Role of Cellular Senescence Genes and Immune Infiltration in Sepsis and Sepsis-Induced ARDS Based on Bioinformatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39588141", "kind": "science-record", "revision": 4, "content_hash": "sha256:5ca63fed7d8987f475502697a240faa1bd7baea96980c460914adbb0f24aa792", "created_at": "2026-11-01T20:29:51Z", "updated_at": "2026-11-01T22:55:40Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Role of Cellular Senescence Genes and Immune Infiltration in Sepsis and Sepsis-Induced ARDS Based on Bioinformatics Analysis.", "doi": "10.2147/JIR.S488463", "pmid": "39588141", "authors": ["Xiao-Ling Wu", "Ya-Nan Guo"], "journal": "Journal of inflammation research", "year": 2024, "abstract": "Introduction: Sepsis is the leading cause of death in critically ill patients; it results in multiorgan dysfunction, including acute respiratory distress syndrome (ARDS). Our study was conducted to determine the role of cellular senescence genes and immune infiltration in sepsis and sepsis-induced ARDS via bioinformatic analyses.\nExperimental Procedures: Datasets GSE66890 and GSE145227 were obtained from the Gene Expression Omnibus (GEO) database and utilized for bioinformatics analyses. Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of the differentially expressed genes (DEGs) were performed to identify the key functional modules. Two machine learning algorithms, least absolute shrinkage and selection operator (LASSO) and support vector machine recursive feature elimination (SVM-RFE), were used to screen for characteristic genes in sepsis and sepsis-induced ARDS. ROC curves were generated to evaluate the predictive ability of gene hubs. Differences in immune infiltration levels between the disease and control groups were compared via ssGSEA. The diagnostic value of the hub genes was verified via quantitative PCR (qPCR) in hospitalized patients.\nResults: Four characteristic genes (ATM, CCNB1, CCNA1, and E2F2) were identified as biomarkers for the progression of sepsis-induced ARDS. E2F2 showed the highest predictive ability for the occurrence of ARDS in patients with sepsis. CD56bright and plasmacytoid dendritic cells exhibited high infiltration in the sepsis-induced ARDS group, whereas eosinophils, MDSCs, macrophages, and neutrophils exhibited low infiltration. In addition, ATM expression was lower in patients with sepsis than in those without sepsis (n = 6).\nConclusion: Sepsis-induced ARDS is correlated with circulating immune responses, and the expression of ATM, CCNB1, CCNA1, and E2F2 may serve as potential diagnostic biomarkers and therapeutic targets in sepsis-induced ARDS.", "claims": [{"id": "claim-method", "statement": "After homogenization, DEGs were screened with a threshold of p < 0.05 using the “limma” package.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39588141", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "After homogenization, DEGs were screened with a threshold of p < 0.05 using the “limma” package."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data used to support the findings of this study are available from the GEO website (https://www.ncbi.nlm.nih.gov/gds/?term=) and KEGG database (https://www.kegg.jp/entry/map04218), and the code is supplied by the corresponding author upon request. This paper has been uploaded to ResearchGate as a preprint: https://www.researchsquare.com/article/rs-4476919/v1.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39588141", "locator": "sections[22] \"Data Sharing Statement\" paragraph 2", "quote": "The data used to support the findings of this study are available from the GEO website (https://www.ncbi.nlm.nih.gov/gds/?term=) and KEGG database (https://www.kegg.jp/entry/map04218), and the code is supplied by the corresponding author upon request. This paper has been uploaded to ResearchGate as a preprint: https://www.researchsquare.com/article/rs-4476919/v1."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Four characteristic genes (ATM, CCNB1, CCNA1, and E2F2) were identified as biomarkers for the progression of sepsis-induced ARDS.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39588141", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Four characteristic genes (ATM, CCNB1, CCNA1, and E2F2) were identified as biomarkers for the progression of sepsis-induced ARDS."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Four characteristic genes (ATM, CCNB1, CCNA1, and E2F2) were identified as biomarkers for the progression of sepsis-induced ARDS.", "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:39588141"], "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. This lane did not download the dataset or read its licence.", "duration_hours": null, "parameters": {}, "inputs": ["data:geo:GSE145227"], "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:GSE145227"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39588141", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.2147/JIR.S488463", "sha256": "sha256:c027cdceb08fed38625b3357ba7a373ee5a604813ad20c5d618576f05168bdde", "bytes": 28065, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.2147/JIR.S488463", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11586271", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE145227", "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=GSE145227", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE145227", "version": null, "note": "The data used to support the findings of this study are available from the GEO website (https://www.ncbi.nlm.nih.gov/gds/?term=) and KEGG database (https://www.kegg.jp/entry/map04218), and the code is supplied by the corresponding author upon request. 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