J Cancer 2019; 10(20):4921-4931. doi:10.7150/jca.30618
Identification of Prognostic Signatures for Predicting the Overall Survival of Uveal Melanoma Patients
1. Department of Dermatology, Zhongshan Hospital, Fudan University, Shanghai 200032, China.
2. State Key Laboratory of Genetic Engineering, School of Life Sciences and Human Phenome Institute, Fudan University, Shanghai 200438, China.
3. Department of Hematology, The First Affiliated Hospital of Soochow University, Shizijie Campus: NO.188, Shizijie Road, Suzhou 215006, P. R. China.
4. Department of Medical Oncology, Fudan University Shanghai Cancer Center, 270 Dong-An Road, Shanghai 200032, China.
5. Department of Hepatobiliary and Pancreatic Surgery, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
# First authors share an equal contribution
Xue M, Shang J, Chen B, Yang Z, Song Q, Sun X, Chen J, Yang J. Identification of Prognostic Signatures for Predicting the Overall Survival of Uveal Melanoma Patients. J Cancer 2019; 10(20):4921-4931. doi:10.7150/jca.30618. Available from http://www.jcancer.org/v10p4921.htm
Uveal melanoma (UM) is an aggressive cancer which has a high percentage of metastasis and with a poor prognosis. Identifying the potential prognostic markers of uveal melanoma may provide information for early detection of metastasis and treatment. In this work, we analyzed 80 uveal melanoma samples from The Cancer Genome Atlas (TCGA). We developed an 18-gene signature which can significantly predict the prognosis of UM patients. Firstly, we performed a univariate Cox regression analysis to identify significantly prognostic genes in uveal melanoma (P<0.01). Then the glmnet Cox analysis was used to generate a powerful prognostic gene model. Further, we established a risk score formula for every patient based on the 18-gene prognostic model with multivariate Cox regression. We stratified patients into high- and low-risk subtypes with median risk score and found that patients in high-risk group had worse prognosis than patients in low-risk group. Multivariate Cox regression analysis demonstrated that 18-gene model risk score was independent of clinical prognostic factors. We identified four genes whose mutations were closely to UM patients' prognosis or risk score. We also explored the relationship between copy number variation and risk score and found that high risk group showed more chromosome aberrations than low risk group. Gene Set Enrichment Analysis (GSEA) analysis showed that the different biological pathways and functions between low and high risk group. In summary, our findings constructed an 18-gene signature for estimating overall survival (OS) of UM. Patients were categorized into two subtypes based on the risk score and we found that high risk group showed more chromosome aberrations than low risk group.
Keywords: Uveal melanoma, Prognosis, Gene mutations, Copy number variants