Bioinformatics analysis of potential common pathogenic mechanisms for COVID-19 infection and primary Sjogren's syndrome.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:35958619", "kind": "science-record", "revision": 4, "content_hash": "sha256:6549bb252eaa0ac592681a277a1d50b8f788ab9487410969d479fbaf480f135d", "created_at": "2026-11-01T18:15:04Z", "updated_at": "2026-11-01T22:07:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Bioinformatics analysis of potential common pathogenic mechanisms for COVID-19 infection and primary Sjogren's syndrome.", "doi": "10.3389/fimmu.2022.938837", "pmid": "35958619", "authors": ["Hong Luo", "Xia Zhou"], "journal": "Frontiers in immunology", "year": 2022, "abstract": "Background: Accumulating evidence has revealed that the prevalence of Coronavirus 2019 (COVID-19) was significantly higher in patients with primary Sjogren's syndrome (pSS) compared to the general population. However, the mechanism remains incompletely elucidated. This study aimed to further investigate the molecular mechanisms underlying the development of this complication.\nMethods: The gene expression profiles of COVID-19 (GSE157103) and pSS (GSE40611) were downloaded from the Gene Expression Omnibus (GEO) database. After identifying the common differentially expressed genes (DEGs) for pSS and COVID-19, functional annotation, protein-protein interaction (PPI) network, module construction and hub gene identification were performed. Finally, we constructed transcription factor (TF)-gene regulatory network and TF-miRNA regulatory network for hub genes.\nResults: A total of 40 common DEGs were selected for subsequent analyses. Functional analyses showed that cellular components and metabolic pathways collectively participated in the development and progression of pSS and COVID-19. Finally, 12 significant hub genes were identified using the cytoHubba plugin, including CMPK2, TYMS, RRM2, HERC5, IFI44L, IFI44, IFIT2, IFIT1, IFIT3, MX1, CDCA2 and TOP2A, which had preferable values as diagnostic markers for COVID-19 and pSS.\nConclusions: Our study reveals common pathogenesis of pSS and COVID-19. These common pathways and pivotal genes may provide new ideas for further mechanistic studies.", "claims": [{"id": "claim-method", "statement": "In this study, Networkanalyst was used to identify DEGs for GSE157103 and we employed GEO2R to analyze DEGs for GSE40611. Genes with adjusted P-value < 0.05 and |log2 fold change (log2FC)| > 1.0 were considered DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35958619", "locator": "sections[4] \"Identification of shared DEGs between COVID-19 and primary Sjogren’s syndrome\" paragraph 2", "quote": "In this study, Networkanalyst was used to identify DEGs for GSE157103 and we employed GEO2R to analyze DEGs for GSE40611. Genes with adjusted P-value < 0.05 and |log2 fold change (log2FC)| > 1.0 were considered DEGs."}], "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 in the article/supplementary material.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:35958619", "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 in the article/supplementary material."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Differential-expression analysis of the COVID-19 and primary Sjogren’s syndrome datasets identified 40 shared DEGs.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:35958619", "locator": "sections[11] \"Identification of DEGs and shared genes between COVID-19 and primary Sjogren’s syndrome\" paragraph 2", "quote": "Then by taking the intersection of DEGs of GSE157103 dataset and GSE40611 dataset, there were 40 shared DEGs selected, which were visualized by Venn diagrams (Figure 2C)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Differential-expression analysis of the COVID-19 and primary Sjogren’s syndrome datasets identified 40 shared DEGs.", "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:35958619"], "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:GSE157103"], "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:GSE157103"], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": []}, "data": {"state": "partial", "assets": [{"id": "source:pmid:35958619", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.3389/fimmu.2022.938837", "sha256": "sha256:a82086a60eb56b54b4bc44ba08f561b6024ac3271fb060de9cc630ed6e36db7e", "bytes": 44056, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.3389/fimmu.2022.938837", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC9360424", "source": "europe_pmc_fulltext", "coverage": "whole_body", "text_hash_scope": "rendered_section_body"}}, {"id": "data:geo:GSE157103", "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=GSE157103", "sha256": null, "bytes": null, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "GSE157103", "version": null, "note": "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 in the article/supplementary material.", "structure": {"repository": "GEO", "access_route": "GEO accession URL", "access_statement_verbatim": "The datasets presented in this study can be found in online repositories. 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