Analysis of Differentially Expressed Genes and Molecular Pathways in Familial Hypercholesterolemia Involved in Atherosclerosis: A Systematic and Bioinformatics Approach.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:32760426", "kind": "science-record", "revision": 4, "content_hash": "sha256:da0e955cfe1e6c57e65a1766c648bcbfccd99fdca5e4b122a4897ab0df0db6ac", "created_at": "2026-11-01T15:41:07Z", "updated_at": "2026-11-01T21:23:35Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Analysis of Differentially Expressed Genes and Molecular Pathways in Familial Hypercholesterolemia Involved in Atherosclerosis: A Systematic and Bioinformatics Approach.", "doi": "10.3389/fgene.2020.00734", "pmid": "32760426", "authors": ["S Udhaya Kumar", "D Thirumal Kumar", "R Bithia", "Srivarshini Sankar", "R Magesh", "Mariem Sidenna", "C George Priya Doss", "Hatem Zayed"], "journal": "Frontiers in genetics", "year": 2020, "abstract": "Background and Aims: Familial hypercholesterolemia (FH) is one of the major risk factor for the progression of atherosclerosis and coronary artery disease. This study focused on identifying the dysregulated molecular pathways and core genes that are differentially regulated in FH and to identify the possible genetic factors and potential underlying mechanisms that increase the risk to atherosclerosis in patients with FH. Methods: The Affymetrix microarray dataset (GSE13985) from the GEO database and the GEO2R statistical tool were used to identify the differentially expressed genes (DEGs) from the white blood cells (WBCs) of five heterozygous FH patients and five healthy controls. The interaction between the DEGs was identified by applying the STRING tool and visualized using Cytoscape software. MCODE was used to determine the gene cluster in the interactive networks. The identified DEGs were subjected to the DAVID v6.8 webserver and ClueGo/CluePedia for functional annotation, such as gene ontology (GO) and enriched molecular pathway analysis of DEGs. Results: We investigated the top 250 significant DEGs (p-value < 0.05; fold two change ≥ 1 or ≤ -1). The GO analysis of DEGs with significant differences revealed that they are involved in critical biological processes and molecular pathways, such as myeloid cell differentiation, peptidyl-lysine modification, signaling pathway of MyD88-dependent Toll-like receptor, and cell-cell adhesion. The analysis of enriched KEGG pathways revealed the association of the DEGs in ubiquitin-mediated proteolysis and cardiac muscle contraction. The genes involved in the molecular pathways were shown to be differentially regulated by either activating or inhibiting the genes that are essential for the canonical signaling pathways. Our study identified seven core genes (UQCR11, UBE2N, ADD1, TLN1, IRAK3, LY96, and MAP3K1) that are strongly linked to FH and lead to a higher risk of atherosclerosis. Conclusion: We identified seven core genes that represent potential molecular biomarkers for the diagnosis of atherosclerosis and might serve as a platform for developing therapeutics against both FH and atherosclerosis. However, functional studies are further needed to validate their role in the pathogenesis of FH and atherosclerosis.", "claims": [{"id": "claim-method", "statement": "The GEO2R inbuilt methods, such as T-test and Benjamini and Hochberg (false discovery rate), were used to calculate the p-value and FDR in order to determine the DEGs among patients with FH and controls (Aubert et al., 2004).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32760426", "locator": "sections[4] \"Data Preprocessing and Identification of DEGs\" paragraph 2", "quote": "The GEO2R inbuilt methods, such as T-test and Benjamini and Hochberg (false discovery rate), were used to calculate the p-value and FDR in order to determine the DEGs among patients with FH and controls (Aubert et al., 2004)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The GEO database from NCBI (Gene Expression Omnibus database, https://www.ncbi.nlm.nih.gov/geo/) was used to access the GSE13985 dataset.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:32760426", "locator": "sections[14] \"Data Availability Statement\" paragraph 2", "quote": "The GEO database from NCBI (Gene Expression Omnibus database, https://www.ncbi.nlm.nih.gov/geo/) was used to access the GSE13985 dataset."}], "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:32760426"], "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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