2026-10-13T11:47:38Z https://api.figshare.com/v2/oai

oai:figshare.com:article/34010278 2026-09-28T06:04:34Z category_4 category_13 category_19 category_21 category_272 category_873 category_39 category_734 category_931 category_64 category_69 category_122 portal_63 item_type_3 month_year_09_2026

Hazard Prioritization and Transcriptomic Characterization of PFASs Based on Machine Learning and Human Liver Spheroids Shiyuan Wang (455732) Li Cui (37567) Huifeng Yue (1461376) Biochemistry Genetics Pharmacology Biotechnology Environmental Sciences not elsewhere classified Chemical Sciences not elsewhere classified Ecology Biological Sciences not elsewhere classified Information Systems not elsewhere classified Cancer Inorganic Chemistry Mental Health provide biological context lipid metabolism disruption induced hepatic perturbations increasingly serious threat different hazard tiers cellular stress responses human liver spheroids 19 pfas compounds public transcriptomic data throughput bioactivity data transcriptomic responses prioritizing data human health hazard prioritization transcriptomic profiling transcriptomic characterization xgboost algorithm tier scheme subsequently analyzed scarce pfas related hazards potential strategy polyfluoroalkyl substances pfas hepatotoxicity operational four ongoing toxicity machine learning integrated computational based prioritization Because of their pervasiveness and ongoing toxicity, particularly to the liver, perfluorinated and polyfluoroalkyl substances (PFASs) present an increasingly serious threat to human health and the environment. On the basis of high-throughput bioactivity data, we developed a quantitative structure–activity relationship (QSAR) model based on the XGBoost algorithm and an operational four-tier scheme for bioactivity-based prioritization of PFAS hepatotoxicity-related hazards. Public transcriptomic data from human liver spheroids (GSE145239) exposed to 19 PFAS compounds were subsequently analyzed to provide biological context for the model-derived hazard prioritization tiers. Transcriptomic response patterns differed among PFASs assigned to different hazard tiers. Transcriptomic profiling further suggested that lipid metabolism disruption and cellular stress responses were among the major biological processes associated with PFAS-induced hepatic perturbations. FABP1, PLIN2, and GDF15 were identified as shared responsive genes across PFAS exposure groups. This integrated computational and in vitro framework links molecular hazard prioritization to transcriptomic responses, providing a potential strategy for prioritizing data-scarce PFAS. 2026-09-28T00:00:00Z Dataset Dataset 10.1021/acs.chemrestox.6c00344.s002 https://figshare.com/articles/dataset/Hazard_Prioritization_and_Transcriptomic_Characterization_of_PFASs_Based_on_Machine_Learning_and_Human_Liver_Spheroids/34010278 CC BY-NC 4.0