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代谢组学-转录组学联合分析:揭示失调的细胞死亡网络并为高级神经母细胞瘤开发诊断模型。

Metabolomics-transcriptomics joint analysis: unveiling the dysregulated cell death network and developing a diagnostic model for high-grade neuroblastoma.

机构信息

Health Commission of Henan Province Key Laboratory for Precision Diagnosis and Treatment of Pediatric Tumor, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China.

Henan International Joint Laboratory for Prevention and Treatment of Pediatric Disease, Children's Hospital Affiliated to Zhengzhou University, Zhengzhou, China.

出版信息

Front Immunol. 2024 Jan 4;14:1345734. doi: 10.3389/fimmu.2023.1345734. eCollection 2023.

Abstract

High-grade neuroblastoma (HG-NB) exhibits a significantly diminished survival rate in comparison to low-grade neuroblastoma (LG-NB), primarily attributed to the mechanism of HG-NB is unclear and the lacking effective therapeutic targets and diagnostic model. Therefore, the current investigation aims to study the dysregulated network between HG-NB and LG-NB based on transcriptomics and metabolomics joint analysis. Meanwhile, a risk diagnostic model to distinguish HG-NB and LG-NB was also developed. Metabolomics analysis was conducted using plasma samples obtained from 48 HG-NB patients and 36 LG-NB patients. A total of 39 metabolites exhibited alterations, with 20 showing an increase and 19 displaying a decrease in HG-NB. Additionally, transcriptomics analysis was performed on NB tissue samples collected from 31 HG-NB patients and 20 LG-NB patients. Results showed that a significant alteration was observed in a total of 1,199 mRNAs in HG-NB, among which 893 were upregulated while the remaining 306 were downregulated. In particular, the joint analysis of both omics data revealed three aberrant pathways, namely the cAMP signaling pathway, PI3K-Akt signaling pathway, and TNF signaling pathway, which were found to be associated with cell death. Notably, a diagnostic model for HG-NB risk classification was developed based on the genes , , and with an area under the receiver operating characteristic curve of 0.915. In the validation set, the sensitivity and specificity were determined to be 75.0% and 80.0%, respectively.

摘要

高级神经母细胞瘤(HG-NB)的存活率明显低于低级神经母细胞瘤(LG-NB),主要原因是 HG-NB 的发生机制尚不清楚,且缺乏有效的治疗靶点和诊断模型。因此,本研究旨在通过转录组学和代谢组学联合分析研究 HG-NB 和 LG-NB 之间失调的网络。同时,还开发了一种用于区分 HG-NB 和 LG-NB 的风险诊断模型。代谢组学分析使用来自 48 例 HG-NB 患者和 36 例 LG-NB 患者的血浆样本进行。共发现 39 种代谢物发生改变,其中 20 种在 HG-NB 中增加,19 种减少。此外,还对 31 例 HG-NB 患者和 20 例 LG-NB 患者的 NB 组织样本进行了转录组学分析。结果表明,HG-NB 中共有 1199 个 mRNAs 发生显著改变,其中 893 个上调,其余 306 个下调。特别是,对两组组学数据的联合分析揭示了三个异常途径,即 cAMP 信号通路、PI3K-Akt 信号通路和 TNF 信号通路,这些途径与细胞死亡有关。值得注意的是,基于基因、和开发了 HG-NB 风险分类的诊断模型,ROC 曲线下面积为 0.915。在验证集中,灵敏度和特异性分别为 75.0%和 80.0%。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2049/10794662/4d1c1dbf5b82/fimmu-14-1345734-g007.jpg

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