Race Matters: Analyzing the Relationship between Colorectal Cancer Mortality Rates and Various Factors within Respective Racial Groups.

Colorectal cancer (CRC) is the third leading cause of mortality due to cancer (with over 50,000 deaths annually), representing 9% of all cancer deaths in the United States (1). In particular, the African-American CRC mortality rate is among the highest reported for any race/ethnic group. Meanwhile, the CRC mortality rate for Hispanics is 15-19% lower than that for non-Hispanic Caucasians (2). While factors such as obesity, age, and socio-economic status are known to associate with CRC mortality, do these and other potential factors correlate with CRC death in the same way across races? This research linked CRC mortality data obtained from the National Cancer Institute with data from the United States Census Bureau, the Centers for Disease Control and Prevention, and the National Solar Radiation Database to examine geographic and racial/ethnic differences, and develop a spatial regression model that adjusted for several factors that may attribute to health disparities among ethnic/racial groups. This analysis showed that sunlight, obesity, and socio-economic status were significant predictors of CRC mortality. The study is significant because it not only verifies known factors associated with the risk of CRC death but, more importantly, demonstrates how these factors vary within different racial groups. Accordingly, education on reducing risk factors for CRC should be directed at specific racial groups above and beyond creating a generalized education plan.

A comparison of imputation procedures and statistical tests for the analysis of two-dimensional electrophoresis data.

BACKGROUND: Numerous gel-based softwares exist to detect protein changes potentially associated with disease. The data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. A particularly important topic is how the various softwares handle missing data. To date, no one has extensively studied the impact that interpolating missing data has on subsequent analysis of protein spots. RESULTS: This work highlights the existing algorithms for handling missing data in two-dimensional gel analysis and performs a thorough comparison of the various algorithms and statistical tests on simulated and real datasets. For imputation methods, the best results in terms of root mean squared error are obtained using the least squares method of imputation along with the expectation maximization (EM) algorithm approach to estimate missing values with an array covariance structure. The bootstrapped versions of the statistical tests offer the most liberal option for determining protein spot significance while the generalized family wise error rate (gFWER) should be considered for controlling the multiple testing error. CONCLUSIONS: In summary, we advocate for a three-step statistical analysis of two-dimensional gel electrophoresis (2-DE) data with a data imputation step, choice of statistical test, and lastly an error control method in light of multiple testing. When determining the choice of statistical test, it is worth considering whether the protein spots will be subjected to mass spectrometry. If this is the case a more liberal test such as the percentile-based bootstrap t can be employed. For error control in electrophoresis experiments, we advocate that gFWER be controlled for multiple testing rather than the false discovery rate.

Feature detection techniques for preprocessing proteomic data.

Numerous gel-based and nongel-based technologies are used to detect protein changes potentially associated with disease. The raw data, however, are abundant with technical and structural complexities, making statistical analysis a difficult task. Low-level analysis issues (including normalization, background correction, gel and/or spectral alignment, feature detection, and image registration) are substantial problems that need to be addressed, because any large-level data analyses are contingent on appropriate and statistically sound low-level procedures. Feature detection approaches are particularly interesting due to the increased computational speed associated with subsequent calculations. Such summary data corresponding to image features provide a significant reduction in overall data size and structure while retaining key information. In this paper, we focus on recent advances in feature detection as a tool for preprocessing proteomic data. This work highlights existing and newly developed feature detection algorithms for proteomic datasets, particularly relating to time-of-flight mass spectrometry, and two-dimensional gel electrophoresis. Note, however, that the associated data structures (i.e., spectral data, and images containing spots) used as input for these methods are obtained via all gel-based and nongel-based methods discussed in this manuscript, and thus the discussed methods are likewise applicable.

Lights, Camera, Action! systematic variation in 2-D difference gel electrophoresis images.

2-D Difference gel electrophoresis (DIGE) circumvents many of the problems associated with gel comparison via the traditional 2-DE approach. DIGE's accuracy and precision, however, is compromised by the existence of other significant sources of systematic variation, including that caused by the apparatus used for imaging proteins (location of the camera and lighting units, background material, imperfections within that material, etc.). Through a series of experiments, we estimate some of these factors, and account for their effect on the DIGE experimental data, thus providing improved estimates of the true relative protein intensities. The model presented here includes 2-DE images as a special case.

Innate immunity modulates adipokines in humans.

CONTEXT: Chronic inflammation converges in type 2 diabetes and atherosclerosis. Modulation of adipokine signaling by innate immunity in humans is of considerable interest given the role of adipokines in insulin resistance and atherosclerosis. OBJECTIVE: The aim of the study was to examine effects of low-grade endotoxemia, a model of human inflammation, on adipokines in vivo. DESIGN/SETTING: An open-label, placebo-controlled, fixed-sequence clinical study was conducted at a General Clinical Research Center. PATIENTS: There were 20 healthy male (50%) and female volunteers aged 18-40 yr. INTERVENTION: Serial blood sampling and adipose biopsies were performed for 24 h before and after iv bolus endotoxin [lipopolysaccharide (LPS), 3 ng/kg]. MAIN OUTCOME MEASURES: We measured plasma leptin, adiponectin, resistin, soluble leptin receptor, cytokines, insulin, and glucose; distribution of adiponectin among multimeric complexes; whole blood, monocyte and adipose mRNA for adipokines and their receptors. RESULTS: LPS induced fever, blood, and adipose TNF and IL-6 and increased homeostasis model assessment of insulin resistance. These were associated with increases in plasma leptin (from 4.1 +/- 1.1 to 6.1 +/- 1.9 ng/ml in men; 21.1 +/- 4.4 to 27.4 +/- 4.7 ng/ml in women; P < 0.005), doubling of the leptin:soluble leptin receptor ratio, and marked induction of whole blood resistin mRNA (13.7 +/- 7.3-fold; P < 0.001) and plasma resistin (8.5 +/- 2.75 to 43.2 +/- 15.3 ng/ml; P < 0.001). Although total adiponectin levels and low and high molecular weight adiponectin complexes were unaltered by LPS treatment, whole blood mRNA for adiponectin receptors 1 (49%; P < 0.005) and 2 (65%; P < 0.001) was suppressed. CONCLUSIONS: Modulation of adipokine signaling may contribute to the insulin resistant, atherogenic state associated with human inflammatory syndromes. Targeting of individual adipokines or their upstream regulation may prove effective in preventing acute and chronic inflammation-related metabolic complications.

Characterization of gene expression profiles of normal canine retina and brain using a retinal cDNA microarray.

PURPOSE: Construction of a canine retinal custom cDNA microarray for comprehensive retinal gene expression profiling and application for the identification of genes that are preferentially expressed in the retina and brain lobes using a brain pool reference tissue. METHODS: A cDNA microarray was constructed utilizing clones obtained from a normalized canine retinal expressed sequence tag library. Gene expression profiles were analyzed for normal retina, as well as the cortex of the frontal, occipital, and temporal brain regions. Each sample was studied against a reference sample of pooled brain RNA. Data from a quantified scanned image were normalized using the loess subgrid procedure. Retina-enriched genes were identified using the Significance Analysis of Microarrays (SAM) algorithm, and confirmed by northern blot analyses for selected genes. Differences between biological samples were displayed using principal component analysis (PCA). RESULTS: Expression profiles for each tissue set were analyzed against the common reference of pooled brain. Changes in expression between the sample and the reference were higher in the retina (27.9%) than the individual brain tissues (2-6.6%). Furthermore, all individual retinal samples were clearly separated from any of the hybridizations using brain tissue in the PCA. The accuracy of observed changes in expression has been confirmed by northern blot analysis using five randomly chosen genes that represented a wide range of different expression levels between retina and brain. CONCLUSIONS: We have established an accurate and robust microarray system suitable for the investigation of expression patterns in the retina and brain. Characterization of the gene expression profiles in normal retina will facilitate the understanding of the processes that underline differences between normal and diseased retinas.