Abstract
Chemotherapy agents can cause serious adverse effects by attacking both cancer tissues and normal tissues. Therefore, we proposed a synthetic lethality (SL) concept-based computational method to identify specific anticancer drug targets. First, a 3-step screening strategy (network-based, frequency-based and function-based screening) was proposed to identify the SL gene pairs by mining 697 cancer genes and the human signaling network, which had 6306 proteins and 62937 protein-protein interactions. The network-based screening was composed of a stability score constructed using a network information centrality measure (the average shortest path length) and the distance-based screening between the cancer gene and the non-cancer gene. Then, the non-cancer genes were extracted and annotated using drug-target interaction and drug description information to obtain potential anticancer drug targets. Finally, the human SL data in SynLethDB, the existing drug sensitivity data and text-mining were utilized for target validation. We successfully identified 2555 SL gene pairs and 57 potential anticancer drug targets. Among them, CDK1, CDK2, PLK1 and WEE1 were verified by all three aspects and could be preferentially used in specific targeted therapy in the future.
Highlights
To identify synthetic lethality (SL) interactions that could be efficacious in treating cancer, many approaches have been proposed
Computational approaches, which can help to identify and prioritize potential SL gene pairs for further experimental validation, represent an attractive alternative compared to genome-wide small interfering RNA (siRNA) or CRISPR-based human cell line screening approaches
Non-cancer genes were paired with cancer genes to form 515780 (740 × 697) gene pairs, which were used as input data for the following 3-step screening strategy for identifying SL gene pairs
Summary
To identify SL interactions that could be efficacious in treating cancer, many approaches have been proposed. Compared to model genetic systems, human cell systems face greater challenges for genome-wide siRNA or CRISPR screening These approaches are considerably more expensive, labor-intensive, time consuming and many of the essential genes so identified turn out to be either restricted to only these cell-line models or are in frequently overexpressed in cancers[16]. Computational approaches, which can help to identify and prioritize potential SL gene pairs for further experimental validation, represent an attractive alternative compared to genome-wide siRNA or CRISPR-based human cell line screening approaches. The SL strategy contributes to the identification of anticancer drug targets and drug redirection
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