Bioinformatics analyses of gene expression profile to identify pathogenic mechanisms for COVID-19 infection and cutaneous lupus erythematosus.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38022551", "kind": "science-record", "revision": 4, "content_hash": "sha256:c61a8463816ca4240aaee0a51dd423569836eced9e4a64b7c46a480bed4c4303", "created_at": "2026-11-01T19:02:56Z", "updated_at": "2026-11-01T22:53:10Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analyses of gene expression profile to identify pathogenic mechanisms for COVID-19 infection and cutaneous lupus erythematosus.", "doi": "10.3389/fimmu.2023.1268912", "pmid": "38022551", "authors": ["Zhenyu Gao", "Xinchao Zhai", "Guoqing Yan", "Yao Tian", "Xia Huang", "Qingchao Wu", "Lin Yuan", "Linchong Su"], "journal": "Frontiers in immunology", "year": 2023, "abstract": "Objective: The global mortality rates have surged due to the ongoing coronavirus disease 2019 (COVID-19), leading to a worldwide catastrophe. Increasing incidents of patients suffering from cutaneous lupus erythematosus (CLE) exacerbations after either contracting COVID-19 or getting immunized against it, have been observed in recent research. However, the precise intricacies that prompt this unexpected complication are yet to be fully elucidated. This investigation seeks to probe into the molecular events inciting this adverse outcome.\nMethod: Gene expression patterns from the Gene Expression Omnibus (GEO) database, specifically GSE171110 and GSE109248, were extracted. We then discovered common differentially expressed genes (DEGs) in both COVID-19 and CLE. This led to the creation of functional annotations, formation of a protein-protein interaction (PPI) network, and identification of key genes. Furthermore, regulatory networks relating to these shared DEGs and significant genes were constructed.\nResult: We identified 214 overlapping DEGs in both COVID-19 and CLE datasets. The following functional enrichment analysis of these DEGs highlighted a significant enrichment in pathways related to virus response and infectious disease in both conditions. Next, a PPI network was constructed using bioinformatics tools, resulting in the identification of 5 hub genes. Finally, essential regulatory networks including transcription factor-gene and miRNA-gene interactions were determined.\nConclusion: Our findings demonstrate shared pathogenesis between COVID-19 and CLE, offering potential insights for future mechanistic investigations. And the identification of common pathways and key genes in these conditions may provide novel avenues for research.", "claims": [{"id": "claim-method", "statement": "In our quest to identify the DEGs separating COVID-19 and HC subjects within the GSE171110 dataset, we adopted the use of limma packages within the R programming language.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38022551", "locator": "sections[4] \"Identification of differentially expressed genes and common DEGs among COVID-19 and CLE\" paragraph 2", "quote": "In our quest to identify the DEGs separating COVID-19 and HC subjects within the GSE171110 dataset, we adopted the use of limma packages within the R programming language."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/under the accession numbers GSE109248/GSE171110.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38022551", "locator": "sections[18] \"Data availability statement\" paragraph 2", "quote": "The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: https://www.ncbi.nlm.nih.gov/geo/under the accession numbers GSE109248/GSE171110."}], "state": "complete"}, {"id": "claim-target-result", "statement": "We identified 214 overlapping DEGs in both COVID-19 and CLE datasets.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38022551", "locator": "sections[0] \"Abstract\" paragraph 3", "quote": "We identified 214 overlapping DEGs in both COVID-19 and CLE datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "We identified 214 overlapping DEGs in both COVID-19 and CLE datasets.", "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:38022551"], "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:GSE109248"], "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:GSE109248"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:38022551", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2023.1268912", "sha256": "sha256:cfe8007e4dbe21fbce902097f67e9af14ef6e31f4ad84e2055b3fb3b82be30ee", "bytes": 40698, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2023.1268912", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10644101", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE109248", "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=GSE109248", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE109248", "version": null, "note": "The datasets presented in this study can be found in online repositories. 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