文献综述显示,AHCY、DPYSL3和NME1是神经母细胞瘤复发率最高的预后基因。

IF 4 3区 生物学 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Biodata Mining Pub Date : 2023-03-04 DOI:10.1186/s13040-023-00325-1
Davide Chicco, Tiziana Sanavia, Giuseppe Jurman
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引用次数: 1

摘要

神经母细胞瘤是一种影响全球数十万儿童的儿童神经系统肿瘤,有关其预后的信息对患者、其家人和临床医生至关重要。相关生物信息学分析的主要目标之一是提供稳定的遗传特征,能够包括表达水平可以有效预测患者预后的基因。在本研究中,我们收集了生物医学文献中发表的神经母细胞瘤预后特征,并注意到其中最常见的基因有三个:AHCY, DPYLS3和NME1。因此,我们通过对诊断为神经母细胞瘤的不同组患者的多基因表达数据集进行生存分析和二元分类来研究这三种基因的预后能力。最后,我们讨论了文献中有关这三个基因与神经母细胞瘤的主要研究。在这三个验证步骤中,我们的结果都证实了AHCY、DPYLS3和NME1的预后能力,并强调了它们在神经母细胞瘤预后中的关键作用。我们的研究结果可能对神经母细胞瘤遗传学研究产生影响:生物学家和医学研究人员可以更多地关注这三个基因在神经母细胞瘤患者中的调控和表达,从而开发出更好的治疗方法,挽救患者的生命。
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Signature literature review reveals AHCY, DPYSL3, and NME1 as the most recurrent prognostic genes for neuroblastoma.

Neuroblastoma is a childhood neurological tumor which affects hundreds of thousands of children worldwide, and information about its prognosis can be pivotal for patients, their families, and clinicians. One of the main goals in the related bioinformatics analyses is to provide stable genetic signatures able to include genes whose expression levels can be effective to predict the prognosis of the patients. In this study, we collected the prognostic signatures for neuroblastoma published in the biomedical literature, and noticed that the most frequent genes present among them were three: AHCY, DPYLS3, and NME1. We therefore investigated the prognostic power of these three genes by performing a survival analysis and a binary classification on multiple gene expression datasets of different groups of patients diagnosed with neuroblastoma. Finally, we discussed the main studies in the literature associating these three genes with neuroblastoma. Our results, in each of these three steps of validation, confirm the prognostic capability of AHCY, DPYLS3, and NME1, and highlight their key role in neuroblastoma prognosis. Our results can have an impact on neuroblastoma genetics research: biologists and medical researchers can pay more attention to the regulation and expression of these three genes in patients having neuroblastoma, and therefore can develop better cures and treatments which can save patients' lives.

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来源期刊
Biodata Mining
Biodata Mining MATHEMATICAL & COMPUTATIONAL BIOLOGY-
CiteScore
7.90
自引率
0.00%
发文量
28
审稿时长
23 weeks
期刊介绍: BioData Mining is an open access, open peer-reviewed journal encompassing research on all aspects of data mining applied to high-dimensional biological and biomedical data, focusing on computational aspects of knowledge discovery from large-scale genetic, transcriptomic, genomic, proteomic, and metabolomic data. Topical areas include, but are not limited to: -Development, evaluation, and application of novel data mining and machine learning algorithms. -Adaptation, evaluation, and application of traditional data mining and machine learning algorithms. -Open-source software for the application of data mining and machine learning algorithms. -Design, development and integration of databases, software and web services for the storage, management, retrieval, and analysis of data from large scale studies. -Pre-processing, post-processing, modeling, and interpretation of data mining and machine learning results for biological interpretation and knowledge discovery.
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