ISSN: 1449-2288International Journal of Biological Sciences
Global reach, higher impact
Int J Biol Sci 2008; 4(6):368-378. doi:10.7150/ijbs.4.368 This issue Cite
Research Paper
Biological and functional analysis of statistically significant pathways deregulated in colon cancer by using gene expression profiles
1. Istituto di Studi sui Sistemi Intelligenti per l'Automazione, CNR, Via Amendola 122/D-I, 70126 Bari, Italy
2. Unità Operativa di Gastroenterologia, IRCCS, “Casa Sollievo della Sofferenza”-Ospedale, 71013 San Giovanni Rotondo (FG), Italy
3. Servizio di Genetica Medica, IRCCS, “Casa Sollievo della Sofferenza”-Ospedale, 71013 San Giovanni Rotondo (FG), Italy
Abstract
Gene expression profiling offers a great opportunity for studying multi-factor diseases and for understanding the key role of genes in mechanisms which drive a normal cell to a cancer state. Single gene analysis is insufficient to describe the complex perturbations responsible for cancer onset, progression and invasion. A deeper understanding of the mechanisms of tumorigenesis can be reached focusing on deregulation of gene sets or pathways rather than on individual genes. We apply two known and statistically well founded methods for finding pathways and biological processes deregulated in pathological conditions by analyzing gene expression profiles. In particular, we measure the amount of deregulation and assess the statistical significance of predefined pathways belonging to a curated collection (Molecular Signature Database) in a colon cancer data set. We find that pathways strongly involved in different tumors are strictly connected with colon cancer. Moreover, our experimental results show that the study of complex diseases through pathway analysis is able to highlight genes weakly connected to the phenotype which may be difficult to detect by using classical univariate statistics. Our study shows the importance of using gene sets rather than single genes for understanding the main biological processes and pathways involved in colorectal cancer. Our analysis evidences that many of the genes involved in these pathways are strongly associated to colorectal tumorigenesis. In this new perspective, the focus shifts from finding differentially expressed genes to identifying biological processes, cellular functions and pathways perturbed in the phenotypic conditions by analyzing genes co-expressed in a given pathway as a whole, taking into account the possible interactions among them and, more importantly, the correlation of their expression with the phenotypical conditions.
Keywords: Microarray, pathway analysis, prediction accuracy, machine learning, gene expression, colon cancer.
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