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IPM
30
YEARS OLD

“School of Biological Sciences”

Paper   IPM / Biological Sciences / 14184
   School of Biological Sciences
  Title: Does the overall shape of gene networks differ between cancer and normal states? Towards a comprehensive understanding of cancer system biology by meta-analysis of various cancer transcriptomes
  Author(s):
1 . P. Khosravi.
2 . E. Ebrahimie.
  Status: Published
  Journal: The RECOMB/ISCB conference on Regulatory and System Genomics with DREAM challenge
  Year: 2015
  Pages: 101-102
  Supported by: IPM
  Abstract:
Recent advances in computational biology have provided the possibility of formulating the characteristics of gene networks in terms of network topology statistics. The aim of the present study is to find thepossible network topology rules which can distinguish different types of cancer from normal state. To this end, meta-analysis is employed to analyse the gene regulatory networks of 8 different types of cancer (breast, cervical, esophageal, head and neck, leukemia, prostate, rectal, lung and two subtypes of lung cancer (small cell lung and non-small cell lung)) in comparison to normal state. Microarray data were downloaded from the GEO database, NCBI. Gene regulatory networks were constructed using the ARACNE algorithm through the Cyni toolbox; consequently, 20 network statistics were calculated using NetworkAnalyzer plugin for Cytoscape. These statistics mainly describe number of edges, clustering coefficient, connected components, network diameter, network centralization, characteristics path length, average number of neighbors, number of nodes, network density, and heterogeneity in networks. Discriminant function analysis show that number of edges, network diameter, and average number of neighbors are the main network topology statistics which discriminate cancer networks from normal ones. Cancer networks have lower number of edges with shorter diameter, and fewer number of neighbors that confirms the extensive networks rewiring during cancer progression. Discriminant function analysis is able to predict gene network of cancer from normal with 70test. PCA analysis demonstrates the similarity in network statistics between cervical cancer and breast cancer. Lung cancer have a distinguished different network pattern with low network centralization and diameter. This study demonstrates the possibility of finding universal pattern in different types of cancers based on network topological statistics. It also shows that decision tree models (pattern recognition) are successful in finding the pattern of cancer induction based on the important network statistics.

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