Systems analysis of cancer progression
It is well-known that the stage of the cancer at diagnosis is significantly correlated with the survival odds of the patient, and the chances of survival fall off rapidly with the progression of cancer. Despite this, few efforts address the mechanisms of progression of the disease. Here we suggest a meta-analysis methodology for studying the progression of cancer. Cancer genome data annotated by stage and site is collected in [DriverDB] (http://ngs.ym.edu.tw/driverdb). Given the cancer of interest, the genome data for the cancer could be interrogated stage-wise for driver genes, which are then used to seed the construction of specialised gene networks representative of each stage of cancer. By comparative analysis of these gene networks across stages, the enrichment of genes (‘hubs’) for each stage could be statistically assessed. By subsequently validating against [Cancer Gene Census] (http://www.sanger.ac.uk/cosmic/census), we could then obtain novel stage-specific genes implicated in the progression of the cancer under study. The stage-specific gene networks could be further interrogated for driver subnetworks and pathways involved in the pathogenesis of cancer progression. Useful methods to cross-validate the results include ontology analyses with tools like [BiNGO] (http://www.psb.ugent.be/cbd/papers/BiNGO/). Such approaches to study cancer progression would yield biomarkers and drug targets helpful in improving response rates to therapy.