OMICS. 2013 Jul;17(7):393-7. doi: 10.1089/omi.2012.0084.

Graphical identification of cancer-associated gene subnetworks based on small proteomics data sets.

Mezhoud K.

UR04CNSTN01-Bio-computing Unit, Life Science Department, National Center for Nuclear Sciences and Technologies, Ariana, Tunisia. kmezhoud@bioinformatics.tn

 

Abstract

Proteomics is a rapidly emerging frontier in post-genomics medicine and biology, but the quantitative analysis and validation of proteomic data are in need of further improvements. Before selecting potential candidate proteomic biomarkers, it is important to understand the broader context of how biological processes are regulated under different conditions or in different phenotypes. The enrichment of proteomic data consists of extracting as much biological meaning as possible from curated, pathway-based, functional protein interaction networks. Currently, most of the enrichment tools are intended for microarray data and require parametric data, whereas proteomic data are often nonparametric. In this study, we aimed to select a suite of interactive tools that can enrich proteomic results with a graphical overview. This facilitated diagnosis and interpretation prior to further analysis. From a list of proteins, a network was constructed using a map of the most severely disrupted biological process, and the disease entity was then identified on the basis of clinical data. Taken together, this graphical and interactive method ranks potential proteins via functional analysis in order to improve the choice of biomarkers for validation with the following advantages: 1) It adds neighbor proteins that are not selected by mass spectrometry analysis, but could in fact be key proteins; 2) pinpoints the biological process most often involved; and 3) predicts the most likely disease on the basis of clinical data.

PMID: 23642253

 

Supplementary

The gene expression profiles from different cancerous tissues may be closely resembling and involve the same pathways and biological process. The use of one biomarker (gene) for screening or predicting the prognosis may not provide conclusive diagnostic evidence.. In order to improve the specificity and efficiency of the test, a panel of genes could be used.

The prognosis could be improved if the gene expression profiles (Figure 1A) were matched to known cancers data (Figure 1B) and integrated into its biological process (Figure 1C). The challenge in Biomedicine is integrating simultaneously complex, wide and different type of OMICS data. Here I demonstrate how graph theory upgrade the biological meaning of huge and  heterogeneous data sets. It facilitates the prognosis and the prediction of cancer predisposition using real clinical data (Figure 1D).

Figure1

Figure 1: Cancer predisposition prediction using graph representation

Video link:  http://www.youtube.com/watch?v=7E4Q_JyTyWw

The video describes  a Cytoscape demonstration of how can built a graph that integrates experimental and known data to predict biomarkers for cancers.

kmezhoud-cnstn

Karim Mezhoud is an Associate Professor in National Center for Nuclear Sciences and Technologies in Tunis, Tunisia. He focuses his research on studying the behavior of life systems exposed to stresses using proteomics, integrative biology, system biology, bioinformatics and biostatistics.

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