Identification of diagnostic candidate genes in COVID-19 patients with sepsis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39377750", "kind": "science-record", "revision": 4, "content_hash": "sha256:633122fc3b2c6ac398c2c842ec4f1284ce4c940183262ea7766ef26f3c53d9a7", "created_at": "2026-11-01T20:29:35Z", "updated_at": "2026-11-01T22:55:21Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of diagnostic candidate genes in COVID-19 patients with sepsis.", "doi": "10.1002/iid3.70033", "pmid": "39377750", "authors": ["Jiuang Li", "Shiqian Pu", "Lei Shu", "Mingjun Guo", "Zhihui He"], "journal": "Immunity, inflammation and disease", "year": 2024, "abstract": "PURPOSE: Coronavirus Disease 2019 (COVID-19) and sepsis are closely related. This study aims to identify pivotal diagnostic candidate genes in COVID-19 patients with sepsis.\nPATIENTS AND METHODS: We obtained a COVID-19 data set and a sepsis data set from the Gene Expression Omnibus (GEO) database. Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, protein-protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID-19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value. Finally, the data set GSE28750 was used to verify the core genes and analyze the immune infiltration.\nRESULTS: The COVID-19 data set contained 3,438 DEGs， and 595 common genes were screened in sepsis. sepsis DEGs were mainly enriched in immune regulation. The intersection of DEGs for COVID-19 and core genes for sepsis was 329, which were also mainly enriched in the immune system. After developing the PPI network, 17 node genes were filtered and thirteen candidate hub genes were selected for diagnostic value evaluation using machine learning. All thirteen candidate hub genes have diagnostic value, and 8 genes with an Area Under the Curve (AUC) greater than 0.9 were selected as diagnostic genes.\nCONCLUSION: Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA-DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID-19 patients with sepsis. This finding contributes to the identification of potential peripheral blood diagnostic candidate genes for COVID-19 patients with sepsis.", "claims": [{"id": "claim-method", "statement": "Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co‐expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID‐19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39377750", "locator": "sections[0] \"Abstract\" paragraph 2", "quote": "Identification of differentially expressed genes (DEGs) and module genes using the Linear Models for Microarray Data (LIMMA) and weighted gene co‐expression network analysis (WGCNA), functional enrichment analysis, protein–protein interaction (PPI) network construction, and machine learning algorithms (least absolute shrinkage and selection operator (LASSO) regression and Random Forest (RF)) were used to identify candidate hub genes for the diagnosis of COVID‐19 patients with sepsis. Receiver operating characteristic (ROC) curves were developed to assess the diagnostic value."}], "state": "complete"}, {"id": "claim-data-access", "statement": "GEO belongs to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open‐source data, so there are no ethical issues and other conflicts of interest.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39377750", "locator": "sections[26] \"DATA AVAILABILITY STATEMENT\" paragraph 2", "quote": "GEO belongs to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open‐source data, so there are no ethical issues and other conflicts of interest."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA‐DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID‐19 patients with sepsis.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39377750", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA‐DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID‐19 patients with sepsis."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Five core genes (CD3D, IL2RB, KLRC, CD5, and HLA‐DQA1) associated with immune infiltration were identified to evaluate their diagnostic utility COVID‐19 patients with sepsis.", "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:39377750"], "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:GSE171110"], "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:GSE171110"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:39377750", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1002/iid3.70033", "sha256": "sha256:f4de5cf4036ea3477884fb96e8dc8a7bc03e2fd6dbf335ac44542d14b52795fc", "bytes": 35479, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1002/iid3.70033", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC11460023", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE171110", "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=GSE171110", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE171110", "version": null, "note": "GEO belongs to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open‐source data, so there are no ethical issues and other conflicts of interest.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "GEO belongs to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. 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The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. Our study is based on open‐source data, so there are no ethical issues and other conflicts of interest.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "GEO belongs to public databases. The patients involved in the database have obtained ethical approval. Users can download relevant data for free for research and publish relevant articles. 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