Dissecting the molecular mechanisms of cancer through bioinformatics-based experimental approaches

Cancer is a disease of aberrant gene expression characterized by inappropriate (temporal or quantitative) expression of positive mediators of cell proliferation in conjunction with diminished expression of negative mediators of cell growth. Alteration of the normal balance of these positive and negative mediators leads to the abnormal growth of cells and tissues that typify neoplastic disease. Development of a better understanding of the genetic and epigenetic mechanisms that induce neoplastic transformation and drive the cancer phenotype is essential for continued progress towards the design of practical molecular diagnostics and effective treatment strategies. Over the past decades, molecular techniques that facilitate the assessment of gene expression, identification of gene mutations, and characterization of chromosome abnormalities (numeric and structural) have been established and applied to cancer research. However, many of these techniques are slow and labor-intensive. More recently, high-throughput technologies have emerged that generate large volumes of data related to the genetics and epigenetics of cancer (or other disorders). These advances in molecular genetic technology required the development of sophisticated bioinformatic tools to manage the large datasets generated. The combination of high-throughput molecular assays and bioinformatic-based data mining strategies has significantly impacted our understanding of the molecular pathogenesis of cancer, classification of tumors, and now the management of cancer patients in the clinic. This article will review basic molecular techniques and bioinformatic-based experimental approaches used to dissect the molecular mechanisms of carcinogenesis.