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The estrogen receptor alpha (ERalpha) plays a critical role in the pathogenesis and clinical behavior of breast cancer. To obtain further insights into the molecular basis of estrogen-dependent forms of this malignancy, we used real-time quantitative reverse transcription (RT)-PCR to compare the mRNA expression of 560 selected genes in ERalpha-positive and ERalpha-negative breast tumors. Fifty-one (9.1%) of the 560 genes were significantly upregulated in ERalpha-positive breast tumors compared with ERalpha-negative breast tumors. In addition to well-known ERalpha-induced genes (PGR, TFF1/PS2, BCL2, ERBB4, CCND1, etc.) and genes recently identified by cDNA microarray-based approaches (GATA3, TFF3, MYB, STC2, HPN/HEPSIN, FOXA1, XBP1, SLC39A6/LIV-1, etc.), an appreciable number of novel genes were identified, many of, which were weakly expressed. This validates the use of large-scale real-time RT-PCR as a method complementary to cDNA microarrays for molecular tumor profiling. Most of the new genes identified here encoded secreted proteins (SEMA3B and CLU), growth factors (BDNF, FGF2 and EGF), growth factor receptors (IL6ST, PTPRT, RET, VEGFR1 and FGFR2) or metabolic enzymes (CYP2B6, CA12, ACADSB, NAT1, LRBA, SLC7A2 and SULT2B1). Importantly, we also identified a large number of genes encoding proteins with either pro-apoptotic (PUMA, NOXA and TATP73) or anti-apoptotic properties (BCL2, DNTP73 and TRAILR3). Surprisingly, only a small proportion of the 51 genes identified in breast tumor biopsy specimens were confirmed to be ERalpha-regulated and/or E2-regulated in vitro (cultured cell lines). Therefore, this study identified a limited number of genes and signaling pathways, which better delineate the role of ERalpha in breast cancer. Some of the genes identified here could be useful for diagnosis or for predicting endocrine responsiveness, and could form the basis for novel therapeutic strategies.

endocrine-related cancer

Identification of novel genes that co-cluster with estrogen receptor alpha in breast tumor biopsy specimens, using a large-scale real-time reverse transcription-PCR approach