A bioinformatics approach for identifying potential molecular mechanisms and key genes involved in COVID-19 associated cardiac remodeling.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:34131597", "kind": "science-record", "revision": 5, "content_hash": "sha256:9fa36317274b90a4c6e701ad2ff8dad3130e8f6b63b0481911275fa1410c6de2", "created_at": "2026-11-01T15:41:39Z", "updated_at": "2026-11-01T22:03:58Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "A bioinformatics approach for identifying potential molecular mechanisms and key genes involved in COVID-19 associated cardiac remodeling.", "doi": "10.1016/j.genrep.2021.101246", "pmid": "34131597", "authors": ["Hamid Ceylan"], "journal": "Gene reports", "year": 2021, "abstract": "In 2019 coronavirus disease (COVID-19), whose main complication is respiratory involvement, different organs may also be affected in severe cases. However, COVID-19 associated cardiovascular manifestations are limited at present. The main purpose of this study was to identify potential candidate genes involved in COVID-19-associated heart damage by bioinformatics analysis. Differently expressed genes (DEGs) were identified using transcriptome profiles (GSE150392 and GSE4172) downloaded from the GEO database. After gene and pathway enrichment analyses, PPI network visualization, module analyses, and hub gene extraction were performed using Cytoscape software. A total of 228 (136 up and 92 downregulated) overlapping DEGs were identified at these two microarray datasets. Finally, the top hub genes (FGF2, JUN, TLR4, and VEGFA) were screened out as the critical genes among the DEGs from the PPI network. Identification of critical genes and mechanisms in any disease can lead us to better diagnosis and targeted therapy. Our findings identified core genes shared by inflammatory cardiomyopathy and SARS-CoV-2. The findings of the current study support the idea that these key genes can be used in understanding and managing the long-term cardiovascular effects of COVID-19.", "claims": [{"id": "claim-method", "statement": "DEGs between SARS-CoV-2 infected hiPSC-CMs and non-infected hiPSC-CMs were screened by using Genevestigator software (Hruz et al., 2008) and the GEO2R tool (Barrett et al., 2013). p-Value <0.05 and a |log2FC| ≥ 0.5 were defined as the cut-off criteria.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34131597", "locator": "sections[4] \"Identification of DEGs\" paragraph 2", "quote": "DEGs between SARS-CoV-2 infected hiPSC-CMs and non-infected hiPSC-CMs were screened by using Genevestigator software (Hruz et al., 2008) and the GEO2R tool (Barrett et al., 2013). p-Value <0.05 and a |log2FC| ≥ 0.5 were defined as the cut-off criteria."}], "state": "complete"}, {"id": "claim-data-access", "statement": "All data used in this study are available in the GEO repository.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:34131597", "locator": "sections[13] \"Data availability\" paragraph 2", "quote": "All data used in this study are available in the GEO repository."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": null, "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:34131597"], "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. 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The overlapping hub genes in the top 10 by five ranked methods respectively in cytoHubba are highlighted in bold.", "structure": {"pmcid": "PMC8192842", "section_index": 10, "section_title": "Identification and validation of hub genes", "row_count": 11, "column_count": 10}, "table": {"label": "Table 3", "caption": "Top 10 genes evaluated in the PPI network using five calculation methods and employing CytoHubba in Cytoscape. The overlapping hub genes in the top 10 by five ranked methods respectively in cytoHubba are highlighted in bold.", "rows": [["Gene", "MCC", "Gene", "MNC", "Gene", "Degree", "Gene", "EcCentricity", "Gene", "EPC"], ["VEGFA", "610.0", "JUN", "25.0", "JUN", "26.0", "JUN", "0.17375", "VEGFA", "28.994"], ["JUN", "609.0", "VEGFA", "24.0", "VEGFA", "26.0", "VEGFA", "0.17375", "JUN", "28.507"], ["FGF2", "434.0", "CCND1", "20.0", "TLR4", "23.0", "TLR4", "0.17375", "CCND1", "27.439"], ["CCND1", "382.0", "TLR4", "20.0", "CCND1", "22.0", "FGF2", "0.17375", "TLR4", "26.957"], ["CDKN1A", "357.0", "FGF2", "17.0", "FGF2", "17.0", "LGALS3", "0.17375", "FGF2", "26.208"], ["TLR4", "285.0", "CDKN1A", "16.0", "CDKN1A", "17.0", "BIRC3", "0.17375", "CDKN1A", "26.047"], ["ITGAM", "245.0", "ITGAM", "16.0", "ITGAM", "17.0", "CXCR4", "0.17375", "ITGAM", "24.942"], ["AURKB", "216.0", "AURKB", "13.0", "PRKACA", "15.0", "DDX58", "0.17375", "LGALS3", "22.287"], ["PSMB8", "202.0", "PSMB8", "10.0", "AURKB", "13.0", "HSPB3", "0.17375", "CXCR4", "22.271"], ["PSMD12", "198.0", "THY1", "10.0", "PSMB8", "12.0", "ABCB1", "0.17375", "BIRC3", "21.898"]], "row_count": 11, "column_count": 10, "numeric_cells": 52, "header_rows": 1, "locator": "section[10]/table-wrap[1]#t0015"}}], "gaps": ["asset:data:geo:GSE150392 not downloaded", "asset:data:geo:GSE150392 licence not read", "asset:data:geo:GSE4172 not downloaded", "asset:data:geo:GSE4172 licence not read"]}, "analysis": {"state": "partial", "methods": [{"id": "method-1", "family": "network_analysis", "specific": "ppi_network", "label": "Network / pathway analysis", "dsl_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "PPI"}}, {"id": "method-2", "family": "network_analysis", "specific": "pathway_enrichment", "label": "Network / pathway analysis", "dsl_operations": ["pathway_enrich"], "missing_operations": [], "expressible": true, "parameters": {}, "data_shapes": ["edge_list"], "evidence": {"score": 0.7, "band": "medium", "quote": "pathway enrichment"}}, {"id": "method-3", "family": null, "specific": null, "label": "Genevestigator software and the GEO2R tool", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "DEGs between SARS-CoV-2 infected hiPSC-CMs and non-infected hiPSC-CMs were screened by using Genevestigator software (Hruz et al., 2008) and the GEO2R tool (Barrett et al., 2013). p-Value <0.05 and a |log2FC| ≥ 0.5 were defined as the cut-off criteria."}}], "targets": [], "gaps": ["method.reader_named_unmapped"]}, "capability": {"state": "complete", "requirements": [{"capability_id": "capability:computational_analysis", "label": "Computational analysis platform", "assay_families": ["meta_analysis", "machine_learning", "survival_analysis", "statistical_regression", "pca", "molecular_docking", "network_analysis", "differential_expression", "gwas"], "availability_state": "owned", "tier_unlocked": "R1", "candidate": "Automated Assays Laravel application and simulated adapters"}], "gaps": []}, "readiness": {"state": "complete", "tier": "R1", "blocking_axis": "none", "blocked_on": "nothing:replicable_now", "axes": {"method": {"tier": "R1", "expressible": true, "required_operations": ["build_graph", "centrality_scores", "pathway_enrich"], "missing_operations": [], "basis": "The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: ppi_network."}, "capability": {"tier": "R1", "capability_id": "capability:computational_analysis", "availability_state": "owned", "basis": "The computational analysis is owned. 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