Leiomyosarcoma: Identification of Hub Genes and Pathways based on Co-expression Network Analysis

Sarcomas are heterogeneous and rare mesenchymal malignancies that grow from different tissues. Leiomyosarcoma is the most popular soft tissue sarcomas. The currently available systematic treatments for this cancer DUHQʼW VWLOO effective. Moreover, treatment options for this cancer are rare due to the insufficient number of patients and clinical trials. In addition, heterogeneous nature of this rare tumor prevents categorization of patients for personalized medicine. Increasing knowledge about molecular characteristics of this type of cancer helps physician for better therapeutic options. Recently, high throughput technologies generate opportunities for discovering new insight into different aspects of the biological system. This opportunity may compensate the rare numbers of clinical trials in finding new treatments in leiomyosarcoma. So, Network constructing and analysis of gene co-expression is one of the advanced approaches for gaining insight via high throughput data set. Some information about gene functions in leiomyosarcoma currently available but it needs more investigation. Here, Weighted Gene Correlation Network Analysis (WGCNA) algorithm is used as a system biology method for constructing leiomyosarcoma co-expression network. The data set of transcriptomics profiling for 80 leiomyosarcoma cases was downloaded via The Cancer Genome Atlas (TCGA) project. We are going to provide functional annotations of genes whose function are unknown. So, clusters (modules) are obtained through the Dynamic Tree Cut method. For exploring hub genes in leiomyosarcoma, Degree centrality analysis of the co-expression network is provided. And for pathway-enrichment analysis, we use the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. During this research, we expect to find some modules that insight us for a better understanding of this cancer. It may help division of patients based on their molecular patterns for personalized medicine and finding probable targets for therapy.

کلیدواژه ها:

Sarcoma ، Leiomyosarcoma ، Gene Co-expression Network ، WGCNA ، Clustering