Identification of Key lncRNAs Associated With Atherosclerosis Progression Based on Public Datasets

Abstract

Atherosclerosis is one of the most common type of cardiovascular disease and the prime cause of mortality in the aging population worldwide. However, the detail mechanisms and special biomarkers of atherosclerosis remain to be further investigated. Lately, long non-coding RNAs (lncRNAs) has attracted much more attention than other types of ncRNAs. In our work, we found and confirmed differently expressed lncRNAs and mRNAs in atherosclerosis by analyzing GSE28829. We performed the weighted gene co-expression network analysis (WGCNA) by analyzing GSE40231 to confirm highly correlated genes. Gene Ontology (GO) analysis were utilized to assess the potential functions of differential expressed lncRNAs in atherosclerosis. Co-expression networks were also constructed to confirm hub lncRNAs in atherosclerosis. A total of 5784 mRNAs and 654 lncRNAs were found to be dysregulated in the progression of atherosclerosis. A total of 15 lncRNA-mRNA co-expression modules were identified in this study based on WGCNA analysis. Moreover, a few lncRNAs, such as ZFAS1, LOC100506730, LOC100506691, DOCK9-AS2, RP11-6I2.3, LOC100130219, were confirmed as important lncRNAs in atherosclerosis. Taken together, bioinformatics analysis revealed these lncRNAs were involved in regulating the leukotriene biosynthetic process, gene expression, actin filament organization, t-circle formation, antigen processing, and presentation, interferon-gamma-mediated signaling pathway, and activation of GTPase activity. We believed that this study would provide potential novel therapeutic and prognostic targets for atherosclerosis.

Identification of Long Noncoding RNAs as Predictors of Survival in Triple-Negative Breast Cancer Based on Network Analysis

“…Exploitation of the function of dysregulated lncRNAs will provide potential clinical applications for TN's diagnosis and treatment. WGCNA can be used to identify key lncRNAs associated with multiple cancer pathogenesis and progression [23][24][25][26]. Survival analysis verified the identified lncRNAs possessing potential indicative roles in TN prognosis.…”

Analysis of long non-coding RNA expression profiles in high-glucose treated vascular endothelial cells

“…Moreover, we identi ed some known lncRNAs may participate in high-glucose treated HUVEC. DAPK1-IT1, DAPK1 intronic transcript 1, has proved involved in cancer progression [41,42] , chemotherapy insensitivity [43] , Atherogenesis [44,45] , etc. In our study, we constructed the coexpression subnet of DAPK1-IT1 and function was analyzed with KEGG pathway (Figure 6), found may function with Terpenoid backbone biosynthesis, Steroid biosynthesis, Vitamin digestion and absorption, Protein processing in endoplasmic reticulum, Phagosome, and some of these pathways have been proved to be involved in the occurrence and development of diabetes.…”

The reuse of public datasets in the life sciences: potential risks and rewards

“…Co-expression analysis to find connected genes, for example identification of long non-coding RNAs associated with atherosclerosis progression (Wang et al, 2019); Co-expression networks, for example related to bamboo development using public RNA-Seq data (Ma et al, 2018) or related to cellulose synthesis using public microarray data (Persson et al, 2005); Construction of regulatory networks using co-expression data, for example co-expression network analysis to reveal genes in growth-defence trade-offs under JA signalling (Zhang et al, 2020) Batch effects might be possible if large sample groups come from the same source. Ideally, networks for different samples should be incorporated as there is high variation between co-expression networks with different samples (Ma et al, 2018).…”