---
title: "PFOA Exposure and MASLD Risk: Integrated Computational Toxicology and Multi-Omics Analysis"
id: "plos-one-3-environmental-pfoa-exposure-and-the-risk-of-metabolic-dysfunction-associated"
canonical_url: "https://medichelpline.com/clinical-feed/plos-one-3-environmental-pfoa-exposure-and-the-risk-of-metabolic-dysfunction-associated"
content_type: "clinical_feed_article"
specialty: "Gastroenterology"
source_name: "PLOS ONE (Medicine)"
source_url: "https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970"
published_at: "2026-09-09T14:00:00.000Z"
evidence_level: "Journal Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# PFOA Exposure and MASLD Risk: Integrated Computational Toxicology and Multi-Omics Analysis
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/plos-one-3-environmental-pfoa-exposure-and-the-risk-of-metabolic-dysfunction-associated
- **Specialty:** [Gastroenterology](https://medichelpline.com/clinical-feed/gastroenterology.md)
- **Primary Source:** PLOS ONE (Medicine)
- **Source URL:** [Original Journal Publication](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970)
- **Published At:** 2026-09-09T14:00:00.000Z
- **Evidence Rating:** Journal Feed
## Executive GIST (TL;DR)
- Perfluorooctanoic acid (**PFOA**) is a persistent, bioaccumulative PFAS detected in environmental and human samples and has been linked to liver injury and lipid metabolism disorders. - The study used an integrative systems-biology strategy combining computational toxicology, transcriptomic and single-cell multi-omics, machine learning, molecular docking/dynamics, and in vitro validation. - Chemical data for PFOA (3D structure and canonical SMILES) were retrieved from PubChem; three ligand/target prediction tools (ChEMBL, SwissTargetPrediction, STITCH) produced 271 predicted human targets. - Nineteen NAFLD/MASLD-related GEO datasets were collected; three microarray datasets (GSE66676, GSE89632, GSE164760) were merged as the training set after batch correction; GSE63067 and GSE135251 were used for validation. - Differential expression analysis on the merged training set identified 652 DEGs; a disease gene library of 902 NAFLD-related targets was assembled from GEO, GeneCards, OMIM, and TTD. - Intersection of PFOA targets and disease genes yielded 17 shared targets. A comprehensive machine-learning pipeline (11 algorithms, 113 model combinations) identified six hub genes with high diagnostic performance: **NR4A2, BCL6, CASP1, SHBG, FABP4, IL10** (AUC up to 0.996). - Single-cell RNA sequencing analysis (14 samples: 7 healthy, 7 NAFLD) characterized cell type–specific expression patterns and cell proportion differences; macrophage/monocyte pseudotime trajectories were inferred. - Molecular docking and 100 ns molecular dynamics simulations indicated stable binding poses of PFOA with **SHBG** and **FABP4**; docking used CB-Dock and MD used Gromacs2023.2 with amber14sb and GAFF2 force fields. - In vitro validation employed an FFA-induced MASLD HepG2 cell model exposed to 80 μg/mL PFOA for 24 hours; PFOA altered mRNA and protein expression of the core genes. - Authors conclude PFOA may mechanistically affect MASLD via disruption of lipid metabolism, inflammation, and immune homeostasis, but note these results do not establish epidemiological causation and call for prospective studies with quantified exposure.
## Clinical Analysis & Structured Key Points
[ Skip to main content ](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#main-content) Advertisement * [plos.org](https://plos.org/) * [Create account](https://community.plos.org/registration/new) * [Sign in](https://journals.plos.org/user/secure/login?page=%2Fplosone%2Farticle%3Fid%3D10.1371%2Fjournal.pone.0357970) * * About * Browse * Publish * [](https://journals.plos.org/plosone/ "PLOS One") * Search [advanced search](https://journals.plos.org/plosone/search) * [Browse Topics](https://journals.plos.org/plosone/subjectAreaBrowse) Browse Subject Areas ? Click through the PLOS taxonomy to find articles in your field. For more information about PLOS Subject Areas, click [here](https://github.com/PLOS/plos-thesaurus/blob/master/README.md "Link opens in new window"). [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970) [](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970) * 0 [Save](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970#savedHeader) [Total Mendeley and Citeulike bookmarks.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970#savedHeader) * 0 [Citation](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970#citedHeader) [Paper's citation count computed by Dimensions.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970#citedHeader) * 36 [View](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970#viewedHeader) [PLOS views and downloads.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970#viewedHeader) * 0 [Share](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970#discussedHeader) [Sum of Facebook, Twitter, Reddit and Wikipedia activity.](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970#discussedHeader) Open Access Peer-reviewed Research Article # Environmental PFOA exposure and the risk of metabolic dysfunction-associated steatotic liver disease: An integrated computational toxicology and multi-omics study * Tianyu Zhang , Contributed equally to this work with: Tianyu Zhang, Yu Yuan Roles Conceptualization, Data curation, Software, Writing – original draft Affiliation Department of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China ⨯ * Yu Yuan , Contributed equally to this work with: Tianyu Zhang, Yu Yuan Roles Conceptualization, Formal analysis, Methodology, Writing – original draft Affiliation Department of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China ⨯ * Chunli Lin, Roles Methodology, Resources, Writing – review & editing Affiliation Department of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China ⨯ * Chao Song, Roles Investigation, Validation, Writing – original draft Affiliation Department of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China ⨯ * Tianrong Liao, Roles Formal analysis, Investigation, Validation, Writing – original draft Affiliation Department of Acupuncture and Tuina, Guangxi University of Chinese Medicine, Nanning, Guangxi, China ⨯ * Yuewen Sun, Roles Data curation, Investigation, Validation, Writing – review & editing Affiliation Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China ⨯ * Hongzhen Tang Roles Funding acquisition, Project administration, Supervision, Writing – review & editing * E-mail: thz201807@126.com Affiliation Ruikang Hospital Affiliated to Guangxi University of Chinese Medicine, Nanning, Guangxi, China [ ![ORCID logo](https://journals.plos.org/resource/img/orcid_16x16.png) https://orcid.org/0009-0004-7419-8483 ](https://orcid.org/0009-0004-7419-8483 "ORCID Registry") ⨯ # 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 ![PLOS](https://journals.plos.org/resource/img/logo-plos-full-color.svg) x * Published: September 9, 2026 * * [Article](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970) * [Authors](https://journals.plos.org/plosone/article/authors?id=10.1371/journal.pone.0357970) * [Metrics](https://journals.plos.org/plosone/article/metrics?id=10.1371/journal.pone.0357970) * [Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357970) * [Media Coverage](http://plos.altmetric.com/details/doi/10.1371/journal.pone.0357970) * [Abstract](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#abstract0) * [Introduction](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#sec005) * [Materials and methods](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#sec006) * [Results](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#sec031) * [Discussion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#sec040) * [Conclusion](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#sec041) * [Study limitations and future directions](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#sec042) * [Supporting information](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#sec043) * [References](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#references) * [Reader Comments](https://journals.plos.org/plosone/article/comments?id=10.1371/journal.pone.0357970) * [Figures](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970) ## 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. ## Figures ![Fig 8](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g008) ![Fig 9](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g009) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g001) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.t001) ![Table 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.t002) ![Fig 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g002) ![Fig 3](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g003) ![Fig 4](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g004) ![Fig 5](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g005) ![Fig 6](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g006) ![Fig 7](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g007) ![Fig 8](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g008) ![Fig 9](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g009) ![Fig 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g001) ![Table 1](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.t001) ![Table 2](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.t002) **Citation:** Zhang T, Yuan Y, Lin C, Song C, Liao T, Sun Y, et al. (2026) Environmental PFOA exposure and the risk of metabolic dysfunction-associated steatotic liver disease: An integrated computational toxicology and multi-omics study. PLoS One 21(9): e0357970. https://doi.org/10.1371/journal.pone.0357970 **Editor:** Inbakandan Dhinakarasamy, Sathyabama Institute of Science and Technology, INDIA **Received:** May 19, 2026; **Accepted:** August 24, 2026; **Published:** September 9, 2026 **Copyright:** © 2026 Zhang et al. This is an open access article distributed under the terms of the [Creative Commons Attribution License](http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. **Data Availability:** The original contributions presented in this study are included in the article. The transcriptomic datasets were obtained from the NCBI Gene Expression Omnibus (GEO): GSE66676, GSE89632 and GSE164760 (training set); GSE135251 and GSE63067 (validation sets). The single-cell RNA-sequencing data were also obtained from GEO (sample-level accessions are listed in [Table 1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone-0357970-t001)). The analysis code is available at . **Funding:** This study was supported by 1. Capacity Enhancement of Guangxi Zhuang and Yao Medicine Engineering Technology Research Center (Guangxi Science and Technology Department - ZY24212016); 2. Research and Promotion of Acupuncture and Massage Techniques for Treating Obesity (Guangxi Administration of Traditional Chinese Medicine - GZSY2025035); 3. Sino-Western Medicine Collaborative Clinical Research Project (China National Center for the Development of Traditional Chinese Medicine - CXZH2025008); 4.Innovation Project of Guangxi Graduate Education of GXUCM(YCSY2025058); 5. Special Cohort Study on Hospital-Level Priority Diseases by 2025 – Interventional Cohort Study on Obesity/Overweight Among Middle School Students in Nanning Urban Area (The Second Affiliated Hospital of Guangxi University of Chinese Medicine - 2025YJ-32). **Competing interests:** The authors have declared that no competing interests exist. ## Introduction Perfluorooctanoic acid (PFOA) is a representative perfluoroalkyl substance (PFAS), widely used in industrial and consumer products due to its hydrophobic, oleophobic, surfactant-like properties, and chemical stability [[1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref001)]. It is extensively employed in the manufacture of fluoropolymers, including non-stick coatings, carpets, textiles, and food packaging for stain, oil, and water resistance. PFOA is highly persistent in the environment and bioaccumulative [[2](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref002)], earning it the designation as a “persistent organic pollutant” [[3](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref003)]. It has been detected globally in water, soil, and biological specimens, including human serum and liver tissue, posing a significant public health risk [[4](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref004),[5](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref005)]. Epidemiological studies suggest that PFOA exposure is closely associated with liver injury and lipid metabolism disorders [[6](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref006)], indicating it may be a potential environmental risk factor for the development and progression of metabolic dysfunction-associated steatotic liver disease (MASLD). Non-alcoholic fatty liver disease (NAFLD) is one of the most prevalent chronic liver diseases worldwide, with an estimated global prevalence of 30.2% [[7](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref007)]. It is characterized by excessive lipid accumulation in hepatocytes in the absence of significant alcohol consumption or other known liver diseases [[8](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref008)]. The NAFLD spectrum includes simple steatosis (NAFL), non-alcoholic steatohepatitis (NASH), fibrosis, and can progress to cirrhosis and hepatocellular carcinoma, and is strongly associated with obesity, type 2 diabetes, metabolic syndrome, and aging [[9](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref009)]. In 2023, a multi-society Delphi consensus process led by the American Association for the Study of Liver Diseases (AASLD), the European Association for the Study of the Liver (EASL), and the Asociación Latinoamericana para el Estudio del Hígado (ALEH) proposed a new nomenclature, renaming NAFLD to MASLD [[10](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref010)]. In clinical practice, the two definitions identify a virtually overlapping population, with concordance rates reported between 96% and 99%, meaning that MASLD can be considered largely equivalent to NAFLD in terms of patient selection [[11](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref011)]. However, their diagnostic philosophies are fundamentally different: NAFLD is an exclusion-based diagnosis (requiring the ruling out of other liver diseases and significant alcohol intake), whereas MASLD is an inclusion-based diagnosis that requires the presence of hepatic steatosis plus at least one of five cardiometabolic risk factors (e.g., obesity, type 2 diabetes, hypertension, hypertriglyceridemia, or low HDL-cholesterol) [[12](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref012)]. This renaming carries multiple profound implications. First, it eliminates the potentially stigmatising terms “non-alcoholic” and “fatty” [[13](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref013)]. Second, it places metabolic dysfunction at the centre of the disease process, shifting the focus from “what it is not” to “what it is” – a positive diagnosis grounded in pathophysiology [[14](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref014)]. Third, the new framework introduces an overarching term, steatotic liver disease (SLD), and further stratifies the disease spectrum by adding a novel category, MetALD, for patients with metabolic dysfunction and an intermediate level of alcohol consumption [[15](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone.0357970.ref015)]. Collectively, the transition from NAFLD to MASLD represents more than a mere change in acronym; it signifies a paradigm shift from a diagnosis of exclusion to one of inclusion, thereby enhancing disease awareness, improving patient-clinician communication, and enabling more precise risk stratification and management. Current research on the risk of PFOA-induced MASLD (formerly known as NAFLD) primarily relies on epidemiological associations or isolated in vitro experiments, lacking a systems biology approach that integrates computational toxicology and multi-omics data to unravel the underlying molecular mechanisms. Therefore, this study aims to utilize public database resources and computational toxicology methods to identify potential targets of PFOA. By integrating transcriptomic data from MASLD, we applied multiple machine learning algorithms to construct high-performance diagnostic models and identify core genes. Furthermore, we analyzed the expression patterns of these core genes at the cellular subtype level using single-cell RNA sequencing (scRNA-seq). Molecular docking and molecular dynamics (MD) simulations were employed to assess the binding stability between PFOA and core targets, followed by preliminary in vitro validation. This study systematically reveals the potential molecular and cellular mechanisms by which PFOA exposure influences MASLD, providing new scientific evidence for assessing its hepatotoxicity risk and exploring potential intervention targets. ## Materials and methods ### Data collection and preprocessing The analytical workflow is detailed in [Fig 1](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0357970#pone-0357970-g001). The 3D structure and canonical SMILES (C(=O)(C(C(C(C(C(C(C(F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)(F)F)O) of PFOA were obtained from the PubChem database. Target prediction employed a multi-strategy approach: the ChEMBL database for ligand-receptor interaction analysis; SwissTargetPrediction (probability > 0) based on chemogenomics; and STITCH (min. interaction score > 0.4). A total of 271 predicted targets, limited to the human proteome, were identified. [![thumbnail](https://journals.plos.org/plosone/article/figure/image?size=inline&id=10.1371/journal.pone.0357970.g001)](https://journals.plos.org/plosone/article/figure/image?size=medium&id=10.1371/journal.pone.0357970.g001 "Click for larger image") Download: * [PNG larger image](https://journals.plos.org/plosone/article/figure/image?download&size=large&id=10.1371/journal.pone.0357970.g001) * [TIFF original image](https://journals.plos.org/plosone/article/figure/image?download&size=original&id=10.1371/journal.pone.0357970.g001) Fig 1. A. Two-dimensional structure of the PFOA molecule. B. Venn diagram of potential PFOA targets predicted by ChEMBL, STITCH, and SwissTargetPrediction databases. C. PCA plot of the training dataset before merging. D. PCA plot of the dataset after merging and batch correction. E. Volcano plot of differentially expressed genes (DEGs) in the NAFLD group. F. Venn diagram of overlapping disease genes from four databases (GEO, OMIM, GeneCard,
## Related Clinical Research

- [GLP-1 Receptor Agonists and Pulmonary Hypertension: Mechanistic Rationale and Evidence Gaps](https://medichelpline.com/clinical-feed/pubmed-42747605.md) (DOI: 10.1007/s00408-026-00939-2)
- [Framework for Assessing Cardiovascular Risk and Fitness for Duty in Tactical Athletes](https://medichelpline.com/clinical-feed/aha-news-0-new-guidance-introduces-framework-for-assessing-cardiovascular-risk-and-fitness.md)
- [New ACC/AHA Guidance on Cardiovascular Care for Tactical Athletes](https://medichelpline.com/clinical-feed/stat-news-1-tactical-athletes-get-new-guidance-for-cardiovascular-fitness.md)
- [Left ventricular hypertrophy and brain atrophy in type 2 diabetes: findings from the D2 cohort](https://medichelpline.com/clinical-feed/medrxiv-0-left-ventricular-hypertrophy-brain-atrophy-and-cognitive-decline-in-type-2.md)
- [GLP-1 Receptor Agonist Start Does Not Reduce Anti-VEGF Injection Frequency in Diabetic Macular Ede](https://medichelpline.com/clinical-feed/medrxiv-15-glp-1-receptor-agonist-initiation-and-anti-vegf-treatment-frequency-in-diabetic.md)

## Navigation
- [← Back to Gastroenterology Feed](https://medichelpline.com/clinical-feed/gastroenterology.md)
- [← All Clinical Specialties](https://medichelpline.com/clinical-feed.md)
## Medical & Regulatory Disclaimer

> [!CAUTION]
> MedicHelpline content is structured for research, educational, and professional discovery purposes. It does not constitute individual medical advice, clinical diagnosis, or treatment recommendations.
> Always verify dosing, contraindications, and regulatory alerts against official product labeling and primary regulatory sources before clinical decision-making.