Shenzhen Research Institute of Big Data, Shenzhen, China.
School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, China.
PLoS Comput Biol. 2021 Jun 1;17(6):e1009044. doi: 10.1371/journal.pcbi.1009044. eCollection 2021 Jun.
Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer.
现有研究表明，microRNAs（miRNAs 或 miRs）的失调参与了癌症的发生和发展。许多研究都致力于将 microRNAs 鉴定为癌症诊断、预后和治疗靶点的潜在生物标志物。随着 miRNA 测序技术的快速发展，已经收集了大量用于多种癌症的 miRNA 表达数据。这些宝贵的数据库为探索 miRNA 与癌症之间的关系提供了新的范例。因此，迫切需要通过整合泛癌范例中的多组学数据来探索复杂的癌症相关 miRNA-基因模式。在这项研究中，我们提出了一种张量稀疏典型相关分析（TSCCA）方法，用于识别多种癌症中与癌症相关的 miRNA-基因模块。TSCCA 能够克服现有解决方案的缺点，并更好地捕捉具有更好生物学解释的癌症共享和特定的 miRNA-基因共表达模块。我们使用来自 TCGA 数据库的 33 种癌症类型的一组模拟数据和匹配的 miRNA/基因表达数据全面评估了 TSCCA 的性能。我们发现了一些具有重要生物学功能和统计学意义的功能失调的 miRNA-基因模块。这些模块可以促进我们对 miRNA 调控癌症机制的理解，并为 miRNA 为基础的癌症治疗提供思路。
