Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:38335213", "kind": "science-record", "revision": 4, "content_hash": "sha256:456d930435305549f72ed556dee51ae0e587b5531a0b1af86f8cf397d470fc33", "created_at": "2026-11-01T19:20:16Z", "updated_at": "2026-11-01T22:53:39Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Identification of abdominal aortic aneurysm subtypes based on mechanosensitive genes.", "doi": "10.1371/journal.pone.0296729", "pmid": "38335213", "authors": ["Chang Sheng", "Qin Zeng", "Weihua Huang", "Mingmei Liao", "Pu Yang"], "journal": "PloS one", "year": 2024, "abstract": "BACKGROUND: Rupture of abdominal aortic aneurysm (rAAA) is a fatal event in the elderly. Elevated blood pressure and weakening of vessel wall strength are major risk factors for this devastating event. This present study examined whether the expression profile of mechanosensitive genes correlates with the phenotype and outcome, thus, serving as a biomarker for AAA development.\nMETHODS: In this study, we identified mechanosensitive genes involved in AAA development using general bioinformatics methods and machine learning with six human datasets publicly available from the GEO database. Differentially expressed mechanosensitive genes (DEMGs) in AAAs were identified by differential expression analysis. Molecular biological functions of genes were explored using functional clustering, Protein-protein interaction (PPI), and weighted gene co-expression network analysis (WGCNA). According to the datasets (GSE98278, GSE205071 and GSE165470), the changes of diameter and aortic wall strength of AAA induced by DEMGs were verified by consensus clustering analysis, machine learning models, and statistical analysis. In addition, a model for identifying AAA subtypes was built using machine learning methods.\nRESULTS: 38 DEMGs clustered in pathways regulating 'Smooth muscle cell biology' and 'Cell or Tissue connectivity'. By analyzing the GSE205071 and GSE165470 datasets, DEMGs were found to respond to differences in aneurysm diameter and vessel wall strength. Thus, in the merged datasets, we formally created subgroups of AAAs and found differences in immune characteristics between the subgroups. Finally, a model that accurately predicts the AAA subtype that is more likely to rupture was successfully developed.\nCONCLUSION: We identified 38 DEMGs that may be involved in AAA. This gene cluster is involved in regulating the maximum vessel diameter, degree of immunoinflammatory infiltration, and strength of the local vessel wall in AAA. The prognostic model we developed can accurately identify the AAA subtypes that tend to rupture.", "claims": [{"id": "claim-method", "statement": "Briefly, the ‘limma’ R algorithm was used to identify DEGs.", "kind": "methodological", "target": null, "evidence": [{"asset": "source:pmid:38335213", "locator": "sections[5] \"2.3 Bioinformatics and statistical analysis\" paragraph 2", "quote": "Briefly, the ‘limma’ R algorithm was used to identify DEGs."}], "state": "complete"}, {"id": "claim-target-result", "statement": "A total of 38 DEMGs were found among the DEGs, of which 8 were upregulated and 30 were downregulated.", "kind": "qualitative", "target": null, "evidence": [{"asset": "source:pmid:38335213", "locator": "sections[7] \"3.1 Identification of DEMGs\" paragraph 2", "quote": "A total of 38 DEMGs were found among the DEGs, of which 8 were upregulated and 30 were downregulated (Fig 2C and S4 Table in S1 File)."}], "state": "complete"}], "gaps": []}, "experiment": {"state": "partial", "objective": "A total of 38 DEMGs were found among the DEGs, of which 8 were upregulated and 30 were downregulated.", "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:38335213"], "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. 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