Uncovering endothelial to mesenchymal transition drivers in atherosclerosis via multi-omics analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39956907", "kind": "science-record", "revision": 4, "content_hash": "sha256:6b529acbd18696d29fdbdf9d1ee70e3431a2b15c9467db8e27ac95935078e16c", "created_at": "2026-11-01T20:30:13Z", "updated_at": "2026-11-01T22:56:08Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Uncovering endothelial to mesenchymal transition drivers in atherosclerosis via multi-omics analysis.", "doi": "10.1186/s12872-025-04571-5", "pmid": "39956907", "authors": ["Qingyan Huang", "Yuhong Gan", "Xiaoqi Zheng", "Zhikang Yu", "Qionghui Huang", "Mingfeng Huang"], "journal": "BMC cardiovascular disorders", "year": 2025, "abstract": "PURPOSE: This study aimed to identify novel candidates that regulate Endothelial to mesenchymal transition(EndMT) in atherosclerosis by integrating multi-omics data.\nMETHODS: The single-cell RNA sequencing (scRNA-seq) dataset GSE159677, bulk RNA-seq dataset GSE118446 and microarray dataset GSE56309 were obtained from the Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) were used for downscaling and cluster identification. Differentially expressed genes (DEGs) from GSE118446 and GSE56309 were analyzed using limma package. Functional enrichment analysis was applied by DAVID functional annotation tool. Quantitative real-time polymerase chain reaction (qPCR) and western blotting were used for further validation.\nRESULTS: Nine endothelial cell (EC) clusters were identified in human plaques, with EC cluster 5 exhibiting an EndMT phenotype. The intersection of genes from EC cluster 5 and common DEGs in vitro EndMT models revealed seven mesenchymal candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1. Validation of these findings was carried out through qPCR analysis.\nCONCLUSION: Through the integration of multi-omics data using bioinformatics methods, our study identified seven novel EndMT candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1.", "claims": [{"id": "claim-method", "statement": "The DEGs were analyzed using limma package (Version 3.5.1).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39956907", "locator": "sections[4] \"Microarray and bulk sequencing data processing\" paragraph 2", "quote": "The DEGs were analyzed using limma package (Version 3.5.1)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The datasets analyzed during the current study are publicly availability.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39956907", "locator": "sections[23] \"notes\" paragraph 1", "quote": "The datasets analyzed during the current study are publicly availability."}], "state": "complete"}, {"id": "claim-target-result", "statement": "Through the integration of multi-omics data using bioinformatics methods, our study identified seven novel EndMT candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39956907", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "Through the integration of multi-omics data using bioinformatics methods, our study identified seven novel EndMT candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "Through the integration of multi-omics data using bioinformatics methods, our study identified seven novel EndMT candidates: PTGS2, TPM1, SERPINE1, FN1, RASD1, SEMA3C, and ESM1.", "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:39956907"], "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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METHODS: The single-cell RNA sequencing (scRNA-seq) dataset GSE159677, bulk RNA-seq", "ta. METHODS: The single-cell RNA sequencing (scRNA-seq) dataset GSE159677, bulk RNA-seq dataset GSE", "methods ScRNA sequencing data processing The scRNA-seq data of GSE159677 was retrieved from the Gen", ", 100-35B-10) for 48 h. Statistical analysis ScRNA-seq and transcriptional data analysis was perfor"], "order_basis": "paper_text_order:236", "id": "assay:1"}, {"role": "data_generation", "definition_id": "qpcr", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["itative real-time polymerase chain reaction (qPCR) and western blotting were used for further", "on of these findings was carried out through qPCR analysis. CONCLUSION: Through the integratio", "rsion 4.2.2). Unpaired t test and graphs for qPCR data were performed by GraphPad Prism softwa"], "order_basis": "paper_text_order:762", "id": "assay:2"}, {"role": "data_generation", "definition_id": "western_blot", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.93, "evidence": ["al-time polymerase chain reaction (qPCR) and western blotting were used for further validation. RESULTS: N"], "order_basis": "paper_text_order:772", "id": "assay:3"}, {"role": "data_generation", "definition_id": "cell_culture", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.77, "evidence": ["SCIENTIFIC (71,074, Jiangsu, China) and were cultured in ECM ( Sciencell, 1001) supplemented with 5%"], "order_basis": "paper_text_order:3088", "id": "assay:4"}, {"role": "analysis", "definition_id": "differential_expression", "definition_version": "f2132e96ae6c7a4e", "definition_digest": null, "required_operations": ["normalise_counts", "differential_expression_fit", "fdr_bh"], "support_state": "supported", "source_hashes": {"dsl_catalogue": "f2132e96ae6c7a4e"}, "confidence": 1.0, "evidence": ["The DEGs were analyzed using limma package (Version 3.5.1)."], "order_basis": "consumes_earlier_output", "id": "assay:5"}]}