Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38912048", "kind": "science-record", "revision": 3, "content_hash": "sha256:1fc6d8e6f80940e3d8d9596102e4a916086fc80a91881701239c3b06603ee1f6", "created_at": "2026-11-01T20:28:59Z", "updated_at": "2026-11-01T22:12:06Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of key genes as potential diagnostic biomarkers in sepsis by bioinformatics analysis.", "doi": "10.7717/peerj.17542", "pmid": "38912048", "authors": ["Guoxin Lin", "Nannan Li", "Jishi Liu", "Jian Sun", "Hao Zhang", "Ming Gui", "Youjie Zeng", "Juan Tang"], "journal": "PeerJ", "year": 2024, "abstract": "Background: Sepsis, an infection-triggered inflammatory syndrome, poses a global clinical challenge with limited therapeutic options. Our study is designed to identify potential diagnostic biomarkers of sepsis onset in critically ill patients by bioinformatics analysis.\nMethods: Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched. Weighted gene co-expression network analysis (WGCNA) was performed and the gene modules most associated with sepsis were identified as key modules. Functional enrichment analysis of the key module genes was then conducted. Moreover, differentially expressed gene (DEG) analysis was conducted by the \"limma\" R package. Protein-protein interaction (PPI) network was created using STRING and Cytoscape, and PPI hub genes were identified with the cytoHubba plugin. The PPI hub genes overlapping with the genes in key modules of WGCNA were determined to be the sepsis-related key genes. Subsequently, the key overlapping genes were validated in an external independent dataset and sepsis patients recruited in our hospital. In addition, CIBERSORT analysis evaluated immune cell infiltration and its correlation with key genes.\nResults: By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p = 2e - 19). 293 DEGs were identified in the merged datasets. The PPI network was created, and the CytoHubba was used to calculate the top 20 genes based on four algorithms (Degree, EPC, MCC, and MNC). Ultimately, LTF, LCN2, ELANE, MPO and CEACAM8 were identified as key overlapping genes as they appeared in the PPI hub genes and the key module genes of WGCNA. These sepsis-related key genes were validated in an independent external dataset (GSE131761) and sepsis patients recruited in our hospital. Additionally, the immune infiltration profiles differed significantly between sepsis and non-sepsis critical illness groups. Correlations between immune cells and these five key genes were assessed, revealing that plasma cells, macrophages M0, monocytes, T cells regulatory, eosinophils and NK cells resting were simultaneously and significantly associated with more than two key genes.\nConclusion: This study suggests a critical role of LTF, LCN2, ELANE, MPO and CEACAM8 in sepsis and may provide potential diagnostic biomarkers and therapeutic targets for the treatment of sepsis.", "claims": [{"id": "claim-method", "statement": "Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules highly associated with sepsis by utilizing “WGCNA” R package (Langfelder & Horvath, 2008).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38912048", "locator": "sections[4] \"Weighted gene co-expression network analysis\" paragraph 2", "quote": "Weighted gene co-expression network analysis (WGCNA) was performed to identify gene modules highly associated with sepsis by utilizing “WGCNA” R package (Langfelder & Horvath, 2008)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The raw data are available in the Supplementary Files.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38912048", "locator": "sections[22] \"Additional Information and Declarations\" paragraph 5", "quote": "The raw data are available in the Supplementary Files."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Using merged and de-batched GSE28750 and GSE74224 data, WGCNA identified the greenyellow module as most positively correlated with sepsis (0.7, p = 2e − 19).", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38912048", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Gene expression profiles of GSE28750 and GSE74224 were obtained from the Gene Expression Omnibus (GEO) database. These datasets were merged, normalized and de-batched."}, {"asset": "source:pmid:38912048", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "By WGCNA, the greenyellow module showed the highest positive correlation with sepsis (0.7, p = 2e − 19)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Using merged and de-batched GSE28750 and GSE74224 data, WGCNA identified the greenyellow module as most positively correlated with sepsis (0.7, p = 2e − 19).", "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:38912048"], "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:GSE131761"], "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:GSE131761"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38912048", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.7717/peerj.17542", "sha256": 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