Elevated MED28 expression predicts poor outcome in women with breast cancer

Abstract Background MED28 (also known as EG-1 and magicin) has been implicated in transcriptional control, signal regulation, and cell proliferation. MED28 has also been associated with tumor progression in in vitro and in vivo models. Here we examined the association of MED28 expression with human breast cancer progression. Methods Expression of MED28 protein was determined on a population basis using a high-density tissue microarray consisting of 210 breast cancer patients. The association and validation of MED28 expression with histopathological subtypes, clinicopathological variables, and disease outcome was assessed. Results MED28 protein expression levels were increased in ductal carcinoma in situ and invasive ductal carcinoma of the breast compared to non-malignant glandular and ductal epithelium. Moreover, MED28 was a predictor of disease outcome in both univariate and multivariate analyses with higher expression predicting a greater risk of disease-related death. Conclusions We have demonstrated that MED28 expression is increased in breast cancer. In addition, although the patient size was limited (88 individuals with survival information) MED28 is a novel and strong independent prognostic indicator of survival for breast cancer

Protein expression based multimarker analysis of breast cancer samples

<p>Abstract</p> <p>Background</p> <p>Tissue microarray (TMA) data are commonly used to validate the prognostic accuracy of tumor markers. For example, breast cancer TMA data have led to the identification of several promising prognostic markers of survival time. Several studies have shown that TMA data can also be used to cluster patients into clinically distinct groups. Here we use breast cancer TMA data to cluster patients into distinct prognostic groups.</p> <p>Methods</p> <p>We apply weighted correlation network analysis (WGCNA) to TMA data consisting of 26 putative tumor biomarkers measured on 82 breast cancer patients. Based on this analysis we identify three groups of patients with low (5.4%), moderate (22%) and high (50%) mortality rates, respectively. We then develop a simple threshold rule using a subset of three markers (p53, Na-KATPase-β1, and TGF β receptor II) that can approximately define these mortality groups. We compare the results of this correlation network analysis with results from a standard Cox regression analysis.</p> <p>Results</p> <p>We find that the rule-based grouping variable (referred to as WGCNA*) is an independent predictor of survival time. While WGCNA* is based on protein measurements (TMA data), it validated in two independent Affymetrix microarray gene expression data (which measure mRNA abundance). We find that the WGCNA patient groups differed by 35% from mortality groups defined by a more conventional stepwise Cox regression analysis approach.</p> <p>Conclusions</p> <p>We show that correlation network methods, which are primarily used to analyze the relationships between gene products, are also useful for analyzing the relationships between patients and for defining distinct patient groups based on TMA data. We identify a rule based on three tumor markers for predicting breast cancer survival outcomes.</p

Differentiation theory and the ontologies of regionalism in Latin America

Abstract In this article, we argue that conventional understandings of regional integration based on neo-functionalism, hitherto often used to describe the diverse projects of Latin American regionalism, are of limited utility in that context. Rather than representing processes of economic or political unification, the various regionalisms could be understood more productively as a reaction to the crisis in legitimacy that social orders in the region have experienced under the conditions of globalized modernity. We then deploy an understanding of regionalism derived from sociological differentiation theory in order to advance this argument

Direct Determination of Total Serum Cholesterol by Use of Double-Wavelength Spectrophotometry

Abstract We describe a simple, accurate method for direct determination of total cholesterol in serum. Systematic investigation of a previously described modified Liebermann— Burchard reagent has indicated the necessity of accounting for both bilirubin interference and decreased specificity owing to exothermia. Double-wavelength spectrophotometry was used to optically null out bilirubin as an interfering factor, whereas adding serum to the cold reagent increases its specificity for the cholesterol color reaction. Comparison of 106 cholesterol values with those obtained by the procedure of Abell et al. [J. Biol. Chem. 195, 357 (1952)] yielded a correlation coefficient greater than 0.99; our inter-run coefficient of variation for pooled laboratory serum was 1.7%.</jats:p

Cancer Detection Using Neural Computing Methodology

This paper describes a novel learning methodology used to analyze bio-materials. The premise of this research is to help pathologists quickly identify anomalous cells in a cost efficient method. Skilled pathologists must methodically, efficiently and carefully analyze manually histopathologic materials for the presence, amount and degree of malignancy and/or other disease states. The prolonged attention required to accomplish this task induces fatigue that may result in a higher rate of diagnostic errors. In addition, automated image analysis systems to date lack a sufficiently intelligent means of identifying even the most general regions of interest in tissue based studies and this shortfall greatly limits their utility. An intelligent data understanding system that could quickly and accurately identify diseased tissues and/or could choose regions of interest would be expected to increase the accuracy of diagnosis and usher in truly automated tissue based image analysis