PredDSMC: A predictor for driver synonymous mutations in human cancers - Wang, Lihua; Sun, Jianhui; Ma, Shunshuai; Xia, Junfeng; Li, Xiaoyan (Frontiers in Genetics, 2023)
Driver mutations play a critical role in the occurrence and development of human cancers. Most studies have focused on missense mutations that function as drivers in cancer. However, accumulating experimental evidence indicates that synonymous mutations also can act as driver mutations. Here, we proposed a computational method called PredDSMC to accurately predict driver synonymous mutations in human cancers. We first systematically explored four categories of multimodal features, including sequence features, splicing features, conservation scores, and functional scores. Further feature selection was carried out to remove redundant features and improve the model performance. Finally, we utilized the random forest classifier to build PredDSMC. Results on two independent test sets indicated that PredDSMC outperformed the state-of-the-art methods in differentiating driver synonymous mutations from passenger mutations. In conclusion, we expect PredDSMC, as a driver synonymous mutation prediction method, will be a valuable method for a deeper understanding of synonymous mutations in human cancers.