Research Paper Volume 12, Issue 21 pp 21481—21503
Two machine learning methods identify a metastasis-related prognostic model that predicts overall survival in medulloblastoma patients
- 1 Department of Neurosurgery, Shanghai East Hospital, Tongji University School of Medicine, Shanghai 200120, P.R. China
- 2 Huamu Community Health Service Center, Shanghai 201204, P.R. China
Received: February 26, 2020 Accepted: July 30, 2020 Published: November 5, 2020https://doi.org/10.18632/aging.103923
How to Cite
Copyright: © 2020 Chen et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Approximately 30% of medulloblastoma (MB) patients exhibit metastasis at initial diagnosis, which often leads to a poor prognosis. Here, by using univariate Cox regression analysis, two machine learning methods (Lasso-penalized Cox regression and random survival forest-variable hunting (RSF-VH)), and multivariate Cox regression analysis, we established two metastasis-related prognostic models, including the 47-mRNA-based model based on the Lasso method and the 21-mRNA-based model based on the RSF-VH method. In terms of the results of the receiver operating characteristic (ROC) curve analyses, we selected the 47-mRNA metastasis-associated model with the higher area under the curve (AUC). The 47-mRNA-based prognostic model could classify MB patients into two subgroups with different prognoses. The ROC analyses also suggested that the 47-mRNA metastasis-associated model may have a better predictive ability than MB subgroup. Multivariable Cox regression analysis demonstrated that the 47-mRNA-based model was independent of other clinical characteristics. In addition, a nomogram comprising the 47-mRNA-based model was built. The results of ROC analyses suggested that the nomogram had good discrimination ability. Our 47-mRNA metastasis-related prognostic model and nomogram might be an efficient and valuable tool for overall survival (OS) prediction and provide information for individualized treatment decisions in patients with MB.
MB: Medulloblastoma; RSF-VH: Random survival forest-variable hunting; ROC: Receiver operating characteristic; AUC: Area under the curve; WGCNA: Weighted gene co-expression network analysis; GO: Gene ontology; OS: Overall survival; GEO: Gene Expression Omnibus; DEGs: Differentially expressed genes; GS: Gene significance; MM: Module membership; MRI: Magnetic resonance imaging; CSF: Cerebrospinal fluid.