Prostate cancer which affects an elderly man is a feature of senescence (cellular) — a biology phenomenon

The aim of this paper was to present late-life form of the prostate cancer, which differs from its aggressive counterpart that affects men between 55–65 years old and younger. The differences can be found in carcinogenesis risk factors, cancer biology and finally patients’ survival. The most important is that these two clinical (age-related) forms of the prostate cancer are still undistinguishable in clinico-pathology reports and patients bearing different diseases are offered the same treatment. Potential mechanisms leading to development of the late-life clinically indolent prostate cancer are discussed. It seems that the key abnormalities are proteins involved in control of regenerative potential and cell senescence

Influence of two pt(iv) complexes on viability, apoptosis and cell cycle of B16 mouse melanoma tumors

Several platinum(IV) complexes are showing considerable promise in initial trials, producing reactive intermediates that then interact with DNA. Aim: To perform in vitro study of two new platinum(IV) complexes cytotoxic effect on B16 mouse melanoma cells. Methods: PtCl₄ (dbtp)₂ and PtCl₂ (6mp)₂ complexes were prepared. PtCl₄ (dbtp)₂ was created as modification of PtCl₄ (dmtp) test previously.Apoptosis and necrosis were examined using flow cytometry, upon Annexin V/PI staining. Results: LC₁₀,LC₅₀ andLC₉₀ parameters established for PtCl₄ (dbtp)₂ were as following: 2.6, 17.0, 58.0 μmol/L. However LC₁₀ andLC₅₀ established for PtCl₂ (6mp)₂ were 1.2 and 14.0μmol/l respectively. The both complexes induced apoptosis. PtCl₂ (6mp)₂ induced cell cycle arrest in G0/G1, while PtCl₄ (dbtp)₂ — in S-phase. Conclusions: PtCl₄ (dbtp)₂ appeared to be more cytotoxic against B16 cells than PtCl₂ (6mp)₂ . Apoptosis was the main mechanism of cell loss in cultures incubated with both tested complexes

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