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

TL;DR

This study identifies key genes linked to GenX exposure in humans and animals, revealing possible toxic effects on mitochondrial and metabolic functions.

Contribution

The study introduces a novel rank-in approach and machine learning to identify cross-species key genes affected by GenX exposure.

Findings

Seven key genes (TTR, ATP6V1B1, EPHX1, ITIH3, ATXN10, UBXN1, HPX) were identified as potential markers for GenX exposure.

GenX exposure may affect mitochondrial function and metabolic processes through these genes.

The findings were validated across human, mouse, and rat datasets.

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.

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