Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39609718", "kind": "science-record", "revision": 3, "content_hash": "sha256:e3044b35eb0f0f0ab6795cc63330c8f6761659d951e0b286de92c218e5066510", "created_at": "2026-11-01T20:29:52Z", "updated_at": "2026-11-01T22:13:12Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of immune-related mitochondrial metabolic disorder genes in septic shock using bioinformatics and machine learning.", "doi": "10.1186/s41065-024-00350-y", "pmid": "39609718", "authors": ["Yu-Hui Cui", "Chun-Rong Wu", "Li-Ou Huang", "Dan Xu", "Jian-Guo Tang"], "journal": "Hereditas", "year": 2024, "abstract": "PURPOSE: Mitochondria are involved in septic shock and inflammatory response syndrome, which severely affects the life security of patients. It is necessary to recognize and explore the immune-mitochondrial genes in septic shock.\nMETHODS: The GSE57065 dataset was acquired from the Gene Expression Omnibus (GEO) database and filtered by limma and the weighted correlation network analysis (WGCNA) to identify mitochondrial-related differentially expressed genes (MitoDEGs) in septic shock. The function of MitoDEGs was analyzed using the Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA), respectively. The Protein-Protein Interaction (PPI) network composed of MitoDEGs was established using Cytoscape. Support Vector Machine Recursive Feature Elimination (SVM-RFE), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO) were used to identify diagnostic MitoDEGs, which were validated using receiver operating characteristic (ROC) analysis and Quantitative Real-time Reverse Transcription Polymerase Chain Reaction (qRT-PCR). Furthermore, the infiltration of immunocytes was analyzed using CIBERSORT, and the correlation between diagnostic MitoDEGs and immunocytes was explored using Spearman.\nRESULTS: A total of 44 MitoDEGs were filtered, and functional enrichment analysis showed they were associated with mitochondrial function, and the PPI network had 457 nodes and 547 edges. Four diagnostic genes, MitoDEGs, PGS1, C6orf136, THEM4, and EPHX2, were identified by three machine learning algorithms, and qRT-PCR results obtained similar expression levels as bioinformatics analysis. Furthermore, the diagnostic model constructed by the diagnostic genes had fine diagnostic efficacy. Immunocyte infiltration analysis showed that activated immunocytes were abundant and correlated with hub genes, with neutrophils accounting for the largest proportion in septic shock.\nCONCLUSIONS: In this study, we recognized four immune-mitochondrial key genes (PGS1, C6orf136, THEM4, and EPHX2) in septic shock and designed a novel gene diagnosis model that provided a new and meaningful way for the diagnosis of septic shock.", "claims": [{"id": "claim-method", "statement": "Finally, the diagnostic model was established using logistic regression and validated using the ROC curves based on the training set (GSE57065) and the validation set (GSE95233).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39609718", "locator": "sections[6] \"Identification of diagnostic MitoDEGs using machine learning algorithms\" paragraph 2", "quote": "Finally, the diagnostic model was established using logistic regression and validated using the ROC curves based on the training set (GSE57065) and the validation set (GSE95233)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "No datasets were generated or analysed during the current study.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39609718", "locator": "sections[23] \"notes\" paragraph 1", "quote": "No datasets were generated or analysed during the current study."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The ROC curves of the diagnostic model in the training set and the validation set are shown in Fig. 4D - E, and the AUROCs were 1.000 and 0.986, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39609718", "locator": "sections[14] \"Validation of diagnostic MitoDEGs\" paragraph 2", "quote": "The ROC curves of the diagnostic model in the training set and the validation set are shown in Fig. 4D - E, and the AUROCs were 1.000 and 0.986, respectively."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The ROC curves of the diagnostic model in the training set and the validation set are shown in Fig. 4D - E, and the AUROCs were 1.000 and 0.986, respectively.", "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:39609718"], "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:GSE57065"], "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:GSE57065"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39609718", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1186/s41065-024-00350-y", "sha256": 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