Limits...
Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks.

Islam MF, Hoque MM, Banik RS, Roy S, Sumi SS, Hassan FM, Tomal MT, Ullah A, Rahman KM - J Clin Bioinforma (2013)

Bottom Line: MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively.In contrast, lower level modular overlapping is found in cancer PINs than the normal ones.The study predicts some major molecular complexes that might act as the important regulators in cancer progression.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh. taufiqbge@gmail.com.

ABSTRACT

Background: Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard.

Methods: In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively.

Results: In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions.

Conclusion: The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer.

No MeSH data available.


Related in: MedlinePlus

Correlation histogram for cancer condition of colon.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3852839&req=5

Figure 24: Correlation histogram for cancer condition of colon.

Mentions: Correlation matrix and correlation histogram in both normal and cancer conditions for each tissue represent the nature of correlation among the nodes of the overlapping modules (Tables 10, 11, 12, 13, 14, 15, 16, 17 and 18 and Figures 20, 21, 22, 23, 24, 25, 26, 27 and 28). Correlation matrix represents all the possible interactions of the overlapping modules. Correlation histogram represents only the valid interactions at certain threshold (here 1.0). From the correlation matrix and histogram, it is found that the interactions among the nodes of overlapping modules differ between normal and cancer cases (Tables 10, 11, 12, 13, 14, 15, 16, 17 and 18 and Figures 20, 21, 22, 23, 24, 25, 26, 27 and 28). The statistical significance test also supports the difference (at p ≤ 0.1) and depicts that valid interactions (at threshold 1.0) of overlapping modules in cancer PINs are significantly increased than the normal PINs (at p = 0.08) (Additional file 3).


Comparative analysis of differential network modularity in tissue specific normal and cancer protein interaction networks.

Islam MF, Hoque MM, Banik RS, Roy S, Sumi SS, Hassan FM, Tomal MT, Ullah A, Rahman KM - J Clin Bioinforma (2013)

Correlation histogram for cancer condition of colon.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3852839&req=5

Figure 24: Correlation histogram for cancer condition of colon.
Mentions: Correlation matrix and correlation histogram in both normal and cancer conditions for each tissue represent the nature of correlation among the nodes of the overlapping modules (Tables 10, 11, 12, 13, 14, 15, 16, 17 and 18 and Figures 20, 21, 22, 23, 24, 25, 26, 27 and 28). Correlation matrix represents all the possible interactions of the overlapping modules. Correlation histogram represents only the valid interactions at certain threshold (here 1.0). From the correlation matrix and histogram, it is found that the interactions among the nodes of overlapping modules differ between normal and cancer cases (Tables 10, 11, 12, 13, 14, 15, 16, 17 and 18 and Figures 20, 21, 22, 23, 24, 25, 26, 27 and 28). The statistical significance test also supports the difference (at p ≤ 0.1) and depicts that valid interactions (at threshold 1.0) of overlapping modules in cancer PINs are significantly increased than the normal PINs (at p = 0.08) (Additional file 3).

Bottom Line: MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively.In contrast, lower level modular overlapping is found in cancer PINs than the normal ones.The study predicts some major molecular complexes that might act as the important regulators in cancer progression.

View Article: PubMed Central - HTML - PubMed

Affiliation: Biotechnology and Genetic Engineering Discipline, Khulna University, Khulna 9208, Bangladesh. taufiqbge@gmail.com.

ABSTRACT

Background: Large scale understanding of complex and dynamic alterations in cellular and subcellular levels during cancer in contrast to normal condition has facilitated the emergence of sophisticated systemic approaches like network biology in recent times. As most biological networks show modular properties, the analysis of differential modularity between normal and cancer protein interaction networks can be a good way to understand cancer more significantly. Two aspects of biological network modularity e.g. detection of molecular complexes (potential modules or clusters) and identification of crucial nodes forming the overlapping modules have been considered in this regard.

Methods: In the current study, the computational analysis of previously published protein interaction networks (PINs) has been conducted to identify the molecular complexes and crucial nodes of the networks. Protein molecules involved in ten major cancer signal transduction pathways were used to construct the networks based on expression data of five tissues e.g. bone, breast, colon, kidney and liver in both normal and cancer conditions. MCODE (molecular complex detection) and ModuLand methods have been used to identify the molecular complexes and crucial nodes of the networks respectively.

Results: In case of all tissues, cancer PINs show higher level of clustering (formation of molecular complexes) than the normal ones. In contrast, lower level modular overlapping is found in cancer PINs than the normal ones. Thus a proposition can be made regarding the formation of some giant nodes in the cancer networks with very high degree and resulting in reduced overlapping among the network modules though the predicted molecular complex numbers are higher in cancer conditions.

Conclusion: The study predicts some major molecular complexes that might act as the important regulators in cancer progression. The crucial nodes identified in this study can be potential drug targets to combat cancer.

No MeSH data available.


Related in: MedlinePlus