Synergistic machine learning models utilizing ferroptosis-related genes for improved neuroblastoma outcome prediction.

IF 1.5 4区 医学 Q2 PEDIATRICS
Translational pediatrics Pub Date : 2024-12-31 Epub Date: 2024-12-27 DOI:10.21037/tp-24-323
Jian Cheng, Xiao Dong, Yang Yang, Xiaohan Qin, Xing Zhou, Da Zhang
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引用次数: 0

Abstract

Background: Neuroblastoma (NB) is a highly heterogeneous and common pediatric malignancy with a poor prognosis. Ferroptosis, an iron-dependent cell death pathway, may play a crucial role in NB tumor progression and immune response. This study aimed to investigate ferroptosis in NB to identify potential therapeutic targets and develop predictive models for prognosis and recurrence.

Methods: Six datasets were accessed from the ArrayExpress database and Gene Expression Omnibus. Ferroptosis-related genes (FRGs) were selected from the FerrDb website. Unsupervised clustering, differential expression analysis, weighted correlation network analysis (WGCNA), and gene set enrichment analysis (GSEA) were adopted to investigate potential pathways associated with ferroptosis in NB and identify the key genes involved. We used the least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression to develop the ferroptosis-related prognostic signatures (FRPS) while using machine learning (ML) algorithms to construct the recurrence model.

Results: Ribosome and cell cycle may be the potential pathways for ferroptosis involved in NB, with MYCN and RRM2 identified as key genes in this regulatory process. Five FRGs-ATG7 (-1.009), ELAVL1 (1.739), PPARA (0.493), RDX6 (1.457), and TERT (0.247)-were screed out for the FRPS, which showed excellent predictive performance in comparison with other published NB signatures. Eight FRGs-ALDH3A2 (48.597), TERT (23.398), ULK2 (21.034), AKR1C1 (20.699), MFN2 (12.575), SLC16A1 (12.342), TF (10.240), and DDR2 (7.598)-were selected based on the importance scores to construct the recurrence model. Among the models, utilizing random forest (RF), XGboost, support vector machine (SVM), K-nearest neighbors (KNN), and linear discriminant analysis (LDA), the RF model exhibited the highest performance.

Conclusions: We investigated the potential ferroptosis-related pathways and hub- FRGs in NB and developed prognosis and recurrence models, providing new potential targets for prognostic evaluation and treatment in NB patients.

利用嗜铁相关基因的协同机器学习模型改善神经母细胞瘤预后预测。
背景:神经母细胞瘤(NB)是一种高度异质性和常见的儿童恶性肿瘤,预后较差。铁凋亡是一种铁依赖性细胞死亡途径,可能在NB肿瘤的进展和免疫反应中起重要作用。本研究旨在研究NB中铁下垂,以确定潜在的治疗靶点,并建立预后和复发的预测模型。方法:从ArrayExpress数据库和Gene Expression Omnibus中获取6个数据集。从FerrDb网站上选择与铁凋亡相关的基因(FRGs)。采用无监督聚类、差异表达分析、加权相关网络分析(WGCNA)和基因集富集分析(GSEA)等方法探讨NB中铁下垂的潜在相关途径,并确定关键基因。我们使用最小绝对收缩和选择算子(LASSO)和多变量Cox回归来开发与铁死相关的预后特征(FRPS),同时使用机器学习(ML)算法构建递归模型。结果:核糖体和细胞周期可能是NB参与铁死亡的潜在途径,MYCN和RRM2是这一调控过程的关键基因。五个FRGs-ATG7 (-1.009), ELAVL1 (1.739), PPARA (0.493), RDX6(1.457)和TERT(0.247)-被筛选出来用于FRPS,与其他已发表的NB特征相比,FRPS表现出出色的预测性能。根据重要性评分选择8个FRGs-ALDH3A2(48.597)、TERT(23.398)、ULK2(21.034)、AKR1C1(20.699)、MFN2(12.575)、SLC16A1(12.342)、TF(10.240)、DDR2(7.598)构建递归模型。其中,利用随机森林(RF)、XGboost、支持向量机(SVM)、k近邻(KNN)和线性判别分析(LDA)的RF模型表现出最高的性能。结论:我们研究了NB中潜在的凋亡相关通路和hub- FRGs,建立了NB患者的预后和复发模型,为NB患者的预后评估和治疗提供了新的潜在靶点。
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来源期刊
Translational pediatrics
Translational pediatrics Medicine-Pediatrics, Perinatology and Child Health
CiteScore
4.50
自引率
5.00%
发文量
108
期刊介绍: Information not localized
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