Evidence mapPaperPMID 40503237Full record

ArticleFrontiers in immunology2025

Identification of mitophagy-related biomarkers in severe acute pancreatitis: integration of WGCNA, machine learning algorithms and scRNA-seq.

Xiaozhou Xie, Zheng Wang, Haoyu Zhang, Jiongdi Lu, Feng Cao, Fei Li

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Article in Frontiers in immunology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

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4citing papers in PubMed
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4 citing papers in PubMed.

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5 · Who and what money

Authors and funding

6 authors.

Xiaozhou XieDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Zheng WangDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Haoyu ZhangDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Jiongdi LuDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Feng CaoDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.
Fei LiDepartment of General Surgery, Xuanwu Hospital, Capital Medical University, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Mitophagy is a highly conserved cellular process in eukaryotic cells that selectively clears dysfunctional or damaged mitochondria through autophagy mechanisms to maintain mitochondrial homeostasis. However, the role of mitophagy in the pathogenesis of severe acute pancreatitis (SAP) has not been fully investigated. In this study, we aimed to identify crucial mitophagy-related genes in SAP to provide a theoretical basis for in-depth mechanistic investigations. Methods: We downloaded the GSE194331 dataset from the Gene Expression Omnibus (GEO), identified differentially expressed genes (DEGs), and used weighted gene co-expression network analysis (WGCNA) and three machine learning algorithms to identify crucial genes. In addition, single sample gene set enrichment analysis (ssGSEA) was conducted to explore the relationship between crucial genes and immune infiltration. The expression of crucial genes at the single-cell level was analyzed using single-cell RNA sequencing (scRNA seq) data from the GSE279876 dataset. Finally, we established the SAP mouse model and conducted preliminary validation of the mechanism of crucial genes in SAP. Result: We identified MAPK14 as a crucial mitophagy-related gene in SAP by intersecting the results of DEGs, WGCNA, and three machine learning algorithms. In addition, ssGSEA revealed that MAPK14 was strongly associated with immune cell infiltration. The analysis of scRNA-seq data revealed that MAPK14 was highly expressed in pancreatic macrophages, suggesting that macrophage-derived MAPK14 may potentially regulate inflammation in SAP. Finally, we preliminarily validated using the SAP mouse model that inhibiting the protein encoded by MAPK14 increased the expression of mitophagy marker proteins and significantly alleviated SAP inflammation. Conclusion: Inhibition of MAPK14 activation may alleviate SAP by enhancing mitophagy. Our study highlights the potential role of the mitophagy-related gene MAPK14 in SAP pathogenesis, providing important insights for future investigations into mitophagy-mediated immune mechanisms in SAP.

Indexed as

Gene Regulatory NetworksMachine LearningMitophagyPancreatitisAlgorithmsAnimalsBiomarkersComputational BiologyDisease Models, AnimalGene Expression ProfilingHumansMiceRNA-SeqSingle-Cell AnalysisSingle-Cell Gene Expression AnalysisBiomarkersimmune cell infiltrationmachine learningMAPK14mitophagyp38αsevere acute pancreatitissingle-cell RNA sequencingWGCNA

Identifiers

PMID40503237
PMCPMC12151840

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.