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

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

A Comparison of Common Health Indicators From Two Surveys of Latinos in the Bronx, New York

Introduction: Population health surveys inform and demonstrate the impact of public health policies. However, the performance of such surveys in specific groups of interest (e.g., Hispanics/Latinos in a neighborhood of New York City) is rarely studied. Method: We compared measures for obesity, hypertension, diabetes, and current smoking based on the New York City Community Health Survey (CHS, a telephone survey of New York City adults) with the Hispanic Community Health Survey/Study of Latinos (HCHS/SOL), an in-person survey of Hispanic/Latino adults in four communities in the United States (2008-2011), including the Bronx. CHS data were limited to Hispanic/Latinos living in the HCHS/SOL Bronx catchment area. Results: Compared with CHS, HCHS/SOL estimated higher prevalence of obesity (in HCHS/SOL, PHCHS/SOL = 45.0% vs. in CHS, PCHS = 30.6%, p \u3c .01) and current smoking (PHCHS/SOL = 21.2% vs. PCHS = 16.2%, p \u3c .01) but similar for hypertension (PHCHS/SOL = 33.1% vs. PCHS = 33.8%, p \u3e .05) and diabetes (PHCHS/SOL = 15.2% vs. PCHS = 15.7%, p \u3e .05). Stratified estimates (by age, sex, education, and Hispanic/Latino heritage) followed similar trends. Conclusion: Our study emphasizes the importance of assessing potential bias in population-based surveys of Hispanics/Latinos and other populations of interest and highlights the complex nature of measuring health outcomes via population-based surveys

Smoking negatively impacts renal cell carcinoma overall and cancer-specific survival

Tobacco use is a leading cause of premature death, yet few studies have investigated the effect of tobacco exposure on the outcome of patients with renal cell carcinoma (RCC). The authors of this report retrospectively studied the impact of smoking on clinicopathologic factors, survival outcomes, and p53 expression status in a large cohort of patients with RCC

Clinical, molecular, and genetic correlates of lymphatic spread in clear cell renal cell carcinoma

While it is well known that clear cell renal cell carcinoma (ccRCC) that presents with lymphatic spread is associated with an extremely poor prognosis, its molecular and genetic biology is poorly understood