The Application of Network Structure Aanalysis in the Study of Disease Mechanisms In this paper, we use the structural parameters of the gene correlation network to study the pathogenesis. Based on the data of gene expression profile (experimental group and control group), their correlation networks are established, and the structural parameters of networks under different thresholds are analyzed, in order to find that the structural parameters of the control group (C) and the experimental group (E) are significantly different within the full threshold. This indicates that there are clear differences in the gene network structure between C and E, and also reveales that the differences are related to the mechanism of the disease. Furthermore, the genes with the greatest contribution to structural parameters are selected as the key genes, and their functional mechanism could be obtained by analyzing their annotation. As an example, this paper mainly establishes two kinds of Pearson correlation networks based on the gene expression profile data of 30 patients with idiopathic pulmonary hypertension (PAH) (the experimental group, E) and 41 healthy people (the control group, C) from GSE33463 in NCBI database, analyzes the average clustering coefficient of the two kinds of networks under different thresholds. By annotating the first 50 genes (structural key genes) that contribute greatly to the difference of the average clustering coefficient, we find that they are mainly related to cancer, blood vessels, immunity and apoptosis, which are consistent with the conclusion of pathological research literature. Therefore, it is of great significance to study the mechanism of gene level of pulmonary hypertension by network structure parameter analysis.

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).