J Cancer 2017; 8(16):3261-3267. doi:10.7150/jca.21261 This issue
1. Dept. Surgical Urology, The Affiliated Longhua District People's Hospital of Southern Medical University, Shenzhen 518109, China;
2. Dept. Cell Biology and Genetics, Shenzhen University Health Science Center, Shenzhen 518060, China;
3. Dept. Surgical Urology, The third affiliated hospital, Sun Yat-Sen University, Guangzhou 510630, China;
4. First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou 310003, China.
*Shengping Zhang and Yafei Xu contributed equally to this article.
Prostate cancer is a leading male malignancy worldwide, while the prognosis prediction remains quite inaccurate. The study aimed to observe whether there was an association between the prognosis of prostate cancer and genetic mutation profile, and to build an accurate prognostic predictor based on the genetic signatures. The patients diagnosed of prostate cancer from The Cancer Genomic Atlas were used for prognostic stratification, while the somatic gene mutation profiles were compared between different prognostic groups. The genetic features were further used for training machine-learning models to predict prostate cancer prognosis. No significant gene with somatic mutation rate difference was found between prognostic groups of prostate cancer. Total 43 atypical genes were screened for building a support vector machine model to predict prostate cancer prognosis, with an average accuracy of 66% and 64% for 5-fold cross-validation or training-testing evaluation respectively. When combined with the National Institute for Health and Care Excellence (NICE) features, the model could be further improved, with the 5-fold cross-validation accuracy of ~71%, much better than NICE itself (62%). To our knowledge, for the first time, the research studied the relationship of genome-wide somatic mutations with prostate prognosis, and developed an effective prognostic prediction model with the atypical genetic signatures.
Keywords: prostate cancer, somatic mutation, prognosis prediction, atypical features, support vector machine