Bioinformatic analysis of differential expression and core GENEs in breast cancer

Abstract

Breast cancer (BRCA) is one of the most common malignancies in women. The gene expression profile of GSE103512 from the GEO database was downloaded in order to find key genes involved in the occurrence and development of BRCA. 75 samples, including 65 cancer and 10 normal samples, were included in this analysis. Differentially expressed genes (DEGs) between BRCA patients and health people were chosen using R tool. We next performed gene ontology (GO) analysis and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway analysis using the Database for Annotation, Visualization and Integrated Discovery (DAVID). Moreover, Cytoscape with Search Tool for the Retrieval of Interacting Genes (STRING) was utilized to visualize protein-protein interaction (PPI) of these DEGs. The related genes and medicines specific to hub genes were predicted by CBioportal. We screened a total of 357 DEGs including 77 up-regulated and 280 down-regulated. A series of BRCA related GO terms and pathways were identified by analysis of these DEGs. Insulin-like growth factor 1 (IGF1); epidermal growth factor receptor (EGFR); v-jun avian sarcoma virus 17 oncogene homolog (JUN) and Estrogen Receptor 1 (ESR1) of the DEGs were screened by construction of the PPI network and the degree of connectivity. IGF1 and ESR1 were finally selected as potential hub genes and treatment targets of BRCA. In conclusion, this bioinformatics analysis demonstrated that DEGs and hub genes, such as IGF1, might regulate the development of gastric cancer. These DEGs could be used as new biomarkers for diagnosis and to guide the combination medicine of BRCA.

Keywords: Breast cancer; bioinformatics analysis; biomarker; differential expression genes; therapeutic.

Figure 1

DEG screening of GSE 103512 datasets. Cassette figures before (A) and after (B) data standardization. (C) Hierarchical clustering of the first 150 DEGs. The color scale shown at the top illustrates the relative expression level of an mRNA. Red color represents a high relative expression level and a green color represents a low relative expression level.

Figure 2

GO and KEGG pathway analysis of DEGs using-log p. A. Analysis of molecular function enrichment. B. Analysis of biological process enrichment. C. Analysis of cellular component enrichment. D. Analysis of KEGG pathway analysis.

Figure 3

PPI network construction of DEGs. The yellow node in the network represents the core node with degree ≥ 25.

Figure 4

Prognostic value of four genes (IGF1 (A), ESR1 (B), JUN (C), EGFR (D)) in BRCA patients. The desired Affymetrix IDs are valid: 209541_at (IGF1), 201464_x_at (ESR1), 202311_s_at (JUN), 1565483_at (EGFR). HR: hazard ratio, CI: confidence interval.

Figure 5

Expression level of IGF1 (A) and ESR1 (B) in cancer and normal tissues. BRCA: breast cancer, T: tumor tissues, N: normal tissues, *P < 0.05.

Figure 6

Medicine and regulatory factors of IGF1 and ESR1 prediction. Pink circles: genes, white hexagon: drugs not approved by FDA, yellow hexagon: FDA approved drugs, Pink arrows: transport controls, turquoise arrows: phosphorylation controls, green arrows: controls expression, yellow lines: targeted by drug.