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Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation.

Khosravi P, Gazestani VH, Akbarzadeh M, Mirkhalaf S, Sadeghi M, Goliaei B - Avicenna J Med Biotechnol (2015 Jan-Mar)

Bottom Line: 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.The results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran ; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.

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.

No MeSH data available.


Related in: MedlinePlus

Number of interactions. This figure shows the GRNs for 14 TFs (orange nodes) that change their interaction numbers dramatically during cancer progression; A) Normal stage; B) Adjacent stage; C) Tumor stage; D) Metastasis stage which reflects the high level of rewiring of gene regulatory interactions.
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Figure 0002: Number of interactions. This figure shows the GRNs for 14 TFs (orange nodes) that change their interaction numbers dramatically during cancer progression; A) Normal stage; B) Adjacent stage; C) Tumor stage; D) Metastasis stage which reflects the high level of rewiring of gene regulatory interactions.

Mentions: Using the CLR algorithm, four independent networks related to the four different cell stages were reconstructed (Normal, Adjacent, Tumor, and Metastasis). The metastasis GRN had the lowest number of interactions with 2505 interactions while the other three GRNs had around 3000 interactions each. Additionally, topological analysis of the GRNs revealed that all four networks exhibited the small-word property (28) and scales-free architecture (29) which are the well-known characteristics of most biological networks (Figure 1). All four reconstructed GRNs were mainly composed of the same set of genes; however, the conserved interactions among these four networks were very low and the metastasis network had the most unique interactions (Figure 2).


Comparative Analysis of Prostate Cancer Gene Regulatory Networks via Hub Type Variation.

Khosravi P, Gazestani VH, Akbarzadeh M, Mirkhalaf S, Sadeghi M, Goliaei B - Avicenna J Med Biotechnol (2015 Jan-Mar)

Number of interactions. This figure shows the GRNs for 14 TFs (orange nodes) that change their interaction numbers dramatically during cancer progression; A) Normal stage; B) Adjacent stage; C) Tumor stage; D) Metastasis stage which reflects the high level of rewiring of gene regulatory interactions.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4388891&req=5

Figure 0002: Number of interactions. This figure shows the GRNs for 14 TFs (orange nodes) that change their interaction numbers dramatically during cancer progression; A) Normal stage; B) Adjacent stage; C) Tumor stage; D) Metastasis stage which reflects the high level of rewiring of gene regulatory interactions.
Mentions: Using the CLR algorithm, four independent networks related to the four different cell stages were reconstructed (Normal, Adjacent, Tumor, and Metastasis). The metastasis GRN had the lowest number of interactions with 2505 interactions while the other three GRNs had around 3000 interactions each. Additionally, topological analysis of the GRNs revealed that all four networks exhibited the small-word property (28) and scales-free architecture (29) which are the well-known characteristics of most biological networks (Figure 1). All four reconstructed GRNs were mainly composed of the same set of genes; however, the conserved interactions among these four networks were very low and the metastasis network had the most unique interactions (Figure 2).

Bottom Line: 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.The results showed that dynamical analysis of biological networks may provide useful information to gain better understanding of the cell.

View Article: PubMed Central - PubMed

Affiliation: Department of Bioinformatics, Institute of Biochemistry and Biophysics (IBB), University of Tehran, Tehran, Iran ; School of Biological Sciences, Institute for Research in Fundamental Sciences (IPM), Tehran, Iran.

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.

No MeSH data available.


Related in: MedlinePlus