'Discovery of oncogenes and tumor suppressor genes using genetic and epigenetic features' (DORGE) is a novel data analysis algorithm developed by researchers at the University of California, Irvine. The technique integrates multiple datasets to use both genetic mutations and epigenetic changes (reversible chemical modifications to DNA) as markers for oncogenes (cancer-promoting genes) and tumour suppressor genes.
'Existing bioinformatics algorithms do not sufficiently leverage epigenetic features to predict cancer driver genes, despite the fact that epigenetic alterations are known to be associated with cancer driver genes,' explained Professor Wei Li, senior author of the paper published in Science Advances. 'Our computational algorithm integrates public data on epigenetic and genetic alterations to improve the prediction of cancer driver genes.'
Genetic data from cancer cells and patients are a rich source of discovery of new cancer-related genes. However, existing methods do not always have the sensitivity to detect cancer-associated mutations against the natural background rate of mutation in cancer cells. This means that many potentially important cancer driver genes can be missed in these analyses.
The DORGE algorithm predicted a number of cancer driver genes which were already known, as well as ones which had not been identified before. The researchers found that some of these genes are important parts of known protein interaction and drug-gene networks, meaning that they have a good likelihood of being effective targets in cancer research and treatment.
'Our DORGE algorithm, successfully leveraged public data to discover the genetic and epigenetic alterations that play significant roles in cancer driver gene dysregulation,' said Professor Li. 'These findings could be instrumental in improving cancer prevention, diagnosis and treatment efforts in the future.'