Exploring the Molecular Mechanism of Thoracic Aortic Aneurysm via Bioinformatics Analysis.

{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:29538353", "kind": "science-record", "revision": 9, "content_hash": "sha256:303a68e53d4e51867fb9b8da4033eb6288f964f43bfbfa1a088e486ab2c96b2f", "created_at": "2026-11-01T15:40:32Z", "updated_at": "2026-11-01T22:43:18Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Exploring the Molecular Mechanism of Thoracic Aortic Aneurysm via Bioinformatics Analysis.", "doi": "10.12659/msm.905970", "pmid": "29538353", "authors": ["Hongfang Li", "Yuzhi Zhen", "Yunshuang Geng", "Junyan Feng", "Jun Wang", "Hongsong Zhang"], "journal": "Medical science monitor : international medical journal of experimental and clinical research", "year": 2018, "abstract": "BACKGROUND The aim of this study was to identify some key genes related to the pathogenesis of thoracic aortic aneurysm (TAA) and gain more insights to the molecular mechanism of TAA. MATERIAL AND METHODS The expression profile of GSE9106 was downloaded from the Gene Expression Omnibus (GEO) database. The data contained 58 TAA peripheral blood samples and 36 normal peripheral blood samples. The differently expressed genes (DEGs) between the TAA samples and the normal samples were identified via limma package of R. Functional enrichment analysis of the DEGs were performed via the Database for Annotation, Visualization and Integrated Discovery (DAVID). The differentially co-expressed genes in TAA samples compared to normal samples were identified via the DCGL package in R. The protein-protein interaction (PPI) network of the DEGs was constructed through the Search Tool for the Retrieval of Interacting Proteins (STARING) database and visualized by Cytoscape software. RESULTS A total of 407 DEGs were obtained in TAA samples compared with normal samples. The DEGs were enriched in 29 Gene Ontology (GO) terms. There were 1,441 co-expression gene pairs that had significant changes in the co-expression status in TAA samples compared with normal samples and a differential co-expression network was constructed based on them. Moreover, a PPI network of the DEGs was constructed, containing 101 nodes. CONCLUSIONS Bioinformatics methods could identify significant biological processes and genes related to TAA. 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