(BQ) Part 2 book “Cancer systems biology” has contents: Cancer gene prediction using a network approach, cancer genomics to cancer biology, tumors and their microenvironments, gene set and pathway-based analysis for cancer omics, and other contents. | Chapter 11 Cancer Gene Prediction Using a Network Approach Xuebing Wu and Shao Li Contents Introduction Molecular Networks and Human Diseases Network Approach for Cancer Gene Prediction Prioritize by Network Proximity Proximity to Known Disease Genes of the Same Disease Proximity of Candidate Gene Pairs: Enabling de Novo Discovery Phenotype Similarity-Assisted Methods Calculating and Validating Phenotypic Similarity Modeling with Molecular Network and Phenotype Similarity Prioritize by Network Centrality Centrality in a Context-Specific Gene Network Centrality in a Genomic-Phenomic Network Other Methods Discussion Acknowledgments References 191 192 195 196 196 200 200 200 202 205 205 205 206 207 208 208 •‡ Introduction Cancer is a genetic disease (Vogelstein and Kinzler 2004). Decades of research in molecular genetics have identified a number of important genes responsible for the genesis of various types of cancer (Futreal et al. 2004) and drugs targeting these mutated cancer genes have brought dramatic therapeutic advances and substantially improved and prolonged the lives of cancer patients (Huang and Harari 1999). However, cancer is extremely complex and heterogeneous. It has been suggested that 5% to 10% of the human genes probably contribute to oncogenesis (Strausberg, Simpson, and Wooster 2003), while current experimentally validated cancer genes only cover 1% of human genome (Futreal et al. 2004), 191 192 ◾ Xuebing Wu and Shao Li suggesting that there are still hundreds or even thousands of cancer genes that remain to be identified. For example, in breast cancer, known susceptibility genes, including BRCA1 (Miki et al. 1994) and BRCA2 (Wooster et al. 1995), can only explain less than 5% of the total breast cancer incidence and less than 25% of the familial risk (Oldenburg et al. 2007). The same challenge is also faced by .
