A novel network-based approach for discovering dynamic metabolic biomarkers in cardiovascular disease.

Metabolic biomarkers may play an important role in the diagnosis, prognostication and assessment of response to pharmacological therapy in complex diseases. The process of discovering new metabolic biomarkers is a non-trivial task which involves a number of bioanalytical processing steps coupled with a computational approach for the search, prioritization and verification of new biomarker candidates. Kinetic analysis provides an additional dimension of complexity in time-series data, allowing for a more precise interpretation of biomarker dynamics in terms of molecular interaction and pathway modulation. A novel network-based computational strategy for the discovery of putative dynamic biomarker candidates is presented, enabling the identification and verification of unexpected metabolic signatures in complex diseases such as myocardial infarction. The novelty of the proposed method lies in combining metabolic time-series data into a superimposed graph representation, highlighting the strength of the underlying kinetic interaction of preselected analytes. Using this approach, we were able to confirm known metabolic signatures and also identify new candidates such as carnosine and glycocholic acid, and pathways that have been previously associated with cardiovascular or related diseases. This computational strategy may serve as a complementary tool for the discovery of dynamic metabolic or proteomic biomarkers in the field of clinical medicine

High proliferation rate and TNM stage but not histomorphological subtype are independent prognostic markers for overall survival in papillary renal cell carcinoma

Papillary renal cell carcinoma (PRCC) is currently divided in 2 subtypes. We reviewed a large cohort of PRCC and correlated subtype, morphological features and diagnostic marker expression with overall survival (OS) to uncover differences between the 2 subtypes. Three hundred seventy-six renal tumors initially diagnosed as PRCC with clinical and survival data were collected from the participating centers. Two hundred forty-six tumors were classified as PRCC1 (65.4%) and 130 as PRCC2 (34.6%) and graded according to the 2016 World Health Organization/Intemational Society of Urological Pathology grading system. Morphological features (abundant cytoplasm, necrosis, fibrous stroma, foamy macrophages and psammoma bodies) were noted. Immunohistochemical stains (MIB1, p53, Racemase, EMA, CK7, CK20, E-Cadherin) were performed using tissue microarrays. chi(2)-Tests, log-rank tests and uni- and multivariate Cox regression analysis were performed. Both subtypes displayed different morphological features and immunohistochemical profiles: abundant cytoplasm was more frequent in PRCC2, while foamy macrophages were more common in PRCC1. Abundant cytoplasm and presence of psammoma bodies were associated with poorer OS. PRCC1 showed more frequent CK7 expression, PRCC2 more frequent E-Cadherin, p53 and higher MIB1 expression (>15%). Expression of Racemase and CK7 was associated with better OS, while high MIB1 (>15%) was associated with poorer OS. In multivariate analysis, the only independent predictors of OS were proliferation (MIB1), tumor stage, metastasis and age at surgery. Subtype was not an independent prognostic factor. Therefore, PRCC subtype on its own is not suitable for estimating survival. More data focusing on PRCC tumor biology is needed to define prognostic subgroups, especially in PRCC2. (C) 2018 Elsevier Inc. All rights reserved