Bioinformatics analysis of long non-coding RNA-associated competing endogenous RNA network in schizophrenia

• Published in: Scientific reports Vol. 11; no. 1; pp. 24413 - 13
• Main Authors: Sabaie, Hani, Moghaddam, Madiheh Mazaheri, Moghaddam, Marziyeh Mazaheri, Ahangar, Noora Karim, Asadi, Mohammad Reza, Hussen, Bashdar Mahmud, Taheri, Mohammad, Rezazadeh, Maryam
• Format: Journal Article
• Language: English
• Published: London Nature Publishing Group UK 24.12.2021; Nature Publishing Group; Nature Portfolio
• Subjects: 631/114; 631/208; 631/477; Bioinformatics; Biomarkers - metabolism; Brain-derived neurotrophic factor; Brodmann's area; CD44 antigen; Computational Biology - methods; DNA microarrays; Drb1 protein; Fibroblast growth factor 2; Gene expression; Genetic factors; Histocompatibility antigen HLA; Humanities and Social Sciences; Humans; Lymphoblasts; Mental disorders; MicroRNAs; miRNA; multidisciplinary; Neostriatum; Non-coding RNA; Osteonectin; Pax8 protein; Peripheral blood; Regulatory sequences; RNA, Long Noncoding - metabolism; Schizophrenia; Schizophrenia - genetics; Science; Science (multidisciplinary); Transcriptomes
• DOI: 10.1038/s41598-021-03993-3
• ISSN: 2045-2322

Schizophrenia (SCZ) is a serious psychiatric condition with a 1% lifetime risk. SCZ is one of the top ten global causes of disabilities. Despite numerous attempts to understand the function of genetic factors in SCZ development, genetic components in SCZ pathophysiology remain unknown. The competing endogenous RNA (ceRNA) network has been demonstrated to be involved in the development of many kinds of diseases. The ceRNA hypothesis states that cross-talks between coding and non-coding RNAs, including long non-coding RNAs (lncRNAs), via miRNA complementary sequences known as miRNA response elements, creates a large regulatory network across the transcriptome. In the present study, we developed a lncRNA-related ceRNA network to elucidate molecular regulatory mechanisms involved in SCZ. Microarray datasets associated with brain regions (GSE53987) and lymphoblasts (LBs) derived from peripheral blood (sample set B from GSE73129) of SCZ patients and control subjects containing information about both mRNAs and lncRNAs were downloaded from the Gene Expression Omnibus database. The GSE53987 comprised 48 brain samples taken from SCZ patients (15 HPC: hippocampus, 15 BA46: Brodmann area 46, 18 STR: striatum) and 55 brain samples taken from control subjects (18 HPC, 19 BA46, 18 STR). The sample set B of GSE73129 comprised 30 LB samples (15 patients with SCZ and 15 controls). Differentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) were identified using the limma package of the R software. Using DIANA-LncBase, Human MicroRNA Disease Database (HMDD), and miRTarBase, the lncRNA- associated ceRNA network was generated. Pathway enrichment of DEmRNAs was performed using the Enrichr tool. We developed a protein–protein interaction network of DEmRNAs and identified the top five hub genes by the use of STRING and Cytoscape, respectively. Eventually, the hub genes, DElncRNAs, and predictive miRNAs were chosen to reconstruct the subceRNA networks. Our bioinformatics analysis showed that twelve key DEmRNAs, including BDNF , VEGFA , FGF2 , FOS , CD44 , SOX2 , NRAS , SPARC , ZFP36 , FGG , ELAVL1 , and STARD13 , participate in the ceRNA network in SCZ. We also identified DLX6-AS1 , NEAT1 , MINCR , LINC01094 , DLGAP1-AS1 , BABAM2-AS1 , PAX8-AS1 , ZFHX4-AS1 , XIST , and MALAT1 as key DElncRNAs regulating the genes mentioned above. Furthermore, expression of 15 DEmRNAs (e.g., ADM and HLA-DRB1 ) and one DElncRNA ( XIST ) were changed in both the brain and LB, suggesting that they could be regarded as candidates for future biomarker studies. The study indicated that ceRNAs could be research candidates for investigating SCZ molecular pathways.