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

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.