Evidence map›Paper›PMID 42724511›Full record

ArticleTranslational cancer research2026

Investigating the mechanisms of PhIP-induced colorectal cancer through network toxicology, machine learning, and molecular dynamics simulation.

Yuqi Gao, Qian Ye, Yunhuan Zhen, Yan Wang, Yi Wang, Yuping Zeng, Xiaoqin Liu, Xiaoru Zhou, Weiwei Chen

Abstract read
In one paragraph

Article in Translational cancer research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

9 authors.

Yuqi GaoDepartment of Clinical Medicine, Guizhou Medical University, Guiyang, China.
Qian YeDepartment of Clinical Medicine, Guizhou Medical University, Guiyang, China.
Yunhuan ZhenDepartment of Clinical Medicine, Guizhou Medical University, Guiyang, China.
Yan WangDepartment of Abdominal Oncology, Affiliated Cancer Hospital of Guizhou Medical University, Guiyang, China.
Yi WangDepartment of Abdominal Oncology, Affiliated Cancer Hospital of Guizhou Medical University, Guiyang, China.
Yuping ZengDepartment of Clinical Medicine, Guizhou Medical University, Guiyang, China.
Xiaoqin LiuDepartment of Clinical Medicine, Guizhou Medical University, Guiyang, China.
Xiaoru ZhouDepartment of Clinical Medicine, Guizhou Medical University, Guiyang, China.
Weiwei ChenDepartment of Clinical Medicine, Guizhou Medical University, Guiyang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Over the past few years, 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine (PhIP)- a compound from grilled or processed meats-has emerged as a major player in cancer development, especially colorectal cancer (CRC). This work dives into its potential links to CRC and uncovers the key genes that bridge this connection. Methods: We tapped into various databases to pinpoint target genes tied to PhIP and CRC, then ran protein-protein interaction (PPI) analyses for visualization. Next, we explored underlying mechanisms through Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. To nail down predictions, we tested 107 machine learning pipelines and picked the best one, validating its accuracy and the core genes' prognostic value across datasets. Next, molecular docking and dynamics simulations probed the interactions between these genes and PhIP. Finally, cell proliferation was assessed using Cell Counting Kit-8 (CCK-8) and 5-ethynyl-2'-deoxyuridine (EdU) assays, and polymerase chain reaction (PCR) was performed to validate the expression levels of the hub genes. Results: Our analysis identified 39 overlapping genes, from which a machine learning model (glmBoost + Enet) identified six candidate targets: CDK4, CEBPB, COMT, SOX9, TIMP1, and TOP2A. To prioritize these, a hierarchical screening framework was applied. Molecular docking and dynamics simulations identified CDK4, COMT, and TIMP1 as the most stable interactors with PhIP. Functional assays confirmed that PhIP treatment significantly enhanced the proliferation of CRC cells. Crucially, quantitative PCR (qPCR) validation in multiple CRC cell lines identified TIMP1 as the primary target, showing the most consistent and significant upregulation upon PhIP exposure. Conclusions: In essence, these genes drive PhIP is role in CRC, offering novel insights into its molecular pathways. This could reshape how we tackle food-related pollutants, paving the way for better prevention and targeted therapies.

Indexed as

Colorectal cancer (CRC)machine learningmolecular dynamics simulationnetwork toxicology

Identifiers

PMID42724511
PMCPMC13559597

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.