A DNA methylation-associated nomogram predicts the overall survival of osteosarcoma

Numerous reports have demonstrated that DNA methylation may be underlying prognostic biomarkers of cancer. However, few studies indicated that DNA methylation was independent biomarker for osteosarcoma prognosis. We aimed to discover and validate a novel DNA methylation signature for prediction of osteosarcoma patients’ overall survival (OS). The DNA methylation data of osteosarcoma patients was researched from The Cancer Genome Atlas (TCGA) database. Overall, 80 samples with 485,577 DNA methylation sites were enrolled in our study. The 80 samples were randomly allocated into training dataset (first two-thirds) and validation dataset (remaining one-third). Initially, the univariate Cox proportional hazard analysis was performed in the training dataset to determine methylation sites significantly (P < .05) relevant to osteosarcoma patients’ OS as underlying indicators. Subsequently, the underlying indicators were employed to carry out the least absolute shrinkage and selection operator (LASSO) Cox regression analysis for further selecting the candidate methylation sites. Then, the selected candidate methylation sites were employed as covariates to perform multivariate Cox proportional hazard model for identifying the predictor of OS in osteosarcoma patients. The validation dataset was used to validate the predictive accuracy by receiver operating characteristic (ROC) analysis and Kaplan–Meier survival analysis. We discovered a 7-DNA methylation signature closely relevant to OS of osteosarcoma patients. AUC at 1, 3, 5 years in training dataset (0.951, 0.922, 0.925, respectively), testing dataset (0.952, 0.918, 0.925, respectively), and entire dataset (0.952, 0.968, 0.968, respectively). Suggesting high predictive values for OS of osteosarcoma patients. In addition, a methylation-associated nomogram suggested good predictive value and clinical application. We discovered and validated a novel 7-DNA methylation-associated nomogram for predicting OS of osteosarcoma patients.