Module-based differential expression for exploring tumor heterogeneity in 22 tumor types

• Main Authors: Lin, Yu-Wei, 林昱葦
• Other Authors: Yang, Jinn-Moon
• Format: Others
• Language: zh-TW
• Published: 2014
• Online Access: http://ndltd.ncl.edu.tw/handle/zz39m3

碩士 === 國立交通大學 === 生物科技系所 === 103 === Cancer is one of the most common causes of death in human. Recent studies have indicated that phenotypic and genetic heterogeneity, including disease mechanisms, metastasis risk, and therapy, exist not only between tumors but also between individuals with same tumor. This heterogeneity is one of the reasons for the difficulties in developing cancer therapy. For comprehensive understanding tumor heterogeneity, high-throughput genomic data (e.g., microarray and next generation sequencing ) has been widely used to explore the difference between normal and cancer cells. These significantly expressed genes were treated as potential biomarkers. In cells, a module is a group of proteins that are often highly connected and perform a certain kind of biological functions. To explore tumor heterogeneity, previous studies used gene expression data to identify modules, and described their involved biological processes using the biological network derived from experimental data or KEGG pathways. However, most of these studies are still limited to the use of well-known pathways and biological networks. In our previous studies, the homologous mapping networks are constructed through experimental protein-protein interactions (PPIs) and homologous predicting PPIs. Next, we have proposed that evolutionary conservation could be used to identify comprehensive modules in homologous mapping networks. Here, we proposed a new strategy, “module-base differential expression”, to integrate gene expression data sets across 22 different tumor types (666 normal samples and 522 tumor samples) from Gene Expression Omnibus database into comprehensive modules. For exploring tumor heterogeneity in 22 tumor types, significant differential expression modules are identified for each tumor types, and are used to establish differential expression network. We examined the analysis applicability of tumor heterogeneity for comprehensive modules, and found that the comprehensive modules contained 79.24% of known cancer-related genes and 79.67% genes that involved in KEGG cancer-related pathway. For predicting cancer-related genes, the precision of our strategy is higher than one of gene-based strategy. In addition, for clustering 22 different types of tumors, our strategy can categorize different types of cancers with same tissue, but gene-based strategy not. Moreover, the results show that up-regulated modules in most of tumor types are involved in the regulation of cell cycle; down-regulated modules are involved in cell-cell signaling and transport. We believe that the strategy of module-based differential expression is useful for exploring tumor heterogeneity and identifying biomarkers in specific tumor type.