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pmid:40665394

Molecular mechanisms of efferocytosis imbalance in the idiopathic pulmonary fibrosis microenvironment: from gene screening to dynamic regulation analysis.

Qian Jin · Yi Kang · Wenwen Jin · Ying Liu · Qian Chen · Jian Liu · Yali Guo · Yuguang Wang

Biology direct · 2025 doi:10.1186/s13062-025-00658-3

Paper

complete

Experiment

partial

Data

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Analysis

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Execution

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pmid:40665394
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2026-11-01T20:30:46Z
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title
Molecular mechanisms of efferocytosis imbalance in the idiopathic pulmonary fibrosis microenvironment: from gene screening to dynamic regulation analysis.
doi
10.1186/s13062-025-00658-3
pmid
40665394
authors array · 8 items
[0]
Qian Jin
[1]
Yi Kang
[2]
Wenwen Jin
[3]
Ying Liu
[4]
Qian Chen
[5]
Jian Liu
[6]
Yali Guo
[7]
Yuguang Wang
journal
Biology direct
year
2025
abstract
BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive pulmonary disease characterized by alveolar structural destruction and fibrosis. In recent years, efferocytosis has been recognized as playing a crucial role in the occurrence and progression of IPF. This study aimed to identify and regulate key efferocytosis-related genes to elucidate their potential roles and clinical significance in IPF. METHODS: IPF-related datasets (GSE32537) were obtained from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were applied to identify key genes associated with IPF, intersecting them with efferocytosis-related genes (ERGs) to obtain IPF-ERGs. Protein‒protein interaction (PPI) network construction and enrichment analysis were performed to elucidate the potential functions of these genes in IPF. Seven machine learning algorithms were employed to screen for hub genes with high diagnostic value. The GSE70866 dataset was used for validation, and a nomogram was constructed. Additionally, the CIBERSORT algorithm was used to analyze immune infiltration levels, and transcriptomic validation of the hub genes was conducted in animal experiments. RESULTS: A total of 21 IPF-ERGs were identified, and machine learning further identified TLR2, ATG7, SPHK1, and ICAM1 as hub genes, which were significantly upregulated in the IPF group. Immune infiltration analysis revealed a significant increase in the infiltration levels of immune cell subsets, including memory B cells, CD8 + T cells, and resting dendritic cells, in the IPF group. Further clinical correlation analysis revealed a strong association between the expression levels of the hub genes and pulmonary function. A nomogram was constructed on the basis of the hub genes and validated for its potential clinical application. Consensus clustering classified IPF patients into two subtypes: C1, which was primarily by metabolic pathway activation, and C2, which was enriched in inflammatory and immune pathways. Transcriptomic analysis of animal experiments also confirmed the upregulation of hub gene expression in IPF. CONCLUSION: This study identified TLR2, ATG7, SPHK1, and ICAM1 as four key hub genes, revealing their potential diagnostic value and biological functions in IPF. These genes may serve as potential diagnostic biomarkers and therapeutic targets, providing new insights for precision treatment. CLINICAL TRIAL NUMBER: Not applicable.
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sections[16] "Statistical analysis" paragraph 2
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The Wilcoxon test was used to analyze the level differences between the two groups.
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sections[38] "Data availability" paragraph 2
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All data included in this study are available upon request by contact with the corresponding author.
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The study identified 21 IPF-ERGs and four hub genes—TLR2, ATG7, SPHK1, and ICAM1—that were significantly upregulated in the IPF group.
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A total of 21 IPF-ERGs were identified, and machine learning further identified TLR2, ATG7, SPHK1, and ICAM1 as hub genes, which were significantly upregulated in the IPF group.
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The study identified 21 IPF-ERGs and four hub genes—TLR2, ATG7, SPHK1, and ICAM1—that were significantly upregulated in the IPF group.
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Single-cell RNA-sequencing reveals profibrotic roles of distinct epithelial and mesenchymal lineages in pulmonary fibrosis
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BAL cell gene expression is predictive of Mortality in Idiopathic Pulmonary Fibrosis and enriched for Genes of Airway Basal Cells (II)
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d to analyze immune infiltration levels, and transcriptomic validation of the hub genes was conducted in
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{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40665394", "kind": "science-record", "revision": 3, "content_hash": "sha256:08eec8fa449e2afa092433b586d71ff3a1e28715b74280e042cde2ed9a7607a0", "created_at": "2026-11-01T20:30:46Z", "updated_at": "2026-11-01T22:14:22Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Molecular mechanisms of efferocytosis imbalance in the idiopathic pulmonary fibrosis microenvironment: from gene screening to dynamic regulation analysis.", "doi": "10.1186/s13062-025-00658-3", "pmid": "40665394", "authors": ["Qian Jin", "Yi Kang", "Wenwen Jin", "Ying Liu", "Qian Chen", "Jian Liu", "Yali Guo", "Yuguang Wang"], "journal": "Biology direct", "year": 2025, "abstract": "BACKGROUND: Idiopathic pulmonary fibrosis (IPF) is a chronic progressive pulmonary disease characterized by alveolar structural destruction and fibrosis. In recent years, efferocytosis has been recognized as playing a crucial role in the occurrence and progression of IPF. This study aimed to identify and regulate key efferocytosis-related genes to elucidate their potential roles and clinical significance in IPF.\nMETHODS: IPF-related datasets (GSE32537) were obtained from the Gene Expression Omnibus (GEO) database. Differential gene expression analysis and weighted gene coexpression network analysis (WGCNA) were applied to identify key genes associated with IPF, intersecting them with efferocytosis-related genes (ERGs) to obtain IPF-ERGs. Protein‒protein interaction (PPI) network construction and enrichment analysis were performed to elucidate the potential functions of these genes in IPF. Seven machine learning algorithms were employed to screen for hub genes with high diagnostic value. The GSE70866 dataset was used for validation, and a nomogram was constructed. Additionally, the CIBERSORT algorithm was used to analyze immune infiltration levels, and transcriptomic validation of the hub genes was conducted in animal experiments.\nRESULTS: A total of 21 IPF-ERGs were identified, and machine learning further identified TLR2, ATG7, SPHK1, and ICAM1 as hub genes, which were significantly upregulated in the IPF group. Immune infiltration analysis revealed a significant increase in the infiltration levels of immune cell subsets, including memory B cells, CD8 + T cells, and resting dendritic cells, in the IPF group. Further clinical correlation analysis revealed a strong association between the expression levels of the hub genes and pulmonary function. A nomogram was constructed on the basis of the hub genes and validated for its potential clinical application. Consensus clustering classified IPF patients into two subtypes: C1, which was primarily by metabolic pathway activation, and C2, which was enriched in inflammatory and immune pathways. Transcriptomic analysis of animal experiments also confirmed the upregulation of hub gene expression in IPF.\nCONCLUSION: This study identified TLR2, ATG7, SPHK1, and ICAM1 as four key hub genes, revealing their potential diagnostic value and biological functions in IPF. These genes may serve as potential diagnostic biomarkers and therapeutic targets, providing new insights for precision treatment.\nCLINICAL TRIAL NUMBER: Not applicable.", "claims": [{"id": "claim-method", "statement": "The Wilcoxon test was used to analyze the level differences between the two groups.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40665394", "locator": "sections[16] \"Statistical analysis\" paragraph 2", "quote": "The Wilcoxon test was used to analyze the level differences between the two groups."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data included in this study are available upon request by contact with the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40665394", "locator": "sections[38] \"Data availability\" paragraph 2", "quote": "All data included in this study are available upon request by contact with the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 21 IPF-ERGs and four hub genes—TLR2, ATG7, SPHK1, and ICAM1—that were significantly upregulated in the IPF group.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40665394", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "A total of 21 IPF-ERGs were identified, and machine learning further identified TLR2, ATG7, SPHK1, and ICAM1 as hub genes, which were significantly upregulated in the IPF group."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study identified 21 IPF-ERGs and four hub genes—TLR2, ATG7, SPHK1, and ICAM1—that were significantly upregulated in the IPF group.", "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:40665394"], "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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