Cyclin A is a prognostic indicator in early stage breast cancer with and without tamoxifen treatment

Overexpression of G1-S regulators cyclin D1 or cyclin A is frequently observed in breast cancer and is also to result in ligand-independent activation of oestrogen receptor in vitro. This might therefore, provide a mechanism for failure of tamoxifen treatment. We examined by immunohistochemical staining the effect of deregulation of these, and other cell cycle regulators on tamoxifen treatment in a group of 394 patients with early stage breast cancer. In univariate analysis, expression of cyclin A, Neu, Ki-67 index, and lack of OR expression were significantly associated with worse prognosis. When adjusted by the clinical model (for lymph node status, age, performance status, T-classification, grade, prior surgery, oestrogen receptor status and tamoxifen use), only overexpression of cyclin A and Neu were significantly associated with worse prognosis with hazard ratios of, respectively, 1.709 (P=0.0195) and 1.884 (P=0.0151). Overexpression of cyclin A was found in 86 out of the 201 OR-positive cases treated with tamoxifen, and was the only independent marker associated with worse prognosis (hazard ratio 2.024, P=0.0462). In conclusion, cyclin A is an independent predictor of recurrence of early stage breast cancer and is as such a marker for response in patients treated with tamoxifen

Computational analysis of expression of human embryonic stem cell-associated signatures in tumors

<p>Abstract</p> <p>Background</p> <p>The cancer stem cell model has been proposed based on the linkage between human embryonic stem cells and human cancer cells. However, the evidences supporting the cancer stem cell model remain to be collected. In this study, we extensively examined the expression of human embryonic stem cell-associated signatures including core genes, transcription factors, pathways and microRNAs in various cancers using the computational biology approach.</p> <p>Results</p> <p>We used the class comparison analysis and survival analysis algorithms to identify differentially expressed genes and their associated transcription factors, pathways and microRNAs among normal vs. tumor or good prognosis vs. poor prognosis phenotypes classes based on numerous human cancer gene expression data. We found that most of the human embryonic stem cell- associated signatures were frequently identified in the analysis, suggesting a strong linkage between human embryonic stem cells and cancer cells.</p> <p>Conclusions</p> <p>The present study revealed the close linkage between the human embryonic stem cell associated gene expression profiles and cancer-associated gene expression profiles, and therefore offered an indirect support for the cancer stem cell theory. However, many interest issues remain to be addressed further.</p