Dataset Information

Microarray analysis of long non-coding RNA expression profiles in low high-density lipoprotein cholesterol disease.

ABSTRACT: BACKGROUND:Low high-density lipoprotein cholesterol (HDL-C) disease with unknown etiology has a high prevalence in the Xinjiang Kazak population. In this study, long noncoding RNAs (lncRNAs) that might play a role in low HDL-C disease were identified. METHODS:Plasma samples from 10 eligible individuals with low HDL disease and 10 individuals with normal HDL-C levels were collected. The lncRNA profiles for 20 Xinjiang Kazak individuals were measured using microarray analysis. RESULTS:Differentially expressed lncRNAs and mRNAs with fold-change values not less than 1.5 and FDR-adjusted P-values less than 0.05 were screened. Bioinformatic analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and network analyses, were used to determine relevant signaling pathways and predict potential target genes. In total, 381 lncRNAs and 370 mRNAs were differentially expressed based on microarray analysis. Compared with those in healthy individuals, several lncRNAs were upregulated or downregulated in patients with low HDL-C disease, among which TCONS_00006679 was most significantly upregulated and TCONS_00011823 was most significantly downregulated. GO and KEGG pathway analyses as well as co-expression networks of lncRNAs and mRNAs revealed that the platelet activation pathway and cardiovascular disease were associated with low HDL-C disease. CONCLUSIONS:Potential target genes integrin beta-3 (ITGB3) and thromboxane A2 receptor (TBXA2R) were regulated by the lncRNAs AP001033.3-201 and AC068234.2-202, respectively. Both genes were associated with cardiovascular disease and were involved in the platelet activation pathway. AP001033.3-201 and AC068234.2-202 were associated with low HDL-C disease and could play a role in platelet activation in cardiovascular disease. These results reveal the potential etiology of dyslipidemia in the Xinjiang Kazakh population and lay the foundation for further validation using large sample sizes.

SUBMITTER: Wang X

PROVIDER: S-EPMC7388226 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Microarray analysis of long non-coding RNA expression profiles in low high-density lipoprotein cholesterol disease.

Wang Xinping X Guo Shuxia S Hu Yunhua Y Guo Heng H Zhang Xianghui X Yan Yizhong Y Ma Jiaolong J Li Yu Y Wang Haixia H He Jia J Ma Rulin R

Lipids in health and disease 20200728 1

<h4>Background</h4>Low high-density lipoprotein cholesterol (HDL-C) disease with unknown etiology has a high prevalence in the Xinjiang Kazak population. In this study, long noncoding RNAs (lncRNAs) that might play a role in low HDL-C disease were identified.<h4>Methods</h4>Plasma samples from 10 eligible individuals with low HDL disease and 10 individuals with normal HDL-C levels were collected. The lncRNA profiles for 20 Xinjiang Kazak individuals were measured using microarray analysis.<h4>Re ...[more]

PMID: 32723322

Similar Datasets

Project description:Long non-coding RNAs (lncRNAs) serve a crucial role in every aspect of cell biological functions as well as in a variety of diseases, including cardiovascular disease, cancer and nervous system disease. However, the differential expression profiles of lncRNAs in Marfan syndrome (MFS) have not been reported. The aim of the present study was to identify potential target genes behind the pathogenesis of MFS by analyzing microarray profiles of lncRNA in aortic tissues from individuals with MFS and normal aortas (NA). The differentially expressed lncRNA profiles between MFS (n=3) and NA (n=4) tissues were analyzed using microarrays. Bioinformatics analyses were used to further investigate the candidate lncRNAs. Reverse transcription-quantitative (RT-qPCR) was applied to validate the results. In total, the present study identified 294 lncRNAs (245 upregulated and 49 downregulated) and 644 mRNAs (455 upregulated and 189 downregulated) which were differential expressed between MFS and NA tissues (fold change ≥1.5; P<0.05). Gene Ontology enrichment analysis indicated that the differentially expressed mRNAs were involved in cell adhesion, elastic fiber assembly, extracellular matrix (ECM) organization, the response to virus and the inflammatory response. Kyoto Encyclopedia of Gene and Genomes pathway analysis indicated that the differentially expressed mRNAs were mainly associated with focal adhesion, the ECM-receptor interaction, the mitogen-activated protein kinase signaling pathway and the tumor necrosis factor signaling pathway. The lncRNA-mRNA coexpression network analysis further elucidated the interaction between the lncRNAs and mRNAs. A total of five lncRNAs (uc003jka.1, uc003jox.1, X-inactive specific transcript, linc-lysophosphatidic acid receptor 1 and linc-peptidylprolyl isomerase domain and WD repeat containing 1) with the highest degree of coexpression were selected and confirmed using RT-qPCR. In the present study, expression profiles of lncRNA and mRNA in MFS were revealed using microarray analysis. These results provided novel candidates for further investigation of the molecular mechanisms and effective targeted therapies for MFS.

Project description:Low-density lipoprotein cholesterol (LDL-C) is an important modifiable risk factor for atherosclerotic cardiovascular disease. It is unclear whether the percentage LDL-C lowering with pharmacotherapies differs on the basis of baseline LDL-C levels. To evaluate the association between baseline LDL-C levels and the percentage LDL-C reduction with a statin, ezetimibe, and a PCSK9 inhibitor. This secondary exploratory study analyzed data from 3 randomized placebo-controlled clinical trials (Aggrastat to Zocor-Thrombolysis in Myocardial Infarction 21 [A to Z-TIMI 21], Improved Reduction of Outcomes: Vytorin Efficacy International Trial [IMPROVE-IT], and Further Cardiovascular Outcomes Research With PCSK9 Inhibition in Subjects With Elevated Risk [FOURIER]) of lipid-lowering therapies (statin, ezetimibe, and a PCSK9 inhibitor) and included participants with atherosclerotic cardiovascular disease. Analyses took place form April to October 2020. In A to Z-TIMI 21, 1:1 randomization to simvastatin, 40 mg, daily for 30 days followed by 80 mg daily thereafter vs placebo for 4 months followed by simvastatin, 20 mg, daily thereafter. In IMPROVE-IT, 1:1 randomization to ezetimibe, 10 mg, daily plus simvastatin, 40 mg, daily vs placebo plus simvastatin, 40 mg, daily. In FOURIER, 1:1 randomization to evolocumab, 140 mg, every 2 weeks or 420 mg monthly vs matching placebo. The percentage LDL-C reduction at either 1 month (A to Z-TIMI 21, IMPROVE-IT) or 3 months (FOURIER) as a function of baseline LDL-C level. Data were modeled using a generalized linear regression model. A total of 3187 patients from A to Z-TIMI 21, 10 680 patients from IMPROVE-IT, and 25 847 patients from FOURIER were analyzed. There was a higher percentage reduction in LDL-C levels with evolocumab in patients with lower baseline LDL-C levels, ranging from 59.4% (95% CI, 59.1%-59.8%) in patients with a baseline LDL-C level of 130 mg/dL to 66.1% (95% CI, 65.6%-66.6%) in patients with a baseline LDL-C level of 70 mg/dL (P < .001). In contrast, across the same range of baseline LDL-C level, there was a more modest difference for simvastatin (44.6% [95% CI, 43.9%-45.2%] vs 47.8% [95% CI, 46.4%-49.2%]; P < .001) and minimal difference with ezetimibe (25.0% [95% CI, 23.3%-26.6%] vs 26.2% [95% CI, 24.2%-28.1%]; P = .007). The percentage LDL-C reduction with statins, ezetimibe, and PCSK9 inhibition is not attenuated in patients starting with lower baseline LDL-C levels and is 6.6% greater for PCSK9 inhibition. These data are encouraging for the use of intensive LDL-C-lowering therapy even for patients with lower LDL-C levels.

Project description:Background Conventional "low-density lipoprotein cholesterol (LDL-C)" assays measure cholesterol content in both low-density lipoprotein and lipoprotein(a) particles. To clarify the consequences of this methodological limitation for clinical care, our study aimed to compare associations of "LDL-C" and corrected LDL-C with risk of cardiovascular disease and to assess the impact of this correction on the classification of patients into guideline-recommended LDL-C categories. Methods and Results Lipoprotein(a) cholesterol content was estimated as 30% of lipoprotein(a) mass and subtracted from "LDL-C" to obtain corrected LDL-C values (LDL-Ccorr30). Hazard ratios for cardiovascular disease (defined as coronary heart disease, stroke, or coronary revascularization) were quantified by individual-patient-data meta-analysis of 5 statin landmark trials from the Lipoprotein(a) Studies Collaboration (18 043 patients; 5390 events; 4.7 years median follow-up). When comparing top versus bottom quartiles, the multivariable-adjusted hazard ratio for cardiovascular disease was significant for "LDL-C" (1.17; 95% CI, 1.05-1.31; P=0.005) but not for LDL-Ccorr30 (1.07; 95% CI, 0.93-1.22; P=0.362). In a routine laboratory database involving 531 144 patients, reclassification of patients across guideline-recommended LDL-C categories when using LDL-Ccorr30 was assessed. In "LDL-C" categories of 70 to <100, 100 to <130, 130 to <190, and ≥190 mg/dL, significant proportions (95% CI) of participants were reassigned to lower LDL-C categories when LDL-Ccorr30 was used: 30.2% (30.0%-30.4%), 35.1% (34.9%-35.4%), 32.9% (32.6%-33.1%), and 41.1% (40.0%-42.2%), respectively. Conclusions "LDL-C" was associated with incident cardiovascular disease only when lipoprotein(a) cholesterol content was included in its measurement. Refinement in techniques to accurately measure LDL-C, particularly in patients with elevated lipoprotein(a) levels, is warranted to assign risk to the responsible lipoproteins.

Dataset Information

Microarray analysis of long non-coding RNA expression profiles in low high-density lipoprotein cholesterol disease.

Publications

Microarray analysis of long non-coding RNA expression profiles in low high-density lipoprotein cholesterol disease.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets