFB1-induced colorectal cancer.</p>

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Integrating machine learning and molecular docking to identify candidate core genes involved in aflatoxin B1-induced colorectal cancer

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年7月22日

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  • Zihan Ji,
  • Aijun Ji

摘要

Aflatoxin B1 (AFB1) contributes to colorectal cancer development through multiple molecular pathways. This study aims to investigate the molecular mechanisms underlying Colorectal cancer (CRC) induced by AFB1. Using integrative transcriptomic differential expression data, we enumerated candidate genes for AFB1-related colorectal cancer based on the list of published AFB1 protein targets. The molecular relationship between AFB1 and its candidates was verified by machine learning, network-based toxicology and molecular docking algorithms. The study screened 55 AFB1-related CRC genes. Through machine learning algorithm, three key genes, ABCG2, PDE3A, and CCND1, were selected. Down-regulation of ABCG2 and PDE3A but upregulation of CCND1 were observed under expression (P < 0.05). Molecular docking indicated that the binding was stable between AFB1 and target proteins. Our analysis determines three key regulatory genes that play a key role in the colorectal cancer induction induced by AFB1. We show that AFB1 directly interacts with the transcripts of these genes, their protein products may disturb the network function. This study provides systems-level, multi-algorithm evidence suggesting that AFB1 may promote CRC pathogenesis by targeting specific genes and signaling pathways. Machine learning identified three core regulatory genes, and molecular docking suggested AFB1's high binding affinity with key targets. These findings provide critical insights for further mechanistic exploration of AFB1-induced colorectal cancer.