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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

Degree distribution in bone normal network.
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Figure 29: Degree distribution in bone normal network.

Mentions: The MCODE study shows that during cancer condition in each tissue, network clustering is increased. The ModuLand study denotes that the crucial nodes with module centrality are decreased in cancer (except breast cancer) representing the reduced level of module overlapping of cancer networks. The possible reason can be explained by degree distribution of the networks (FiguresĀ 29, 30, 31, 32, 33, 34, 35, 36, 37 and 38). Degree distribution of the networks can account for a possible explanation for counter behaving such clustering and overlapping. In all cancer PINs, few selective nodes with much higher degree are found contrary to the normal PINs. From this observation, a plausible argument can be proposed that some giant nodes are formed in cancer PINs covering a huge degree number and result in many randomly dispersed nodes. Such instance reduces the number of nodes with module centrality and subsequently overlapping modules with reduced number of nodes and edges are formed.


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)

Degree distribution in bone normal network.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 29: Degree distribution in bone normal network.
Mentions: The MCODE study shows that during cancer condition in each tissue, network clustering is increased. The ModuLand study denotes that the crucial nodes with module centrality are decreased in cancer (except breast cancer) representing the reduced level of module overlapping of cancer networks. The possible reason can be explained by degree distribution of the networks (FiguresĀ 29, 30, 31, 32, 33, 34, 35, 36, 37 and 38). Degree distribution of the networks can account for a possible explanation for counter behaving such clustering and overlapping. In all cancer PINs, few selective nodes with much higher degree are found contrary to the normal PINs. From this observation, a plausible argument can be proposed that some giant nodes are formed in cancer PINs covering a huge degree number and result in many randomly dispersed nodes. Such instance reduces the number of nodes with module centrality and subsequently overlapping modules with reduced number of nodes and edges are formed.

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