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pmid:38887981

Single-cell combined with transcriptome sequencing to explore the molecular mechanism of cell communication in idiopathic pulmonary fibrosis.

Minggao Zhu · Yuhu Yi · Kui Jiang · Yongzhi Liang · Lijun Li · Feng Zhang · Xinglong Zheng · Haiyan Yin

Journal of cellular and molecular medicine · 2024 doi:10.1111/jcmm.18499

Paper

complete

Experiment

complete

Data

partial

Analysis

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Execution

absent

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Record details sci_version · id · kind · revision · content_hash · schema · created_at · updated_at · generator
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pmid:38887981
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2026-11-01T20:28:57Z
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title
Single-cell combined with transcriptome sequencing to explore the molecular mechanism of cell communication in idiopathic pulmonary fibrosis.
doi
10.1111/jcmm.18499
pmid
38887981
authors array · 8 items
[0]
Minggao Zhu
[1]
Yuhu Yi
[2]
Kui Jiang
[3]
Yongzhi Liang
[4]
Lijun Li
[5]
Feng Zhang
[6]
Xinglong Zheng
[7]
Haiyan Yin
journal
Journal of cellular and molecular medicine
year
2024
abstract
Idiopathic pulmonary fibrosis (IPF) is a common, chronic, and progressive lung disease that severely impacts human health and survival. However, the intricate molecular underpinnings of IPF remains elusive. This study aims to delve into the nuanced molecular interplay of cellular interactions in IPF, thereby laying the groundwork for innovative therapeutic approaches in the clinical field of IPF. Sophisticated bioinformatics methods were employed to identify crucial biomarkers essential for the progression of IPF. The GSE122960 single-cell dataset was obtained from the Gene Expression Omnibus (GEO) compendium, and intercellular communication potentialities were scrutinized via CellChat. The random survival forest paradigm was established using the GSE70866 dataset. Quintessential genes were selected through Kaplan-Meier (KM) curves, while immune infiltration examinations, functional enrichment critiques and nomogram paradigms were inaugurated. Analysis of intercellular communication revealed an intimate potential connections between macrophages and various cell types, pinpointing five cardinal genes influencing the trajectory and prognosis of IPF. The nomogram paradigm, sculpted from these seminal genes, exhibits superior predictive prowess. Our research meticulously identified five critical genes, confirming their intimate association with the prognosis, immune infiltration and transcriptional governance of IPF. Interestingly, we discerned these genes' engagement with the EPITHELIAL_MESENCHYMAL_TRANSITION signalling pathway, which may enhance our understanding of the molecular complexity of IPF.
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We utilized the RSF algorithm, a machine learning approach, to sift through hallmark genes employing the randomForestSRC suite.
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Our research meticulously identified five critical genes, confirming their intimate association with the prognosis, immune infiltration and transcriptional governance of IPF.
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Our research meticulously identified five critical genes, confirming their intimate association with the prognosis, immune infiltration and transcriptional governance of IPF.
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Our research meticulously identified five critical genes, confirming their intimate association with the prognosis, immune infiltration and transcriptional governance of IPF.
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Single-Cell Transcriptomic Analysis of Human Lung Reveals Complex Multicellular Changes During Pulmonary Fibrosis II
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title
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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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38887981", "kind": "science-record", "revision": 2, "content_hash": "sha256:c3738f357746ae0eebdfe7c61f190c0f1b036cdea1a83811c0dbf872202ab595", "created_at": "2026-11-01T20:28:57Z", "updated_at": "2026-11-01T21:33:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Single-cell combined with transcriptome sequencing to explore the molecular mechanism of cell communication in idiopathic pulmonary fibrosis.", "doi": "10.1111/jcmm.18499", "pmid": "38887981", "authors": ["Minggao Zhu", "Yuhu Yi", "Kui Jiang", "Yongzhi Liang", "Lijun Li", "Feng Zhang", "Xinglong Zheng", "Haiyan Yin"], "journal": "Journal of cellular and molecular medicine", "year": 2024, "abstract": "Idiopathic pulmonary fibrosis (IPF) is a common, chronic, and progressive lung disease that severely impacts human health and survival. However, the intricate molecular underpinnings of IPF remains elusive. This study aims to delve into the nuanced molecular interplay of cellular interactions in IPF, thereby laying the groundwork for innovative therapeutic approaches in the clinical field of IPF. Sophisticated bioinformatics methods were employed to identify crucial biomarkers essential for the progression of IPF. The GSE122960 single-cell dataset was obtained from the Gene Expression Omnibus (GEO) compendium, and intercellular communication potentialities were scrutinized via CellChat. The random survival forest paradigm was established using the GSE70866 dataset. Quintessential genes were selected through Kaplan-Meier (KM) curves, while immune infiltration examinations, functional enrichment critiques and nomogram paradigms were inaugurated. Analysis of intercellular communication revealed an intimate potential connections between macrophages and various cell types, pinpointing five cardinal genes influencing the trajectory and prognosis of IPF. The nomogram paradigm, sculpted from these seminal genes, exhibits superior predictive prowess. Our research meticulously identified five critical genes, confirming their intimate association with the prognosis, immune infiltration and transcriptional governance of IPF. Interestingly, we discerned these genes' engagement with the EPITHELIAL_MESENCHYMAL_TRANSITION signalling pathway, which may enhance our understanding of the molecular complexity of IPF.", "claims": [{"id": "claim-method", "statement": "We utilized the RSF algorithm, a machine learning approach, to sift through hallmark genes employing the randomForestSRC suite.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38887981", "locator": "sections[6] \"Random survival forest algorithm\" paragraph 2", "quote": "We utilized the RSF algorithm, a machine learning approach, to sift through hallmark genes employing the randomForestSRC suite."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. 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This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122960"], "outputs": ["the source data with its repository provenance"], "instruments": [], "reagents": []}, {"id": "run_analysis", "order": 3, "name": "Run the published analysis", "kind": "analysis", "state": "complete", "blocked_on": null, "description": "Apply the paper's statistical method to the resolved series and compare the result with the published target.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE122960"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38887981", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1111/jcmm.18499", "sha256": "sha256:97934fddbed75c86fdae15ceaef4155ff2c4def67c5c082c39dca44446002219", "bytes": 37041, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1111/jcmm.18499", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11184282", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE122960", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE122960", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE122960", "version": null, "note": "The datasets presented in this study can be found in online repositories. The names of the repositories/repositories and accession number(s) can be found in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. The names of the repositories/repositories and accession number(s) can be found in the article/supplementary material.", "access_statement_kind": "data_availability_statement", "sample_accessions": ["GSM3489182", "GSM3489183", "GSM3489184", "GSM3489185", "GSM3489186", "GSM3489187", "GSM3489188", "GSM3489189", "GSM3489190", "GSM3489191", "GSM3489192", "GSM3489193", "GSM3489194", "GSM3489195", "GSM3489196", "GSM3489197", "GSM3489198"], "sample_count": 17, "group_sizes": null, "group_sizes_basis": null, "platform": "20301", "taxon": "Homo sapiens", "title": "Single-Cell Transcriptomic Analysis of Human Lung Reveals Complex Multicellular Changes During Pulmonary Fibrosis II", "resolution_checked_at": "2026-11-01T15:39:54Z", "resolution_check": "identifier_resolution_only_no_download_no_licence_read", "downloaded": false, "licence_read": false}}, {"id": "data:geo:GSE70866", "role": "raw", "format": "geo-series", "media_type": "application/vnd.ncbi.geo-series+json", "uri": "https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE70866", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE70866", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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