Frontiers in Immunology (Jun 2024)

Identification and validation of cuproptosis-related genes in acetaminophen-induced liver injury using bioinformatics analysis and machine learning

  • Zhenya Guo,
  • Zhenya Guo,
  • Zhenya Guo,
  • Jiaping Liu,
  • Jiaping Liu,
  • Jiaping Liu,
  • Guozhi Liang,
  • Guozhi Liang,
  • Guozhi Liang,
  • Haifeng Liang,
  • Haifeng Liang,
  • Haifeng Liang,
  • Mingbei Zhong,
  • Mingbei Zhong,
  • Mingbei Zhong,
  • Stephen Tomlinson,
  • Songqing He,
  • Songqing He,
  • Songqing He,
  • Guoqing Ouyang,
  • Guoqing Ouyang,
  • Guoqing Ouyang,
  • Guandou Yuan,
  • Guandou Yuan,
  • Guandou Yuan

DOI
https://doi.org/10.3389/fimmu.2024.1371446
Journal volume & issue
Vol. 15

Abstract

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BackgroundAcetaminophen (APAP) is commonly used as an antipyretic analgesic. However, acetaminophen overdose may contribute to liver injury and even liver failure. Acetaminophen-induced liver injury (AILI) is closely related to mitochondrial oxidative stress and dysfunction, which play critical roles in cuproptosis. Here, we explored the potential role of cuproptosis-related genes (CRGs) in AILI.MethodsThe gene expression profiles were obtained from the Gene Expression Omnibus database. The differential expression of CRGs was determined between the AILI and control samples. Protein protein interaction, correlation, and functional enrichment analyses were performed. Machine learning was used to identify hub genes. Immune infiltration was evaluated. The AILI mouse model was established by intraperitoneal injection of APAP solution. Quantitative real-time PCR and western blotting were used to validate hub gene expression in the AILI mouse model. The copper content in the mouse liver samples and AML12 cells were quantified using a colorimetric assay kit. Ammonium tetrathiomolybdate (ATTM), was administered to mouse models and AML12 cells in order to investigate the effects of copper chelator on AILI.ResultsThe analysis identified 7,809 differentially expressed genes, 4,245 of which were downregulated and 3,564 of which were upregulated. Four optimal feature genes (OFGs; SDHB, PDHA1, NDUFB2, and NDUFB6) were identified through the intersection of two machine learning algorithms. Further nomogram, decision curve, and calibration curve analyses confirmed the diagnostic predictive efficacy of the four OFGs. Enrichment analysis indicated that the OFGs were involved in multiple pathways, such as IL-17 pathway and chemokine signaling pathway, that are related to AILI progression. Immune infiltration analysis revealed that macrophages were more abundant in AILI than in control samples, whereas eosinophils and endothelial cells were less abundant. Subsequently, the AILI mouse model was successfully established, and histopathological analysis using hematoxylin–eosin staining along with liver function tests revealed a significant induction of liver injury in the APAP group. Consistent with expectations, both mRNA and protein levels of the four OFGs exhibited a substantial decrease. The administration of ATTAM effectively mitigates copper elevation induced by APAP in both mouse model and AML12 cells. However, systemic administration of ATTM did not significantly alleviate AILI in the mouse model.ConclusionThis study first revealed the potential role of CRGs in the pathological process of AILI and offered novel insights into its underlying pathogenesis.

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