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Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis.
Zhou, Qian; Liu, Xiaofeng; Lv, Mingming; Sun, Erhu; Lu, Xun; Lu, Cheng.
Afiliación
  • Zhou Q; Department of Breast, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210004, China.
  • Liu X; Department of Breast, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210004, China.
  • Lv M; Department of Breast, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210004, China.
  • Sun E; Department of Breast, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210004, China.
  • Lu X; School of Public Health, Yale University, New Haven, CT 06520, USA.
  • Lu C; Department of Breast, Women's Hospital of Nanjing Medical University, Nanjing Maternity and Child Health Care Hospital, Nanjing 210004, China.
Biomed Res Int ; 2021: 6649660, 2021.
Article en En | MEDLINE | ID: mdl-33959662
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.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Biología Computacional Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Biomed Res Int Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos
Texto completo
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XML
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Neoplasias de la Mama / Biología Computacional Tipo de estudio: Diagnostic_studies / Prognostic_studies / Risk_factors_studies Límite: Female / Humans Idioma: En Revista: Biomed Res Int Año: 2021 Tipo del documento: Article País de afiliación: China Pais de publicación: Estados Unidos