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Unraveling the PFOS-NSCLC axis

Integrated network toxicology, machine learning, and causal inference identify EIF4EBP1 as a key molecular hub

Bibliographic Data

ID22069324
AuthorsTing Huang (0000-0003-4186-8714, Zhejiang Chinese Medical University), Huaxin Pang (0000-0003-4998-4576, China Academy of Chinese Medical Sciences), Jundan Wang (0000-0002-9353-3492, Department of Oncology, Hangzhou TCM Hospital Affiliated to Zhejiang Chinese Medical University), Yi-Xiang Wang (0000-0001-5697-0717, Zhejiang Chinese Medical University), Junhua Guo (0000-0002-4729-163X, Zhejiang Chinese Medical University), Keke Hu (0009-0004-7846-9957, Zhejiang Chinese Medical University), Heran Zhou (0009-0002-2519-2654, Zhejiang Chinese Medical University, corresponding author)
Year2026
Volume14
Pages1751125-1751125
Publication date2026-02-12
Open AccessYes
TypeARTICLE
VenueFrontiers in Public Health (JOURNAL)
Journal identifiersISSN: 2296-2565 • E-ISSN: 2296-2565
PublisherFrontiers Media SA (PUBLISHER • CH)
DOI10.3389/fpubh.2026.1751125
PMID41769099
OpenAlexW7128710016
LanguageEN
References cited34

Background: Perfluorooctanesulfonic acid (PFOS) is a persistent environmental pollutant with suspected carcinogenic potential; however, the molecular mechanisms driving PFOS-associated non-small cell lung cancer (NSCLC) remain obscure. In particular, the interplay between chemical exposure, oncogenic signaling nodes, and tumor microenvironment (TME) remodeling is poorly defined. This study integrates systems toxicology with multi-omics to elucidate the role of EIF4EBP1 as a mechanistic bridge connecting PFOS exposure to NSCLC pathogenesis. Methods: We synthesized chemical-protein interactions from toxicological databases (ChEMBL, STITCH, and SwissTargetPrediction) and disease-associated genes to map the PFOS-NSCLC intersection. Robust feature selection, utilizing LASSO and SVM-RFE algorithms, was applied to transcriptomic data from the GSE33532 discovery cohort to identify core targets. Key findings were substantiated through external validation in The Cancer Genome Atlas (TCGA) dataset, including differential expression and survival analyses. Causal associations were investigated via two-sample Mendelian randomization (MR), and the immune landscape was characterized using the CIBERSORT algorithm. Molecular docking simulations and an adverse outcome pathway (AOP) framework were further employed to assess mechanistic plausibility. Results: Network analysis identified 41 shared targets significantly enriched in PPAR signaling and xenobiotic metabolism. Machine learning consensus prioritized EIF4EBP1 as a critical hub gene. EIF4EBP1 was significantly upregulated in both the discovery (AUC = 0.936) and TCGA validation cohorts. Clinical analysis revealed subtype-specific prognostic value, where high EIF4EBP1 expression correlated with poor survival in lung adenocarcinoma (LUAD) but favorable outcomes in squamous cell carcinoma (LUSC). Immunologically, EIF4EBP1 expression tracked with an adaptive immune-skewed profile, characterized by increased plasma cell and activated CD4 + memory T cell infiltration. MR analysis indicated a potential causal effect of genetically predicted EIF4EBP1 expression on increased LUAD risk (OR = 4.196, 95% CI: 1.209-14.565), but not squamous cell carcinoma. Structural docking confirmed a stable, non-covalent interaction between PFOS and the EIF4EBP1 binding pocket (-7.2 kcal/mol). Conclusion: This study identifies EIF4EBP1 as a putative molecular initiating node linking PFOS exposure to LUAD susceptibility and immune modulation. The constructed AOP framework suggests a mechanism wherein PFOS-mediated translational dysregulation contributes to subtype-specific carcinogenesis. These findings provide a data-driven rationale for risk assessment and warrant further experimental verification in toxicological models

Causal inference · Inference · Warrant · Health, Environment, Cognitive Aging · Per- and polyfluoroalkyl substances research · Single-cell and spatial transcriptomics

  • David

    Open Access•Brad T Sherman, Ming Hao et al.•Nucleic Acids Research•2022

  • Global Cancer Statistics 2020

    Open Access•Hyuna Sung, Adalberto M Filho et al.•CA: A Cancer Journal for Clinicians•2021

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