Identification of hub genes and prediction of the ceRNA network in adult sepsis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:40821971", "kind": "science-record", "revision": 3, "content_hash": "sha256:65395142619fcb6264e0fc2dc3e92ca0fd260054d060c8d31b7e7699b23410d1", "created_at": "2026-11-01T20:30:54Z", "updated_at": "2026-11-01T22:14:34Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of hub genes and prediction of the ceRNA network in adult sepsis.", "doi": "10.7717/peerj.19619", "pmid": "40821971", "authors": ["Kangyi Xue", "Kan Wu", "Haoxian Luo", "Haihua Luo", "Zhaoqian Zhong", "Fen Li", "Lei Li", "Li Chen"], "journal": "PeerJ", "year": 2025, "abstract": "Background: Sepsis refers to a dysregulated host immune response to infection. It carries a high risk of morbidity and mortality, and its pathogenesis has yet to be fully elucidated. The main aim of this study was to identify prognostic hub genes for sepsis and to predict a competitive endogenous RNA (ceRNA) network that regulates the hub genes.\nMethods: Six transcriptome datasets from the peripheral blood of septic patients were retrieved from the Gene Expression Omnibus (GEO) database. The robust rank aggregation (RRA) method was used to screen differentially expressed genes (DEGs) across these datasets. A comprehensive bioinformatics investigation was conducted, encompassing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses using the \"clusterProfiler\" package in R, as well as gene set enrichment analysis (GSEA) to further elucidate the biological functions and pathways associated with the DEGs. Weighted gene co-expression network analysis (WGCNA) was performed to identify a module significantly associated with sepsis. Integration of this module with protein-protein interaction (PPI) network analysis facilitated the identification of five hub genes. These hub genes were subsequently validated using an independent dataset and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) analysis of peripheral blood samples from septic patients. The prognostic values of these hub genes were assessed via receiver operating characteristic (ROC) curve analysis. Finally, a ceRNA network regulating the prognostic hub genes was constructed by integrating data from a literature review as well as five online databases.\nResults: RRA analysis identified 164 DEGs across six training cohorts. Bioinformatics analyses revealed concurrent hyperinflammation and immunosuppression in sepsis patients. Five hub genes were identified via WGCNA and PPI network analysis, and their differential expression was verified by the validation dataset (GSE28750) and RT-qPCR analysis in the peripheral blood of septic patients. ROC analysis confirmed four hub genes with prognostic value, and a ceRNA network was predicted to elucidate their regulatory mechanisms.\nConclusion: This study identified four hub genes (CLEC4D, GPR84, S100A12, and HK3) with significant prognostic value in sepsis and predicted a ceRNA network (NEAT1-hsa-miR-495-3p-ELF1) regulating their expression. The integrated analysis reconfirmed the concurrent presence of hyperinflammation and immunosuppression in hospitalized sepsis patients. These findings enhance the understanding of sepsis pathogenesis and identify potential therapeutic targets.", "claims": [{"id": "claim-method", "statement": "Statistically changed genes and differentially expressed genes (DEGs) were screened using the robust rank aggregation (RRA) method, which is an unbiased and effective approach for integrating data from diverse chip platforms using a comprehensive ranking list algorithm (Jia & Zhai, 2019; Yan et al., 2018).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40821971", "locator": "sections[4] \"Robust rank aggregation analysis\" paragraph 2", "quote": "Statistically changed genes and differentially expressed genes (DEGs) were screened using the robust rank aggregation (RRA) method, which is an unbiased and effective approach for integrating data from diverse chip platforms using a comprehensive ranking list algorithm (Jia & Zhai, 2019; Yan et al., 2018)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets are available at GEO: GSE137340, GSE69063, GSE69528, GSE54514, GSE57065, GSE95233, and GSE28570.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:40821971", "locator": "sections[29] \"Additional Information and Declarations\" paragraph 6", "quote": "The datasets are available at GEO: GSE137340, GSE69063, GSE69528, GSE54514, GSE57065, GSE95233, and GSE28570."}], "state": "complete"}, {"id": "claim-target-result", "statement": "This study identified four hub genes (CLEC4D, GPR84, S100A12, and HK3) with significant prognostic value in sepsis and predicted a ceRNA network (NEAT1-hsa-miR-495-3p-ELF1) regulating their expression.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:40821971", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "This study identified four hub genes (CLEC4D, GPR84, S100A12, and HK3) with significant prognostic value in sepsis and predicted a ceRNA network (NEAT1-hsa-miR-495-3p-ELF1) regulating their expression."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "This study identified four hub genes (CLEC4D, GPR84, S100A12, and HK3) with significant prognostic value in sepsis and predicted a ceRNA network (NEAT1-hsa-miR-495-3p-ELF1) regulating their expression.", "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:40821971"], "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:GSE137340"], "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": {"n_clusters": "clustering tree showing 11 cluster modules"}, "inputs": ["data:geo:GSE137340"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:40821971", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.19619", 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