[基于高通量 RNA 测序的 Wilms 肿瘤关键基因鉴定及其对预后和免疫反应的影响]。

Q3 Medicine
Z Gao, J Lin, P Hong, Z Hu, J Dong, Q Shi, X Tian, F Liu, G Wei
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引用次数: 0

摘要

目的:确定 Wilms 肿瘤中差异表达的关键基因,并分析其对患者预后和免疫反应的潜在影响:方法:利用高通量RNA测序技术鉴定Wilms肿瘤临床样本和配对正常组织中差异表达的mRNA,并利用GO、KEGG和GSEA富集分析其生物学功能。利用 STRING 数据库确定了枢纽基因,并在此基础上利用 LASSO 回归法构建了预后模型。利用 cBioPortal 平台分析了关键枢纽基因的突变,并预测了它们对免疫疗法疗效的影响。利用 RT-qPCR 验证了关键枢纽基因在 Wilms 肿瘤中的差异表达:结果:在 Wilms 肿瘤中发现的 1612 个差异表达基因中,1030 个上调,582 个下调,主要涉及细胞周期过程和免疫反应。发现了 10 个中心基因,其中 4 个基因(TP53、MED1、CCNB1 和 EGF)与 Wilms 肿瘤患儿的生存密切相关。通过LASSO回归分析构建了3个基因的预后特征,根据该特征将患者分为高危和低危两组,其生存结果有显著差异(HR=1.814,log-rank P=0.002)。该模型的 3 年、5 年和 7 年生存率 ROC 曲线的 AUC 均大于 0.7。关键枢纽基因的整体突变或TP53/CCNB1的单个突变与较低的生存率密切相关,而TP53的高表达与较差的免疫治疗效果相关。RT-qPCR证实,关键枢纽基因在Wilms肿瘤组织和细胞中的表达存在显著差异:结论:TP53 基因在 Wilms 肿瘤中发挥着重要作用,有可能成为新的免疫治疗生物标志物和治疗靶点。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
[Identification of key genes in Wilms tumor based on high-throughput RNA sequencing and their impacts on prognosis and immune responses].

Objective: To identify the key genes differentially expressed in Wilms tumor and analyze their potential impacts on prognosis and immune responses of the patients.

Methods: High-throughput RNA sequencing was used to identify the differentially expressed mRNAs in clinical samples of Wilms tumor and paired normal tissues, and their biological functions were analyzed using GO, KEGG and GSEA enrichment analyses. The hub genes were identified using STRING database, based on which a prognostic model was constructed using LASSO regression. The mutations of the key hub genes were analyzed and their impacts on immunotherapy efficacy was predicted using the cBioPortal platform. RT-qPCR was used to verify the differential expressions of the key hub genes in Wilms tumor.

Results: Of the 1612 differentially expressed genes identified in Wilms tumor, 1030 were up-regulated and 582 were down-regulated, involving mainly cell cycle processes and immune responses. Ten hub genes were identified, among which 4 genes (TP53, MED1, CCNB1 and EGF) were closely related to the survival of children with Wilms tumor. A 3-gene prognostic signature was constructed through LASSO regression analysis, and the patients stratified into with high- and low-risk groups based on this signature had significantly different survival outcomes (HR=1.814, log-rank P=0.002). The AUCs of the 3-, 5- and 7-year survival ROC curves of this model were all greater than 0.7. The overall mutations in the key hub genes or the individual mutations in TP53/CCNB1 were strongly correlated with a lower survival rates, and a high TP53 expression was correlated with a poor immunotherapy efficacy. RT-qPCR confirmed that the key hub genes had significant differential expressions in Wilms tumor tissues and cells.

Conclusion: TP53 gene plays an important role in the Wilms tumor and may potentially serve as a new immunotherapeutic biomarker as well as a therapeutic target.

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