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. 2008 Dec 5:2:106.
doi: 10.1186/1752-0509-2-106.

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

André Fujita  1 , Luciana Rodrigues Gomes, João Ricardo Sato, Rui Yamaguchi, Carlos Eduardo Thomaz, Mari Cleide Sogayar, Satoru Miyano
Affiliations

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

André Fujita et al. BMC Syst Biol. .

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 approximately 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.

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Figures

Figure 1
Figure 1
A pictorial scheme of the combination of PCA+MLDA and dependence network analysis for two populations (normal and tumoral prostatic tissues).
Figure 2
Figure 2
The discriminative weight of each simulated feature. The features are sorted (in decreasing order) by the absolute value of the weight. Red crosses represent the 500 features that have their functional connectivities alterated between conditions 1 and 2. Blue crosses represent the 24,500 features which have their functional connectivities unaltered.
Figure 3
Figure 3
The discriminative weight of each gene. The genes are sorted (in decreasing order) by the absolute value of the weight. The horizontal red line indicates the 100th gene.
Figure 4
Figure 4
A normal prostate relevance network constructed with the top 100 most discriminative genes and FDR of 5%. Core genes are represented in red.
Figure 5
Figure 5
A tumoral prostate relevance network constructed with the top 100 most discriminative genes and FDR of 5%. Core genes are represented in red.
See this image and copyright information in PMC

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