Androgen receptor protein is down-regulated by basic fibroblast growth factor in prostate cancer cells

Interactions between polypeptide growth factors and the androgen receptor (AR) are important for regulation of cellular events in carcinoma of the prostate. Basic fibroblast growth factor (bFGF), the prototype of heparin-binding growth factors, and the AR are commonly expressed in prostate cancer. bFGF diminished prostate-specific antigen protein in the supernatants of androgen-stimulated human prostate cancer cells LNCaP by 80%. In the present study, we asked whether the bFGF effect on prostate-specific antigen is preceded by action on AR expression. LNCaP cells were treated with bFGF and AR protein expression was determined by immunoblotting and ligand binding assay. bFGF down-regulated AR protein in a dose-dependent manner showing a maximal effect at 50 ng ml−1both in the presence or absence of dihydrotestosterone. Down-regulation of AR protein expression occurred already after 8 h of bFGF treatment and a maximal inhibition was observed 24 h after addition of bFGF to culture media. As AR expression can be reduced by an increase in intracellular calcium levels, we investigated whether the bFGF effect on AR protein is mediated by this mechanism. Calcium release from intracellular stores and store-operated calcium influx after treatment with either bFGF or calcium ionophore A 23187 were measured by single cell fluorescence technique. The ionophore A 23187 was able to induce calcium influx and an increase in cytoplasmic calcium concentration in LNCaP cells. In contrast, bFGF was incapable of eliciting a similar effect. In contrast to AR protein, AR mRNA levels were not affected by bFGF as shown by semiquantitative reverse transcription polymerase chain reaction. In summary, these studies show that bFGF is a potent negative regulator of AR protein expression in the human prostate cancer cell line LNCaP. © 2000 Cancer Research Campaig

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