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鉴定参与肾母细胞瘤的12基因特征和核心基因:综合生物信息学分析

Identification of a 12-Gene Signature and Hub Genes Involved in Kidney Wilms Tumor Integrated Bioinformatics Analysis.

作者信息

Huang Guoping, Mao Jianhua

机构信息

Department of Nephrology, The Children's Hospital, Zhejiang University School of Medicine, National Clinical Research Center for Child Health, Hangzhou, China.

出版信息

Front Oncol. 2022 Apr 11;12:877796. doi: 10.3389/fonc.2022.877796. eCollection 2022.

Abstract

Wilms tumor (WT), also known as nephroblastoma, is a rare primary malignancy in all kinds of tumor. With the development of second-generation sequencing, the discovery of new tumor markers and potential therapeutic targets has become easier. This study aimed to explore new WT prognostic biomarkers. In this study, WT-miRNA datasets GSE57370 and GSE73209 were selected for expression profiling to identify differentially expressed genes. The key gene miRNA, namely hsa-miR-30c-5p, was identified by overlapping, and the target gene of candidate hsa-miR-30c-5p was predicted using an online database. Furthermore, 384 genes were obtained by intersecting them with differentially expressed genes in the TARGET-WT database, and the genes were analyzed for pathway and functional enrichment. Kaplan-Meier survival analysis of the 384 genes yielded a total of 25 key genes associated with WT prognosis. Subsequently, a prediction model with 12 gene signatures (BCL6, CCNA1, CTHRC1, DGKD, EPB41L4B, ERRFI1, LRRC40, NCEH1, NEBL, PDSS1, ROR1, and RTKN2) was developed. The model had good predictive power for the WT prognosis at 1, 3, and 5 years (AUC: 0.684, 0.762, and 0.774). Finally, ERRFI1 (hazard ratios [HR] = 1.858, 95% confidence intervals [CI]: 1.298-2.660) and ROR1 (HR = 0.780, 95% CI: 0.609-0.998) were obtained as independent predictors of prognosis in WT patients by single, multifactorial Cox analysis.

摘要

肾母细胞瘤(WT),也称为肾胚胎瘤,是各类肿瘤中一种罕见的原发性恶性肿瘤。随着二代测序技术的发展,发现新的肿瘤标志物和潜在治疗靶点变得更加容易。本研究旨在探索新的WT预后生物标志物。在本研究中,选择WT-miRNA数据集GSE57370和GSE73209进行表达谱分析以鉴定差异表达基因。通过重叠鉴定出关键基因miRNA,即hsa-miR-30c-5p,并使用在线数据库预测候选hsa-miR-30c-5p的靶基因。此外,通过将它们与TARGET-WT数据库中的差异表达基因进行交叉分析获得了384个基因,并对这些基因进行了通路和功能富集分析。对这384个基因进行Kaplan-Meier生存分析,共得到25个与WT预后相关的关键基因。随后,开发了一个具有12个基因特征(BCL6、CCNA1、CTHRC1、DGKD、EPB41L4B、ERRFI1、LRRC40、NCEH1、NEBL、PDSS1、ROR1和RTKN2)的预测模型。该模型对WT患者1年、3年和5年的预后具有良好的预测能力(AUC:0.684、0.762和0.774)。最后,通过单因素、多因素Cox分析,获得ERRFI1(风险比[HR]=1.858,95%置信区间[CI]:1.298-2.660)和ROR1(HR=0.780,95%CI:0.609-0.998)作为WT患者预后的独立预测因子。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/3083/9038080/b53c8a28c575/fonc-12-877796-g001.jpg

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