Publications and Research
Document Type
Article
Publication Date
1-2015
Abstract
Background
Prostate cancer is one of the most widespread cancers in men and is fundamentally a genetic disease. Identifying regulators in cancer using novel systems biology approaches will potentially lead to new insight into this disease. It was sought to address this by inferring gene regulatory networks (GRNs). Moreover, dynamical analysis of GRNs can explain how regulators change among different conditions, such as cancer subtypes.
Methods
In our approach, independent gene regulatory networks from each prostate state were reconstructed using one of the current state-of-art reverse engineering approaches. Next, crucial genes involved in this cancer were highlighted by analyzing each network individually and also in comparison with each other.
Results
In this paper, a novel network-based approach was introduced to find critical transcription factors involved in prostate cancer. The results led to detection of 38 essential transcription factors based on hub type variation. Additionally, experimental evidence was found for 29 of them as well as 9 new transcription factors.
Conclusion
The results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell.
Comments
This article was originally published in Avicenna Journal of Medical Biotechnology, available at https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4388891/
This work is distributed under a Creative Commons Attribution 3.0 Unported License (CC BY 3.0).