The aim of the first part of this study was to find out the expression profiles of the genes, which were selected from the former BRCA1-induced gene list (OVCA1, OVCA2, ERBIN, RAD21, XRN2, RENT2, SMG1 and MAC30) in normal-matched primary breast tumors and to correlate the gene expression profiles of selected candidate genes with BRCA1 and various pathology parameters. Among the target genes, the expression of ERBIN, SMG1 and RAD21 were found to be highly correlated with that of BRCA1 both in BRCA1 up- and down-regulated cells and this result was validated with qRT-PCR expression profiling of the eight genes in 32 normal-matched primary breast tumor samples. These genes were found to be discriminative between ER(-) and ER(+) tumors as well as grade 1 and grade 3 tumors. Target genes were also analyzed in independent microarray datasets to assess their predictive power for breast tumor grade, subtype and patient survival. ERBIN, SMG1 and RAD21 were found to have predictive roles in these datasets. The aim of the second part of the study was to found appropriate reference genes (RGs) for accurate quantification of target gene expressions in breast tumor tissues. The expression patterns of fifteen widely-used endogenous RGs and three candidate genes that were selected through analysis of two independent microarray datasets were determined in 23 primary breast tumors and their matched normal tissues using qRT-PCR. Additionally, 18S rRNA, ACTB, and SDHA were tested using randomly primed cDNAs from 13 breast tumor pairs to assess the rRNA/mRNA ratio. The tumors exhibited significantly lower rRNA/mRNA ratio when compared to their normals. Among the eighteen tested endogenous reference genes, ACTB and SDHA were identified as the most suitable reference genes for the normalization of qRTPCR data in the analysis of normal-matched tumor breast tissue pairs. The aim of the third part of this study was to develop a resampling-based metaanalysis strategy. Two independent microarray datasets that contain normal breast, invasive ductal carcinoma (IDC), and invasive lobular carcinoma (ILC) samples were used for the meta-analysis. The resampling-based meta-analysis has led to the identification of a highly stable set of genes for classification of normal breast samples and breast tumors encompassing both the ILC and IDC subtypes. A subset of this meta-gene list was shown to predict well-established molecular tumor subtypes, e.g., basal vs luminal or ER+/ER-, with high accuracy and sensitivity based on class prediction analysis of existing breast cancer microarray datasets. Expression of selected genes, tested on 10 independent primary IDC samples and matched nontumor controls by real-time qRT-PCR, supported the meta-analysis results.