Differential Expression of Lncrna-Mirna-mrna and Their Related Functional Networks in New-Onset Type 2 Diabetes Mellitus among Chinese Rural Adults
Genes(2022)
摘要
Increasing evidence suggested that the expression and inter-regulation of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA) were related to the development of diabetes. Based on bioinformatics analysis, this study aimed to comprehensively analyze the dysregulated RNA molecules related to new-onset type 2 diabetes mellitus (T2DM). Twenty-four patients with new-onset T2DM were included as cases, and sex- and age-matched participants were included as controls. The differentially expressed lncRNAs, miRNAs, and mRNAs between the two groups were screened by RNA sequencing. LncRNA-miRNA-mRNA network and enrichment analysis were used to reveal the RNA molecules that were potentially associated with T2DM and their early changes. A total of 123 lncRNAs, 49 miRNAs, and 312 mRNAs were differentially expressed in the new-onset T2DM (fold change ≥ 1.5 and p value < 0.05). Functional analysis revealed that differentially expressed RNAs were likely to play essential roles in diabetes-related pathways. In addition, the protein–protein interaction (PPI) network screened multiple hub mRNAs, and lncRNA-miRNA-mRNA networks showed that a single miRNA could be related to multiple lncRNAs, and then they coregulated more mRNAs. SLC25A4, PLCB1, AGTR2, PRKN, and SCD5 were shown to be important mRNAs in T2DM, and miR-199b-5p, miR-202-5p, miR-548o-3p as well as miR-1255b-5p could be involved in their regulation. In conclusion, several new and previously identified dysregulated lncRNAs, miRNAs, and mRNAs were found to be vital biomarkers in T2DM. Their alterations and interactions could modulate the pathophysiology of T2DM. Those findings may provide new insights into the molecular mechanisms underlying the development of T2DM.
更多查看译文
关键词
long noncoding RNA,microRNA,messenger RNA,new-onset type 2 diabetes mellitus
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
数据免责声明
页面数据均来自互联网公开来源、合作出版商和通过AI技术自动分析结果,我们不对页面数据的有效性、准确性、正确性、可靠性、完整性和及时性做出任何承诺和保证。若有疑问,可以通过电子邮件方式联系我们:report@aminer.cn