Abstract
Finding the biomarkers of cancers and the analysis of cancer-driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Gene expression profiling has been widely used for cancer research, and its patterns, combined with statistical and computational techniques have been explored in many types of cancer. Genes having correlations in terms of expression may form complexes, pathways, or participate in regulatory and signaling circuits [1]-[3]. Clusters of genes in co-expression networks are commonly used as functional units. Gene co-expressed across multiple samples are more likely to correspond to functional groups. Analysis of gene co-expression networks of different types of cancer found that various cancers share common characteristics and functions and genes related to these characteristics were found in [4]-[6]. This work is based on the hypothesis established by previous works that the dense clusters or communities in the gene co-expression networks of cancer patients may represent functional units regarding cancer initiation and progression.
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