Multivariate gene expression analysis reveals functional connectivity changes between normal-tumoral prostatesReport as inadecuate




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BMC Systems Biology

, 2:106

First Online: 05 December 2008Received: 29 August 2008Accepted: 05 December 2008

Abstract

BackgroundProstate 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.

ResultsPrincipal 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.

ConclusionWe 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.

Electronic supplementary materialThe online version of this article doi:10.1186-1752-0509-2-106 contains supplementary material, which is available to authorized users.

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Author: André Fujita - Luciana Rodrigues Gomes - João Ricardo Sato - Rui Yamaguchi - Carlos Eduardo Thomaz - Mari Cleide Sogay

Source: https://link.springer.com/



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