Predicting cancer involvement of genes from heterogeneous data

Abstract

BackgroundSystematic approaches for identifying proteins involved in different types of cancer are needed. Experimental techniques such as microarrays are being used to characterize cancer, but validating their results can be a laborious task. Computational approaches are used to prioritize between genes putatively involved in cancer, usually based on further analyzing experimental data.ResultsWe implemented a systematic method using the PIANA software that predicts cancer involvement of genes by integrating heterogeneous datasets. Specifically, we produced lists of genes likely to be involved in cancer by relying on: (i) protein-protein interactions; (ii) differential expression data; and (iii) structural and functional properties of cancer genes. The integrative approach that combines multiple sources of data obtained positive predictive values ranging from 23% (on a list of 811 genes) to 73% (on a list of 22 genes), outperforming the use of any of the data sources alone. We analyze a list of 20 cancer gene predictions, finding that most of them have been recently linked to cancer in literature.ConclusionOur approach to identifying and prioritizing candidate cancer genes can be used to produce lists of genes likely to be involved in cancer. Our results suggest that differential expression studies yielding high numbers of candidate cancer genes can be filtered using protein interaction networks.

A Transcriptome-proteome Integrated Network Identifies Endoplasmic Reticulum thiol oxidoreductase (ERp57) as a Hub that Mediates Bone Metastasis

“…By revealing the context of a given protein in the interaction network, the systems-level view can yield useful insights into molecular and cell function (15). These cellular network models are obtained through a combination of mRNA expression profiles and curated protein-protein interaction data, which have recently become abundant (16). Identifying subnetworks induced in a certain phenotype using such models can facilitate biological validation (17).…”

Knowledge environments representing molecular entities for the virtual physiological human

“…We hypothesized that proteins whose partners are genes of a pathway involved in aneurysm are likely to be involved as well. This hypothesis was successfully applied previously to predict the protein fold between proteins connected by a linker (Espadaler et al 2005b), candidate sequence fragments for interactions (iMotifs; Aragues et al 2007) or new putative cancer genes (Espana et al 2004;Sanz et al 2007;Aragues et al 2008). Accordingly, we wish to score the validity of a gene being implied on the aneurysm using an ALD threshold of N. However, our expectations need to be proved for the APIN using the threshold N as parameter.…”

An Integrative Approach to Identifying Biologically Relevant Genes

“…Since most existing work is designed for specific research purposes, they can only handle one or limited types of knowledge of the same category. For instance, the models proposed in [40,1,2] can only handle knowledge about genes, but not knowledge about samples. To address this limitation, we propose a integrative approach to systematically incorporate different types of knowledge in gene selection.…”

“…In [28], gene annotation are used for choosing gene ranking criterion. In [2], protein interaction, gene-disease association and gene function annotation are used for choosing cancer related genes. Gene selection approaches using gene regulatory network and gene ontology are also studied in [18] and [30,38], respectively.…”