基于生物信息学分析的主要MicroRNA基因在乳腺癌中的表达及预后意义

Qiong Wang, Jiuli Hu, Junwei Liang, Lanfang Liu, Shuoyang Xiao, Rui Wang, Chanchan Hu
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引用次数: 0

摘要

目的:乳腺癌(Breast Cancer, BC)具有高度复杂性和异质性的特点,microRNA (miRNA)与BC的发生发展密切相关。在这项研究中,我们评估了miRNA在BC中的预后价值。背景:乳腺导管癌和小叶癌是最常见的乳腺癌类型,并显示出这种疾病的高度复杂性和异质性。每个BC患者都有独特的形态学和分子特征。MicroRNAs (miRNAs)在人类肿瘤的发生、进展和预后中起着至关重要的作用。我们的研究旨在确定乳腺导管癌和小叶癌的潜在预后生物标志物,以预测总体生存结果。方法:所有分析的miRNA测序和临床数据均来自基因组数据共享数据门户。使用R软件中的edgeR软件包分析差异miRNA表达谱。获得完整的生存信息和差异表达的miRNA表达,使用Caret包装将样本及其图谱随机分为两组(训练组和试验组)。我们对训练组的mirna进行了单变量Cox回归分析。我们使用了三种不同的基于web的工具来鉴定miRNA的靶基因,并使用Perl语言来评估miRNA标记的靶基因。采用STRING数据库评估ppi。结果:共鉴定出304个差异表达的mirna(213个上调,91个下调)。其中,9个(hsa-miR-204-5p),通过Cox回归分析和miRNA签名风险评分建立。然后建立BC患者三年生存风险模型,训练组、测试组和整组的ROC auc分别为0.804、0.667、0.739。通过功能富集和生物信息学分析,发现mirna在肿瘤相关生物学过程和途径中的差异表达。结论:目前的研究为BC中基于mi- rna的mRNA网络提供了新的见解。9个miRNA和10个枢纽基因可能是预测BC患者生存的独立预后标志。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
The Expression and Prognostic Significance of Major MicroRNA Genes in Breast Cancer Based on Bioinformatics Analysis
Objective: Breast Cancer (BC) is characterized by high complexity and heterogeneity, and microRNA (miRNA) is bound up with the occurrence and development of BC. In this study, we evaluated the prognostic value of miRNA in BC. Background: Breast ductal and lobular cancers are the most common types of Breast Carcinomas (BC) and indicate the high complexity heterogeneity in this disease. Each BC patient has unique morphological and molecular features. MicroRNAs (miRNAs) play a critical role in human oncogenesis, progression, and prognosis. Our study aimed to identify potential prognostic biomarkers of breast ductal and lobular cancers to predict the overall survival outcome. Methods: All analyzed miRNA sequencing and clinical data were obtained from the Genomic Data Commons Data Porta. edgeR package in R software was used to analyze the differential miRNA expression profiles. Complete survival information and differentially expressed miRNA expression were obtained and the Caret package was used for random division of the samples along with their profiles into two groups (training group and test group). We performed univariate Cox regression analyses for miRNAs in the training group. We utilized three different web-based tools to identify the target genes of miRNAs and used the Perl language to evaluate the target genes for miRNA signature. STRING database was used to assess PPIs. Results: A total of 304 differentially expressed miRNAs were identified (213 were upregulated and 91 were downregulated). Among these, nine (hsa-miR-204-5p, by Cox regression analysis and miRNA signature risk score built. And then we performed the model of BC patients for three years survival risk, the AUCs of ROC were 0.804, 0.667, and 0.739 in the training, test, and entire groups, respectively. miRNAs were differentially expressed in tumor-related biological processes and pathways by functional enrichment and bioinformatic analysis. Conclusion: The current study provided novel insights into the mi-RNA-based mRNA network in BC. The nine miRNA and ten hub genes may be independent prognostic signatures for survival prediction in BC patients.
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