Cancer core modules identification through genomic and transcriptomic changes correlation detection at network level

In this study, we describe a novel network analysis to identify the driver mutation through integrating both cancer genomes and transcriptomes. Our method successfully identified a significant genotype-phenotype change correlation in all six solid tumor types and revealed core modules that contain both significantly enriched somatic mutations and aberrant expression changes specific to tumor development. Moreover, we found that the majority of these core modules contained well known cancer driver mutations, and that their mutated genes tended to occur at hub genes with central regulatory roles. In these mutated genes, the majority were cancer-type specific and exhibited a closer relationship within the same cancer type rather than across cancer types. The remaining mutated genes that exist in multiple cancer types led to two cancer type clusters, one cluster consisted of three neural derived or related cancer types, and the other cluster consisted of two adenoma cancer types.Our approach can successfully identify the candidate drivers from the core modules. Comprehensive network analysis on the core modules potentially provides critical insights into convergent cancer development in different organs.Cancer occurs when cells grow out of control due to genetic mutations [1]. It is not a single disease, but exhibits a wide spectrum of phenotypic variations involving numerous critical genes and pathways, e.g. TGF-β, NK-κB, TNF-α that may play multiple and even opposite roles [2,3]. Accordingly, a wide range of genetic mutations is involved, and the same mutations may exhibit a different impact. Further elucidation of the functional link between the genetic mutations and phenotypic changes in cancer development is of central importance, but remains a challenge [4]. Moreover, these genetic mutations disrupt the DNA repair pathways, resulting in many associated non-functional mutations [5].Thus, this poses a big challenge to the central goal in cancer research to identify