Genes That Predict Poor Prognosis in Breast Cancer via Bioinformatical Analysis
Qian Zhou, Xiaofeng Liu, Mingming Lv, Erhu Sun, Xun Lu, Cheng Lu
Abstract
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: ) were found to highly expressed in breast cancer tissues with poor prognosis. They differentially expressed between different subclasses of breast cancer. CONCLUSION: ) could be potential targets for therapy in breast cancer and prediction of prognosis on the basis of bioinformatical analysis.