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| Paper IPM / Biological Sciences / 14184 |
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| Abstract: | |||||
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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 ï¬nd 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 coefï¬cient, 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 conï¬rms 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 ï¬nding universal
pattern
in different types of cancers based on network topological
statistics.
It also shows that decision tree models (pattern recognition)
are
successful in ï¬nding the pattern of cancer induction based on the
important
network statistics.
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