Identifying Structural Changes in Correlation Networks Models of Cancer Gene Expression by Stage Gene expression analysis using correlation network modeling can help to identify systemslevel cellular changes and cooperation among genes. Network modeling is a relatively novel method for comparing changes across stages of cancer, or between primary tumor tissue and the metastatic tissue. In this study, we develop a pipeline to identify the dynamic changes of cancer in gene expression level through timedependent network analysis of cancer data from The Cancer Genome Atlas (TCGA). A total of 16 correlation networks were built from four (4) stages in four (4) different types of cancers: Thyroid Carcinoma, Colon Adenocarcinoma and Rectum Adenocarcinoma, Stomach Adenocarcinoma, and Kidney Renal Clear Cell Carcinoma. To identify the basic changes in network structure, we performed Jaccard similarity comparison of structurally relevant nodes. We employed mutation analysis to measure and present the timebased changes in mutation rate of genes that are specific to each cancer type. Finally, we present a case study to identify the gene expression changes among primary tumor tissue and metastatic tissue in skin cutaneous melanoma (SKCM).

Cancer Biomarker Discovery from Gene Coexpression Networks Using Community Detection Methods Finding the network biomarkers of cancers and the analysis of cancer driving genes that are involved in these biomarkers are essential for understanding the dynamics of cancer. Clusters of genes in coexpression networks are commonly known as functional units. This work is based on the hypothesis that the dense clusters or communities in the gene coexpression networks of cancer patients may represent functional units regarding cancer initiation and progression. In this study, RNAseq gene expression data of three cancers  Breast Invasive Carcinoma (BRCA), Colorectal Adenocarcinoma (COAD) and Glioblastoma Multiforme (GBM)  from The Cancer Genome Atlas (TCGA) are used to construct gene coexpression networks using Pearson Correlation. Six wellknown community detection algorithms are applied on these networks to identify communities with five or more genes. A permutation test is performed to further mine the communities that are conserved in other cancers, thus calling them conserved communities. Then survival analysis is performed on clinical data of three cancers using the conserved community genes as prognostic covariates. The communities that could distinguish the cancer patients between high and lowrisk groups are considered as cancer biomarkers. In the present study, 16 such network biomarkers are discovered.