Evidence map›Paper›PMID 42715255›Full record

ArticlePloS one2026

Environmental PFOA exposure and the risk of metabolic dysfunction-associated steatotic liver disease: An integrated computational toxicology and multi-omics study.

Tianyu Zhang, Yu Yuan, Chunli Lin, Chao Song, Tianrong Liao, Yuewen Sun, Hongzhen Tang

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Article in PloS one, 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

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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

7 authors.

Tianyu ZhangDepartment of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Yu YuanDepartment of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Chunli LinDepartment of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Chao SongDepartment of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Tianrong LiaoDepartment of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Yuewen SunRuikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China.
Hongzhen TangRuikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China.ORCID https://orcid.org/0009-0004-7419-8483

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundPerfluorooctanoic 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.

methodsThis 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.

resultsWe 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.

conclusionOur 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.

Indexed as

CaprylatesEnvironmental ExposureEnvironmental PollutantsFatty LiverFluorocarbonsNon-alcoholic Fatty Liver DiseaseComputational BiologyHep G2 CellsHumansMachine LearningMolecular Docking SimulationMultiomicsCaprylatesEnvironmental PollutantsFluorocarbonsperfluorooctanoic acid

Identifiers

PMID42715255
PMCPMC13557357

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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.