Identification of grade-specific diagnostic and prognostic biomarkers of key candidate genes and pathways in breast cancer by integrated bioinformatics analysis

Purpose: Breast cancer (BC) is the most common malignant tumor in women. Due to the mechanism of BC has not yet been completely clear, we aim to identify the key pathway and genes in BC based on bioinformatics method. Methods: Samples were obtained from NCBI-GEO website. Then, GEO2R tools and Venn diagram software were used to identify the differentially expressed genes (DEGs). Next, analyze Kyoto Encyclopedia of Gene and Genome (KEGG) pathway and gene ontology (GO) were analyzed by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction (PPI) network was drew by Search Tool for the Retrieval of Interacting Genes (STRING). Afterwards, we selected the core genes from PPI network by Molecular Complex Detection (MCODE) plug-in. And we performed Kaplan-Meier analysis to assess the overall survival of the core genes. Last Gene Expression Profiling Interactive Analysis (GEPIA) was used to discover highly expressed genes in BC. Results: DEGs contained 23 up-regulated and 32 down-regulated genes. GO described molecular function (MF), cellular component (CC), and biological process (BP). KEGG pathway showed DEGs were mainly involved in ECM-receptor interaction, p53 signaling pathway, PPAR signaling pathway, signaling pathways regulating pluripotency of stem cells, cGMP-PKG signaling pathway and Tyrosine metabolism. Finally, we screened 15 core genes, 14 of which had adverse prognosis and high expression in BC. Conclusions: In the current study, 14 core genes of BC were identified based on bioinformatics method, which could useful to provide essential information for early diagnosis and treatment of BC.