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 eigengene features regarding association to the risk of metastasis. Nine eigengenes were selected which show strong association with patient survival prognosis. All of the nine corresponding gene modules also have highly enriched biological functions or cytoband locations. Three of them are unique modules to RNA-Seq data, which complement the modules from microarray data in terms of survival prognosis. We then merged all eigengenes from these unique modules and used an integrative method called Similarity Network Fusion to test the prognostic power of these eigengenes for prognosis. The prognostic accuracies are significantly improved as compared to using all eigengenes, and a subgroup of patients with very poor survival rate was identified. Conclusions We first compared GCNs mined from microarray and RNA-seq data. We discovered that each data modality yields unique GCNs, which are enriched with clear biological functions. Then we do module unique analysis and use lasso-cox model to select survival-associated eigengenes. Integration of unique and survival-associated eigengenes from both data types provides complementary information that leads to more accurate survival prognosis. Reviewers Reviewed by Susmita Datta, Marco Chierici and Dimitar Vassilev. Electronic supplementary material The online version of this article (10.1186/s13062-018-0229-2) contains supplementary material, which is available to authorized users.

Significantly different immunological score in lung adenocarcinoma and squamous cell carcinoma and a proposal for a new immune staging system

“…Besides, our model contained myeloid cells, checkpoints, and multi-labeled markers, and was established based on the LASSO-COX regression model, which significantly improved the accuracy of prediction. 25,26,28 Through classifying the patient into low-IS NSCLC or high-IS NSCLC group, the prognostic risk information is easily available. After stratified by the clinicopathological factors, IS NSCLC was still a good predictor of prognosis.…”

“…Finally, a scoring system based on multi-immune features was constructed for survival predicting. [25][26][27][28] LASSO-COX regression model analysis was performed using the package "glmnet" of R software (version 3.5.3).…”

“…LASSO-COX proportional hazards (LASSO-COX) model is also broadly used in survival analysis when the data are complex and multidimensional. [25][26][27][28] In this study, we used the LASSO-COX model to construct a novel IS system named IS NSCLC on the basis of 112 immune features, covering most of the immune cell subsets, for the prediction of postoperative recurrences and clinical outcomes of LUAD (IS LUAD ) and LUSC (IS LUSC ). We further combined the IS NSCLC with TNM to form a TN-Immunoscore (TN-I) staging system for better risk stratification of NSCLC.…”

“…LASSO-COX proportional hazards (LASSO-COX) model is also broadly used in survival analysis when the data are complex and multidimensional. 25 – 28 …”

Nomogram for predicting overall survival in children with neuroblastoma based on SEER database

“…Multimodal therapy currently seems an effective treatment for NB, which includes operation, chemotherapy, radiotherapy and immunotherapy [ 4 5 6 7 8 ]. Generally, children with low-risk NB have a good prognosis and seldom need intensified therapy [ 9 10 11 ], while patients with high-risk NB are diagnosed with obvious tumor progression, showing a bad prognosis, with the long-term survival rate being 50% [ 12 13 ].…”