Exosome-related gene identification and diagnostic model construction in hepatic ischemia-reperfusion injury.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39341981", "kind": "science-record", "revision": 4, "content_hash": "sha256:5633be5fbf8c3eee286cf9fca2f0de80b2e4d7fad260f1f3b96fec2cb4cabc9e", "created_at": "2026-11-01T20:29:32Z", "updated_at": "2026-11-01T22:55:17Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exosome-related gene identification and diagnostic model construction in hepatic ischemia-reperfusion injury.", "doi": "10.1038/s41598-024-73441-5", "pmid": "39341981", "authors": ["Yujuan You", "Shoulin Chen", "Binquan Tang", "Xianliang Xing", "Huanling Deng", "Yiguo Wu"], "journal": "Scientific reports", "year": 2024, "abstract": "Hepatic ischemia-reperfusion injury (HIRI) may cause severe hepatic impairment, acute hepatic insufficiency, and multiorgan system collapse. Exosomes can alleviate HIRI. Therefore, this study explored the role of exosomal-related genes (ERGs) in HIRI using bioinformatics to determine the underlying molecular mechanisms and novel diagnostic markers for HIRI. We merged the GSE12720, GSE14951, and GSE15480 datasets obtained from the Gene Expression Omnibus (GEO) database into a combined gene dataset (CGD). CGD was used to identify differentially expressed genes (DEGs) based on a comparison of the HIRI and healthy control cohorts. The impact of these DEGs on HIRI was assessed through gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA). ERGs were retrieved from the GeneCards database and prior studies, and overlapped with the identified DEGs to yield the set of exosome-related differentially expressed genes (ERDEGs). Functional annotations and enrichment pathways of these genes were determined using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. Diagnostic models for HIRI were developed using least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM) algorithms. Key genes with diagnostic value were identified from the overlap, and single-sample gene-set enrichment analysis (ssGSEA) was conducted to evaluate the immune infiltration characteristics. A molecular regulatory interaction network was established using Cytoscape software to elucidate the intricate regulatory mechanisms of key genes in HIRI. Finally, exosome score (Es) was obtained using ssGSEA and the HIRI group was divided into the Es_High and Es_Low groups based on the median Es. Gene expression was analyzed to understand the impact of all genes in the CGD on HIRI. Finally, the relative expression levels of the five key genes in the hypoxia-reoxygenation (H/R) model were determined using quantitative real-time PCR (qRT-PCR). A total of 3810 DEGs were identified through differential expression analysis of the CGD, and 61 of these ERDEGs were screened. Based on GO and KEGG enrichment analyses, the ERDEGs were mainly enriched in wound healing, MAPK, protein kinase B signaling, and other pathways. GSEA and GSVA revealed that these genes were mainly enriched in the TP53, MAPK, TGF[Formula: see text], JAK-STAT, MAPK, and NFKB pathways. Five key genes (ANXA1, HNRNPA2B1, ICAM1, PTEN, and THBS1) with diagnostic value were screened using the LASSO regression and SVM algorithms and their molecular interaction network was established using Cytoscape software. Based on ssGSEA, substantial variations were found in the expression of 18 immune cell types among the groups (p < 0.05). Finally, the Es of each HIRI patient was calculated. ERDEGs in the Es_High and Es_Low groups were enriched in the IL18, TP53, MAPK, TGF[Formula: see text], and JAK-STAT pathways. The differential expression of these five key genes in the H/R model was verified using qRT-PCR. Herein, five key genes were identified as potential diagnostic markers. Moreover, the potential impact of these genes on pathways and the regulatory mechanisms of their interaction network in HIRI were revealed. Altogether, our findings may serve as a theoretical foundation for enhancing clinical diagnosis and elucidating underlying pathogeneses.", "claims": [{"id": "claim-method", "statement": "Diagnostic models for HIRI were developed using least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM) algorithms.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39341981", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Diagnostic models for HIRI were developed using least absolute shrinkage and selection operator (LASSO) regression and support vector machine (SVM) algorithms."}], "state": "complete"}, {"id": "claim-data-access", "statement": "Data is provided within the manuscript or supplementary information files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39341981", "locator": "sections[35] \"Data availability\" paragraph 2", "quote": "Data is provided within the manuscript or supplementary information files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five key genes (ANXA1, HNRNPA2B1, ICAM1, PTEN, and THBS1) with diagnostic value were screened using the LASSO regression and SVM algorithms and their molecular interaction network was established using Cytoscape software.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39341981", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "Five key genes (ANXA1, HNRNPA2B1, ICAM1, PTEN, and THBS1) with diagnostic value were screened using the LASSO regression and SVM algorithms and their molecular interaction network was established using Cytoscape software."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Five key genes (ANXA1, HNRNPA2B1, ICAM1, PTEN, and THBS1) with diagnostic value were screened using the LASSO regression and SVM algorithms and their molecular interaction network was established using Cytoscape software.", "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:39341981"], "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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