Identifying HIF1A and HGF as two hub genes in aortic dissection and function analysis by integrating RNA sequencing and single-cell RNA sequencing data.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39479394", "kind": "science-record", "revision": 4, "content_hash": "sha256:d37cb15b093a0540f448d675e22d2f7351f6031de64e143d4b3acd1fe485b495", "created_at": "2026-11-01T20:29:44Z", "updated_at": "2026-11-01T22:55:31Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identifying HIF1A and HGF as two hub genes in aortic dissection and function analysis by integrating RNA sequencing and single-cell RNA sequencing data.", "doi": "10.3389/fcvm.2024.1475991", "pmid": "39479394", "authors": ["Hai-Bing Li", "Chang Liu", "Xiang-Di Mao", "Shu-Zheng Yuan", "Li Li", "Xin Cong"], "journal": "Frontiers in cardiovascular medicine", "year": 2024, "abstract": "Objective: Aortic dissection (AD) is a severe aortic disease with high mortality, and its pathogenesis remains elusive. To explore the regulatory mechanisms of AD, we integrated public RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets to screen the hub genes of AD and further analyzed their functions, which may provide references to the diagnosis and treatment of AD.\nMethods: Four AD-related datasets were obtained from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis and differential expression analysis were applied to identify overlapping genes in dataset GSE153434. Protein-protein interaction (PPI) network was constructed based on overlapping genes. Five methods (closeness, degree, EPC, MCC, and MNN) were used to pick hub genes. The receiver operating characteristic curve was used to evaluate the diagnostic efficiency of the hub genes in extra datasets GSE98770 and GSE52093. scRNA-seq dataset GSE213740 was used to explore the expression and function of the hub genes at the single-cell level. Quantitative real-time polymerase chain reaction was used to verify the expression of hub genes in beta-aminopropionitrile (BAPN)-induced mouse thoracic aortic aneurysm and dissection (TAAD) model.\nResults: A total of 71 overlapping genes were screened by intersecting the significant genes in the pink module and the differentially expressed genes. A PPI network with 45 nodes and 74 edges was generated, and five top hub genes (HIF1A, HGF, HMOX1, ITGA5, and ITGB3) were identified. All the hub genes had area under the curve values above 0.55. scRNA-seq data analysis showed that HIF1A was significantly upregulated in macrophages and HGF was significantly upregulated in vascular smooth muscle cells (SMCs) of the ascending aortas in AD patients. HIF1A may transcriptionally regulate multiple downstream target genes involving inflammation (TLR2, ALOX5AP, and MIF), glycolysis (ENO1, LDHA, and GAPDH), tissue remodeling (PLAU), and angiogenesis (SERPIN and VEGFA). HGF may participate in the signaling among SMCs, fibroblasts, and endothelial cells through binding to different receptors (MET, EGFR, IGF1R, and KDR). The mRNA expression of Hif1a, Hgf, and their target genes, including Alox5ap, Serpine1, Tlr2, Plau, Egfr, and Igf1r, was significantly upregulated in aortic tissues of BAPN-treated mice.\nConclusion: By integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD. HIF1A in macrophages may promote AD formation by promoting inflammation, glycolysis, tissue remodeling, and angiogenesis, and HGF may mediate signaling among SMCs, fibroblasts, and endothelial cells in the development of AD.", "claims": [{"id": "claim-method", "statement": "GEO2R online tool and limma R package (version 3.58.1) were used to analyze DEGs of GSE153434, GSE98770, and GSE52093 datasets (19).", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39479394", "locator": "sections[5] \"Differentially expressed gene analysis\" paragraph 2", "quote": "GEO2R online tool and limma R package (version 3.58.1) were used to analyze DEGs of GSE153434, GSE98770, and GSE52093 datasets (19)."}], "state": "complete"}, {"id": "claim-data-access", "statement": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:39479394", "locator": "sections[24] \"Data availability statement\" paragraph 2", "quote": "The original contributions presented in the study are included in the article/Supplementary Material, further inquiries can be directed to the corresponding author."}], "state": "complete"}, {"id": "claim-target-result", "statement": "By integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:39479394", "locator": "sections[0] \"Abstract\" paragraph 4", "quote": "By integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "complete", "objective": "By integrating RNA-seq and scRNA-seq data, we identified HIF1A and HGF as two hub genes with good diagnostic efficiency for AD.", "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:39479394"], "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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Conclusion: By integrating RNA-seq and scRNA-seq data, we identified HIF1A and"], "order_basis": "paper_text_order:56", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["n analysis by integrating RNA sequencing and single-cell RNA sequencing data.", "tegrated public RNA sequencing (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets to screen the hub genes", "ng (RNA-seq) and single-cell RNA sequencing (scRNA-seq) datasets to screen the hub genes of AD and", "nes in extra datasets GSE98770 and GSE52093. scRNA-seq dataset GSE213740 was used to explore the ex"], "order_basis": "paper_text_order:75", "id": "assay:2"}, {"role": "data_generation", "definition_id": "animal_model", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "partial", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.69, "evidence": ["es in beta-aminopropionitrile (BAPN)-induced mouse thoracic aortic aneurysm and dissection (TAA"], "order_basis": "paper_text_order:1322", "id": "assay:3"}, {"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": ["GEO2R online tool and limma R package (version 3.58.1) were used to analyze DEGs of GSE153434, GSE98770, and GSE52093 datasets (19)."], "order_basis": "consumes_earlier_output", "id": "assay:4"}]}