A novel prognostic biomarker for muscle invasive bladder urothelial carcinoma based on 11 DNA methylation signature
| Autor: | Yun Jiang, Qingting Feng, Yiqing Jiang, Lingkai Xu, Xiaochen Shu, Fang Meng, Yueyi Feng |
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| Rok vydání: | 2020 |
| Předmět: |
0301 basic medicine
Cancer Research Poor prognosis Bladder Urothelial Carcinoma 03 medical and health sciences 0302 clinical medicine Risk Factors Biomarkers Tumor Medicine Humans Prognostic biomarker Pharmacology Invasive carcinoma business.industry Muscle invasive Methylation Nomogram DNA Methylation Middle Aged Prognosis Survival Analysis 030104 developmental biology Oncology Urinary Bladder Neoplasms 030220 oncology & carcinogenesis DNA methylation Cancer research Molecular Medicine business Research Paper |
| Zdroj: | Cancer Biol Ther |
| ISSN: | 1555-8576 |
| Popis: | Muscle-invasive bladder urothelial carcinoma (MIBC) is a highly invasive cancer, which leads to prevalent recurrence and poor prognosis. Exploring the association of DNA methylation and the prognosis of MIBC will thus be of important value in clinical management and treatment. Bumphunter method and adaptive lasso regression were used to explore the relationship between different methylation regions (DMRs) and the prognosis of MIBC. Next, we constructed a risk prognosis model and validated this model. Moreover, the performance of this risk model was examined by using time-dependent receiver operating characteristic curve (ROC). We identified 58,449 different methylation sites and 490 different methylation regions. Among them, 11 DMRs were associated with the prognosis of MIBC through rigorous screening. Through the linear combination of 11 DMRs, a putative marker was developed, which can distinguish the survival risk in both the training dataset (HR = 2.58, 95% CI = (1.64, 4.05)) and the verification dataset (HR = 2.77, 95% CI = (1.25, 6.15)). Relatively high predictive values were observed from this model for training dataset (AUC = 0.791) and verification dataset (AUC = 0.668). Stratified analysis showed that the association was independent of gender. A nomogram was additionally generated to predict 5-year survival probability containing risk score and pathological stage. Its performance was evaluated by applying calibration curve. The methylation signature risk model based on 11 DMRs may be a reliable prognostic signature for MIBC, which provides new insights into development of individualized therapy for MIBC. |
| Databáze: | OpenAIRE |
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