Identification of differently expressed genes with specific SNP Loci for breast cancer by the integration of SNP and gene expression profiling analyses.

This study aims to explore the relationship between gene polymorphism and breast cancer, and to screen DEGs (differentially expressed genes) with SNPs (single nucleotide polymorphisms) related to breast cancer. The SNPs of 17 patients and the preprocessed SNP profiling GSE 32258 (38 cases of normal breast cells) were combined to identify their correlation with breast cancer using chi-square test. The gene expression profiling batch8_9 (38 cases of patients and 8 cases of normal tissue) was preprocessed with limma package, and the DEGs were filtered out. Then fisher's method was applied to integrate DEGs and SNPs associated with breast cancer. With NetBox software, TRED (Transcriptional Regulatory Element Database) and UCSC (University of California Santa Cruz) database, genes-associated network and transcriptional regulatory network were constructed using cytoscape software. Further, GO (Gene Ontology) and KEGG analyses were performed for genes in the networks by using siggenes. In total, 332 DEGs were identified. There were 160 breast cancer-related SNPs related to 106 genes of gene expression profiling (19 were significant DEGs). Finally, 11co-correlated DEGs were selected. In genes-associated network, 9 significant DEGs were correlated to 23 LINKER genes while, in transcriptional regulatory network, E2F1 had regulatory relationships with 7 DEGs including MTUS1, CD44, CCNB1 and CCND2. KRAS with SNP locus of rs1137282 was involved in 35 KEGG pathways. The genes of MTUS1, CD44, CCNB1, CCND2 and KRAS with specific SNP loci may be used as biomarkers for diagnosis of breast cancer. Besides, E2F1 was recognized as the transcription factor of 7 DEGs including MTUS1, CD44, CCNB1 and CCND2.