ArticleScientific reports2024
Identification of PANoptosis-related genes for idiopathic pulmonary fibrosis by machine learning and molecular subtype analysis.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Regulated Cell Death in Idiopathic Pulmonary Fibrosis.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026Review
- Molecular subtyping and prognostic evaluation in idiopathic pulmonary fibrosis: a focus on mechanical-related genes.Journal of translational medicine · 2025Article
- Diagnostic PANoptosis-related genes in acute kidney injury: bioinformatics, machine learning, and validation.Annals of medicine · 2025Article
- Metabolic syndrome-related gene signature for prognosis and immune microenvironment prediction in hepatocellular carcinoma.Scientific reports · 2025Article
- Long-Term Maternal Exposure to Triclosan Provokes Pulmonary Fibrosis in Mouse Offspring.Environment & health (Washington, D.C.) · 2025Article
- Supervised Machine-Based Learning and Computational Analysis to Reveal Unique Molecular Signatures Associated with Wound Healing and Fibrotic Outcomes to Lens Injury.International journal of molecular sciences · 2025Article
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Authors and funding
11 authors.
Funding
Abstract
Idiopathic pulmonary fibrosis (IPF) is a severe interstitial lung disease characterized by a grim prognosis, in which various forms of cell death are significant contributors to its development. The objective of this study is to explore diagnostic biomarkers and molecular subtypes associated with PANoptosis in IPF, and to develop reliable diagnostic models based on PANoptosis-related mechanisms. The peripheral blood transcriptomic data of IPF from the Gene Expression Omnibus (GEO) database and PANoptosis-related genes from the GeneCards database were utilized to conduct differential gene expression analysis and weighted gene co-expression network analysis (WGCNA), identifying PANoptosis-related differentially expressed genes (PDEGs). We yielded 9 PDEGs and employed machine learning algorithms to identify 3 key diagnostic biomarkers for PANoptosis in IPF: MMP9, FCMR, NIBAN3. Consensus clustering algorithm was applied to recognize two PANoptosis-related subtypes. Cluster 1 exhibited higher abundance of adaptive immune response cells and significant enrichment in DNA damage and repair-related pathways. Cluster 2 exhibited greater prevalence of innate immune response cells and predominant enhancement in pathways related to lipid cholesterol metabolism and vascular remodeling. Diagnostic models were developed with the aid of clinical decision-making and a novel approach to the diagnosis and treatment for IPF.
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