Title:The Integrative Network of Gene Expression, MicroRNA, Methylation and Copy Number Variation in Colon and Rectal Cancer
VOLUME: 11 ISSUE: 1
Author(s):Tao Huang, Bi-Qing Li and Yu-Dong Cai
Affiliation:College of Life Science, Shanghai University, Shanghai 200444, China
Keywords:Integrative network, gene expression, microRNA, methylation, copy number variation, colon and rectal cancer.
Abstract:Gene expression level changes in cancer patients have been studied for a long time and have
proven to be useful in classifying patients, predicting drug response, etc. But factors that control the gene
expression in pathological conditions are still unclear. Identifying the putative causal factors could greatly
help in understanding the mechanisms of cancer development and progression. It is believed that the microRNA, methylation
and copy number variation (CNV) are possible regulators in gene expression. We analyzed the profiles of gene expression,
microRNA, methylation and CNV in colon and rectal cancer patients. A multiple regression based method was developed to
construct an integrative network including different levels of components. The regulatory effects of microRNA, methylation
and copy number variation on gene expression were evaluated and compared. The cis-regulation effects from methylation to
gene expression are very strong. 43.2% of cis methylation-expression appears as the most important methylation-expression
regulator. 80.0% of cis methylation-expression is within the top five most important methylation-expression regulators. The
functions of microRNA dominated genes, methylation dominated genes and CNV dominated genes were analyzed. The CNV
dominated genes were involved in gene expression, protein modification and protein kinase activity. The methylation
dominated genes showed notable immune response and calcium ion binding. The microRNA dominated genes were notable
with regard to biological regulation and signal transduction. By decomposing the networks of gene, microRNA, methylation
and CNV, the network modules were identified. Some modules provided useful clues about the mechanisms of gene expression
regulation. Our methods provide a general framework for studying the integrative network derived from multiple large-scale
biological data. The R script for our integrative network construction method is available from supporting information.
