Feature Genes in Neuroblastoma Distinguishing High-Risk and Non-high-Risk Neuroblastoma Patients: Development and Validation Combining Random Forest With Artificial Neural Network

作者全名："Yang, Sha; Zeng, Lingfeng; Jin, Xin; Lin, Huapeng; Song, Jianning"

作者地址："[Yang, Sha] Chongqing Med Univ, Childrens Hosp, Dept Surg, Chongqing, Peoples R China; [Yang, Sha; Jin, Xin] Minist Educ, Key Lab Child Dev & Disorders, Chongqing, Peoples R China; [Yang, Sha; Jin, Xin] Natl Clin Res Ctr Child Hlth & Disorders, Chongqing, Peoples R China; [Yang, Sha; Jin, Xin] China Int Sci & Technol Cooperat Base Child Dev &, Chongqing, Peoples R China; [Yang, Sha; Jin, Xin] Chongqing Key Lab Pediat, Chongqing, Peoples R China; [Yang, Sha; Jin, Xin] Chongqing Engn Res Ctr Stem Cell Therapy, Chongqing, Peoples R China; [Yang, Sha; Jin, Xin] Chongqing Med Univ, Childrens Hosp, Chongqing, Peoples R China; [Zeng, Lingfeng] Cent South Univ, Xiangya Hosp 2, Dept Nephrol, Changsha, Peoples R China; [Jin, Xin] Chongqing Med Univ, Childrens Hosp, Dept Cardiacthorac, Chongqing, Peoples R China; [Lin, Huapeng] Zhejiang Univ, Affiliated Hangzhou Peoples Hosp 1, Sch Med, Dept Intens Care Unit, Hangzhou, Peoples R China; [Song, Jianning] Guiqian Int Gen Hosp, Dept Gen Surg, Guiyang, Peoples R China"

通信作者："Song, JN (通讯作者)，Guiqian Int Gen Hosp, Dept Gen Surg, Guiyang, Peoples R China."

来源：FRONTIERS IN MEDICINE

ESI学科分类：CLINICAL MEDICINE

WOS号：WOS:000834259200001

JCR分区：Q1

影响因子：3.9

年份：2022

卷号：9

期号：　

开始页：　

结束页：　

文献类型：Article

关键词：neuroblastoma; random forest; artificial neural network; high-risk category; genes

摘要："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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