Integrative analysis based on survival associated co-expression gene modules for predicting Neuroblastoma patients' survival time.

ABSTRACT:

Background

More than 90% of neuroblastoma patients are cured in the low-risk group while only less than 50% for those with high-risk disease can be cured. Since the high-risk patients still have poor outcomes, we need more accurate stratification to establish an individualized precise treatment plan for the patients to improve the long-term survival rate.

Results

We focus on extracting features and providing a workflow to improve survival prediction for neuroblastoma patients. With a workflow for gene co-expression network (GCN) mining in microarray and RNA-Seq datasets, we extracted molecular features from each co-expressed module and summarized them into eigengenes. Then we adopted the lasso-regularized Cox proportional hazards model to select the most informative eigengen

SUBMITTER: Han Y 

PROVIDER: S-EPMC6375203 | biostudies-literature | 2019 Feb

REPOSITORIES: biostudies-literature