Predicting cancer involvement of genes from heterogeneous data.
BACKGROUND
Systematic 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.


RESULTS
We 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.


CONCLUSIONS
Our 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.
Aragues
Ramon
R
Structural Bioinformatics Lab, (GRIB), Universitat Pompeu Fabra-IMIM, Barcelona Research Park of Biomedicine (PRBB), 08003-Barcelona, Catalonia, Spain. ramon.aragues@upf.edu
Sander
Chris
C
Oliva
Baldo
B
D000465:Algorithms; D014408:Biomarkers, Tumor / Q000032:analysis / Q000378:metabolism*; D003198:Computer Simulation; D020869:Gene Expression Profiling / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D009363:Neoplasm Proteins / Q000032:analysis / Q000378:metabolism*; D009369:Neoplasms / Q000378:metabolism*; D025941:Protein Interaction Mapping / Q000379:methods*; D015398:Signal Transduction; D012984:Software
D014408:Biomarkers, Tumor; D009363:Neoplasm Proteins