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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

MCODE analysis of normal condition protein interaction network in breast.
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Figure 3: MCODE analysis of normal condition protein interaction network in breast.

Mentions: Molecular complex detection (MCODE) method has been used to evaluate yeast protein interaction compilation using known molecular complex data from mass spectrometry of the proteome [19,37]. This leads to the observation that highly interconnected, or dense regions of the network may represent molecular complexes [38]. The numbers of possible modules that can be said as molecular complexes, differ between normal and cancer conditions in each of the five tissues (Figures 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10). The ranked molecular complex numbers of normal and cancer protein interaction networks are 15 and 19 for bone, 22 and 28 for breast, 22 and 27 for colon, 21 and 30 for kidney and 19 and 28 for liver respectively (Figures 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10). In all cases, possible molecular complex numbers increase in cancer condition. The statistical significance test also supports the difference (at p ≤ 0.05) and depicts that the molecular complex numbers of cancer PINs are significantly increased than the normal PINs (at p = 0.02) (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)

MCODE analysis of normal condition protein interaction network in breast.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: MCODE analysis of normal condition protein interaction network in breast.
Mentions: Molecular complex detection (MCODE) method has been used to evaluate yeast protein interaction compilation using known molecular complex data from mass spectrometry of the proteome [19,37]. This leads to the observation that highly interconnected, or dense regions of the network may represent molecular complexes [38]. The numbers of possible modules that can be said as molecular complexes, differ between normal and cancer conditions in each of the five tissues (Figures 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10). The ranked molecular complex numbers of normal and cancer protein interaction networks are 15 and 19 for bone, 22 and 28 for breast, 22 and 27 for colon, 21 and 30 for kidney and 19 and 28 for liver respectively (Figures 1, 2, 3, 4, 5, 6, 7, 8, 9 and 10). In all cases, possible molecular complex numbers increase in cancer condition. The statistical significance test also supports the difference (at p ≤ 0.05) and depicts that the molecular complex numbers of cancer PINs are significantly increased than the normal PINs (at p = 0.02) (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