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| Paper IPM / Biological / 13717 |
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| Abstract: | |||||||||||||
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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.
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