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DiME: a scalable disease module identification algorithm with application to glioma progression.

Liu Y, Tennant DA, Zhu Z, Heath JK, Yao X, He S - PLoS ONE (2014)

Bottom Line: We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules.We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison.Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, University of Birmingham, Birmingham, United Kingdom.

ABSTRACT
Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. Examination of the interconnectivity of the identified modules have revealed changes in topology and module activity (expression) between low- and high- grade tumours, which are characteristic of the major shifts in the constitution and physiology of tumour cells during glioma progression. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at http://www.cs.bham.ac.uk/~szh/DiME.

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Heat map showing expression landscape of all genes in the 7 conserved common modules across grade II glioma and GBM samples.Rows correspond to genes grouped by modules and columns correspond to samples grouped by tumour grade.
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pone-0086693-g007: Heat map showing expression landscape of all genes in the 7 conserved common modules across grade II glioma and GBM samples.Rows correspond to genes grouped by modules and columns correspond to samples grouped by tumour grade.

Mentions: Figure 7 shows a heat map of the expression level of individual genes in the 7 modules grouped by modules (rows) and samples by tumour grade (columns). The clear differential expression patterns of genes belonging to the same module across grades are easily observable in Figure 7. For example, activity of modules 1 and 7, corresponding to the regulation of immune response, increased with malignant progression - i.e. grade II to GBM. Taking into account that the expression arrays were performed on samples of the total tumour mass (not isolated glial cells), and the nature of the transcripts represented by the immune-associated modules, this may be a significant observation. We hypothesize that the significant loss of co-expression observed between the modules associated with cell cycle and glial differentiation and those involved in immune function is indicative of the infiltration of immune cells into the tumour mass in GBM samples. Indeed, this is in agreement with literature reports that have shown an increase in T cell infiltration into GBMs which is around 5 times more than that observed in grade II gliomas [34].


DiME: a scalable disease module identification algorithm with application to glioma progression.

Liu Y, Tennant DA, Zhu Z, Heath JK, Yao X, He S - PLoS ONE (2014)

Heat map showing expression landscape of all genes in the 7 conserved common modules across grade II glioma and GBM samples.Rows correspond to genes grouped by modules and columns correspond to samples grouped by tumour grade.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3921127&req=5

pone-0086693-g007: Heat map showing expression landscape of all genes in the 7 conserved common modules across grade II glioma and GBM samples.Rows correspond to genes grouped by modules and columns correspond to samples grouped by tumour grade.
Mentions: Figure 7 shows a heat map of the expression level of individual genes in the 7 modules grouped by modules (rows) and samples by tumour grade (columns). The clear differential expression patterns of genes belonging to the same module across grades are easily observable in Figure 7. For example, activity of modules 1 and 7, corresponding to the regulation of immune response, increased with malignant progression - i.e. grade II to GBM. Taking into account that the expression arrays were performed on samples of the total tumour mass (not isolated glial cells), and the nature of the transcripts represented by the immune-associated modules, this may be a significant observation. We hypothesize that the significant loss of co-expression observed between the modules associated with cell cycle and glial differentiation and those involved in immune function is indicative of the infiltration of immune cells into the tumour mass in GBM samples. Indeed, this is in agreement with literature reports that have shown an increase in T cell infiltration into GBMs which is around 5 times more than that observed in grade II gliomas [34].

Bottom Line: We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules.We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison.Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression.

View Article: PubMed Central - PubMed

Affiliation: School of Computer Science, University of Birmingham, Birmingham, United Kingdom.

ABSTRACT
Disease module is a group of molecular components that interact intensively in the disease specific biological network. Since the connectivity and activity of disease modules may shed light on the molecular mechanisms of pathogenesis and disease progression, their identification becomes one of the most important challenges in network medicine, an emerging paradigm to study complex human disease. This paper proposes a novel algorithm, DiME (Disease Module Extraction), to identify putative disease modules from biological networks. We have developed novel heuristics to optimise Community Extraction, a module criterion originally proposed for social network analysis, to extract topological core modules from biological networks as putative disease modules. In addition, we have incorporated a statistical significance measure, B-score, to evaluate the quality of extracted modules. As an application to complex diseases, we have employed DiME to investigate the molecular mechanisms that underpin the progression of glioma, the most common type of brain tumour. We have built low (grade II)--and high (GBM)--grade glioma co-expression networks from three independent datasets and then applied DiME to extract potential disease modules from both networks for comparison. Examination of the interconnectivity of the identified modules have revealed changes in topology and module activity (expression) between low- and high- grade tumours, which are characteristic of the major shifts in the constitution and physiology of tumour cells during glioma progression. Our results suggest that transcription factors E2F4, AR and ETS1 are potential key regulators in tumour progression. Our DiME compiled software, R/C++ source code, sample data and a tutorial are available at http://www.cs.bham.ac.uk/~szh/DiME.

Show MeSH
Related in: MedlinePlus