Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network.
Background and Objective. Anaplastic thyroid cancer (ATC) gains the definition as an aggressive tumor that has been found in human beings. It has been put in researches that complex gene interaction networks exert an influence on ATC tumor in terms of occurrence and prognosis. Therefore, this study...
| Publicado en: | BioMed Research International pp. 1 - 18 |
|---|---|
| Autores principales: | , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
6/23/2022
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=157685359&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 157685359 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 6/23/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 157685359 157685359 157685359 10.1155/2022/2244228 157685359 ppf: 1 ppct: 17 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Recognition of Key Genes in Human Anaplastic Thyroid Cancer via the Weighing Gene Coexpression Network. aug: au: Gong, Yun Xu, Fanghua Deng, Lifei Peng, Lifen affil: Health Management Center, Jiangxi Provincial People's Hospital (the First Affiliated Hospital of Nanchang Medical College), Nanchang, Jiangxi 330006, China sug: subj: Thyroid Carcinoma, Anaplastic Physiopathology Thyroid Carcinoma, Anaplastic Prognosis Genes Classification Gene Expression Profiling Human Metabolic Networks and Pathways Bioinformatics Oncogenes Classification Gene Expression Ontologies Thyroid Hormones Metabolism Amino Acids Metabolism Neoplastic Processes ab: Background and Objective. Anaplastic thyroid cancer (ATC) gains the definition as an aggressive tumor that has been found in human beings. It has been put in researches that complex gene interaction networks exert an influence on ATC tumor in terms of occurrence and prognosis. Therefore, this study is conducted with the purpose of recognizing possible key genes that have relation with prognosis and pathogenesis of ATC. Methods. For determining pathways and key genes that have relation with development of ATC, differentially expressed genes (DEGs) from GSE33630 as well as GSE65144 expression microarray were screened. Furthermore, we also worked on carrying out the task of constructing a protein-protein interaction (PPI) network and the work of weighing gene coexpression network (WGCNA). DAVID was utilized for the performance of the Gene Ontology (GO) as well as KEGG pathway enrichment analyses for DEGs. We used TCGA THCA data and GSE53072 to further verify the hub gene and hub pathway. Results. We came to the conclusion of the recognition of a total of 1063 genes as DEGs. Analysis regarding functional and pathway enrichment showed that there existed a notable enrichment of upregulated DEGs in the organization of extracellular structure and matrix organization, as well as in organelle fission and nuclear division. The downregulated DEG was markedly gathered in the thyroid hormone metabolic process and generation, as well as in the metabolic process of cellular modified amino acid. We identified 10 hub genes (CXCL8, CDH1, AURKA, CCNA2, FN1, CDK1, ITGAM, CDC20, MMP9, and KIF11) through the PPI network, which might be strongly linked to the carcinogenesis and the development of ATC. In the coexpression network, 6 modules that were relevant to ATC were recognized. The modules were related to the interaction of signaling pathway of p53, Hippo, PI3K/Akt, and ECM-receptor. This hub genes and hub pathway were further successfully validated as a potential biomarker for carcinogenesis and prediction in another database GSE53072. Conclusion. To summarize, this research displayed an illustration of hub genes and pathways that had relation with ATC development, which suggested that DEGs and hub genes, recognized on the basis of bioinformatics analyses, were valuable in the diagnosis for patients with ATC. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|