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

Erythrocyte sedimentation rate and anaemia are independent predictors of survival in patients with clear cell renal cell carcinoma

BACKGROUND: The 1997 international consensus conference on renal cell cancer (RCC) prognosis suggested erythrocyte sedimentation rate (ESR), alkaline phosphatase (ALP), and anaemia as prognostic biomarkers, but most studies reviewed were limited by small sample sizes. METHODS: The Cox proportional hazards model was used to evaluate whether ESR, ALP, haemoglobin (Hb), and haematocrit (Hct) could predict survival outcomes in 1307 patients with clear cell RCC (ccRCC) who underwent nephrectomy during 1994–2008. RESULTS: During a median follow-up of 43 months, we found that the patients with preoperative high levels of ESR, had a 2.10-fold (95% confidence interval (CI): 1.21–3.67) greater risk of dying from RCC compared with patients with low levels (normal range). Patients with preoperative anaemia, assessed by Hb and Hct, had a 3.11-fold (95% CI: 1.17–8.25) and 6.20-fold (95% CI: 2.30–16.72) greater risk of dying from other illnesses, respectively, compared with patients without anaemia. ALP levels were not associated with ccRCC patients' survival. These associations for ESR and anaemia were more pronounced in patients with body mass index (BMI) <25 compared with patients with BMI ⩾25 kg m(−2). CONCLUSION: Preoperative high ESR, but not ALP, was a significant predictor for cancer-specific survival among ccRCC patients. Anaemia increases the risk of death from other illness