Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40599778", "kind": "science-record", "revision": 4, "content_hash": "sha256:54d783c9f421080cbc900ef168521f06434b60ef36be0129aa238a9ac73f1b2e", "created_at": "2026-11-01T20:30:41Z", "updated_at": "2026-11-01T22:56:45Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying potential three key targets gene for septic shock in children using bioinformatics and machine learning methods.", "doi": "10.3389/fimmu.2025.1586584", "pmid": "40599778", "authors": ["Wei Guo", "Hao Chen", "Feng Wang", "Yingjiao Chi", "Wei Zhang", "Shan Wang", "Kezhu Chen", "Hong Chen"], "journal": "Frontiers in immunology", "year": 2025, "abstract": "Background: Septic shock in children is an infectious disease caused by low immunity, and its mortality is very high. Early prediction of the risk of death in children with septic shock is helpful for clinicians to judge the severity of the disease, take active treatment measures, and improve the adverse outcomes of patients. However, the mechanism of death from sepsis in children remains unclear. This study aims to use bioinformatics and machine learning algorithms to identify key genes and pathways associated with fatal sepsis in children, and provide theoretical basis for rational drug use in follow-up TCM treatment.\nMethods: Gene expression profiles were obtained from the GEO database (GSE4607) for 15 blank patients and 14 children with sepsis death. Differentially expressed genes (DEGs) were enriched by GO and KEGG pathways. Construct and visualize protein-protein interaction (PPI) networks to identify candidate genes responsible for fatal sepsis in children. Three kinds of machine learning models were established, and the candidate genes were screened by intersection to obtain the core genes with diagnostic value. ROC curve was drawn for core genes to clarify the diagnostic value of genetic markers.\nResults: Analysis of differences in the preprocessed dataset identified 83 genes, including 78 up-regulated genes and 5 down-regulated genes. 17 candidate genes were screened by protein interaction network analysis. Three machine learning algorithms LASSO, random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) were used to finally screen out three core genes: CD163, MCEMP1 and RETN. CD163, MCEMP1 and RETN may jointly regulate complement and coagulation cascades, toll like receptor signaling pathway, graft versus host disease, type I diabetes mellitus.\nConclusion: In this study, three core genes (CD163, MCEMP1 and RETN) that lead to sepsis death in children were screened out, providing a new understanding of the lethal mechanism of sepsis in children and a promising new therapeutic approach.", "claims": [{"id": "claim-method", "statement": "Three machine learning algorithms LASSO, random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) were used to finally screen out three core genes: CD163, MCEMP1 and RETN.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40599778", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "Three machine learning algorithms LASSO, random forest (RF), and support vector machine recursive feature elimination (SVM-RFE) were used to finally screen out three core genes: CD163, MCEMP1 and RETN."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40599778", "locator": "sections[21] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "CD163, MCEMP1, and RETN were identified as core diagnostic genes, with GSE4607 AUC values of 0.862, 0.936, and 0.995, respectively.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40599778", "locator": "sections[16] \"Identification of characteristic genes\" paragraph 2", "quote": "In the GSE4607 dataset, the AUC value of the CD163 classification model is 0.862 ( Figure 6B ), that of the MCEMP1 classification model is 0.936 ( Figure 6C ), and that of the RETN classification model is 0.995 ( Figure 6D )."}, {"asset": "source:pmid:40599778", "locator": "sections[16] \"Identification of characteristic genes\" paragraph 2", "quote": "Therefore, we focused our research on three genes, CD163, MCEMP1 and RETN ( Figure 6A )."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "CD163, MCEMP1, and RETN were identified as core diagnostic genes, with GSE4607 AUC values of 0.862, 0.936, and 0.995, 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:40599778"], "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:GSE4607"], "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:GSE4607"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40599778", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2025.1586584", "sha256": "sha256:e23d4cca01df92261e8f110bd2ed784e574ff5dced3cfecbcc5b897211d277aa", "bytes": 42923, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2025.1586584", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC12209225", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE4607", "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=GSE4607", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE4607", "version": null, "note": "The original contributions presented in the study are included in the article. 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