Information Engineering CollegeShanghai Maritime University Shanghai 201306 China.
Yangpu District Central Hospital Shanghai 200433 China.
IEEE J Transl Eng Health Med. 2023 Jun 7;11:384-393. doi: 10.1109/JTEHM.2023.3283519. eCollection 2023.
Long non-coding RNAs (lncRNAs) have been shown to be associated with the pathogenesis of different kinds of diseases and play important roles in various biological processes. Although numerous lncRNAs have been found, the functions of most lncRNAs and physiological/pathological significance are still in its infancy. Meanwhile, their expression patterns and regulation mechanisms are also far from being fully understood.
In order to reveal functional lncRNAs and identify the key lncRNAs, we develop a new sparse independence component analysis (ICA) method to identify lncRNA-mRNA-miRNA expression co-modules based on the competitive endogenous RNA (ceRNA) theory using the sample-matched lncRNA, mRNA and miRNA expression profiles. The expression data of the three RNA combined together is approximated sparsely to obtain the corresponding sparsity coefficient, and then it is decomposed by using ICA constraint optimization to obtain the common basis and modules. Subsequently, affine propagation clustering is used to perform cluster analysis on the common basis under multiple running conditions to obtain the co-modules for the selection of different RNA elements.
We applied sparse ICA to Liver Hepatocellular Carcinoma (LIHC) dataset and the experiment results demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules.
It may provide insights into the function of lncRNAs and molecular mechanism of LIHC. Clinical and Translational Impact Statement-The results on LIHC dataset demonstrate that the proposed sparse ICA method can effectively discover biologically functional expression common modules, which may provide insights into the function of IncRNAs and molecular mechanism of LIHC.
长链非编码 RNA（lncRNA）已被证明与多种疾病的发病机制有关，并在各种生物过程中发挥重要作用。尽管已经发现了许多 lncRNA，但大多数 lncRNA 的功能和生理/病理意义仍处于起步阶段。同时，它们的表达模式和调节机制也远未被充分理解。
为了揭示功能性 lncRNA 并鉴定关键 lncRNA，我们根据竞争内源性 RNA（ceRNA）理论，开发了一种新的稀疏独立成分分析（ICA）方法，使用样本匹配的 lncRNA、mRNA 和 miRNA 表达谱，基于竞争内源性 RNA（ceRNA）理论，识别 lncRNA-mRNA-miRNA 表达共模块。将三种 RNA 的表达数据组合在一起进行稀疏近似，以获得相应的稀疏系数，然后通过使用 ICA 约束优化对其进行分解，以获得共同的基础和模块。随后，使用仿射传播聚类在多个运行条件下对共同基础进行聚类分析，以获得不同 RNA 元素的共模块。
我们将稀疏 ICA 应用于 Liver Hepatocellular Carcinoma（LIHC）数据集，实验结果表明，所提出的稀疏 ICA 方法可以有效地发现具有生物学功能的表达共模块。
这可能为 lncRNA 的功能和 LIHC 的分子机制提供新的见解。
在 LIHC 数据集上的结果表明，所提出的稀疏 ICA 方法可以有效地发现具有生物学功能的表达共模块，这可能为 lncRNA 的功能和 LIHC 的分子机制提供新的见解。
