Multivariate gene expression analysis reveals functional connectivity changes between normal/tumoral prostates

Abstract Background Prostate cancer is a leading cause of death in the male population, therefore, a comprehensive study about the genes and the molecular networks involved in the tumoral prostate process becomes necessary. In order to understand the biological process behind potential biomarkers, we have analyzed a set of 57 cDNA microarrays containing ~25,000 genes. Results Principal Component Analysis (PCA) combined with the Maximum-entropy Linear Discriminant Analysis (MLDA) were applied in order to identify genes with the most discriminative information between normal and tumoral prostatic tissues. Data analysis was carried out using three different approaches, namely: (i) differences in gene expression levels between normal and tumoral conditions from an univariate point of view; (ii) in a multivariate fashion using MLDA; and (iii) with a dependence network approach. Our results show that malignant transformation in the prostatic tissue is more related to functional connectivity changes in their dependence networks than to differential gene expression. The MYLK, KLK2, KLK3, HAN11, LTF, CSRP1 and TGM4 genes presented significant changes in their functional connectivity between normal and tumoral conditions and were also classified as the top seven most informative genes for the prostate cancer genesis process by our discriminant analysis. Moreover, among the identified genes we found classically known biomarkers and genes which are closely related to tumoral prostate, such as KLK3 and KLK2 and several other potential ones. Conclusion We have demonstrated that changes in functional connectivity may be implicit in the biological process which renders some genes more informative to discriminate between normal and tumoral conditions. Using the proposed method, namely, MLDA, in order to analyze the multivariate characteristic of genes, it was possible to capture the changes in dependence networks which are related to cell transformation.

Trends in aortic aneurysm- and dissection-related mortality in the state of São Paulo, Brazil, 1985–2009: multiple-cause-of-death analysis

Background: Aortic aneurysm and dissection are important causes of death in older people. Ruptured aneurysms show catastrophic fatality rates reaching near 80%. Few population-based mortality studies have been published in the world and none in Brazil. The objective of the present study was to use multiple-cause-of-death methodology in the analysis of mortality trends related to aortic aneurysm and dissection in the state of Sao Paulo, between 1985 and 2009. Methods: We analyzed mortality data from the Sao Paulo State Data Analysis System, selecting all death certificates on which aortic aneurysm and dissection were listed as a cause-of-death. The variables sex, age, season of the year, and underlying, associated or total mentions of causes of death were studied using standardized mortality rates, proportions and historical trends. Statistical analyses were performed by chi-square goodness-of-fit and H Kruskal-Wallis tests, and variance analysis. The joinpoint regression model was used to evaluate changes in age-standardized rates trends. A p value less than 0.05 was regarded as significant. Results: Over a 25-year period, there were 42,615 deaths related to aortic aneurysm and dissection, of which 36,088 (84.7%) were identified as underlying cause and 6,527 (15.3%) as an associated cause-of-death. Dissection and ruptured aneurysms were considered as an underlying cause of death in 93% of the deaths. For the entire period, a significant increased trend of age-standardized death rates was observed in men and women, while certain non-significant decreases occurred from 1996/2004 until 2009. Abdominal aortic aneurysms and aortic dissections prevailed among men and aortic dissections and aortic aneurysms of unspecified site among women. In 1985 and 2009 death rates ratios of men to women were respectively 2.86 and 2.19, corresponding to a difference decrease between rates of 23.4%. For aortic dissection, ruptured and non-ruptured aneurysms, the overall mean ages at death were, respectively, 63.2, 68.4 and 71.6 years; while, as the underlying cause, the main associated causes of death were as follows: hemorrhages (in 43.8%/40.5%/13.9%); hypertensive diseases (in 49.2%/22.43%/24.5%) and atherosclerosis (in 14.8%/25.5%/15.3%); and, as associated causes, their principal overall underlying causes of death were diseases of the circulatory (55.7%), and respiratory (13.8%) systems and neoplasms (7.8%). A significant seasonal variation, with highest frequency in winter, occurred in deaths identified as underlying cause for aortic dissection, ruptured and non-ruptured aneurysms. Conclusions: This study introduces the methodology of multiple-causes-of-death to enhance epidemiologic knowledge of aortic aneurysm and dissection in São Paulo, Brazil. The results presented confer light to the importance of mortality statistics and the need for epidemiologic studies to understand unique trends in our own population.

Functional clustering of time series gene expression data by Granger causality

Background: A common approach for time series gene expression data analysis includes the clustering of genes with similar expression patterns throughout time. Clustered gene expression profiles point to the joint contribution of groups of genes to a particular cellular process. However, since genes belong to intricate networks, other features, besides comparable expression patterns, should provide additional information for the identification of functionally similar genes. Results: In this study we perform gene clustering through the identification of Granger causality between and within sets of time series gene expression data. Granger causality is based on the idea that the cause of an event cannot come after its consequence. Conclusions: This kind of analysis can be used as a complementary approach for functional clustering, wherein genes would be clustered not solely based on their expression similarity but on their topological proximity built according to the intensity of Granger causality among them.