scholarly journals The Evolutionary Landscape of Pan-Cancer Drives Clinical Aggression

2018 ◽  
Author(s):  
Shichao Pang ◽  
Leilei Wu ◽  
Xin Shen ◽  
Yidi Sun ◽  
Jingfang Wang ◽  
...  
Keyword(s):  
Evolutionary Process ◽  
Protein Interaction Networks ◽  
Cancer Evolution ◽  
Evolutionary Patterns ◽  
Protein Protein Interaction ◽  
Differentially Expressed Protein ◽  
Cancer Types ◽  
Protein Protein Interaction Networks ◽  
The Difference ◽  
Pan Cancer

AbstractAlthough cancer mechanisms differ from occurrence and development, some of them have similar oncogenesis, which leads to similar clinical phenotypes. Most existing genotyping studies look at “omics” data, but intentionally or unintentionally avoided that cancer is a time-dependent evolutionary process, biologically represented by the time evolution of tumor clones. We used the Bayesian mutation landscape approach to reconstruct the evolutionary process of cancer by acquiring somatic mutation data consisting of 21 cancer types. Four representative evolution patterns of pan-cancer have been discovered: trees, chaos, biconvex, and Cambrian, and a strong correlation between these four evolutionary patterns and clinical aggressivity. We further explained the characteristics of the corresponding biological systems in the evolution of pan cancer by analyzing the function of differentially expressed protein-protein interaction networks. Our results explained the difference in clinical aggressivity between cancer evolution patterns from the evolution of tumor clones and exposed the functional mechanism behind.

scholarly journals Discovering Distinct Functional Modules of Specific Cancer Types Using Protein-Protein Interaction Networks

2015 ◽  
Vol 2015 ◽  
pp. 1-10 ◽  
Author(s):  
Ru Shen ◽  
Xiaosheng Wang ◽  
Chittibabu Guda
Keyword(s):  
Protein Interaction ◽  
Cellular Systems ◽  
Protein Interaction Networks ◽  
Interaction Networks ◽  
Cancer Type ◽  
Functional Modules ◽  
Protein Protein Interaction ◽  
Specific Cancer ◽  
Cancer Types ◽  
Protein Protein Interaction Networks

Background. The molecular profiles exhibited in different cancer types are very different; hence, discovering distinct functional modules associated with specific cancer types is very important to understand the distinct functions associated with them. Protein-protein interaction networks carry vital information about molecular interactions in cellular systems, and identification of functional modules (subgraphs) in these networks is one of the most important applications of biological network analysis.Results. In this study, we developed a new graph theory based method to identify distinct functional modules from nine different cancer protein-protein interaction networks. The method is composed of three major steps: (i) extracting modules from protein-protein interaction networks using network clustering algorithms; (ii) identifying distinct subgraphs from the derived modules; and (iii) identifying distinct subgraph patterns from distinct subgraphs. The subgraph patterns were evaluated using experimentally determined cancer-specific protein-protein interaction data from the Ingenuity knowledgebase, to identify distinct functional modules that are specific to each cancer type.Conclusion. We identified cancer-type specific subgraph patterns that may represent the functional modules involved in the molecular pathogenesis of different cancer types. Our method can serve as an effective tool to discover cancer-type specific functional modules from large protein-protein interaction networks.

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