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Yang S, Zeng L, Jin X, Lin H, Song J. Feature Genes in Neuroblastoma Distinguishing High-Risk and Non-high-Risk Neuroblastoma Patients: Development and Validation Combining Random Forest With Artificial Neural Network. Front Med (Lausanne) 2022; 9:882348. [PMID: 35911385 PMCID: PMC9336509 DOI: 10.3389/fmed.2022.882348] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/13/2022] [Indexed: 11/13/2022] Open
Abstract
There is a significant difference in prognosis among different risk groups. Therefore, it is of great significance to correctly identify the risk grouping of children. Using the genomic data of neuroblastoma samples in public databases, we used GSE49710 as the training set data to calculate the feature genes of the high-risk group and non-high-risk group samples based on the random forest (RF) algorithm and artificial neural network (ANN) algorithm. The screening results of RF showed that EPS8L1, PLCD4, CHD5, NTRK1, and SLC22A4 were the feature differentially expressed genes (DEGs) of high-risk neuroblastoma. The prediction model based on gene expression data in this study showed high overall accuracy and precision in both the training set and the test set (AUC = 0.998 in GSE49710 and AUC = 0.858 in GSE73517). Kaplan–Meier plotter showed that the overall survival and progression-free survival of patients in the low-risk subgroup were significantly better than those in the high-risk subgroup [HR: 3.86 (95% CI: 2.44–6.10) and HR: 3.03 (95% CI: 2.03–4.52), respectively]. Our ANN-based model has better classification performance than the SVM-based model and XGboost-based model. Nevertheless, more convincing data sets and machine learning algorithms will be needed to build diagnostic models for individual organization types in the future.
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Affiliation(s)
- Sha Yang
- Department of Surgery, Children’s Hospital of Chongqing Medical University, Chongqing, China
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
- Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Lingfeng Zeng
- Department of Nephrology, The Second Xiangya Hospital of Central South University, Changsha, China
| | - Xin Jin
- Ministry of Education Key Laboratory of Child Development and Disorders, Chongqing, China
- National Clinical Research Center for Child Health and Disorders, Chongqing, China
- China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing, China
- Chongqing Key Laboratory of Pediatrics, Chongqing, China
- Chongqing Engineering Research Center of Stem Cell Therapy, Chongqing, China
- Children’s Hospital of Chongqing Medical University, Chongqing, China
- Department of Cardiacthoracic, Children’s Hospital of Chongqing Medical University, Chongqing, China
| | - Huapeng Lin
- Department of Intensive Care Unit, Affiliated Hangzhou First People’s Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Jianning Song
- Department of General Surgery, Guiqian International General Hospital, Guiyang, China
- *Correspondence: Jianning Song, ,
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