Construction of breast cancer gene regulatory networks and drug target optimization.

Abstract

The purpose of this study was to construct the breast cancer gene regulatory networks through the high-throughput techniques and optimize the drug target genes of breast cancer using bioinformatics analysis, and thus accelerate the process of drug development and improve the cure rate of breast cancer. The gene expression profile data of breast cancer were downloaded from GEO database and the transcriptional regulation data were obtained from UCSC database. Then we identified the differentially expressed genes (DEGs) by SAM algorithm and built gene regulatory networks by the supervised algorithm SIRENE. Finally, the drug targets of the DEGs with changed regulation relations were optimized based on the CancerResource database. A total of 584 DEGs were identified and the gene regulatory networks in the normal state and tumorous state were constructed. By comparing the new predicted regulatory relation in cancer state and normal state, the regulatory relation of 18 genes was found to be changed in the two states, showing the possibility to be applied as drug target genes. After the searches in the CancerResources, 7 genes were screened as the drug target genes, such as PFKFB3. Our present findings shed new light on the molecular mechanism of breast cancer and provide some drug targets which have the potential to be used in clinic for the treatment of breast cancer in future.