Estrogen receptor beta expression in prostate adenocarcinoma

<p>Abstract</p> <p>Background</p> <p>Prostate cancer is the most commonly diagnosed cancer in men and the second leading cause of cancer death in men. Estrogen induction of cell proliferation is a crucial step in carcinogenesis of gynecologic target tissues, and there are many studies recently done, showing that prostate cancer growth is also influenced by estrogen. The characterization of estrogen receptor beta (ER-b) brought new insight into the mechanisms underlying estrogen signalling. In the present study, we investigated the expression of estrogen receptor-b (ER-b) in human prostate cancer tissues.</p> <p>Methods</p> <p>We selected 52 paraffin-embedded blocks of prostate needle biopsies in a cross-sectional study to determine frequency and rate of ER-b expression in different grades of prostate adenocarcinoma according to Gleason grading system. Immunohistochemical staining of tissue sections by monoclonal anti ER-b antibody was performed using an Envision method visualising system.</p> <p>Results</p> <p>ER-b expression was seen in tumoral cells of prostatic carcinoma in all 29 cases with low and intermediate tumors (100%) and 19 of 23 cases with high grade tumor (83%). Mean rate of ER-b expression in low & intermediate grade cancers was 68.41% (SD = 25.63) whereas high grade cancers showed 49.48% rate of expression (SD = 28.79).</p> <p>Conclusions</p> <p>ER-b expression is reduced in high grade prostate cancers compared to low & intermediate grade ones (<it>P </it>value 0.027).</p

Cancer Biomarker Discovery: The Entropic Hallmark

Background: It is a commonly accepted belief that cancer cells modify their transcriptional state during the progression of the disease. We propose that the progression of cancer cells towards malignant phenotypes can be efficiently tracked using high-throughput technologies that follow the gradual changes observed in the gene expression profiles by employing Shannon's mathematical theory of communication. Methods based on Information Theory can then quantify the divergence of cancer cells' transcriptional profiles from those of normally appearing cells of the originating tissues. The relevance of the proposed methods can be evaluated using microarray datasets available in the public domain but the method is in principle applicable to other high-throughput methods. Methodology/Principal Findings: Using melanoma and prostate cancer datasets we illustrate how it is possible to employ Shannon Entropy and the Jensen-Shannon divergence to trace the transcriptional changes progression of the disease. We establish how the variations of these two measures correlate with established biomarkers of cancer progression. The Information Theory measures allow us to identify novel biomarkers for both progressive and relatively more sudden transcriptional changes leading to malignant phenotypes. At the same time, the methodology was able to validate a large number of genes and processes that seem to be implicated in the progression of melanoma and prostate cancer. Conclusions/Significance: We thus present a quantitative guiding rule, a new unifying hallmark of cancer: the cancer cell's transcriptome changes lead to measurable observed transitions of Normalized Shannon Entropy values (as measured by high-throughput technologies). At the same time, tumor cells increment their divergence from the normal tissue profile increasing their disorder via creation of states that we might not directly measure. This unifying hallmark allows, via the the Jensen-Shannon divergence, to identify the arrow of time of the processes from the gene expression profiles, and helps to map the phenotypical and molecular hallmarks of specific cancer subtypes. The deep mathematical basis of the approach allows us to suggest that this principle is, hopefully, of general applicability for other diseases