Exploring diagnostic gene markers and immune infiltration in idiopathic pulmonary fibrosis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:41158346", "kind": "science-record", "revision": 4, "content_hash": "sha256:94e417153acdc65155cfc45cc9173b737ccd6662092711873e7bcf76bd442c76", "created_at": "2026-11-01T20:31:12Z", "updated_at": "2026-11-01T22:57:23Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring diagnostic gene markers and immune infiltration in idiopathic pulmonary fibrosis.", "doi": "10.21037/jtd-2025-1293", "pmid": "41158346", "authors": ["Shuang Sun", "Sibo Wang", "Yuichi Sakairi", "Giulia Maria Stella", "Wei Zhao"], "journal": "Journal of thoracic disease", "year": 2025, "abstract": "Background: Idiopathic pulmonary fibrosis (IPF) is one of the most severe pulmonary disorders, has poor outcomes, and is difficult to predict. This study sought to identify the critical genes involved in IPF progression, and to explore the relationship between these genes and immunogenic invasion.\nMethods: The GSE10667 dataset was downloaded from the Gene Expression Omnibus (GEO) database to identify differentially expressed genes (DEGs) between IPF patients and normal participants. IPF-related hub genes were screened using differential gene analysis and weighted gene co-expression network analysis (WGCNA). For evaluating the diagnostic efficacy of these hub genes, a nomogram was constructed, and receiver operating characteristic (ROC) analysis was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to explore the potential biological functions and pathways of the IPF-related hub genes. To reveal key protein-based genetic networks, protein-protein interaction (PPI) networks were constructed as follows: The key candidate genes associated with IPF, identified through WGCNA and differential gene analysis, were input into the Search Tool for the Retrieval of Interacting Genes/Proteins (STRING) database, for PPI prediction and visualization. The PPI data derived from STRING were further processed and visualized using Cytoscape software. Additionally, the association between MMP2 and immune infiltration was analyzed. Finally, a Mendelian randomization (MR) analysis was performed using genome-wide association study (GWAS) data to establish the causal relationship between MMP2 and IPF.\nResults: In total, 486 DEGs in IPF were identified. A genetic co-expression network was established by the WGCNA to select the most relevant genes. The genes were mainly involved in processes such as the extracellular matrix (ECM), cellular aging, endoplasmic reticulum (ER) stress, and metalloproteinase (MT) activation signaling pathways. Crosslinks between the DEG-related modules and WGCNA modules were assessed to identify the key genes. A PPI network was then established that identified COL1A1, COL3A1, COL1A2, POSTN, and MMP2. Using the ROC curves, we examined the precision of the model and found that the five key genes may be markers of IPF. Finally, MMP2 was chosen to investigate the causes and outcomes of IPF by means of MR and immuno-invasion analysis in IPF.\nConclusions: MMP2 was found to be essential to IPF onset and development. MMP2 might be useful in early diagnosing IPF before symptomatic treatment, and in predicting patient outcomes.", "claims": [{"id": "claim-method", "statement": "We performed differential gene expression analysis using the “limma” R package (version 3.54.0) (9). Linear models were then fitted to identify differentially expressed messenger RNAs (mRNAs) between IPF and control specimens.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41158346", "locator": "sections[5] \"Identification of DEGs\" paragraph 2", "quote": "We performed differential gene expression analysis using the “limma” R package (version 3.54.0) (9). Linear models were then fitted to identify differentially expressed messenger RNAs (mRNAs) between IPF and control specimens."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GSE10667 dataset was downloaded from the Gene Expression Omnibus (GEO) database (6,7). The GSE10667 [GEO Accession Viewer (nih.gov)] dataset comprised 46 pulmonary specimens, including 31 from IPF patients, and 15 from healthy individuals, and clinical data.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:41158346", "locator": "sections[4] \"Microarray data analysis\" paragraph 2", "quote": "The GSE10667 dataset was downloaded from the Gene Expression Omnibus (GEO) database (6,7). The GSE10667 [GEO Accession Viewer (nih.gov)] dataset comprised 46 pulmonary specimens, including 31 from IPF patients, and 15 from healthy individuals, and clinical data."}], "state": "complete"}, {"id": "claim-target-result", "statement": "In total, 486 DEGs in IPF were identified.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:41158346", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "In total, 486 DEGs in IPF were identified."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "In total, 486 DEGs in IPF were identified.", "steps": [{"id": "read_published_estimate", "order": 1, "name": "Read the published estimate from the source", "kind": "other", "state": "complete", "blocked_on": null, "description": "The claim, method and evidence span published with the source article.", "duration_hours": null, "parameters": {}, "inputs": ["source:pmid:41158346"], "outputs": ["the published estimate and its evidence"], "instruments": [], "reagents": []}, {"id": "obtain_source_data", "order": 2, "name": "Obtain the source data from the named repository", "kind": "data-preparation", "state": "complete", "blocked_on": null, "description": "The GEO series accession resolves and its sample accessions are listed. 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