Identification of the Genetic Influence of SARS-CoV-2 Infections on IgA Nephropathy Based on Bioinformatics Method.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:37040729", "kind": "science-record", "revision": 3, "content_hash": "sha256:39f2f613e5ea44cf5b5a62d3a9af7df325072c61b034b1110a49db46f3e74610", "created_at": "2026-11-01T19:01:41Z", "updated_at": "2026-11-01T22:09:14Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of the Genetic Influence of SARS-CoV-2 Infections on IgA Nephropathy Based on Bioinformatics Method.", "doi": "10.1159/000529687", "pmid": "37040729", "authors": ["Xiaoqi Deng", "Yu Luo", "Tianjun Guan", "Xiaodan Guo"], "journal": "Kidney & blood pressure research", "year": 2023, "abstract": "INTRODUCTION: Coronavirus disease-2019 (COVID-19) is caused by severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection. It was initially detected in Wuhan, China, in December 2019. In March 2020, the World Health Organization (WHO) declared COVID-19 a global pandemic. Compared to healthy individuals, patients with IgA nephropathy (IgAN) are at a higher risk of SARS-CoV-2 infection. However, the potential mechanisms remain unclear. This study explores the underlying molecular mechanisms and therapeutic agents for the management of IgAN and COVID-19 using the bioinformatics and system biology way.\nMETHODS: We first downloaded GSE73953 and GSE164805 from the Gene Expression Omnibus (GEO) database to obtain common differentially expressed genes (DEGs). Then, we performed the functional enrichment analysis, pathway analysis, protein-protein interaction (PPI) analysis, gene regulatory networks analysis, and potential drug analysis on these common DEGs.\nRESULTS: We acquired 312 common DEGs from the IgAN and COVID-19 datasets and used various bioinformatics tools and statistical analyses to construct the PPI network to extract hub genes. Besides, we performed gene ontology (GO) and pathway analyses to reveal the common correlation between IgAN and COVID-19. Finally, on the basis of common DEGs, we determined the interactions between DEGs-miRNAs, the transcription factor-genes (TFs-genes), protein-drug, and gene-disease networks.\nCONCLUSION: We successfully identified hub genes that may act as biomarkers of COVID-19 and IgAN and also screened out some potential drugs to provide new ideas for COVID-19 and IgAN treatment.", "claims": [{"id": "claim-method", "statement": "First, we used the GEO.2R [17] web tool to acquire the DEGs, where |logFC|≥2.4 and p value < 0.05.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37040729", "locator": "sections[4] \"Identification of Differentially Expressed Genes and Common Differentially Expressed Genes\" paragraph 2", "quote": "First, we used the GEO.2R [17] web tool to acquire the DEGs, where |logFC|≥2.4 and p value < 0.05."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The data that support the findings of this study are available in the Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/, reference number [14]. Further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:37040729", "locator": "sections[24] \"Data Availability Statement\" paragraph 2", "quote": "The data that support the findings of this study are available in the Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/, reference number [14]. Further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "The study identified 312 common differentially expressed genes between IgAN and COVID-19.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:37040729", "locator": "sections[11] \"Identification of DEGs and Common DEGs between IgAN and COVID-19\" paragraph 2", "quote": "Of these, 312 common DEGs were identified using the Venn diagram, as shown in Figure 2."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "The study identified 312 common differentially expressed genes between IgAN and COVID-19.", "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:37040729"], "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": "blocked", "blocked_on": "data:accession_absent", "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": [], "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": [], "outputs": ["the replicated result and its comparison"], "instruments": [], "reagents": []}], "materials": [], "instruments": [], "reagents": [], "conditions": {}, "gaps": ["step:obtain_source_data blocked on data:accession_absent"]}, "data": {"state": "partial", "assets": [{"id": "source:pmid:37040729", "role": "manuscript", "format": "xml", "media_type": "application/xml", "uri": "https://doi.org/10.1159/000529687", "sha256": "sha256:5074b4eb15b2ac7d413c251b9327bdd6d917960877a2cefab5e53742cc39d065", "bytes": 32530, "access": "open", "state": "resolved", "reader": null, "reader_status": "reader_required", "identifier": "10.1159/000529687", "version": null, "note": "Full article fetched from PMC; the checksum covers the rendered section body, not the original XML bytes.", "structure": {"pmcid": "PMC10308545", "source": "europe_pmc_fulltext", "coverage": "whole_body_without_references", "text_hash_scope": "rendered_section_body"}}], "gaps": []}, "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": "protein-protein interaction"}}, {"id": "method-2", "family": null, "specific": null, "label": "GEO.2R", "dsl_operations": [], "missing_operations": [], "expressible": null, "parameters": {}, "data_shapes": [], "evidence": {"score": 1.0, "band": "verbatim", "quote": "First, we used the GEO.2R [17] web tool to acquire the DEGs, where |logFC|≥2.4 and p value < 0.05."}}], "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": "blocked", "tier": "R4", "blocking_axis": "data", "blocked_on": "data:accession_absent", "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. The physical bench the paper used is not, so a physical validation cannot be run here."}, "data": {"tier": "R4", "reachable": false, "accessions": [], "resolved_accessions": [], "statements": ["The data that support the findings of this study are available in the Gene Expression Omnibus at https://www.ncbi.nlm.nih.gov/geo/, reference number [14]. 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