title: Predicting essential genes and synthetic lethality via influence propagation in signaling pathways of cancer cell fates.
abstract: A major goal of personalized anti-cancer therapy is to increase the drug effects while reducing the side effects as much as possible. A novel therapeutic strategy called synthetic lethality (SL) provides a great opportunity to achieve this goal. SL arises if mutations of both genes lead to cell death while mutation of either single gene does not. Hence, the SL partner of a gene mutated only in cancer cells could be a promising drug target, and the identification of SL pairs of genes is of great significance in pharmaceutical industry. In this paper, we propose a hybridized method to predict SL pairs of genes. We combine a data-driven model with knowledge of signalling pathways to simulate the influence of single gene knock-down and double genes knock-down to cell death. A pair of genes is considered as an SL candidate when double knock-down increases the probability of cell death significantly, but single knock-down does not. The single gene knock-down is confirmed according to the human essential genes database. Our validation against literatures shows that the predicted SL candidates agree well with wet-lab experiments. A few novel reliable SL candidates are also predicted by our model.
authors: School of Computer Engineering, Nanyang Technological University, Singapore 639798, Singapore.
authors: Fan
authors: F
authors: Zhang
authors: Min
authors: M
authors: Wu
authors: Xue-Juan
authors: XJ
authors: Li
authors: Xiao-Li
authors: XL
authors: Li
authors: Chee Keong
authors: CK
authors: Kwoh
authors: Jie
authors: J
authors: Zheng
mesh_terms: D000465:Algorithms; D016923:Cell Death / Q000235:genetics; D003198:Computer Simulation; D055785:Gene Knockdown Techniques / Q000379:methods*; D020043:Genes, Essential; D006801:Humans; D008957:Models, Genetic; D009369:Neoplasms / Q000235:genetics* / Q000473:pathology; D057285:Precision Medicine / Q000379:methods*; D015398:Signal Transduction / Q000235:genetics
keywords: Synthetic lethality; data-driven; signaling pathways