Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis.
Background. Breast cancer is one of the most commonly diagnosed cancers all over the world, and it is now the leading cause of cancer death among females. The aim of this study was to find DEGs (differentially expressed genes) which can predict poor prognosis in breast cancer and be effective target...
| Publicado en: | BioMed Research International pp. 1 - 9 |
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| Autores principales: | , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
4/19/2021
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| 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=149888064&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 149888064 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 4/19/2021 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 149888064 149888064 149888064 10.1155/2021/6649660 149888064 ppf: 1 ppct: 8 formats: fmt: @attributes: type: P tig: atl: Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis. aug: au: Zhou, Qian Liu, Xiaofeng Lv, Mingming Sun, Erhu Lu, Xun Lu, Cheng affil: Department of Breast, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210004, China sug: subj: Breast Neoplasms Prognosis Gene Expression Profiling Bioinformatics Human Female Cancer Patients Cell Line, Tumor Analysis Gene Expression Proteins Genome, Human Metabolic Networks and Pathways Kaplan-Meier Estimator Female ab: Background. Breast cancer is one of the most commonly diagnosed cancers all over the world, and it is now the leading cause of cancer death among females. The aim of this study was to find DEGs (differentially expressed genes) which can predict poor prognosis in breast cancer and be effective targets for breast cancer patients via bioinformatical analysis. Methods. GSE86374, GSE5364, and GSE70947 were chosen from the GEO database. DEGs between breast cancer tissues and normal breast tissues were picked out by GEO2R and Venn diagram software. Then, DAVID (Database for Annotation, Visualization, and Integrated Discovery) was used to analyze these DEGs in gene ontology (GO) including molecular function (MF), cellular component (CC), and biological process (BP) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway. Next, STRING (Search Tool for the Retrieval of Interacting Genes) was used to investigate potential protein-protein interaction (PPI) relationships among DEGs and these DEGs were analyzed by Molecular Complex Detection (MCODE) in Cytoscape. After that, UALCAN, GEPIA (gene expression profiling interactive analysis), and KM (Kaplan–Meier plotter) were used for the prognostic information and core genes were qualified. Results. There were 96 upregulated genes and 98 downregulated genes in this study. 55 upregulated genes were selected as hub genes in the PPI network. For validation in UALCAN, GEPIA, and KM, 5 core genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) were found to highly expressed in breast cancer tissues with poor prognosis. They differentially expressed between different subclasses of breast cancer. Conclusion. These five genes (KIF4A, RACGAP1, CKS2, SHCBP1, and HMMR) could be potential targets for therapy in breast cancer and prediction of prognosis on the basis of bioinformatical analysis. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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