Dysregulation of atrazine-associated core gene networks and risk prediction in human cancers: Insights from integrated transcriptomics and network toxicology analyses

Atrazine, a widely used chlorotriazine herbicide, persists in aquatic environments and poses potential carcinogenic risks. While epidemiological studies link atrazine exposure to malignancies, its intrinsic molecular mechanisms across organ systems remain incompletely understood. This study employed integrated network toxicology and transcriptomic analyses to clarify atrazine-associated oncogenic pathways in liver hepatocellular carcinoma (LIHC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), and sarcoma (SARC). Transcriptomic data from The Cancer Genome Atlas (TCGA) for these cancers were analyzed to identify atrazine-related genes. Protein-protein interaction networks were constructed and analyzed to identify hub genes. Functional enrichment, immune microenvironment analyses, and survival analysis were performed. Molecular docking validated atrazine-target binding, and independent datasets were used for hub gene expression and pan-cancer relevance validation. We identified 92 (LUAD), 136 (LIHC), 137 (KIRC), and 161 (SARC) atrazine-associated targets. Hub genes including CDC6, MCM5/7, UBE2C, FEN1, CDCA8, and VIM were differentially expressed across these cancers. Enrichment analyses revealed atrazine disruption of core pathways, including cell cycle progression and chromosomal instability, epithelial-mesenchymal transition, metabolic reprogramming, and senescence-associated secretory pathways. Molecular docking confirmed high-affinity binding between atrazine and key targets. Pan-cancer validation implicated these hub genes in multiple additional malignancies. Transcription factor analysis nominated HSD17B8 as a key regulatory node. This study demonstrates that atrazine promotes carcinogenesis by dysregulating conserved networks governing genomic stability, cell proliferation, metabolic adaptation, and immune microenvironment remodeling, providing a mechanistic framework linking aquatic atrazine exposure to multi-organ carcinogenesis and nominating HSD17B8-associated pathways for therapeutic intervention. These findings underscore the imperative for enhanced environmental monitoring of atrazine contamination.

Authors

Institutions

Publication Details

Journal
Ecotoxicology and Environmental Safety
Published
2026-01-01
DOI
https://doi.org/10.1016/j.ecoenv.2026.119676
Primary Topic
Pesticide and Herbicide Environmental Studies
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Dysregulation of atrazine-associated core gene networks and risk prediction in human cancers: Insights from integrated transcriptomics and network toxicology analyses

Changqing Yin, Ping Tang, Yifeng Shang, Xiangxin Zhang et al.
Ecotoxicology and Environmental Safety
Pesticide and Herbicide Environmental Studies
article

Dysregulation of atrazine-associated core gene networks and risk prediction in human cancers: Insights from integrated transcriptomics and network toxicology analyses

Changqing Yin, Ping Tang, Yifeng Shang, Xiangxin Zhang, Xinyu Liu, Lianlei Wang, Qing Yang, Xi Chen, Liu Yang, Qunye Zhang, Yaoyu Wang
article en

Abstract

Atrazine, a widely used chlorotriazine herbicide, persists in aquatic environments and poses potential carcinogenic risks. While epidemiological studies link atrazine exposure to malignancies, its intrinsic molecular mechanisms across organ systems remain incompletely understood. This study employed integrated network toxicology and transcriptomic analyses to clarify atrazine-associated oncogenic pathways in liver hepatocellular carcinoma (LIHC), kidney renal clear cell carcinoma (KIRC), lung adenocarcinoma (LUAD), and sarcoma (SARC). Transcriptomic data from The Cancer Genome Atlas (TCGA) for these cancers were analyzed to identify atrazine-related genes. Protein-protein interaction networks were constructed and analyzed to identify hub genes. Functional enrichment, immune microenvironment analyses, and survival analysis were performed. Molecular docking validated atrazine-target binding, and independent datasets were used for hub gene expression and pan-cancer relevance validation. We identified 92 (LUAD), 136 (LIHC), 137 (KIRC), and 161 (SARC) atrazine-associated targets. Hub genes including CDC6, MCM5/7, UBE2C, FEN1, CDCA8, and VIM were differentially expressed across these cancers. Enrichment analyses revealed atrazine disruption of core pathways, including cell cycle progression and chromosomal instability, epithelial-mesenchymal transition, metabolic reprogramming, and senescence-associated secretory pathways. Molecular docking confirmed high-affinity binding between atrazine and key targets. Pan-cancer validation implicated these hub genes in multiple additional malignancies. Transcription factor analysis nominated HSD17B8 as a key regulatory node. This study demonstrates that atrazine promotes carcinogenesis by dysregulating conserved networks governing genomic stability, cell proliferation, metabolic adaptation, and immune microenvironment remodeling, providing a mechanistic framework linking aquatic atrazine exposure to multi-organ carcinogenesis and nominating HSD17B8-associated pathways for therapeutic intervention. These findings underscore the imperative for enhanced environmental monitoring of atrazine contamination.

Ecotoxicology and Environmental SafetyVol. 309
Qilu Hospital of Shandong University (CN), Shandong First Medical University (CN)
Good health and well-being
Openalex Percentile: Top 94%
Pesticide and Herbicide Environmental Studies
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.