Research Paper Volume 14, Issue 20 pp 8374—8393
Identification of circRNA-miRNA-mRNA networks to explore the molecular mechanism and immune regulation of postoperative neurocognitive disorder
- 1 Department of Anesthesiology, Affiliated Foshan Maternity and Child Healthcare Hospital, Southern Medical University, Foshan, Guangdong, China
- 2 Department of Anesthesiology, Shenyang Women’s and Children’s Hospital, Shenyang, Liaoning, China
Received: June 21, 2022 Accepted: October 14, 2022 Published: October 21, 2022
https://doi.org/10.18632/aging.204348How to Cite
Copyright: © 2022 Bao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Postoperative neurocognitive disorder (PND) is a common complication in older patients. However, its pathogenesis has still remained elusive. Recent studies have shown that circular RNA (circRNA) plays an important role in the development of neurodegenerative diseases, such as PND after surgery. CircRNA, as a competitive endogenous RNA (ceRNA), mainly acts as a molecular sponge for miRNA to “adsorb” microRNA (miRNA) and to reduce the inhibitory effects of miRNAs on target mRNA. The sequencing data of circRNA were obtained from the Gene Expression Omnibus (GEO) database.
By bioinformatic methods, circAtlas, miRDB, miRTarBase and miRwalk databases were applied to construct circRNA-miRNA-mRNA networks and screen differentially expressed mRNAs. To improve the accuracy of the data, we randomly divided aging mice into control (non-PND group) and PND groups, and used high-throughput sequencing to analyze their brain hippocampal tissue for analysis. Three key genes were cross-detected in the data of both groups, which were Unc13c, Tbx20 and St8sia2 (as hub genes), providing new targets for PND treatment. According to the results of the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses, immune cell infiltration analysis, gene set enrichment analysis (GSEA), Connectivity Map (CMap) analysis, quantitative real-time polymerase chain reaction (qRT-PCR), the genes that were not related to the central nervous system were removed, and finally, mmu_circ_0000331/miR-1224-3p/Unc13c and mmu_circ_0000406/miR-24-3p/St8sia2 ceRNA networks were identified. In addition, the CMap method was used to select the top 4 active compounds with the largest negative correlation absolute values, including cimaterol, Rucaparib, FG-7142, and Hydrocortisone.