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Rank-In Integrated Machine Learning and Bioinformatic Analysis Identified the Key Genes in HFPO-DA (GenX) Exposure to Human, Mouse, and Rat Organisms

Authors :
Xinyang Li
Hua Xiao
Liye Zhu
Qisijing Liu
Bowei Zhang
Jin Wang
Jing Wu
Yaxiong Song
Shuo Wang
Source :
Toxics, Vol 12, Iss 7, p 516 (2024)
Publication Year :
2024
Publisher :
MDPI AG, 2024.

Abstract

Hexafluoropropylene Oxide Dimer Acid (HFPO-DA or GenX) is a pervasive perfluorinated compound with scant understood toxic effects. Toxicological studies on GenX have been conducted using animal models. To research deeper into the potential toxicity of GenX in humans and animals, we undertook a comprehensive analysis of transcriptome datasets across different species. A rank-in approach was utilized to merge different transcriptome datasets, and machine learning algorithms were employed to identify key genetic mechanisms common among various species and humans. We identified seven genes—TTR, ATP6V1B1, EPHX1, ITIH3, ATXN10, UBXN1, and HPX—as potential variables for classification of GenX-exposed samples, and the seven genes were verified in separate datasets of human, mouse, and rat samples. Bioinformatic analysis of the gene dataset further revealed that mitochondrial function and metabolic processes may be modulated by GenX through these key genes. Our findings provide insights into the underlying genetic mechanisms and toxicological impacts of GenX exposure across different species and offer valuable references for future studies using animal models to examine human exposure to GenX.

Details

Language :
English
ISSN :
23056304
Volume :
12
Issue :
7
Database :
Directory of Open Access Journals
Journal :
Toxics
Publication Type :
Academic Journal
Accession number :
edsdoj.14d1a61be44a4dce80654449135894f1
Document Type :
article
Full Text :
https://doi.org/10.3390/toxics12070516