Environmental PFOA exposure and the risk of metabolic dysfunction-associated steatotic liver disease: An integrated computational toxicology and multi-omics study
- 1Department of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China
- 2Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China
Abstract
Background: Perfluorooctanoic acid (PFOA), a pervasive environmental pollutant, has been implicated in hepatic injury and metabolic dysfunction. However, its role as an environmental risk factor in the pathogenesis of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) remains incompletely understood, particularly from a systems biology perspective. Methods: This study employed an integrative approach combining computational toxicology, multi-omics data analysis, and machine learning. Public databases were utilized to identify PFOA-related targets and MASLD-associated genes. A comprehensive machine learning framework comprising 113 model combinations was applied to transcriptomic datasets (GSE66676, GSE89632, GSE164760) to identify hub genes. Single-cell RNA sequencing (scRNA-seq) analysis delineated cell type-specific expression patterns. Molecular docking and dynamics simulations assessed the binding stability between PFOA and core targets, which was further validated in vitro using an FFA-induced MASLD HepG2 cell model. Results: We identified 17 shared targets between PFOA and NAFLD. Machine learning pinpointed six hub genes (NR4A2, BCL6, CASP1, SHBG, FABP4, IL10) with high diagnostic accuracy (AUC up to 0.996). scRNA-seq revealed distinct expression patterns of these genes across liver cell subtypes in MASLD. Molecular docking and dynamics simulations demonstrated stable binding of PFOA to SHBG and FABP4. In vitro experiments confirmed that PFOA exposure significantly altered the mRNA and protein expression levels of these core genes in the MASLD model. Conclusion: Our findings suggest a potential mechanistic association between PFOA exposure and MASLD pathogenesis, characterized by disruption of lipid metabolism, inflammatory responses, and immune homeostasis. While these results identify biologically plausible pathways, they do not establish epidemiological causation, and further prospective studies with quantified PFOA exposure are required to confirm causality in humans.
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BibTeX
@article{zhang2026environmental,
title = {{Environmental PFOA exposure and the risk of metabolic dysfunction-associated steatotic liver disease: An integrated computational toxicology and multi-omics study}},
author = {Tianyu Zhang and Yu Yuan and Chunli Lin and Chao Song and Tianrong Liao and Yuewen Sun and Hongzhen Tang},
journal = {PLOS One},
year = {2026},
month = sep,
doi = {10.1371/journal.pone.0357970},
url = {https://doi.org/10.1371/journal.pone.0357970},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Environmental PFOA exposure and the risk of metabolic dysfunction-associated steatotic liver disease: An integrated computational toxicology and multi-omics study
AU - Tianyu Zhang
AU - Yu Yuan
AU - Chunli Lin
AU - Chao Song
AU - Tianrong Liao
AU - Yuewen Sun
AU - Hongzhen Tang
PY - 2026
DA - 2026/09/09
JO - PLOS One
DO - 10.1371/journal.pone.0357970
UR - https://doi.org/10.1371/journal.pone.0357970
AB - Background: Perfluorooctanoic acid (PFOA), a pervasive environmental pollutant, has been implicated in hepatic injury and metabolic dysfunction. However, its role as an environmental risk factor in the pathogenesis of Metabolic Dysfunction-Associated Steatotic Liver Disease (MASLD) remains incompletely understood, particularly from a systems biology perspective. Methods: This study employed an integrative approach combining computational toxicology, multi-omics data analysis, and machine learning. Public databases were utilized to identify PFOA-related targets and MASLD-associated genes. A comprehensive machine learning framework comprising 113 model combinations was applied to transcriptomic datasets (GSE66676, GSE89632, GSE164760) to identify hub genes. Single-cell RNA sequencing (scRNA-seq) analysis delineated cell type-specific expression patterns. Molecular docking and dynamics simulations assessed the binding stability between PFOA and core targets, which was further validated in vitro using an FFA-induced MASLD HepG2 cell model. Results: We identified 17 shared targets between PFOA and NAFLD. Machine learning pinpointed six hub genes (NR4A2, BCL6, CASP1, SHBG, FABP4, IL10) with high diagnostic accuracy (AUC up to 0.996). scRNA-seq revealed distinct expression patterns of these genes across liver cell subtypes in MASLD. Molecular docking and dynamics simulations demonstrated stable binding of PFOA to SHBG and FABP4. In vitro experiments confirmed that PFOA exposure significantly altered the mRNA and protein expression levels of these core genes in the MASLD model. Conclusion: Our findings suggest a potential mechanistic association between PFOA exposure and MASLD pathogenesis, characterized by disruption of lipid metabolism, inflammatory responses, and immune homeostasis. While these results identify biologically plausible pathways, they do not establish epidemiological causation, and further prospective studies with quantified PFOA exposure are required to confirm causality in humans.
SN - 1932-6203
ER -