Using computers to sort through gene regulatory networks, researchers with the University of Queensland's Institute for Molecular Bioscience were able to uncover hard-to-find cancer treatment targets, according to a study published this month in the journal Genome Medicine.
Initially, nine different computational methods for examining the gene regulatory networks were studied. After determining the most effective method--Supervised Inference of Regulatory Networks (SIRENE)--the researchers then applied that method to an ovarian cancer dataset, which ultimately revealed a plethora of new drug targets.
"Cancer is a disease, not of single genes, but rather of genomes and/or networks of molecular interaction and control," the authors wrote. "Reconstructing gene regulatory networks in health and diseased tissue is therefore critical to understanding cancer phenotypes and devising effective therapeutics."
Conventional approaches, the authors added, tend to focus on individual genes, thus making them "too time-consuming."
In an accompanying editorial, Duojiao Wu--with the Biomedical Research Center at Fudan University Zhongshan Hospital in Shanghai--and colleagues said that computational methods for identifying new cancer treatment targets should be "seriously considered."
"Cancer bioinformatics as an emerging strategy is one of the most critical and useful approaches to systems clinical medicine for clinical research and applications," Wu and colleagues said.