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

Sabaie, Hani; Moghaddam, Madiheh Mazaheri; Moghaddam, Marziyeh Mazaheri; Ahangar, Noora Karim; Asadi, Mohammad Reza; Hussen, Bashdar Mahmud; Taheri, Mohammad; Rezazadeh, Maryam

doi:10.1038/s41598-021-03993-3

Cited by 19 publications

(17 citation statements)

References 97 publications

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“…Since our study is the first to identify a link between ASB16-AS1 , LINC01094 , and MS, the findings should be confirmed through further research. In agreement with our findings, glioma ( Zhang et al, 2019 ) and schizophrenia ( Sabaie et al, 2021c ), two additional nervous system diseases, exhibit significantly higher expression levels of ASB16-AS1 and LINC01094 , respectively.…”

Section: Discussionsupporting

confidence: 92%

“…The lncRNA ASB16-AS1 regulates cell proliferation, migration, invasion, and apoptosis through a ceRNA manner in a number of cancers, including glioma ( Zhang et al, 2019 ) and cervical cancer, according to earlier research ( Liu et al, 2020 ). A new long intergenic nonprotein coding RNA called LINC01094 is found on chromosome four and functions as a ceRNA in a variety of conditions, such as calcified aortic valve disease ( Huang et al, 2022 ), clear cell renal cell carcinoma ( Jiang et al, 2020 ), schizophrenia ( Sabaie et al, 2021c ), and glioma ( Liu et al, 2022 ). Since our study is the first to identify a link between ASB16-AS1 , LINC01094 , and MS, the findings should be confirmed through further research.…”

Section: Discussionmentioning

confidence: 99%

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Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis

Sabaie

Rouz²,

Kouchakali³

et al. 2022

Front. Genet.

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Slow-burning inflammation at the lesion rim is connected to the expansion of chronic multiple sclerosis (MS) lesions. However, the underlying processes causing expansion are not clearly realized. In this context, the current study used a bioinformatics approach to identify the expression profiles and related lncRNA-associated ceRNA regulatory axes in the periplaque region in MS patients. Expression data (GSE52139) from periplaque regions in the secondary progressive MS spinal cord and controls were downloaded from the Gene Expression Omnibus database (GEO), which has details on mRNAs and lncRNAs. Using the R software’s limma package, the differentially expressed lncRNAs (DElncRNAs) and mRNAs (DEmRNAs) were found. The RNA interactions were also found using the DIANA-LncBase, miRTarBase, and HMDD databases. The Pearson correlation coefficient was used to determine whether there were any positive correlations between DEmRNAs and DElncRNAs in the ceRNA network. Finally, lncRNA-associated ceRNA axes were created based on co-expression and connections between DElncRNA, miRNA, and DEmRNA. We used the Enrichr tool to enrich the biological process, molecular function, and pathways for DEmRNAs and DElncRNAs. A network of DEmRNAs’ protein-protein interactions was developed, and the top five hub genes were found using Cytoscape and STRING. The current study indicates that 15 DEmRNAs, including FOS, GJA1, NTRK2, CTNND1, and SP3, are connected to the MS ceRNA network. Additionally, four DElncRNAs (such as TUG1, ASB16-AS1, and LINC01094) that regulated the aforementioned mRNAs by sponging 14 MS-related miRNAs (e.g., hsa-miR-145-5p, hsa-miR-200a-3p, hsa-miR-20a-5p, hsa-miR-22-3p, hsa-miR-23a-3p, hsa-miR-27a-3p, hsa-miR-29b-3p, hsa-miR-29c-3p, hsa-miR-34a-5p) were found. In addition, the analysis of pathway enrichment revealed that DEmRNAs were enriched in the pathways for the “MAPK signaling pathway”, “Kaposi sarcoma-associated herpesvirus infection”, “Human immunodeficiency virus one infection”, “Lipid and atherosclerosis”, and “Amphetamine addiction”. Even though the function of these ceRNA axes needs to be investigated further, this study provides research targets for studying ceRNA-mediated molecular mechanisms related to periplaque demyelination in MS.

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Section: Discussionsupporting

confidence: 92%

Section: Discussionmentioning

confidence: 99%

Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis

Sabaie

Rouz²,

Kouchakali³

et al. 2022

Front. Genet.

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“…lncRNA identification: Differentially expressed lncRNA identified compared to the approved lncRNA list from the HUGO database ( https://www.genenames.org/ ) [ 25 ]. |LogFC| > 0.5 and adj p-value less than 0.05 were considered as significant during the identification.…”

Section: Methodsmentioning

confidence: 99%

Esophageal squamous cell carcinoma: Integrated bioinformatics analysis for differential gene expression with identification of hub genes and lncRNA

Hasib

2022

Biochemistry and Biophysics Reports

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“…As more and more evidence of ceRNA interactions accumulated, a number of studies leveraging this ncRNA interaction network for ncDA prediction have emerged. Along with co-expression analysis of lncRNA–miRNA–mRNAs, the ceRNA network was integrated for network-based analysis of ncRNAs related to gastric cancer [ 96 ], schizophrenia [ 112 ], and coronary artery disease [ 117 ]. Another component of the ceRNA hypothesis, circRNA, has been also investigated based on its regulatory network with other transcripts.…”

Section: Computational Ncrna-disease Association Studiesmentioning

confidence: 99%

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Bang

Park

et al. 2022

IJMS

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Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

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

Cited by 19 publications

References 97 publications

Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis

Identification of potential regulatory long non-coding RNA-associated competing endogenous RNA axes in periplaque regions in multiple sclerosis

Esophageal squamous cell carcinoma: Integrated bioinformatics analysis for differential gene expression with identification of hub genes and lncRNA

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

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