Zou ZL, Ye Y, Zhou B, Zhang Y. Identification and characterization of noncoding RNAs-associated competing endogenous RNA networks in major depressive disorder. World J Psychiatry 2023; 13(2): 36-49 [PMID: 36925948 DOI: 10.5498/wjp.v13.i2.36]
Corresponding Author of This Article
Yuan Zhang, MS, Assistant Professor, Personalized Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, No. 32 First Ring Road West 2, Chengdu 610072, Sichuan Province, China. 447415054@qq.com
Research Domain of This Article
Psychiatry
Article-Type of This Article
Basic Study
Open-Access Policy of This Article
This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
World J Psychiatry. Feb 19, 2023; 13(2): 36-49 Published online Feb 19, 2023. doi: 10.5498/wjp.v13.i2.36
Identification and characterization of noncoding RNAs-associated competing endogenous RNA networks in major depressive disorder
Zhi-Li Zou, Yu Ye, Bo Zhou, Yuan Zhang
Zhi-Li Zou, Bo Zhou, Department of Psychosomatic, Sichuan Academy of Medical Science & Sichuan Provincial People’s Hospital, Chengdu 610072, Sichuan Province, China
Yu Ye, Sichuan Provincial Center for Mental Health, Sichuan Academy of Medical Sciences & Sichuan Provincial People's Hospital, Chengdu 611130, Sichuan Province, China
Yuan Zhang, Personalized Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, Chengdu 610072, Sichuan Province, China
Author contributions: Zou ZL contributed to study design, manuscript preparation, inspection, and revision; Ye Y contributed to manuscript preparation; Bo Z contributed to study design; Zhang Y contributed to study design and manuscript preparation.
Supported bythe National Key Research and Development Program of China, No. 2020YFC2005500.
Institutional review board statement: This study involves no human or animal subjects.
Conflict-of-interest statement: The authors report no conflict of interest.
Data sharing statement: The datasets using in this study were from NCBI GEO public databases.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: https://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Yuan Zhang, MS, Assistant Professor, Personalized Drug Therapy Key Laboratory of Sichuan Province, Sichuan Academy of Medical Sciences & Sichuan Provincial People’s Hospital, No. 32 First Ring Road West 2, Chengdu 610072, Sichuan Province, China. 447415054@qq.com
Received: October 9, 2022 Peer-review started: October 9, 2022 First decision: November 27, 2022 Revised: December 6, 2022 Accepted: January 23, 2023 Article in press: January 23, 2023 Published online: February 19, 2023 Processing time: 130 Days and 21.4 Hours
Abstract
BACKGROUND
Major depressive disorder (MDD) is a common and serious mental illness. Many novel genes in MDD have been characterized by high-throughput methods such as microarrays or sequencing. Recently, noncoding RNAs (ncRNAs) were suggested to be involved in the complicated environmental-genetic regulatory network of MDD occurrence; however, the interplay among RNA species, including protein-coding RNAs and ncRNAs, in MDD remains unclear.
AIM
To investigate the RNA expression datasets downloaded from a public database and construct a network based on differentially expressed long noncoding RNA (lncRNAs), microRNAs (miRNAs), and mRNAs between MDD and controls.
METHODS
Gene expression data were searched in NCBI Gene Expression Omnibus using the search term “major depressive disorder.” Six array datasets from humans were related to the search term: GSE19738, GSE32280, GSE38206, GSE52790, GSE76826, and GSE81152. These datasets were processed for initial assessment and subjected to quality control and differential expression analysis. Differentially expressed lncRNAs, miRNAs, and mRNAs were determined, Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed, and protein-protein interaction network was generated. The results were analyzed for their association with MDD.
RESULTS
After analysis, 3 miRNAs, 12 lncRNAs, and 33 mRNAs were identified in the competing endogenous RNA network. Two of these miRNAs were earlier shown to be involved in psychiatric disorders, and differentially expressed mRNAs were found to be highly enriched in pathways related to neurogenesis and neuroplasticity as per Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses. The expression of hub gene fatty acid 2-hydroxylase was enriched, and the encoded protein was found to be involved in myelin formation, indicating that neurological development and signal transduction are involved in MDD pathogenesis.
CONCLUSION
The present study presents candidate ncRNAs involved in the neurogenesis and neuroplasticity pathways related to MDD.
Core Tip: Competing endogenous RNAs (ceRNAs) are novel regulatory molecules involved in a wide range of biological processes and diseases. This study explored the potential ceRNA networks (ceRNETs) involved in the pathogenesis of major depressive disorder (MDD) using bioinformatics data mining. A ceRNET comprising 3 miRNAs, 12 lncRNAs, and 33 mRNAs was constructed based on two public datasets obtained from the Gene Expression Omnibus database. Elucidating the correlation of the ceRNET with MDD opens new avenues to discover specific diagnostic biomarkers for MDD and expands our knowledge about this disease.