Bioinformatics analysis of aggressive behavior of breast cancer via an integrated gene regulatory network.

Background: Breast cancer is one of the most frequently diagnosed cancers in women. Though death from this disease is mainly caused by the metastases of the aggressive cancer cells, few studies have expounded the aggressive behavior of breast cancer.

Materials And Methods: We downloaded the gene expression profiles of GSE40057, including four aggressive and six less-aggressive breast cancer cell lines, from Gene Expression Omnibus and identified the differentially expressed genes (DEGs) between the aggressive and less-aggressive samples. An integrated gene regulatory network was built including DEGs, microRNAs (miRNAs), and transcription factors. Then, motifs and modules of the network were identified. Modules were further analyzed at a functional level using Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway to study the aggressive behavior of breast cancer.

Results: A total of 764 DEGs were found and two modules were filtered from the integrated gene regulatory network. Totally two motifs and modules for DEGs were identified. Significant GO terms associated with cell proliferation and hormone stimulus of the modules were found and the target genes identified were  CAV1, CD44, and TGFβR2. The KEGG pathway analysis discovered that CAV1 and FN1 were significantly enriched in focal adhesion, extracellular matrix (ECM)-receptor interaction, and pathways in cancer.

Conclusion: Aggressive behavior of breast cancer was proved to be related to cell proliferation and hormone stimulus. Genes such as CAV1, CD44, TGFβR2, and FN1 might be potential targets to diagnose the aggressive behavior of breast cancer cells.