Biological networks in gestational diabetes mellitus: insights into the mechanism of crosstalk between long non-coding RNA and N6-methyladenine modification

Abstract

Background: Gestational diabetes mellitus (GDM) is one of the most common complications of pregnancy. The mechanism underlying the crosstalk between long non-coding RNAs (lncRNAs) and N6-methyladenine (m6A) modification in GDM remain unclear. Methods: We generated a lncRNA-mediated competitive endogenous RNA (ceRNA) network using comprehensive data from the Gene Expression Omnibus database, published data, and our preliminary findings. m6A-related lncRNAs were identified based on Pearson correlation coefficient (PCC) analysis using our previous profiles. An integrated pipeline was established to constructed a m6A-related subnetwork thereby predicting the potential effects of the m6A-related lncRNAs. Results: The ceRNA network was composed of 16 lncRNAs, 17 microRNAs, 184 mRNAs, and 338 edges. Analysis with the Kyoto Encyclopedia of Genes and Genomes database demonstrated that genes in the ceRNA network were primarily involved in the development and adverse outcomes of GDM, such as those in the fatty acid-metabolism pathway, the peroxisome proliferator-activated receptor signaling pathway, and thyroid hormone signaling pathway. Four m6A-related lncRNAs were involved in the ceRNA network, including LINC00667, LINC01087, AP000350.6, and CARMN. The m6A-related subnetwork was generated based on these four lncRNAs, their ceRNAs, and their related m6A regulators. Genes in the subnetwork were enriched in certain GDM-associated hormone (thyroid hormone and oxytocin) signaling pathways. LINC00667 was positively correlated with an m6A “reader” (YTHDF3; PCC = 0.95) and exhibited the highest node degree in the ceRNA network. RIP assays showed that YTHDF3 directly bind LINC00667. We further found that MYC possessed the highest node degree in a protein–protein interaction network and competed with LINC00667 for miR-33a-5p. qPCR analysis indicated that LINC00667, YTHDF3 and MYC levels were upregulated in the GDM placentas, while miR-33a-5p was downregulated. In a support-vector machine classifier, an m6A-related module composed of LINC00667, YTHDF3, MYC, and miR-33a-5p showed excellent classifying power for GDM in both the training and the testing dataset, with an accuracy of 76.19 and 71.43%, respectively. Conclusions: Our results shed insights into the potential role of m6A-related lncRNAs in GDM and have implications in terms of novel therapeutic targets for GDM.