Long noncoding RNAs (lncRNAs) have been increasingly accepted to function importantly in human diseases by serving as competing endogenous RNAs (ceRNAs). To date, the ceRNA mechanisms of lncRNAs in the progression of atherosclerosis (AS) remain largely unclear. On the basis of ceRNA theory, we implemented a multistep computational analysis to construct an lncRNA-mRNA network for AS progression (ASpLMN). The probe reannotation method and microRNA-target interactions from databases were systematically integrated. Three lncRNAs (GS1-358P8.4, OIP5-AS1, and TUG1) with central topological features in the ASpLMN were firstly identified. By using subnetwork analysis, we then obtained two highly clustered modules and one dysregulated module from the ASpLMN network. These modules, sharing three lncRNAs (GS1-358P8.4, OIP5-AS1, and RP11-690D19.3), were significantly enriched in biological pathways such as regulation of actin cytoskeleton, tryptophan metabolism, lysosome, and arginine and proline metabolism. In addition, random walking in the ASpLMN network indicated that lncRNA RP1-39G22.7 and MBNL1-AS1 may also play an essential role in the pathology of AS progression. The identified six lncRNAs from the aforementioned steps could distinguish advanced- from early-staged AS, with a strong diagnostic power for AS occurrence. In conclusion, the results of this study will improve our understanding about the ceRNA-mediated regulatory mechanisms in AS progression, and provide novel lncRNAs as biomarkers or therapeutic targets for acute cardiovascular events.
Keywords: atherosclerosis; biological network; competing endogenous RNAs; lncRNAs; progression.