Background To assess the mRNAs expression profile and explore the hub mRNAs and potential molecular mechanisms in the pathogenesis of human thoracic aortic dissection (TAD). Methodology: mRNA microarray expression signatures of TAD tissues (n=6) and no TAD tissues (NT;n=6) were analyzed by Arraystar human mRNAs microarray. Real-time PCR (qRT-PCR) were used to validate the result of mRNAs microarray. Bioinformatic tools including gene ontology, and Kyoto Encyclopedia of Genes and Genomes pathway analysis were utilized. The protein-protein interaction networks were constructed based on data from the STRING database. Molecular Complex Detection (MCODE) and cytohubba analysis were used to infer the most hug gene and pathways. Results: The top 10 hub genes CDK1, CDC20, CCNB2, CCNB1, MAD2L1, AURKA, C3AR1, NCAPG,CXCL12 and ASPM were identified from the PPI network. Module analysis revealed that TAD was associated with cell cycle, oocyte meiosis, p53 signaling pathway, progesterone-mediated oocyte maturation. The qRT-PCR result showed that the expression of all hug genes was significantly increased in TAD samples (p < 0.05). Conclusions: These candidate genes could be used as potential diagnostic biomarkers and therapeutic targets of TAD. Author summary Many basic characteristics underlying the establishment of aortic dissection have not been studied in detail. The presented work sought to understand the pathogenesis of human thoracic aortic dissection by employing bioinformatic tools to explore the hub mRNAs and potential molecular mechanisms of thoracic aortic dissection. Many pathway were thought to have relevant with this disease, but the most important pathway was not define. We used bio-mathematical analysis to explore the potential functions in thoracic aortic dissection and identified the hub genes and explored the intrinsic molecular mechanisms involved in thoracic aortic dissection between two microarray analysis. Finally, we indentified the cell cycle maybe the key pathway in thoracic aortic dissection.
- Downloaded 300 times
- Download rankings, all-time:
- Site-wide: 106,698
- In bioinformatics: 9,001
- Year to date:
- Site-wide: 152,492
- Since beginning of last month:
- Site-wide: 112,986
Downloads over time
Distribution of downloads per paper, site-wide
- 27 Nov 2020: The website and API now include results pulled from medRxiv as well as bioRxiv.
- 18 Dec 2019: We're pleased to announce PanLingua, a new tool that enables you to search for machine-translated bioRxiv preprints using more than 100 different languages.
- 21 May 2019: PLOS Biology has published a community page about Rxivist.org and its design.
- 10 May 2019: The paper analyzing the Rxivist dataset has been published at eLife.
- 1 Mar 2019: We now have summary statistics about bioRxiv downloads and submissions.
- 8 Feb 2019: Data from Altmetric is now available on the Rxivist details page for every preprint. Look for the "donut" under the download metrics.
- 30 Jan 2019: preLights has featured the Rxivist preprint and written about our findings.
- 22 Jan 2019: Nature just published an article about Rxivist and our data.
- 13 Jan 2019: The Rxivist preprint is live!