One of the major problems in genomics and medicine is the identification of gene networks and pathways deregulated in complex and polygenic diseases, like cancer. In this paper, we address the problem of assessing the variability of results of pathways analysis identified in different and independent genome wide expression studies, in which the same phenotypic conditions are assayed. To this end, we assessed the deregulation of 1891 curated gene sets in four independent gene expression data sets of subjects affected by colorectal cancer (CRC). In this comparison we used two well-founded statistical models for evaluating deregulation of gene networks. We found that the results of pathway analysis in expression studies are highly reproducible. Our study revealed 53 pathways identified by the two methods in all the four data sets analyzed with high statistical significance and strong biological relevance with the pathology examined. This set of pathways associated to single markers as well as to whole biological processes altered constitutes a signature of the disease which sheds light on the genetics bases of CRC.
- Colorectal cancer
- Microarray data
ASJC Scopus subject areas
- Computer Science Applications
- Health Informatics