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. 2012:2012:842727.
doi: 10.1100/2012/842727. Epub 2012 May 2.

Biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method

Carlos Roberto Arias  1 , Hsiang-Yuan Yeh, Von-Wun Soo
Affiliations

Biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method

Carlos Roberto Arias et al. ScientificWorldJournal. 2012.

Abstract

Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.

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Figures

Figure 1
Figure 1
General gene prioritization overview.
Figure 2
Figure 2
GP-MIDAS-VXEF workflow.
Figure 3
Figure 3
ROC curves comparing the performance of GP-MIDAS-VXEF with existent state-of-the-art network-based prioritization methods.
Figure 4
Figure 4
Target genes retrieved. Showing the amount of target genes retrieve on different ranks, on top of each bar the average position of the found genes is shown.
Figure 5
Figure 5
Venn diagram shows how the set of target genes is found amongst the different methods tested.
Figure 6
Figure 6
Edge flux values distribution.
Figure 7
Figure 7
Prostate-normal experiment top 50 genes induced result network. Red color shows the higher difference in expression between prostate cancer and normal tissue sample, on the other hand the green color shows the smaller difference in expression between the samples. Nodes with node circle denote seed genes.
Figure 8
Figure 8
Prostate-metastatis experiment top 50 genes induced result network. Red color shows the higher difference in expression between prostate cancer and lymph node metastasis tissue sample, on the other hand the green color shows the smaller difference in expression between the samples. Nodes with node circle denote Seed Genes.
Figure 9
Figure 9
Venn diagram of top 50 genes.
Algorithm 1
Algorithm 1
Overview of NetWalk Phase.
See this image and copyright information in PMC

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