Research status and development trends of Omics in Neuroblastoma a bibliometric and visualization analysis
神经母细胞瘤组学研究现状及发展趋势的文献计量与可视化分析

Mengliang Han, Huizhong Niu*, Fei Duan, Zhaolong Wang, Zhiguang Zhang, Hui Ren

First Department of General Surgery, Hebei Children's Hospital, Shijiazhuang, Hebei, 050000, China
河北儿童医院普外科一科, 河北 石家庄, 050000

* Correspondence: nrInhz@163.com; Tel.: +86+0311+ 85911010
* 通讯地址：nrInhz@163.com；电话：+86+0311+85911010

Abstract
抽象的

Background: Neuroblastoma (NB), a prevalent extracranial solid tumor in children, stems from the neural crest. Omics technologies are extensively employed in NB, and We analyzed published articles on NB omics to understand the research trends and hot topics in NB omics.
背景：神经母细胞瘤（NB）是儿童中常见的颅外实体瘤，起源于神经嵴。 组学技术在NB中得到广泛应用，我们对NB组学上已发表的文章进行分析，以了解NB组学的研究趋势和热点话题。

Method: We collected all articles related to NB omics published from 2005 to 2023 from the Web of Science Core Collection database. Subsequently, we conducted analyses using VOSviewer, CiteSpace, Bibliometrix, and the Bibliometric online analysis platform (https://bibliometric.com/).
方法：我们从 Web of Science 核心合集数据库中收集了 2005 年至 2023 年发表的所有与 NB 组学相关的文章。随后，我们使用VOSviewer、CiteSpace、Bibliometrix和文献计量在线分析平台（https://bibliometric.com/）进行分析。

Results: We included a total of 514 articles in our analysis. The increasing number of publications in this field since 2020 indicates growing attention to NB omics, gradually entering a mature development stage. These articles span 50 countries and 1,000 institutions, involving 3,669 authors and 292 journals. The United States has the highest publication output and collaboration with other countries, with Germany being the most frequent collaborator. Capital Medical University and the German Cancer Research Center are the institutions with the highest publication count. The Journal of Proteome Research and the Journal of Biological Chemistry are the most prolific journal and most co-cited journal, respectively. Wang, W, and Maris, JM are the scholars with the highest publication count and co-citations in this field. "Neuroblastoma" and "Expression" are the most frequent keywords, while "classification," "Metabolism," "Cancer," and "Diagnosis" are recent key terms. The article titled "Neuroblastoma" by John M. Maris is the most cited reference in this analysis.
结果： 我们的分析中总共纳入了 514 篇文章。 2020年以来该领域的论文数量不断增加，表明NB组学受到越来越多的关注，并逐渐进入成熟的发展阶段。这些文章横跨 50 个国家和 1,000 个机构，涉及 3,669 位作者和 292 种期刊。美国的出版物产量和与其他国家的合作最多，其中德国是最频繁的合作者。首都医科大学和德国癌症研究中心是发表论文数量最多的机构。 《Journal of Proteome Research》和《Journal of Biological Chemistry》分别是最多产的期刊和同时被引用最多的期刊。 Wang, W和Maris, JM是该领域发表次数和共被引次数最多的学者。 “神经母细胞瘤”和“表达”是最常见的关键词，而“分类”、“代谢”、“癌症”和“诊断”是最近的关键词。 John M. Maris 题为“神经母细胞瘤”的文章是本分析中被引用最多的参考文献。

Conclusion: In this study, we conducted a bibliometric analysis on NB omics, unveiling research trends and frontiers. This study empowers scholars with a more scientific, objective, and comprehensive understanding of the field, fostering its development.
结论： 在这项研究中，我们对 NB 组学进行了文献计量分析，揭示了研究趋势和前沿。这项研究使学者们对该领域有了更加科学、客观、全面的认识，促进了该领域的发展。

Keywords：bibliometrics, omics, Neuroblastoma, CiteSpace,VOSviewer, Bibliometrix

Introduction
介绍

Neuroblastoma (NB), the most prevalent extracranial solid tumor in children, is a malignant tumor arising from neural crest cells^[1]. NB originates from neural crest cells found in the sympathetic nervous system, typically situated in either the adrenal medulla or paravertebral ganglia. Consequently, it presents as tumor formations in regions such as the neck, chest, abdomen, or pelvic area^[2]. Clinical presentations vary from asymptomatic masses to severe conditions resulting from local invasion and widespread disease dissemination^{[3, 4]}. The incidence rate stands at 10.2 cases per million children below the age of 15, with the majority (90%) occurring in those under 10 years old. These cases make up 15% of pediatric tumor-related fatalities^{[5, 6]}.
神经母细胞瘤(NB)是儿童中最常见的颅外实体瘤，是一种起源于神经嵴细胞的恶性肿瘤^[1] 。 NB 起源于交感神经系统中的神经嵴细胞，通常位于肾上腺髓质或椎旁神经节。因此，它表现为颈部、胸部、腹部或骨盆区域等区域的肿瘤形成^[2] . 临床表现多种多样，从无症状肿块到局部侵袭和广泛疾病传播导致的严重病症^{[3, 4]} 。 发病率为每百万 15 岁以下儿童 10.2 例，其中大多数（90%）发生在 10 岁以下儿童。这些病例占儿科肿瘤相关死亡人数的 15% ^{[5, 6]} 。

Omics refers to the study of a certain molecular group, mainly including genomics, transcriptomics, proteomics, metabolomics, imaging omics, and metabolomics^[7]. Genomics is a scientific field that studies the structure, function, and heredity of the entire genome of an organism. It involves a systematic investigation of the overall genetic information of all genes in an organism, aiming to understand the composition, structure, function, and interactions among genes within the genome^[8]. Transcriptomics is the comprehensive study of the entire collection of messenger RNA (mRNA) within the cells of living organisms. Its goal is to understand the patterns of gene expression, specifically which genes are transcribed into mRNA under specific conditions and their relative levels^[9]. Proteomics investigates the composition, structure, function, and interactions of all proteins within a biological organism. The objective is to comprehensively understand the overall expression of proteins in the organism and how these proteins contribute to the structure, function, and regulation of cells^[10]. Metabolomics is the study of the composition, structure, and changes of all metabolites generated during the metabolic processes in organisms, including amino acids, lipids, sugars, and other metabolic products. The goal is to offer a thorough comprehension of the metabolic features exhibited by organisms in diverse physiological states, environmental conditions, or disease states^[11]. Radiomics is an interdisciplinary approach that integrates various disciplines such as medical imaging, computer science, and statistics. It involves the quantitative analysis and mining of medical imaging data with the aim of revealing imaging features associated with the occurrence, development, treatment, and prognosis of diseases^[12].
组学是指对某一分子群的研究，主要包括基因组学、转录组学、蛋白质组学、代谢组学、成像组学和代谢组学等^[7] 。 基因组学是研究生物体整个基因组的结构、功能和遗传的科学领域。它涉及对生物体中所有基因的整体遗传信息的系统研究，旨在了解基因组内基因的组成、结构、功能和相互作用^[8] 。 转录组学是对活体细胞内全部信使 RNA ( m RNA)集合的综合研究。其目标是了解基因表达模式，特别是哪些基因在特定条件下转录为 mRNA 及其相对水平^[9] . 蛋白质组学研究生物有机体内所有蛋白质的组成、结构、功能和相互作用。 目的是全面了解生物体中蛋白质的整体表达以及这些蛋白质如何对细胞的结构、功能和调节做出贡献^[10] . 代谢组学是研究生物体代谢过程中产生的所有代谢物的组成、结构和变化的学科，包括氨基酸、脂类、糖类和其他代谢产物。目标是全面了解生物体在不同生理状态、环境条件或疾病状态下所表现出的代谢特征^[11] . 放射组学是一种跨学科方法，整合了医学成像、计算机科学和统计学等多个学科。它涉及对医学影像数据的定量分析和挖掘，旨在揭示与疾病发生、发展、治疗和预后相关的影像特征^[12] .

Bibliometrics, an emerging field, reveals hotspots and trends in specific research areas, contributing to a deeper understanding of the knowledge structure within the academic domain^[13].
文献计量学作为一个新兴领域，揭示了特定研究领域的热点和趋势，有助于更深入地理解学术领域内的知识结构^[13] 。

We employ bibliometrics as a research tool to conduct visual analysis on NB omics in terms of countries, institutions, journals, authors, references, keywords, etc. This approach offers distinctive insights into the present state and research trends of NB omics, enhancing its research by strengthening its analysis.
我们采用文献计量学作为研究工具，从国家、机构、期刊、作者、参考文献、关键词等方面对NB组学进行可视化分析。这种方法对NB组学的现状和研究趋势提供了独特的见解，增强了加强分析研究。

Methods

Data Collection
数据采集

We systematically searched the Web of Science Core Collection (WoSCC) database using the following expressions: TS=(Omics OR Genomics OR Proteomics OR Metabolomics OR transcriptomics OR lipidomics OR Immunomics OR glycomics OR RNomics OR Radiomics OR Ultrasomics) AND TS=(Neuroblastoma) AND The search was limited to articles or reviews, and publications were restricted to English. The search results were obtained in "Full Record and Cited References" and "Plain Text" formats, which were then saved as "Download .txt" files. To ensure reliability, two independent reviewers conducted the literature search, with a third reviewer reviewing the results. All data were retrieved from WoSCC on Jan 17, 2024, to prevent bias from continuous updates. The retrieval strategy process is illustrated in Figure 1A.
我们使用以下表达式系统地检索了 Web of Science 核心合集 (WoSCC) 数据库： TS=(组学 OR 基因组学 OR 蛋白质组学 OR 代谢组学 OR 转录组学 OR 脂质组学 OR 免疫组学 OR 糖组学 OR RNomics OR 放射组学 OR 超声组学) AND TS=(神经母细胞瘤) AND搜索仅限于文章或评论，出版物仅限于英文。检索结果以“全记录和引用的参考文献”和“纯文本”格式获得，然后保存为“下载.txt”文件。为了确保可靠性，两名独立评审员进行了文献检索，第三名评审员对结果进行了评审。所有数据均于 2024 年1 月 17 日从 WoSCC检索，以防止持续更新带来的偏差。检索策略流程如图1A所示。

Data analysis

We utilized VOSviewer (version 1.6.18), CiteSpace (version 6.2.R7), and Bibliometrix (version 4.1.3) (https://www.bibliometrix.org), along with the Bibliometric Online Analysis Platform (https://bibliometric.com/), to analyze the data in the literature^[14].
我们使用了 VOSviewer（版本 1.6.18）、CiteSpace （版本 6.2.R7）和 Bibliometrix（版本 4.1.3）(https://www.bibliometrix.org)，以及文献计量在线分析平台 (https:// bibliometric.com/)，分析文献中的数据^[14] .

VOSviewer, developed by the Leiden University Centre for Science and Technology Studies, is utilized for visualizing and analyzing literature networks. In this study, we employed it for visual analysis of institutions, journals, co-cited journals, authors, co-cited authors, keywords, etc^[15]. The VOSviewer graph displays cluster types, density colors, or overlays. In the clustering graph, nodes reflect co-occurrence frequencies; links represent collaboration, co-occurrence, or co-citation relationships between two nodes, with the thickness of the links proportional to the number of publications co-authored by two researchers or the simultaneous occurrence of two keywords, Colors represent different clusters^[16].
VOSviewer 由莱顿大学科学技术研究中心开发，用于可视化和分析文献网络。在本研究中，我们将其用于机构、期刊、共同被引期刊、作者、共同被引作者、关键词等的可视化分析^[15] 。 VOSviewer 图表显示簇类型、密度颜色或叠加。在聚类图中，节点反映了共现频率；链接代表两个节点之间的协作、同现或同被引关系，链接的粗细与两个研究人员共同撰写的出版物数量或两个关键词同时出现的数量成正比，颜色代表不同的簇^{[16] ]} 。

CiteSpace, created by Professor Chaomei Chen of Drexel University, serves as a tool for visually representing and analyzing literature^[17]. In this study, we utilized it to analyze keywords and references.
CiteSpace由德雷克塞尔大学陈超梅教授创建，是一种可视化地表示和分析文献的工具^[17] 。 在这项研究中，我们用它来分析关键词和参考文献。

Additionally, the global distribution and thematic evolution of publications were analyzed using the R package "Bibliometrix". National collaboration analysis was also performed using the online platform https://bibliometric.com/^[18].
此外，还使用 ​​R 包“Bibliometrix ”分析了出版物的全球分布和主题演变。还使用在线平台 https://bibliometric.com/ ^[18]进行了国家合作分析。

We analyzed annual publications using Microsoft Office Excel 2019. The 2023 Impact Factor (IF) and JCR (Journal Citation Reports) categories of the journal were obtained from the Journal Citation Reports by Clarivate Analytics.
我们使用 Microsoft Office Excel 2019 分析了年度出版物。该期刊的 2023 年影响因子 (IF) 和 JCR（期刊引文报告）类别来自Clarivate Analytics 的期刊引文报告。

Results
结果

General trend
大趋势

A total of 514 papers were searched and analyzed, including 473 articles and 41 review articles. The annual publication figures from 2005 to 2023 are as follows: Figure 1B. These papers were written by 3,669 authors who are from 50 countries/regions, and 1000 institutes and published in 292 different journals from 2005 to 2023. We further conducted a detailed visual analysis of the relationships among countries, institutions, authors, journals, and keywords. Additionally, we found the journal's categorization and impact factor on the Clarivate Analytics platform.
共检索分析论文514篇，其中文献473篇，综述文章41篇。 2005年至2023年的年度发表数字如下：图1B。这些论文由2005年至2023年来自50个国家/地区、1000个机构的3,669名作者撰写，发表在292种不同期刊上。我们进一步对国家、机构、作者、期刊和关键词之间的关系进行了详细的可视化分析。此外，我们还在科睿唯安分析平台上找到了该期刊的分类和影响因子。

Countries and institutions analysis
国家和机构分析

In the NB omics research, 50 countries and 1,000 institutions collaborated. The top 10 countries involved are distributed across North America, Asia, and Oceania. Figure 2A illustrates the map of international collaborations, with the United States being the most active collaborator, primarily with Germany, followed by Italy and the United Kingdom. The United States (USA) leads in the number of publications (164, 31.9%), followed by China (96, 18.7%), Germany (67, 13.0%), and Italy (55, 10.7%). As depicted in Figure 2B, different-colored regions represent different countries/regions, with the size of each colored block indicating the number of published articles. The connecting lines between blocks signify collaboration between countries. Compared to other regions, countries represented by the blue, orange, and green blocks have a higher publication count. These blocks correspond to the United States, China, and Germany, respectively. The numerous connecting lines with the blue block indicate more frequent collaborations between the United States and other countries.
在NB组学研究方面，有50个国家和1000个机构进行了合作。参与度排名前10位的国家分布在北美、亚洲和大洋洲。图 2A 展示了国际合作地图，其中美国是最活跃的合作者，主要与德国合作，其次是意大利和英国。美国（USA）在出版物数量上领先（164篇，占31.9%），其次是中国（96篇，占18.7%）、德国（67篇，占13.0%）和意大利（55篇，占10.7%）。如图2B所示，不同颜色的区域代表不同的国家/地区，每个颜色块的大小表示发表文章的数量。区块之间的连接线象征着国家之间的合作。与其他地区相比，蓝色、橙色和绿色块代表的国家的发表数量较高。这些区块分别对应美国、中国和德国。与蓝色块的众多连接线表明美国与其他国家之间的合作更加频繁。

In NB omics research, the top five institutions, according to publication rankings, are Capital Medical University (14, 2.7%), German Cancer Research Center (14, 2.7%), Medical University of Vienna (11, 2.1%), Chinese Academy of Sciences (10, 1.9%), and Zhengzhou University (10, 1.9%). In Figure 2C, each node symbolizes an institution, with the circle's size proportional to the institution's publication count. Node centrality indicates its frequency in shortest paths across the network, reflecting influence and significance. The connections between nodes indicate the strength of associations, with more connections implying greater collaboration.
在NB组学研究中，按发表排名排名前五的机构分别是首都医科大学（14位，2.7%）、德国癌症研究中心（14位，2.7%）、维也纳医科大学（11位，2.1%）、中国科学院（10，1.9%）和郑州大学（10，1.9%）。在图2C中，每个节点代表一个机构，圆圈的大小与该机构的出版物数量成正比。节点中心性表示其在网络最短路径中出现的频率，反映影响力和重要性。节点之间的连接表明关联的强度，连接越多意味着协作程度越高。

Table 1 Top 10 countries and institutions in the field of NB omics research.
表1 NB组学研究领域排名前10位的国家和机构。

Journals and co-cited-journal analysis
期刊和同被引期刊分析

The articles related to NB omics were published in a total of 292 journals. Table 2 shows the top 10 journals in terms of publication volume and total citations, along with their corresponding Impact Factors (IF, JCR 2023). The top 10 journals are all JCR Q1 and JCR Q2.
NB组学相关文章共发表在292种期刊上。表 2 显示了发表量和总引用量排名前 10 的期刊及其相应的影响因子（IF、JCR 2023）。排名前10的期刊均为JCR Q1和JCR Q2 。

The journal boasting the most publications is the Journal of Proteome Research (n=23, 4.5%), followed by PLoS ONE (n=11, 2.1%), and Molecular and Cellular Proteomics (n=10, 1.9%).
发表论文最多的期刊是《Journal of Proteome Research》（n=23，4.5%），其次是《PLoS ONE》（n=11，2.1%）和《Molecular and Cellular Proteomics》（n=10，1.9%）。

Figure 3A shows the network diagram of NB omics related journals, with each node representing a journal. Nodes of the same color are part of the same cluster. The node size and link thickness correspond to the frequency of journal collaboration.
图3A显示了NB组学相关期刊的网络图，每个节点代表一个期刊。相同颜色的节点属于同一簇。节点大小和链接厚度对应于期刊合作的频率。

Co-citation frequency refers to the rate at which two documents are cited in conjunction. In the 3,254 journals collectively cited, the Journal of Biological Chemistry had the highest co-citation frequency (n=828), followed by Nature (n=815), and Proceedings of the National Academy of Sciences of the United States of America (n=748). Furthermore, Nature had the highest impact factor (IF=64.8), followed by Cell (IF=64.5). In Figure 3B, we present the co-citation network of journals with a frequency equal to or exceeding 20. Notably, PLoS ONE stands out as the sole journal listed in the top 10 for both publication and citation counts.
同被引频率是指两篇文献同时被引用的比率。在总共被引用的 3,254 种期刊中，《Journal of Biological Chemistry》的共被引频率最高（n=828），其次是《Nature》（n=815）和《Proceedings of the National Academy of Sciences of America》（n=815）。 =748）。此外，《Nature》的影响因子最高（IF=64.8），其次是《Cell》（IF=64.5）。在图 3B 中，我们展示了频率等于或超过 20 的期刊的同被引网络。值得注意的是，PLoS ONE 是唯一一本在发表次数和引用次数上均跻身前 10 名的期刊。

Table 2 Top 10 journals and co-cited journals in NB omics research.
表2 NB组学研究中排名前10位的期刊及共同被引期刊。

Authors and co-cited authors analysis
作者和共同被引作者分析

The NB omics study involved a total of 3,669 authors. Table 3 presents the top 10 authors in this field. Wang, W emerged as the most prolific scholar with 10 published articles, followed closely by Yang, JG, who contributed 9 articles. The collaboration network among authors is illustrated in Figure 3C, where Wang, W, Yang, JG, and Eggert, A are prominent due to their numerous publications, resulting in larger nodes. Wang, W, and Yang, JG exhibit a close collaboration.
NB 组学研究总共涉及 3,669 名作者。表 3 列出了该领域的前 10 位作者。 Wang, W 成为最多产的学者，发表了 10 篇文章，紧随其后的是 Yang, JG，发表了 9 篇文章。作者之间的协作网络如图 3C 所示，其中 Wang, W、Yang, JG 和 Eggert, A 由于发表的论文数量众多而很突出，从而产生了更大的节点。 Wang, W 和 Yang, JG 表现出密切的合作。

The most frequently cited authors are Maris, JM (n=139), followed by Brodeur, GM (n=116), and Katoh, M (n=95). Figure 3D depicts a network graph of co-cited authors with citations equal to or greater than 15. Nodes of different colors represent authors with distinct collaboration relationships, while nodes of the same color indicate authors within the same cluster. The size of nodes and the thickness of connecting lines positively correlate with the frequency of co-citations.
最常被引用的作者是 Maris, JM (n=139)，其次是 Brodeur, GM (n=116) 和 Katoh, M (n=95)。图 3D 描绘了被引用等于或大于 15 次的同被引作者的网络图。不同颜色的节点代表具有不同协作关系的作者，而相同颜色的节点表示同一簇内的作者。节点的大小和连接线的粗细与同被引的频率正相关。

Table 3 The top 10 authors and co-cited authors on the research of omics in NB.
表3 NB组学研究排名前10位的作者及共同被引作者。

Analysis of keywords and frontiers
关键词和前沿分析

Table 4 displays the top 20 most frequently occurring keywords in the NB groupomics. Two keywords, Neuroblastoma (n=174) and Expression (n=112), have frequencies exceeding 100 times each. The total link strength of seven keywords surpasses 200, while the remaining keywords exceed 100.
表 4 显示了 NB 组组中最常出现的 20 个关键词。神经母细胞瘤 (n=174) 和表达 (n=112) 这两个关键词的频率均超过 100 次。其中7个关键词的总链接强度超过200，其余关键词超过100。

Figure 4A displays keywords with an occurrence equal to or greater than 5 times, resulting in a total of 7 clusters representing 7 research directions. The closest 5 keywords in each of these 7 major clusters (red, green, blue, yellow, purple, cyan, orange) are as follows:Red: proteomics, oxidative stress, Alzheimer's disease, metabolism, protein; Green: n-myc, mycn, receptor, breast-cancer, activating mutations; Blue: gene expression, apoptosis, activation, pathway, binding; Yellow: neuroblastoma, cancer, classification, radiomics, biology; Purple: expression, cells, differentiation, gene, inhibition; Cyan: identification, phosphorylation, bioinformatics, mass spectrometry, comparative proteomics; Orange: genomics, mutations, risk, association, polymorphism.
图4A显示了出现次数等于或大于5次的关键词，总共产生了7个簇，代表7个研究方向。这 7 个主要簇（红、绿、蓝、黄、紫、青色、橙色）中每一个最接近的 5 个关键词如下：红色：蛋白质组学、氧化应激、阿尔茨海默病、代谢、蛋白质；绿色：n-myc、mycn、受体、乳腺癌、激活突变；蓝色：基因表达、凋亡、激活、途径、结合；黄色：神经母细胞瘤、癌症、分类、放射组学、生物学；紫色：表达、细胞、分化、基因、抑制；青色：鉴定、磷酸化、生物信息学、质谱、比较蛋白质组学；橙色：基因组学、突变、风险、关联、多态性。

Figure 4B displays the clustering analysis graph based on keywords, with different colors indicating popular keywords from different years. Lighter colors represent more recent publication years.
图4B显示了基于关键词的聚类分析图，不同颜色表示不同年份的热门关键词。较浅的颜色代表较新的出版年份。

Figure 4C illustrates the trend analysis of NB omics themes to further insight into the trends in the field. The keywords from 2008 to 2011 include NF-kappa-B, rat brain, endoplasmic reticulum, rat, micrornas, 2-dimensional electrophoresis, family, human neuroblastoma cells. The keywords from 2021 to the present are: survival, therapy, classification, biology, a-beta, landscape, diagnosis, relapse, children.
图4C展示了NB组学主题的趋势分析，以进一步洞察该领域的趋势。 2008年至2011年的关键词包括NF - κ- B 、大鼠脑、内质网、大鼠、微小RNA、二维电泳、家族、人神经母细胞瘤细胞。 2021 年至今的关键词是：生存、治疗、分类、生物学、a-beta、景观、诊断、复发、儿童。

Figure 4D lists the top 17 keywords with the most significant citation bursts, sorted by the starting year. Blue bars represent published references, while red bars indicate citation bursts. The earliest is "comparative proteomics," followed by "gene expression," "cancer cells," and "oxidative stress." In recent years, the key terms include "classification," "Metabolism," "Cancer," and "Diagnosis".
图 4D 列出了引文爆发次数最多的前 17 个关键词，按起始年份排序。蓝色条代表已发表的参考文献，而红色条代表引用爆发。最早的是“比较蛋白质组学”，其次是“基因表达”、“癌细胞”和“氧化应激”。近年来，关键术语包括“分类”、“代谢” 、“癌症”和“诊断” 。

Table 4 The top 20 keywords with the highest occurrence of NB omics and their total link strength.
表4 NB组学出现次数最高的前20个关键词及其总链接强度。

Analysis of references
参考文献分析

The co-cited references for NB omics amount to a total of 25,042 articles, with 26 of them being cited more than 20 times. Table 5 illustrates the top 10 cited references, among which 2 articles have been cited 50 times or more, while the rest have citations exceeding 20 times.
NB组学共被引用参考文献25,042篇，其中被引用次数超过20次的有26篇。表5列出了被引次数最多的10篇文献，其中被引50次及以上的文献有2篇，其余均被引超过20次。

Figure 5A displays the MOST locally cited references in NB omics. The most frequently cited reference is a paper by John M. Maris, published in Lancet in 2007, titled "Neuroblastoma," which has been cited 55 times. The second most cited reference is also by John M. Maris, published in the New England Journal of Medicine in 2010, titled "Recent advances in neuroblastoma," with 50 citations.
图 5A 显示了 NB 组学中本地引用最多的参考文献。最常被引用的参考文献是 John M. Maris 于 2007 年在《柳叶刀》上发表的一篇论文，题为“神经母细胞瘤”，已被引用 55 次。被引用次数第二多的参考文献也是 John M. Maris 的，他于 2010 年发表在《新英格兰医学杂志》上，题为“神经母细胞瘤的最新进展”，被引用 50 次。

Figure 5B presents references in the field of NB omics, with the highest number of citations going to the paper by Katherine K. Matthay published in 2016, titled "Neuroblastoma" (n=20), followed by Trevor J. Pugh's 2013 paper titled "The genetic landscape of high-risk neuroblastoma" (n=15).
图 5B 显示了 NB 组学领域的参考文献，其中被引用次数最高的是 Katherine K. Matthay 于 2016 年发表的题为“Neuroblastoma”的论文（n=20），其次是 Trevor J. Pugh 2013 年题为“Neuroblastoma”的论文高危神经母细胞瘤的遗传景观”（n=15）。

Figure 5C depicts the top 17 references experiencing the most substantial citation surges in NB omics. The references are arranged by the year of origin, with blue bars representing time intervals and red bars indicating the start and end years of each citation surge. These surges first appeared in 2008, with an intensity range from 3.0 to 8.34. The reference with the highest citation surge intensity (8.34) was a paper published in 2016 in Nature Reviews Disease Primers, titled "Neuroblastoma." The second-highest intensity citation surge (7.18) was associated with a paper published in 2013 in Nature Genetics, titled "The genetic landscape of high-risk neuroblastoma ".
图 5C 描绘了 NB 组学中引用激增最多的 17 篇参考文献。参考文献按起源年份排列，蓝色条代表时间间隔，红色条代表每次引用激增的开始和结束年份。这些激增首次出现于 2008 年，强度范围为 3.0 至 8.34。引用激增强度最高的参考文献（8.34）是2016年发表在《Nature Reviews Drugs Primers》上的一篇论文，题为“神经母细胞瘤”。第二高强度的引用激增（7.18）与 2013 年《自然遗传学》上发表的一篇题​​为“高危神经母细胞瘤的遗传景观”的论文有关。

Table 5 The top 10 co-cited references on the research of omics in NB.
表5 NB组学研究共被引前10的文献。

Discussion
讨论

General information

In this research, a bibliometric analysis of 514 articles in NB omics from the WOSCC database was carried out using VOSviewer, CiteSpace, and Bibliometrix. The objective was to visually analyze research hotspots and trends in the NB omics field. Findings indicated a consistent growth in publications since 2005, averaging 8.3 articles annually from 2005 to 2020. A notable increase in relevant papers was observed from 2015 to 2020, indicating an outbreak period in NB omics research during that time. After 2020, there was a rapid surge in the number of relevant papers, suggesting a growing interest among scholars and the gradual entry of NB omics into a mature development stage.
在本研究中，使用 VOSviewer、CiteSpace 和 Bibliometrix 对 WOSCC 数据库中 NB 组学的 514 篇文章进行了文献计量分析。 目的是直观地分析NB组学领域的研究热点和趋势。调查结果表明，自 2005 年以来，出版物数量持续增长，2005 年至 2020 年平均每年 8.3 篇文章。 2015年至2020年相关论文数量明显增加，这表明NB组学研究在该时期处于爆发期。 2020年后，相关论文数量迅速激增，表明学者们对NB组学的兴趣日益浓厚，NB组学逐渐进入成熟发展阶段。

Table 1 highlights the prominent contributors to this field, namely the United States, China, and Germany. The United States leads in both publication numbers and collaborations with other countries or regions. Six of the top ten institutions, based on the number of published papers, hail from China. The institution boasting the most publications is the Capital Medical University in China, followed by the German Cancer Research Center in Germany. While China ranks second in the number of publications, Chinese institutions lack collaboration with other countries or regions, and there is a need for active cooperation with other institutions to promote the development of Chinese NB omics.
表 1 重点列出了该领域的主要贡献者，即美国、中国和德国。美国在出版物数量和与其他国家或地区的合作方面均处于领先地位。 根据发表论文数量排名前十的机构中有六家来自中国。 发表论文最多的机构是中国的首都医科大学，其次是德国的德国癌症研究中心。虽然中国的出版物数量位居第二，但中国的机构缺乏与其他国家或地区的合作，需要与其他机构积极合作，以促进中国的发展 注意组学。

The journals and co-cited counterparts fall into three main categories, namely biological and molecular journals, comprehensive journals, and oncology journals, all positioned within the Q1 or Q2 categories. The Journal of Proteome Research (IF = 4.4, Q1) boasts the highest publication count among scientific journals in the field, followed by PLoS ONE (IF = 3.7, Q2), and the Journal of Biological Chemistry (IF = 4.8, Q2) has the highest number of co-citations.
期刊和共被引期刊主要分为三大类，即生物分子期刊、综合期刊和肿瘤学期刊，均位于 Q1 或 Q2 类别。 《Journal of Proteome Research》（IF = 4.4，Q1）在该领域的科学期刊中发表数量最多，其次是 PLoS ONE（IF = 3.7，Q2）和《Journal of Biological Chemistry》（IF = 4.8，Q2）同被引次数最多。

Wang, W from the United States stands out as the most prolific author, boasting the highest publication count among researchers in the field. In 2021, Wang published an article titled "Molecular targeting therapies for neuroblastoma: Progress and challenges," summarizing research on molecular pathways, such as MYCN, BIRC5, PHOX2B, LIN28B, and epigenetic regulatory factors, in the current understanding of the mechanisms underlying NB development^[19].
来自美国的 Wang, W 是最多产的作者，在该领域的研究人员中发表论文数量最多。 2021年，王教授发表了题为《神经母细胞瘤的分子靶向治疗：进展与挑战》的文章，总结了目前对神经母细胞瘤机制认识中对MYCN、BIRC5、PHOX2B、LIN28B、表观遗传调控因子等分子通路的研究。发展^[19] .

In the realm of co-cited authors, the most frequently cited individual is Maris, JM. His most cited article, published in 2017 under the title "Advances in the translational genomics of neuroblastoma: From improving risk stratification and revealing novel biology to identifying actionable genomic alterations," summarizes the knowledge in NB genetics and genomics. The article emphasizes that a deeper understanding of lineage susceptibility, recurrent segmental chromosomal changes, somatic point mutations, translocations, and the latest research on the clonal evolution of recurrent NB can offer new possibilities for improving prognosis and exploring potential therapeutic opportunities^[20].
在共同被引作者领域，最常被引用的个人是 Maris, JM。他被引用次数最多的文章发表于 2017 年，标题为“神经母细胞瘤转化基因组学的进展：从改善风险分层和揭示新颖的生物学到识别可操作的基因组改变”，总结了NB遗传学和基因组学的知识。文章强调，更深入地了解谱系易感性、复发性节段性染色体变化、体细胞点突变、易位以及复发性NB克隆进化的最新研究，可以为改善预后和探索潜在治疗机会提供新的可能性^[20] .

The second most frequently cited author is Brodeur, GM, whose article titled "Neuroblastoma: developmental biology, cancer genomics, and immunotherapy" was published in 2013. This publication offers a refreshed insight into the NB neural crest and its cellular origins, emphasizing the unique potential therapeutic targets in NB. The article also analyzed the the efficacy of anti-ganglioside GD2 antibody therapy in immunosuppressive tumor microenvironments and proposed future translational research roadmaps, including high-throughput drug screening and next-generation animal models^[21].
第二个最常被引用的作者是 GM Brodeur，他的文章题为“神经母细胞瘤：发育生物学、癌症基因组学和免疫疗法”于 2013 年发表。该出版物提供了对 NB 神经嵴及其细胞起源的全新见解，强调了独特的NB 的潜在治疗靶点。文章还分析了抗神经节苷脂GD2抗体疗法在免疫抑制肿瘤微环境中的疗效，并提出了未来的转化研究路线图，包括高通量药物筛选和下一代动物模型^[21] 。

Hotspots and frontiers
热点和前沿

Exploring hot keywords, citation burst references, and trending topics aids in grasping the current research trends and frontiers within this domain^[22]. By analyzing through bibliometrics and visualization software, we summarize the hot topics in the academic field, objectively evaluate the forefront research directions in the field of NB omics. The current research focus includes the identification of potential biomarkers using omics in NB and the application of personalized medicine, as well as the challenges and future directions in NB omics.
探索热门关键词、引用爆发参考文献和趋势主题有助于掌握该领域当前的研究趋势和前沿^[22] 。 通过文献计量学和可视化软件分析，总结学术领域的热点话题，客观评价NB组学领域的前沿研究方向。目前的研究重点包括利用组学识别NB中潜在的生物标志物和个体化医疗的应用，以及NB组学中的挑战和未来方向。

1.Applications of omics in the identification of potential biomarkers and personalized medicine in NB
1.组学在NB潜在生物标志物鉴定和个体化医疗中的应用

NB is categorized into familial NB and sporadic NB, with familial NB accounting for 2% of all NB cases^[23]. The first susceptibility gene mutation identified by NB is in PHOX2B, which contains two polyalanine repeat sequences. This gene encodes a paired-like homeobox transcription factor with functions that can promote cell cycle exit and neuronal differentiation^{[24, 25]}. The most common genetic mutation linked to familial NB occurs in the ALK receptor tyrosine kinase gene expressed in the sympathetic adrenal lineage during neural crest development^{[23, 26, 27]}. This gene is implicated in modulating the equilibrium between proliferation and differentiation via diverse cellular pathways, including the mitogen-activated protein kinase (MAPK) and Ras-related protein 1 (RAP1) signaling pathways^{[28, 29]}. In addition, PHOX2B can directly regulate the expression of the ALK gene^[30]. The susceptibility single nucleotide polymorphisms (SNPs) associated with familial NB in genome-wide association studies (GWAS) include: BARD1, DUSP, DDX4-IL31RA, HACE1, HSD17B, LMO1, LIN28B, LINC00340, and LOC729177 (FLJ44180)^{[31, 32]}.
NB分为家族性NB和散发性NB，其中家族性NB占全部NB病例的2% ^[23] 。 NB鉴定出的第一个易感基因突变位于PHOX2B中，它含有两个多聚丙氨酸重复序列。该基因编码配对样同源盒转录因子，具有促进细胞周期退出和神经元分化的功能^{[24, 25]} 。 最常见的基因突变与 家族性 NB 发生在神经嵴发育期间交感肾上腺谱系中表达的 ALK 受体酪氨酸激酶基因中^[23,26,27] 。 该基因涉及通过多种细胞途径调节增殖和分化之间的平衡，包括丝裂原激活蛋白激酶 (MAPK) 和 Ras 相关蛋白 1 (RAP1) 信号途径^{[28, 29]} 。 此外，PHOX2B可以直接调控ALK基因的表达^[30] . 全基因组关联研究 (GWAS) 中与家族性NB相关的易感性单核苷酸多态性 (SNP)包括：BARD1、DUSP、DDX4-IL31RA、HACE1、HSD17B、LMO1、LIN28B、LINC00340 和 LOC729177 (FLJ44180) ^{[31, 32]} 。

In sporadic NB, PHOX2B mutations and GWAS susceptibility loci are infrequent. Activating mutations in ALK are found in about 6-10% of patients, while high-level ALK gene amplification is present in 3-4% of cases^{[23, 26, 33]}. The most common genetic mutation in sporadic NB involves the amplification of MYCN, expressed in the neural crest during development and plays a crucial role in regulating the proliferation, growth, differentiation, and survival of cells within the developing central nervous system. This amplification is observed in around 22% of patients and is linked to an unfavorable prognosis^[34]. Another common genetic mutation in sporadic NB is the ATRX mutation, and its relationship with the age of diagnosis is statistically significant. In NB patients harboring ATRX mutations, 17% manifest in children aged 18 months to 12 years with stage 4 disease, while 44% are observed in patients aged 12 and above^[35].
在散发性 NB 中，PHOX2B 突变和 GWAS 易感位点并不常见。约 6-10% 的患者存在 ALK 激活突变，而 3-4% 的病例存在高水平 ALK 基因扩增^[23,26,33] 。 散发性 NB 中最常见的基因突变涉及 MYCN 的扩增，MYCN 在发育过程中在神经嵴中表达，在调节发育中的中枢神经系统内细胞的增殖、生长、分化和存活方面发挥着至关重要的作用。这种扩增在大约 22% 的患者中观察到，并且与不良预后相关^[34] 。 散发性NB中另一种常见的基因突变是ATRX突变，其与诊断年龄的关系具有统计学意义。在携带 ATRX 突变的 NB 患者中，17% 出现在 18 个月至 12 岁的 4 期儿童中，而 44% 出现在12 岁及以上的患者中^[35] .

Gene amplification in cancer and chromosomal aberrations may result in the dysregulation of messenger RNA, microRNA, and other non-coding RNAs, thereby disrupting cell apoptosis, differentiation, and immune surveillance^{[36, 37]}. The adverse prognosis in NB patients is specifically marked by deletions of 1p, 11q, and amplification of 17q. Additionally, deletions of 3p, 4p, 9p, and 14q, as well as amplifications of 1q, 2p, 7q, and 11p, also impact the prognosis^{[38, 39]}. The patient's age, disease stage at the time of diagnosis, presence of MYCN amplification in NB cells, deletion of the 11q chromosome, histological characteristics, and ploidy have become crucial factors for stratifying risk groups in NB patients^[40]. High-risk NB patients have chromosomal rearrangements, and the frequency of these aberrations rises with the age at diagnosis, exerting a strong predictive impact on outcomes^{[41, 42]}.
癌症中的基因扩增和染色体畸变可能导致信使 RNA、microRNA 和其他非编码 RNA 的失调，从而破坏细胞凋亡、分化和免疫监视^{[36, 37]} 。 NB 患者的不良预后特别以 1p、11q 缺失和 17q 扩增为标志。此外，3p、4p、9p 和 14q 的缺失以及 1q、2p、7q 和 11p 的扩增也会影响预后^{[38, 39]} 。 患者的年龄、诊断时的疾病阶段、NB细胞中是否存在MYCN扩增、11q染色体的缺失、组织学特征和倍性已成为对NB患者进行危险人群分层的关键因素^[40] . 高危 NB 患者存在染色体重排，并且这些畸变的频率随着诊断年龄的增加而增加，对结果产生强大的预测影响^{[41, 42]} 。

Like genomics, proteomics research plays a crucial role in understanding the mechanisms of tumor formation in NB, discovering biomarkers, and elucidating pathways related to treatment response and drug resistance. Hsu's study revealed a significant upregulation of GRP75, heat shock protein 2, protein disulfide isomerase A3 precursor, TCP1-containing chaperonin protein subunit 1β, and Eno1 protein during NB differentiation induction^[43]. GRP75, a member of the heat shock protein 70 family, is thought to function in tumor development control. Additionally, GRP78, another family member, together with GRP75, acts as a positive prognostic marker for NB in a separate study^{[44, 45]}. The mechanism involves the formation of the GRP75/78-RHAMM complex, which binds to hyaluronic acid-mediated motility receptor (RHAMM) associated with the progression and metastasis of cancer, and interacts with microtubules to stabilize them during the interphase, preventing microtubule depolymerization and promoting the progression of mitosis^{[46, 47]}.
与基因组学一样，蛋白质组学研究在理解 NB 肿瘤形成机制、发现生物标志物以及阐明与治疗反应和耐药性相关的途径方面发挥着至关重要的作用。 Hsu的研究揭示了NB分化诱导过程中GRP75、热休克蛋白2、蛋白二硫键异构酶A3前体、包含TCP1的伴侣蛋白亚基1β和Eno1蛋白的显着上调^[43] 。 GRP75 是热休克蛋白 70 家族的成员，被认为在肿瘤发展控制中发挥作用。此外，在另一项研究中，另一个家族成员 GRP78 与 GRP75 一起作为 NB 的阳性预后标志物^{[44, 45]} 。 其机制涉及GRP75/78-RHAMM复合物的形成，该复合物与与癌症进展和转移相关的透明质酸介导的运动受体（RHAMM）结合，并与微管相互作用以在间期稳定它们，防止微管解聚和促进有丝分裂的进展^{[46, 47]} 。

Valérie employed surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to detect serum amyloid A (SAA) protein, revealing its close association with the prognosis of NB patients^{[48, 49]}. Targeted therapy for tumor-specific mutations can effectively and precisely eradicate cancer while sparing patients from the acute and chronic toxicities that may occur during chemotherapy and radiation therapy. However, in NB, traditional immunotherapy models are not applicable. Treatment approaches based on antibodies rather than T cells, especially those targeting cancer embryonic differentiation antigens, offer viable alternative strategies^[21]. Currently, research has found that the construction of T-cell vaccines targeting cytotoxic T lymphocytes (CTL) recognition, including cancer testis antigens (MAGE and NY-ESO-1), MYCN 110, and survivin in NB antigens, can stimulate T cell-mediated immunity to some extent^[50-52].
Valérie采用表面增强激光解吸电离飞行时间质谱（SELDI-TOF-MS）检测血清淀粉样蛋白A（SAA）蛋白，揭示其与NB患者预后密切相关^{[48, 49]} 。 针对肿瘤特异性突变的靶向治疗可以有效、精准地根除癌症，同时使患者免受化疗和放疗期间可能发生的急慢性毒性的影响。然而，在NB，传统的免疫治疗模式并不适用。基于抗体而非 T 细胞的治疗方法，尤其是针对癌症胚胎分化抗原的治疗方法，提供了可行的替代策略^[21] 。 目前，研究发现，构建针对细胞毒性T淋巴细胞（CTL）识别的T细胞疫苗，包括癌睾丸抗原（MAGE和NY-ESO-1）、MYCN 110以及NB抗原中的生存素，可以刺激T细胞-在一定程度上介导免疫^[50-52] 。

GD2, a cancer embryonic differentiation antigen, is expressed not only during fetal development, but also in mature neurons, pain fibers, and skin cells^[53]. Treatment with anti-GD2 monoclonal antibodies (MAb) administered intravenously, particularly the anti-GD2 IgG antibodies (ch14.18 and murine 3F8), especially in combination with interleukin-2 (IL-2), granulocyte-macrophage colony-stimulating factor (GM-CSF), and oral 13-cis-retinoic acid (CRA), can effectively alleviate symptoms in high-risk NB patients. The potential mediating mechanism is granulocyte-mediated Antibody-Dependent Cellular Cytotoxicity (ADCC) and NK-ADCC^{[54, 55]}. Due to the depletion of white blood cells required for ADCC after induction chemotherapy or autologous stem cell transplantation, the combination of cytokines IL-2 and GM-CSF can be used with monoclonal antibodies (MAb). IL-2 can activate NK cells, natural killer T (NKT) cells, T cells, and regulatory T cells (T reg). Similarly, IL-15, like IL-2, can activate NK, NKT, and CD8+ T cells. However, in non-human primates studies, IL-15 does not lead to capillary leakage, activation-induced cell death, or an increase in T reg activation^[56].
GD2是一种癌胚胎分化抗原，不仅在胎儿发育过程中表达，还在成熟神经元、疼痛纤维和皮肤细胞中表达^[53] 。 使用静脉注射抗 GD2 单克隆抗体 (MAb) 进行治疗，特别是抗 GD2 IgG 抗体（ch14.18 和鼠 3F8），尤其是与白细胞介素 2 (IL-2)、粒细胞巨噬细胞集落刺激因子联合使用（ GM-CSF）和口服13-顺式维甲酸（CRA）可以有效缓解高危NB患者的症状。潜在的介导机制是粒细胞介导的抗体依赖性细胞毒性（ADCC）和NK-ADCC ^{[54, 55]} 。 由于诱导化疗或自体干细胞移植后 ADCC 所需的白细胞被消耗，细胞因子 IL-2 和 GM-CSF 的组合可与单克隆抗体 (MAb) 一起使用。 IL-2 可以激活 NK 细胞、自然杀伤 T (NKT) 细胞、T 细胞和调节性 T 细胞 (T reg)。同样，IL-15 与 IL-2 一样，可以激活 NK、NKT 和 CD8+ T 细胞。然而，在非人类灵长类动物研究中，IL-15 不会导致毛细血管渗漏、激活诱导的细胞死亡或 T reg 激活增加^[56] 。

Zhang combined metabolomics and transcriptomics analyses, uncovering noteworthy distinctions in the cAMP, PI3K-Akt, and TNF signaling pathways between high-grade NB (HG-NB) and low-grade NB (LG-NB). HG-NB corresponds to stage 4 according to the International NB Staging System (INSS), while LG-NB includes stages 1, 2, and 3. Additionally, three biomarkers, MGST1, SERPINE1, and REBB3, were identified^[57]. The specific mechanism centers around cAMP as a second messenger, engaging in diverse cellular processes like growth, differentiation, and gene transcription. It influences four effector proteins, namely cAMP-activated exchange proteins, cyclic nucleotide-gated ion channels, myosin heavy chain proteins, and the cAMP-dependent PKA pathway^{[58, 59]}. The PI3K-Akt pathway exerts its influence on tumors through the stimulation of receptor tyrosine kinases and somatic mutations in pathway-specific components in somatic cells. Inhibiting this pathway can halt the progression of cancer^[60]. The dysregulation of PI3K-Akt in HG-NB has been linked to PTEN tumor suppressor gene dysfunction in several studies^[61]. In addition, a study found that activation of the PI3K-Akt signaling pathway can prompt cell death by suppressing autophagy^[62]. TNF plays a role in both tumor angiogenesis and cell death, promoting the progression and metastasis of tumors^[63]. Moreover, TNF can stimulate the expression and activation of downstream molecules, such as nuclear factor κB and p38 mitogen-activated protein kinase, thereby controlling diverse biological processes, including cell death^[64].
张结合代谢组学和转录组学分析，揭示了高级别 NB (HG-NB) 和低级别 NB (LG-NB) 之间 cAMP、PI3K-Akt 和 TNF 信号通路的显着差异。 HG-NB 对应于国际NB分期系统 (INSS) 的第 4 期，而 LG-NB 包括 1、2 和 3 期。此外，还鉴定了 MGST1、SERPINE1 和 REBB3 三个生物标志物^[57] . 具体机制以 cAMP 作为第二信使为中心，参与生长、分化和基因转录等多种细胞过程。它影响四种效应蛋白，即 cAMP 激活的交换蛋白、环核苷酸门控离子通道、肌球蛋白重链蛋白和 cAMP 依赖性 PKA 途径^{[58, 59]} 。 PI3K-Akt 通路通过刺激受体酪氨酸激酶和体细胞中通路特异性成分的体细胞突变对肿瘤发挥影响。抑制该途径可以阻止癌症的进展^[60] 。 多项研究表明 HG-NB 中 PI3K-Akt 的失调与 PTEN 抑癌基因功能障碍有关^[61] 。 此外，一项研究发现，PI3K-Akt信号通路的激活可以通过抑制自噬来促使细胞死亡^[62] 。 TNF在肿瘤血管生成和细胞死亡中发挥作用，促进肿瘤的进展和转移^[63] 。 此外，TNF可以刺激下游分子的表达和激活，例如核因子κB和p38丝裂原激活蛋白激酶，从而控制包括细胞死亡在内的多种生物过程^[64] 。

The analysis conducted by Prasinou using fatty acid methyl ester (FAME) gas chromatography revealed that in apolipoprotein E (apoE), both apoE3 and apoE4 increased the levels of saturated fatty acids (SFA) and monounsaturated fatty acids (MUFA), as well as the omega-6/omega-3 ratio, in NBSK-N-SH cell membranes. They reduced total polyunsaturated fatty acids (PUFA), leading to decreased membrane stability indicators like PUFA balance, unsaturation index, and peroxidation index. Meanwhile, ApoE3 elevated stearic acid and dihomo-γ-linolenic acid (DGLA) levels, with apoE4 exhibiting the opposite impact. This study emphasizes the importance of membrane lipidomics in understanding the role of apolipoproteins in neurodegenerative diseases^[65].
Prasinou 使用脂肪酸甲酯 (FAME) 气相色谱法进行的分析表明，在载脂蛋白 E (apoE) 中，apoE3 和 apoE4 均增加了饱和脂肪酸 (SFA) 和单不饱和脂肪酸 (MUFA) 的水平，以及NBSK-N-SH 细胞膜中的 omega-6/omega-3 比率。它们减少了多不饱和脂肪酸 (PUFA) 总量，导致 PUFA 平衡、不饱和指数和过氧化指数等膜稳定性指标下降。同时，ApoE3 升高硬脂酸和二高-γ-亚麻酸 (DGLA) 水平，而 apoE4 则表现出相反的影响。 这项研究强调了膜脂质组学在理解载脂蛋白在神经退行性疾病中的作用方面的重要性^[65] 。

Radiomics, a swiftly advancing domain, concentrates on the extraction and analysis of numerous quantitative features from medical images. Utilizing advanced image processing algorithms, radiomics transforms medical images into rapidly exploitable data^[66]. These characteristics encompass information about tumor morphology, voxel texture, intensity, and spatial relationships within the tumor and its surrounding environment. In NB, various medical imaging modalities are currently employed, including computed tomography (CT), positron emission tomography-computed tomography (PET-CT), magnetic resonance imaging (MRI), and metaiodobenzylguanidine (MIBG) scintigraphy.
放射组学是一个快速发展的领域，专注于从医学图像中提取和分析大量定量特征。利用先进的图像处理算法，放射组学将医学图像转换为可快速利用的数据^[66] 。 这些特征包含有关肿瘤形态、体素纹理、强度以及肿瘤及其周围环境内的空间关系的信息。 在NB，目前采用各种医学成像模式，包括计算机断层扫描（CT）、正电子发射断层扫描-计算机断层扫描（PET-CT）、磁共振成像（MRI）和间碘苄胍（MIBG）闪烁扫描。

The International Neuroblastoma Pathology Classification (INPC) is an important classification system for NB. It categorizes NB into two subgroups, favorable histology (FH) and unfavorable histology (UFH), based on several risk factors linked to poor prognosis such as patient age, tumor histologic subtypes, differentiation grade, and mitosis-karyorrhexis index (MKI). This classification system provides a reference for different treatment stratifications^[67]. The pathological heterogeneity of NB results in inconsistent outcomes when multiple pathologists analyze the same patient. The heterogeneity within tumors results in diverse levels of differentiation across different tumor regions, diminishing the accuracy of the INPC. Assessing the MKI proves challenging and subjective, primarily due to the requirement for manually counting 5000 cells under a microscope^[68]. However, radiomics can automate and objectively extract quantitative features to reflect the heterogeneity of lesions, providing significant advantages in tumor staging, pathological subtypes, and prognosis prediction^[69].
国际神经母细胞瘤病理学分类（INPC）是NB的重要分类系统。它根据与不良预后相关的几个危险因素，如患者年龄、肿瘤组织学亚型、分化等级和有丝分裂核碎裂指数 (MKI)，将NB分为两个亚组：有利组织学 (FH) 和不利组织学(UFH)。该分类系统为不同治疗分层提供了参考^[67] 。 当多个病理学家分析同一患者时，NB 的病理异质性会导致结果不一致。 肿瘤内的异质性导致不同肿瘤区域的分化水平不同，从而降低了 INPC 的准确性。评估 MKI 具有挑战性和主观性，主要是因为需要在显微镜下手动计数 5000 个细胞^[68] . 然而，放射组学可以自动化、客观地提取定量特征来反映病变的异质性，在肿瘤分期、病理亚型和预后预测方面提供显着优势^[69] .

Studies indicate that radiogenomics has the potential to identify the pathological subtypes and genetic aberrations of NB^[70]. WU has developed a radiomics model based on CT images to predict MYCN amplification in pediatric NB. In the training group, the model achieved an Area Under the Curve (AUC) of 0.93, compared to 0.92 in the testing group^[71]. Wang employed a CT-based radiomics method for predicting the INPC of NB. The AUC values in the training and testing groups were 0.851 and 0.816, respectively. Decision curve analysis further validated the radiomics model's outstanding performance across various high-risk thresholds^[72]. Qian created a radiomics model utilizing 18F-FDG PET/CT images for distinguishing INPC subgroups. The model demonstrated AUCs of 0.877 and 0.868 in the training and validation cohorts, respectively^[73].
研究表明放射基因组学有潜力识别 NB 的病理亚型和遗传畸变^[70] . WU 开发了一种基于 CT 图像的放射组学模型，用于预测儿科 NB 中的 MYCN 扩增。在训练组中，模型的曲线下面积 (AUC) 为 0.93，而测试组为 0.92 ^[71] . Wang 采用基于 CT 的放射组学方法来预测 NB 的 INPC。训练组和测试组的 AUC 值分别为 0.851 和 0.816。决策曲线分析进一步验证了放射组学模型在各种高风险阈值上的出色表现^[72] . 钱先生利用 18F-FDG PET/CT 图像创建了放射组学模型来区分 INPC 亚群。该模型在训练组和验证组中的 AUC 分别为 0.877 和 0.868 ^[73] 。

2. Challenges and Future Directions in NB Omics
2. NB Omics 的挑战和未来方向

Due to the smaller number of pediatric patients compared to adults and significant differences in drug metabolism, acute toxicity, and late effects between children and adults, the translation and clinical research of pediatric cancer face a series of challenges compared to adult cancer. Additionally, most preclinical studies of pediatric cancers use murine cell lines or xenograft models, but for immunological analysis, mouse effector cells are not ideal human surrogates. To address this issue, it is recommended to use humanized mice implanted with human immune cells to better simulate the human immune system. The high-throughput capabilities of proteomic technologies provide a rich source for discovering potential biomarkers. In the future, NB proteomics can explore biomarkers through the study of protein fragment subtypes, glycoproteins, phosphorylated proteins, and autoantibodies^[74]. In future experiments using mouse models, it is possible to test the rational integration of antitumor monoclonal antibodies, effector cells, and cytokines for inducing tumor cell death. Additionally, the inclusion of small molecule drugs can be explored to prevent the escape of cell death^[21].
由于儿童患者数量相对成人较少，且儿童与成人在药物代谢、急性毒性和迟发效应方面存在显着差异，因此儿童癌症的转化和临床研究与成人癌症相比面临一系列挑战。此外，大多数儿科癌症的临床前研究都使用小鼠细胞系或异种移植模型，但对于免疫学分析，小鼠效应细胞并不是理想的人类替代品。针对这一问题，建议使用植入人体免疫细胞的人源化小鼠，以更好地模拟人体免疫系统。 蛋白质组技术的高通量能力为发现潜在的生物标志物提供了丰富的资源。未来，NB蛋白质组学可以通过蛋白质片段亚型、糖蛋白、磷酸化蛋白和自身抗体的研究来探索生物标志物^[74] 。 在未来使用小鼠模型的实验中，有可能测试抗肿瘤单克隆抗体、效应细胞和细胞因子的合理整合以诱导肿瘤细胞死亡。此外，可以探索加入小分子药物来防止细胞死亡的逃逸^[21] 。

The radiomic approach in NB holds the potential to enhance diagnostic accuracy, treatment planning, and prognosis prediction. However, realizing these goals faces several challenges. Firstly, the limited sample size of NB, due to its relative rarity, makes it difficult to obtain large sample cohorts with sufficient clinical and imaging data for radiomic analysis. Collaborative efforts, data sharing, and multi-institutional cooperation are essential means to overcome this limitation. Secondly, despite the quantitative nature of radiomics, transforming these features into biologically relevant information remains challenging. Lastly, the inherent heterogeneity in imaging protocols and data collection methods, stemming from multiple centers, may impact the reproducibility of research results. Implementing standardized imaging protocols, promoting the sharing of imaging datasets, and developing coordinated technologies are effective strategies to address this issue.
NB 的放射组学方法有可能提高诊断准确性、治疗计划和预后预测。然而，实现这些目标面临着一些挑战。首先，由于NB的相对稀有性，其样本量有限，因此很难获得具有足够临床和影像数据的大样本队列进行放射组学分析。协同努力、数据共享和多机构合作是克服这一限制的重要手段。其次，尽管放射组学具有定量性质，但将这些特征转化为生物学相关信息仍然具有挑战性。最后，来自多个中心的成像协议和数据收集方法固有的异质性可能会影响研究结果的可重复性。实施标准化成像协议、促进成像数据集共享以及开发协调技术是解决这一问题的有效策略。

Another current issue is that most preclinical studies on childhood cancers, including NB, are short-term, utilize clinically irrelevant drug doses and regimens, fail to adequately consider combination chemotherapy, and lack appropriate benchmarks. Moreover, these studies often lack statistical design, proper randomization of animal groups for treatment plans, and mechanisms to keep the research team "blind" to the treatments administered to the study subjects. To address these issues, future efforts should involve building interdisciplinary teams comprising clinical researchers, laboratory scientists, pharmacologists, and biostatisticians.
当前的另一个问题是，大多数针对儿童癌症（包括NB）的临床前研究都是短期的，使用与临床无关的药物剂量和方案，未能充分考虑联合化疗，并且缺乏适当的基准。此外，这些研究通常缺乏统计设计、治疗计划中动物组的适当随机化，以及使研究团队对研究对象的治疗“盲目”的机制。为了解决这些问题，未来的努力应该包括建立由临床研究人员、实验室科学家、药理学家和生物统计学家组成的跨学科团队。

The future direction of NB omics research should pivot towards interdisciplinary translational research teams that utilize validated preclinical models. These teams seek to pinpoint optimal combinations of molecularly targeted therapies, broad-spectrum chemotherapy, and immunotherapy to treat patients with unfavorable prognoses. Simultaneously, they aim to scale down treatment intensity for individuals with more favorable prognoses. The primary objective is to prioritize targeted therapies and minimize cytotoxic treatments, with the overarching goal of curing NB and mitigating treatment-related adverse effects in pediatric patients.
NB组学研究的未来方向应转向利用经过验证的临床前模型的跨学科转化研究团队。这些团队致力于找出分子靶向疗法、广谱化疗和免疫疗法的最佳组合，以治疗预后不良的患者。同时，他们的目标是降低预后较好的个体的治疗强度。主要目标是优先考虑靶向治疗并尽量减少细胞毒性治疗，总体目标是治愈儿科患者的 NB 并减轻治疗相关的不良反应。

Limitations

This study has certain limitations. Firstly, Our search was confined to the WoSCC, which may overlook influential articles from PubMed or Scopus, potentially impacting the final results. However, WoSCC, a citation index database encompassing around 34,000 core journals worldwide and spanning various disciplines, stands out as the most commonly used and most suitable database for bibliometric analysis^[75].
这项研究有一定的局限性。首先，我们的搜索仅限于WoSCC，这可能会忽略来自 PubMed 或 Scopus 的有影响力的文章，从而可能影响最终结果。然而，WoSCC 这个涵盖全球约 34,000 种核心期刊、跨学科的引文索引数据库脱颖而出，成为最常用、最适合文献计量分析的数据库^[75] 。

Secondly, this study only includes English articles and reviews, thereby excluding some non-English publications. Additionally, the WoSCC database remains open and is updated daily with new research, which may result in the omission of the latest publications and incomplete descriptions. However, the collection and analysis for this study were conducted within a short time frame, and the research findings essentially align with the latest statistical data. Therefore, our study still offers relatively objective information and insights.
其次，本研究仅包括英文文章和评论，从而排除了一些非英文出版物。此外，WoSCC 数据库保持开放，并每天根据新研究进行更新，这可能会导致最新出版物的遗漏和不完整的描述。但本研究的收集和分析时间较短，研究结果与最新统计数据基本相符。因此，我们的研究仍然提供了相对客观的信息和见解。

Conclusion

Our bibliometric analysis of NB omics publications involved 514 articles, showcasing a significant surge since 2020. This trend indicates a growing interest in NB omics, suggesting its gradual entry into a mature development stage. The analyzed articles span 50 countries and involve 1,000 institutions, 3,669 authors, and 292 journals. The United States led in publications and collaborations, particularly with Germany. Capital Medical University and the German Cancer Research Center are the institutions with the most published papers. The Journal of Proteome Research and the Journal of Biological Chemistry were the most prolific in terms of publications and co-citations, respectively. Scholars Wang, W, and Maris, JM, were identified as the most prolific authors in terms of publication quantity and co-cited authors in this field. "Neuroblastoma" and "Expression" were the most frequently occurring keywords, while "classification," "Metabolism," "Cancer," and "Diagnosis" emerged as recent keywords. An article titled "Neuroblastoma" by John M. Maris had the highest number of citations among the references. We explored the potential applications of omics in identifying biomarkers and personalized medicine for NB, as well as challenges and future directions in NB omics. In conclusion, our study unveils NB omics research trends and frontiers. This study empowers scholars with a more scientific, objective, and comprehensive understanding of the field, fostering its development.
我们对 NB 组学出版物的文献计量分析涉及 514 篇文章，自 2020 年以来出现大幅增长。这一趋势表明人们对NB 组学的兴趣日益浓厚，表明其逐渐进入成熟发展阶段。分析的文章跨越 50 个国家，涉及 1,000 个机构、3,669 位作者和 292 种期刊。美国在出版物和合作方面处于领先地位，特别是与德国的合作。首都医科大学和德国癌症研究中心是发表论文最多的机构。 《蛋白质组研究杂志》和《生物化学杂志》分别在出版物和共被引方面最多产。学者 Wang, W 和 Maris, JM 被认为是该领域发表数量和共同被引作者最多产的作者。 “神经母细胞瘤”和“表达”是最常出现的关键词，而“分类”、“代谢”、“癌症”和“诊断”是最近出现的关键词。约翰·M·马里斯 (John M. Maris) 发表的题为“神经母细胞瘤”的文章在参考文献中被引用次数最高。我们探讨了组学在识别 NB 生物标志物和个性化医疗方面的潜在应用，以及 NB 组学的挑战和未来方向。总之，我们的研究揭示了 NB 组学研究趋势和前沿。这项研究使学者们对该领域有了更加科学、客观、全面的认识，促进了该领域的发展。

Data availability statement
数据可用性声明

The original contributions presented in the study are included in the article. Further inquiries can be directed to the corresponding authors.
研究中提出的原始贡献包含在文章中。进一步的询问可以直接联系相应的作者。

Author contributions
作者贡献

Mengliang Han, Huizhong Niu designed the research. Fei Duan, Zhaolong Wang, Zhiguang Zhang Collected the data. Hui Ren, Mengliang Han involved in analyzing and interpreting the data. Mengliang Han, Fei Duan, Zhaolong Wang mainly completed the manuscript. Huizhong Niu contributed to the revision of the manuscript. All authors read and approved the final draft of the manuscript.

Funding

Not applicable.
不适用。

Ethics approval and consent to participate
道德批准并同意参与

Not applicable.
不适用。

Competing interests
利益争夺

The authors declared that no competing of interests existing in this study
作者声明本研究不存在利益竞争

References
参考

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Figure 1

(A) The flowchart of publications screening. (B) Annual number of publications on omics in NB.
（一）出版物筛选流程图。 (B) NB 中组学的年度出版物数量。

Figure 2

(A) Collaborative Map of Country in NB Omics Research. (B) Map of International Cooperation Between Countries/regions. (C) Map of visualization of institutions on research of omics in NB
( A ) NB 组学研究国家合作地图。 （二）国家（地区）国际合作地图。 ( C ) NB组学研究机构可视化图

Figure 3

(A) Network of journals on research of omics in NB. (B) Network of co-cited journals on research of omics in NB. (C) Network of authors on research of omics in NB. (D) Network of co-cited authors on research of omics in NB
( A ) NB 组学研究期刊网络。 ( B ) NB 组学研究共同被引期刊网络。 ( C ) NB 组学研究作者网络。 ( D ) NB 组学研究共同被引作者网络

Figure 4

(A) Network of keyword on research of omics in NB. (B) Keyword co-occurrence network analysis diagram on research of omics in NB. (C) Trending topics on research of omics in NB. (D) The top 17 keywords with the strongest citations on research of omics in NB
(一) NB组学研究关键词网络。 ( B ) NB组学研究关键词共现网络分析图。 ( C ) NB组学研究的热门话题。 ( D ) NB组学研究被引次数最多的17个关键词

Figure 5

(A) MOST local cited referecnces on the research of omics in NB. (B) reference' map on the research of omics in NB. (C) The top 17 references with the strongest citation bursts for publications on omics in NB
( A ) NB组学研究中被引用最多的本地参考文献。 ( B ) NB组学研究参考图。 ( C ) NB 组学出版物中引用爆发最强的前 17 篇参考文献