Common genetic aspects between COVID-19 and sarcoidosis: A network-based approach using gene expression data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35128085", "kind": "science-record", "revision": 5, "content_hash": "sha256:3a742dcb6bd836c892c456d359732430fad19f714f766b56cbcda9ac73089f6f", "created_at": "2026-11-01T17:36:53Z", "updated_at": "2026-11-01T22:48:25Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Common genetic aspects between COVID-19 and sarcoidosis: A network-based approach using gene expression data.", "doi": "10.1016/j.bbrep.2022.101219", "pmid": "35128085", "authors": ["Md Roman Mogal", "Sagarika Adhikary Sompa", "Asadullah Junayed", "Md Rashel Mahmod", "Md Zainul Abedin", "Md Asaduzzaman Sikder"], "journal": "Biochemistry and biophysics reports", "year": 2022, "abstract": "The pandemic situation of novel coronavirus disease 2019 (COVID-19) is a global threat on our current planet, with its rapid spread and high mortality rate. Sarcoidosis patients are at high risk to COVID-19 severity for having lung injuries as well as treating with immunosuppressive agents. So, physicians are in dilemma whether they should use immunosuppressive agents or not for the patients with sarcoidosis history and COVID-19 infection. Therefore, common factors should be identified to provide effective treatment. For determining the common genes between COVID-19 and sarcoidosis, GSE164805 and GSE18781 were retrieved from the Gene Expression Omnibus (GEO) database. Common upregulated genes were identified by using R language to investigate their involved pathways and gene ontologies (GO). With the aid of the STRING Cytoscape plugin tool, protein-protein interactions (PPIs) network was constructed. From the PPIs network, Hub genes and essential modules were detected by using Cytohubba, and MCODE respectively. For hub genes, TFs, TFs-miRNA, and drug, interaction networks were built through the NetworkAnalyst web platform. A total of 34 common upregulated genes were identified and among them, five hub genes, including TET2, MUC5AC, VDR, NFE2L2, and BCL6 were determined. In addition, a cluster having VDR and NFE2L2 was detected from the PPIs network. Moreover, 32 transcription factors and 9 miRNA were recognized for hub genes. Furthermore, vitamin D and some of its analogous compounds were obtained from the drug interaction network. In conclusion, hub genes identified in this study might have potential roles in modulating COVID-19 infection and sarcoidosis. However, further studies are required to corroborate this study.", "claims": [{"id": "claim-method", "statement": "For identifying common differentially expressed genes (DEGs) from both datasets, GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) was applied. GEO2R is used to identify DEGs, using GEOquery and limma R packages from the Bioconductor project, having compared two or more groups of samples [16]. In order to control the false discovery rate, Benjamini-Hochberg was conducted [17].", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35128085", "locator": "sections[4] \"Identification of common upregulated genes\" paragraph 2", "quote": "For identifying common differentially expressed genes (DEGs) from both datasets, GEO2R (https://www.ncbi.nlm.nih.gov/geo/geo2r/) was applied. GEO2R is used to identify DEGs, using GEOquery and limma R packages from the Bioconductor project, having compared two or more groups of samples [16]. In order to control the false discovery rate, Benjamini-Hochberg was conducted [17]."}], "state": "complete"}, {"id": "claim-data-access", "statement": "No data was used for the research described in the article.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35128085", "locator": "sections[25] \"Data availability\" paragraph 2", "quote": "No data was used for the research described in the article."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 34 common upregulated genes between COVID-19 and sarcoidosis and five hub genes: TET2, MUC5AC, VDR, NFE2L2, and BCL6.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35128085", "locator": "sections[0] \"Abstract\" paragraph 1", "quote": "A total of 34 common upregulated genes were identified and among them, five hub genes, including TET2, MUC5AC, VDR, NFE2L2, and BCL6 were determined."}, {"asset": "source:pmid:35128085", "locator": "sections[0] \"Abstract\" paragraph 5", "quote": "34 common upregulated genes were identified from GSE164805 and GSE18781 datasets."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "The study identified 34 common upregulated genes between COVID-19 and sarcoidosis and five hub genes: TET2, MUC5AC, VDR, NFE2L2, and BCL6.", "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:35128085"], "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:GSE164805"], "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:GSE164805"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35128085", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1016/j.bbrep.2022.101219", "sha256": "sha256:20b85c66c596bc1c343e3a3a884b17b8fda85d9996f67f9951f87c34ef011971", "bytes": 39693, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1016/j.bbrep.2022.101219", "version": null, "note": "Full article fetched from PMC; 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