Dataset Information

A Potential Autophagy-Related Competing Endogenous RNA Network and Corresponding Diagnostic Efficacy in Schizophrenia.

ABSTRACT: Competing endogenous RNA (ceRNA) and autophagy were related to neurological diseases. But the relationship among ceRNA, autophagy and Schizophrenia (SZ) was not clear. In this study, we obtained gene expression profile of SZ patients (GSE38484, GSE54578, and GSE16930) from Gene Expression Omnibus (GEO) database. Then we screened the autophagy-related differentially expressed lncRNA, miRNA, and mRNA (DElncRNA, DEmiRNA, and DEmRNA) combined with Gene database from The National Center for Biotechnology Information (NCBI). In addition, we performed enrichment analysis. The result showed that biological processes (BPs) mainly were associated with cellular responses to oxygen concentration. The enriched pathways mainly included ErbB, AMPK, mTOR signaling pathway and cell cycle. Furthermore, we constructed autophagy-related ceRNA network based on the TargetScan database. Moreover, we explored the diagnostic efficiency of lncRNA, miRNA and mRNA in ceRNA, through gene set variation analysis (GSVA). The result showed that the diagnostic efficiency was robust, especially miRNA (AUC = 0.884). The miRNA included hsa-miR-423-5p, hsa-miR-4532, hsa-miR-593-3p, hsa-miR-618, hsa-miR-4723-3p, hsa-miR-4640-3p, hsa-miR-296-5p, and hsa-miR-3943. The result of this study may be helpful for deepening the pathophysiology of SZ. In addition, our finding may provide a guideline for the clinical diagnosis of SZ.

SUBMITTER: Li R

PROVIDER: S-EPMC7940829 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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A Potential Autophagy-Related Competing Endogenous RNA Network and Corresponding Diagnostic Efficacy in Schizophrenia.

Li Rongjie R Wang Qiaoye Q Qiu Yufen Y Meng Youshi Y Wei Lei L Wang Hao H Mo Ruikang R Zou Donghua D Liu Chunbin C

Frontiers in psychiatry 20210223

Competing endogenous RNA (ceRNA) and autophagy were related to neurological diseases. But the relationship among ceRNA, autophagy and Schizophrenia (SZ) was not clear. In this study, we obtained gene expression profile of SZ patients (GSE38484, GSE54578, and GSE16930) from Gene Expression Omnibus (GEO) database. Then we screened the autophagy-related differentially expressed lncRNA, miRNA, and mRNA (DElncRNA, DEmiRNA, and DEmRNA) combined with Gene database from The National Center for Biotechno ...[more]

PMID: 33708146

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Project description: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.

Project description:BackgroundOsteoarthritis (OA) is one of the most common diseases in elderly people; however, the correlation between molecular alterations and the occurrence and progression of OA are still not well understood. We conducted this study to investigate the molecular changes in OA via the competing endogenous ribonucleic acid (ceRNA) network.MethodsWe downloaded the messenger RNA (mRNA) data set, GSE48556, the microRNA (miRNA) data set, GSE105027, and the long non-coding (lncRNA) data set, GSE126963 from the Gene Expression Omnibus (GEO) database, and examined the differentially expressed genes (DEGs) in these data sets. Further, we constructed a ceRNA network of the differentially expressed miRNAs, mRNAs, and lncRNAs. To determine the biological functions of the ceRNA network, we performed Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses. Finally, we conducted an immune cell infiltration analysisusing single-sample gene set enrichment analysis to examine the abundance of immune cells in healthy and OA patients, and compared the infiltration of 28 immune cells between the healthy and OA samples. We also analyzed the relationship between the abundance of immune cells and mRNA expression levels in the ceRNA network.ResultsUltimately hsa-mir-425-3p, dual specificity phosphatase 1, and 24 lncRNAs were identified in the ceRNA network. The functional enrichment analyses showed that these lncRNAs, miRNAs, and mRNAs are involved in various significant biological process, such as the regulation of leukocyte migration, Mitogen-Activated Protein (MAP) kinase tyrosine/serine/threonine phosphatase activity, the interleukin-17 signaling pathway, the tumor necrosis factor signaling pathway, and osteoclast differentiation, and can also have a strong effect on immune cell infiltration.ConclusionsThe dual-specificity phosphatase 1-specific ceRNA network can be used as a diagnostic tool to assess the progression of OA patients.

Project description:The present study aimed to screen all types of RNAs involved in the development of papillary thyroid carcinoma (PTC). RNA‑sequencing data of PTC and normal samples were used for screening differentially expressed (DE) microRNAs (DE‑miRNAs), long non‑coding RNAs (DE‑lncRNAs) and genes (DEGs). Subsequently, lncRNA‑miRNA, miRNA‑gene (that is, miRNA‑mRNA) and gene‑gene interaction pairs were extracted and used to construct regulatory networks. Feature genes in the miRNA‑mRNA network were identified by topological analysis and recursive feature elimination analysis. A support vector machine (SVM) classifier was built using 15 feature genes, and its classification effect was validated using two microarray data sets that were downloaded from the Gene Expression Omnibus (GEO) database. In addition, Gene Ontology function and Kyoto Encyclopedia Genes and Genomes pathway enrichment analyses were conducted for genes identified in the ceRNA network. A total of 506 samples, including 447 tumor samples and 59 normal samples, were obtained from The Cancer Genome Atlas (TCGA); 16 DE‑lncRNAs, 917 DEGs and 30 DE‑miRNAs were screened. The miRNA‑mRNA regulatory network comprised 353 nodes and 577 interactions. From these data, 15 feature genes with high predictive precision (>95%) were extracted from the network and were used to form an SVM classifier with an accuracy of 96.05% (486/506) for PTC samples downloaded from TCGA, and accuracies of 96.81 and 98.46% for GEO downloaded data sets. The ceRNA regulatory network comprised 596 lines (or interactions) and 365 nodes. Genes in the ceRNA network were significantly enriched in 'neuron development', 'differentiation', 'neuroactive ligand‑receptor interaction', 'metabolism of xenobiotics by cytochrome P450', 'drug metabolism' and 'cytokine‑cytokine receptor interaction' pathways. Hox transcript antisense RNA, miRNA‑206 and kallikrein‑related peptidase 10 were nodes in the ceRNA regulatory network of the selected feature gene, and they may serve import roles in the development of PTC.

Dataset Information

A Potential Autophagy-Related Competing Endogenous RNA Network and Corresponding Diagnostic Efficacy in Schizophrenia.

Publications

A Potential Autophagy-Related Competing Endogenous RNA Network and Corresponding Diagnostic Efficacy in Schizophrenia.

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