Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38701300", "kind": "science-record", "revision": 4, "content_hash": "sha256:eb3aa2fbbc26aa0662225a6fbc4d33d649202963f1d7e36d27c24d2ae95f863e", "created_at": "2026-11-01T19:20:40Z", "updated_at": "2026-11-01T22:54:15Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification and validation of oxidative stress-related genes in sepsis-induced myopathy.", "doi": "10.1097/MD.0000000000037933", "pmid": "38701300", "authors": ["Ning Zhang", "Dan Huang", "Xiang Li", "JinXia Yan", "Qi Yan", "WeiXing Ge", "Jun Zhou"], "journal": "Medicine", "year": 2024, "abstract": "BACKGROUND: Sepsis-induced myopathy (SIM) a complication of sepsis that results in prolonged mechanical ventilation, long-term functional disability, and increased patient mortality. This study was performed to identify potential key oxidative stress-related genes (OS-genes) as biomarkers for the diagnosis of SIM using bioinformatics.\nMETHODS: The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language. Simultaneously, we searched for the genes related to oxidative stress in the Gene Ontology (GO) database. The intersection of the genes selected from the GO database and the genes from the GSE13205 was considered as OS-genes of SIM, where the differential genes were regarded as OS-DEGs. OS-DEGs were analyzed using GO enrichment, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, and protein-protein interaction (PPI) networks. Hub genes in OS-DEGs were selected based on degree, and diagnostic genes were further screened by gene expression and receiver operating characteristic (ROC) curve. Finally, a miRNA-gene network of diagnostic genes was constructed.\nRESULTS: A total of 1089 DEGs were screened from the GSE13205, and 453 OS-genes were identified from the GO database. The overlapping DEGs and OS-genes constituted 25 OS-DEGs, including 15 significantly upregulated and 10 significantly downregulated genes. The top 10 hub genes, including CD36, GPX3, NQO1, GSR, TP53, IDH1, BCL2, HMOX1, JAK2, and FOXO1, were screened. Furthermore, 5 diagnostic genes were identified: CD36, GPX3, NQO1, GSR, and TP53. The ROC analysis showed that the respective area under the curves (AUCs) of CD36, GPX3, NQO1, GSR, and TP53 were 0.990, 0.981, 0.971, 0.971, and 0.971, which meant these genes had very high diagnostic values of SIM. Finally, based on these 5 diagnostic genes, we found that miR-124-3p and miR-16-5p may be potential targets for the treatment of SIM.\nCONCLUSIONS: The results of this study suggest that OS-genes might play an important role in SIM. CD36, GPX3, NQO1, GSR, and TP53 have potential as specific biomarkers for the diagnosis of SIM.", "claims": [{"id": "claim-method", "statement": "Differential expression analysis was conducted using the “limma” package in R 4.2.1 software, and “ggplot2” and “ComplexHeatmap” were used to depict volcano/difference ranking plots and heatmap plots, respectively.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38701300", "locator": "sections[4] \"2.2. Analysis of differentially expressed genes\" paragraph 2", "quote": "Differential expression analysis was conducted using the “limma” package in R 4.2.1 software, and “ggplot2” and “ComplexHeatmap” were used to depict volcano/difference ranking plots and heatmap plots, respectively."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38701300", "locator": "sections[20] \"fn-group\" paragraph 1", "quote": "The datasets generated during and/or analyzed during the current study are available from the corresponding author on reasonable request."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using limma on the GSE13205 expression dataset, the study identified 1,089 differentially expressed genes, including 494 significantly upregulated and 595 significantly downregulated genes.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38701300", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "The GSE13205 was obtained from the Gene Expression Omnibus (GEO) database, including 13 SIM samples and 8 healthy samples, and the differentially expressed genes (DEGs) were identified by limma package in R language."}, {"asset": "source:pmid:38701300", "locator": "sections[10] \"3.1. Identification of DEGs and OS-DEGs in SIM\" paragraph 2", "quote": "Using the thresholds of adjusted |log2(FC)| > 1 and P value < .05, a total of 1089 DEGs were obtained from the GSE13205, including 494 significantly upregulated and 595 significantly downregulated genes (Table S2, Supplemental Digital Content, http://links.lww.com/MD/M268)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "Using limma on the GSE13205 expression dataset, the study identified 1,089 differentially expressed genes, including 494 significantly upregulated and 595 significantly downregulated genes.", "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:38701300"], "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": "blocked", "blocked_on": "data:accession_absent", "description": "The GEO series accession resolves and its sample accessions are listed. 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