Li Xianbin, Ai Hannan, Li Bizhou, Zhang Chaohui, Meng Fanmei, Ai Yuncan
State Key Laboratory for Biocontrol, School of Life Sciences, Sun Yat-sen University, Guangzhou, China.
Department of Electrical and Computer Engineering, The Grainger College of Engineering, University of Illinois at Urbana-Champaign, Urbana, IL, United States.
Front Genet. 2022 Jan 27;13:825318. doi: 10.3389/fgene.2022.825318. eCollection 2022.
Identifying cancer-related miRNAs (or microRNAs) that precisely target mRNAs is important for diagnosis and treatment of cancer. Creating novel methods to identify candidate miRNAs becomes an imminent Frontier of researches in the field. One major obstacle lies in the integration of the state-of-the-art databases. Here, we introduce a novel method, MIMRDA, which incorporates the RNA and RNA expression profiles for predicting miNA-isease ssociations to identify key miRNAs. As a proof-of-principle study, we use the MIMRDA method to analyze TCGA datasets of 20 types (BLCA, BRCA, CESE, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, SKCM, STAD, THCA and UCEC) of cancer, which identified hundreds of top-ranked miRNAs. Some (as Category 1) of them are endorsed by public databases including TCGA, miRTarBase, miR2Disease, HMDD, MISIM, ncDR and mTD; others (as Category 2) are supported by literature evidences. miR-21 (representing Category 1) and miR-1258 (representing Category 2) display the excellent characteristics of biomarkers in multi-dimensional assessments focusing on the function similarity analysis, overall survival analysis, and anti-cancer drugs' sensitivity or resistance analysis. We compare the performance of the MIMRDA method over the Limma and SPIA packages, and estimate the accuracy of the MIMRDA method in classifying top-ranked miRNAs via the Random Forest simulation test. Our results indicate the superiority and effectiveness of the MIMRDA method, and recommend some top-ranked key miRNAs be potential biomarkers that warrant experimental validations.
识别精确靶向mRNA的癌症相关miRNA（或微小RNA）对于癌症的诊断和治疗至关重要。创建新方法来识别候选miRNA成为该领域研究迫在眉睫的前沿。一个主要障碍在于整合最先进的数据库。在此，我们介绍一种新方法MIMRDA，它整合RNA和RNA表达谱以预测miRNA-疾病关联，从而识别关键miRNA。作为原理验证研究，我们使用MIMRDA方法分析20种类型（BLCA、BRCA、CESE、CHOL、COAD、ESCA、HNSC、KICH、KIRC、KIRP、LIHC、LUAD、LUSC、PAAD、PRAD、READ、SKCM、STAD、THCA和UCEC）癌症的TCGA数据集，识别出数百个排名靠前的miRNA。其中一些（类别1）得到包括TCGA、miRTarBase、miR2Disease、HMDD、MISIM、ncDR和mTD等公共数据库的认可；其他一些（类别2）得到文献证据的支持。miR-21（代表类别1）和miR-1258（代表类别2）在聚焦功能相似性分析、总生存分析以及抗癌药物敏感性或耐药性分析的多维度评估中显示出作为生物标志物的优异特征。我们比较了MIMRDA方法与Limma和SPIA软件包的性能，并通过随机森林模拟测试估计MIMRDA方法在对排名靠前的miRNA进行分类时的准确性。我们的结果表明MIMRDA方法的优越性和有效性，并推荐一些排名靠前的关键miRNA作为潜在生物标志物，值得进行实验验证。
