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The present study aimed to detect the differences between breast cancer cells and normal breast cells, and investigate the potential pathogenetic mechanisms of breast cancer. The sample GSE9574 series was downloaded, and the microarray data was analyzed to identify differentially expressed genes (DEGs). Gene Ontology (GO) cluster analysis using the GO Enrichment Analysis Software Toolkit platform and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis for DEGs was conducted using the Gene Set Analysis Toolkit V2. In addition, a protein-protein interaction (PPI) network was constructed, and target sites of potential transcription factors and potential microRNA (miRNA) molecules were screened. A total of 106 DEGs were identified in the current study. Based on these DEGs, a number of bio-pathways appear to be altered in breast cancer, including a number of signaling pathways and other disease-associated pathways, as indicated by KEGG pathway clustering analysis. ATF3 , JUND , FOSB and JUNB were detected in the PPI network. Finally, the most significant potential target sites of transcription factors and miRNAs in breast cancer, which are important in the regulation of gene expression, were identified. The results indicated that miR-93, miR-302A, miR-302B, miR-302C, miR-302D, miR-372, miR-373, miR-520E and miR-520A were closely associated with the occurrence and development of breast cancer. Therefore, changes in the expression of these miRNAs may alter cell metabolism and trigger the development of breast cancer and its complications.

Integrated analysis of differentially expressed genes in breast cancer pathogenesis