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通过生物信息学分析对儿童颅内肿瘤的临床和流行病学研究及最常见肿瘤亚型的诊断生物标志物的鉴定及其与免疫微环境的关系。

Clinical and Epidemiological Study of Intracranial Tumors in Children and Identification of Diagnostic Biomarkers for the Most Common Tumor Subtype and Their Relationship with the Immune Microenvironment Through Bioinformatics Analysis.

机构信息

Department of Neurosurgery, Xijing Hospital, Airforce Military Medical University (Fourth Military Medical University), Xi'an, 710032, China.

Department of Neurosurgery, PLA 163Rd Hospital (Second Affiliated Hospital of Hunan Normal University), Changsha, 410000, China.

出版信息

J Mol Neurosci. 2022 Jun;72(6):1208-1223. doi: 10.1007/s12031-022-02003-z. Epub 2022 Mar 28.

DOI:10.1007/s12031-022-02003-z
PMID:35347632
Abstract

Brain tumors are the second most common pediatric malignancy and have poor prognosis. Understanding the pathogenesis of tumors at the molecular level is essential for clinical treatment. We conducted a retrospective study on the epidemiology of brain tumors in children based on clinical data obtained from a neurosurgical center. After identifying the most prevalent tumor subtype, we identified new potential diagnostic biomarkers through bioinformatics analysis of the public database. All children (0-15 years) with brain tumors diagnosed histopathologically between 2010 and 2020 at the Department of Neurosurgery, Xijing Hospital, were reviewed retrospectively for age distribution, sex predilection, native location, tumor location, symptoms, and histological grade, and identified the most common tumor subtypes. Two datasets (GSE44971 and GSE44684) were downloaded from the Gene Expression Omnibus database, whereas the GSE44971 dataset was used to screen the differentially expressed genes between normal and tumor samples. Gene ontology, disease ontology, and gene set enrichment analysis enrichment analyses were performed to investigate the underlying mechanisms of differentially expressed genes in the tumor. Combined with methylation data in the GSE44684 dataset, we further analyzed the correlation between methylation and gene expression levels. Two algorithms, LASSO and SVM-RFE, were used to select the hub genes of the tumor. The diagnostic value of the hub genes was assessed using the receiver operating characteristic (ROC) curve. Finally, we further evaluated the relationship between the hub gene and the tumor microenvironment and immune gene sets. Overall, 650 children from 18 provinces in China were included in this study. The male-to-female ratio was 1.41:1, and the number of patients reached a peak in the 10-15-year-old group (41.4%).The most common symptoms we encountered in our institute were headache and dizziness 250 (28.2%), and nausea and vomiting 228 (25.7%). The predominant location is supratentorial, with a supratentorial to infratentorial ratio of 1.74:1. Low-grade tumors (WHO I/II) constituted 60.9% of all cases and were predominant in every age group. According to basic classification, the most common tumor subtype is pilocytic astrocytoma (PA). A total of 3264 differentially expressed genes were identified in the GSE44971 dataset, which are mainly involved in the process of neural signal transduction, immunity, and some diseases. Correlation analysis indicated that the expression of 45 differentially expressed genes was negatively correlated with promoter DNA methylation. Next, we acquired five hub genes (NCKAP1L, GPR37L1, CSPG4, PPFIA4, and C8orf46) from the 45 differentially expressed genes by intersecting the LASSO and SVM-RFE models. The ROC analysis revealed that the five hub genes had good diagnostic value for patients with PA (AUC > 0.99). Furthermore, the expression of NCKAP1L was negatively correlated with immune, stromal, and estimated scores, and positively correlated with immune gene sets. This study, based on the data analysis of intracranial tumors in children in a single center over the past 10 years, reflected the clinical and epidemiological characteristics of intracranial tumors in children in Northwest China to a certain extent. PA is considered the most common subtype of intracranial tumors in children. Through bioinformatics analysis, we suggested that NCKAP1L, GPR37L1, CSPG4, PPFIA4, and C8orf46 are potential biomarkers for the diagnosis of PA.

摘要

脑肿瘤是儿童第二大常见的恶性肿瘤,预后较差。了解肿瘤在分子水平上的发病机制对于临床治疗至关重要。我们基于神经外科中心的临床数据,对儿童脑肿瘤的流行病学进行了回顾性研究。在确定最常见的肿瘤亚型后,我们通过公共数据库的生物信息学分析确定了新的潜在诊断生物标志物。

所有(0-15 岁)在西京医院神经外科经组织病理学诊断为脑肿瘤的儿童,均回顾性分析其年龄分布、性别倾向、发病部位、肿瘤部位、症状和组织学分级,并确定最常见的肿瘤亚型。从基因表达综合数据库(Gene Expression Omnibus database)下载了两个数据集(GSE44971 和 GSE44684),其中 GSE44971 数据集用于筛选正常和肿瘤样本之间差异表达的基因。进行了基因本体论、疾病本体论和基因集富集分析富集分析,以研究肿瘤中差异表达基因的潜在机制。结合 GSE44684 数据集的甲基化数据,我们进一步分析了甲基化与基因表达水平之间的相关性。使用 LASSO 和 SVM-RFE 两种算法筛选肿瘤的枢纽基因。使用接收者操作特征(receiver operating characteristic,ROC)曲线评估枢纽基因的诊断价值。最后,我们进一步评估了枢纽基因与肿瘤微环境和免疫基因集之间的关系。

总之,这项研究共纳入了来自中国 18 个省的 650 名儿童。男女性别比为 1.41:1,患者数量在 10-15 岁组达到峰值(41.4%)。我们研究所遇到的最常见症状是头痛和头晕(250 例,28.2%)和恶心和呕吐(228 例,25.7%)。主要发病部位为幕上,幕上与幕下的比例为 1.74:1。低级别肿瘤(WHO I/II)构成了所有病例的 60.9%,且在每个年龄组均占主导地位。根据基本分类,最常见的肿瘤亚型是毛细胞星形细胞瘤(PA)。GSE44971 数据集中共鉴定出 3264 个差异表达基因,主要参与神经信号转导、免疫和一些疾病过程。相关性分析表明,45 个差异表达基因的表达与启动子 DNA 甲基化呈负相关。接下来,我们通过 LASSO 和 SVM-RFE 模型的交集,从 45 个差异表达基因中获得了五个枢纽基因(NCKAP1L、GPR37L1、CSPG4、PPFIA4 和 C8orf46)。ROC 分析显示,这五个枢纽基因对 PA 患者具有良好的诊断价值(AUC>0.99)。此外,NCKAP1L 的表达与免疫、基质和估计评分呈负相关,与免疫基因集呈正相关。

这项研究基于过去 10 年在单个中心对颅内肿瘤的数据分析,在一定程度上反映了中国西北地区儿童颅内肿瘤的临床和流行病学特征。PA 被认为是儿童最常见的颅内肿瘤亚型。通过生物信息学分析,我们提出 NCKAP1L、GPR37L1、CSPG4、PPFIA4 和 C8orf46 可能是诊断 PA 的潜在生物标志物。

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