Dysregulation of long noncoding RNA MEG3 and NLRC5 expressions in patients with relapsing-remitting multiple sclerosis: is there any correlation?

Torkamandi, Shahram; Bahrami, Shima; Ghorashi, Tahereh; Dehani, Mohammad; Bayat, Hadi; Hoseini, Seyyed Mohamad; Rezaei, Somaye; Soosanabadi, Mohsen

doi:10.1038/s41435-021-00154-4

Cited by 8 publications

(8 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…WNT1 [236], RTN4R [237], MEF2D [238], CX3CL1 [239], PIN1 [240], UNC13A [241], CDK5 [242], SLC30A3 [243], TUBA4A [244], BCL2A1 [245], CHI3L1 [246], SERPINA1 [247], CCR5 [248], C7 [249], S100A4 [250], C1QB [251], SPP1 [252], TLR7 [253], TLR2 [254], NEFH (neurofilament heavy chain) [255], GPNMB (glycoprotein nmb) [256], B2M [257], COX2 [258], YAP1 [259], MYD88 [260], CSF1 [261], REST (RE1 silencing transcription factor) [262], DDX58 [263], LRP4 [264] and KCNJ10 [265] contributes to the progression of amyotrophic lateral sclerosis. Previous studies had shown that the altered expression of ADCYAP1 [266], CCK (cholecystokinin) [267], LINGO1 [268], CX3CL1 [269], NECTIN1 [270], IL9 [271], TLR8 [272], CCR5 [273], NOD2 [274], C7 [275], TLR7 [276], HGF (hepatocyte growth factor) [277], TLR2 [278], PTPRC (protein tyrosine phosphatase receptor type C) [279], C3 [280], IFI16 [281], GLI1 [282], CYBB (cytochrome b-245 beta chain) [283], TLR1 [284], NEFH (neurofilament heavy chain) [285], CLIC1 [286], PDK4 [287], NFATC2 [288], GPNMB (glycoprotein nmb) [289], CD58 [290], NQO1 [291], B2M [292], ANXA2 [293], FLT1 [294], IFIH1 [295], COX2 [296], NLRC5 [297], CFH (complement factor H) [298], YAP1 [299], MYD88 [300], IQGAP1 [301], ANXA1 [302] and DDX58 [303] were closely related to the occurrence of multiple sclerosis. Studies had shown that NEUROD6 [304], CHRNA7 [305], NRGN (neurogranin) [306]...…”

Section: Discussionmentioning

confidence: 99%

Bioinformatics and next generation sequencing data analysis to identify key genes and pathways influencing in Parkinson’s disease

Vastrad¹,

Vastrad²

2022

Preprint

View full text Add to dashboard Cite

Parkinson's disease (PD) is the most commonly diagnosed neurodegenerative disorder Identification of novel prognostic and pathogenesis biomarkers plays a pivotal role in the management of the PD. Next generation sequencing (NGS) dataset from the GEO (Gene Expression Omnibus) database were used to identify differentially expressed genes (DEGs) in PD. Gene Ontology (GO) and REACTOME pathway enrichmental analyses were performed to elucidate the functional roles of the DEGs. Protein-protein interaction (PPI), modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were established. The receiver operating characteristic curve (ROC) analysis was used to explore the diagnostic values of hub genes in PD. In total, 957 DEGs were identified, of which 478 were up regulated genes and 479 were down regulated genes. GO and pathway enrichment analysis results revealed that the up regulated genes were mainly enriched in nervous system development, cell junction, transporter activity and neuronal system, whereas down regulated genes were mainly enriched in response to stimulus, cell periphery, identical protein binding and immune system. The top hub genes in the constructed PPI network, modules, miRNA-hub gene regulatory network and TF-hub gene regulatory network were OTUB1, PPP2R1A, AP2M1, PIN1, USP11, CDK2, IQGAP1, NEDD4, VIM and CDK1. Furthermore, ROC analysis showed that hub genes were having good diagnostic values. We identified a series of essential genes along with the pathways that were most closely related with PD initiation and progression. Our results provide a more detailed molecular mechanism for the advancement of PD, shedding light on the potential biomarkers and therapeutic targets.

show abstract

Section: Discussionmentioning

confidence: 99%

Bioinformatics and next generation sequencing data analysis to identify key genes and pathways influencing in Parkinson’s disease

Vastrad¹,

Vastrad²

2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Evidence has also demonstrated an increase in the expression of NEAT1, TUG1, and PANDAR in interferon-β-responsive MS population compared with the healthy individuals. In addition, TUG1 and NEAT1 expressions were inversely correlated with onset age and disease duration in female MS patients 23 . NEAT1 and TUG1 over-expressions were also reported in Italian MS patients in comparison with healthy controls 18 .…”

Section: Introductionmentioning

confidence: 89%

“…In relation to the shared features between MS and NMOSD, in the current study, we have focused on the above lncRNAs to measure their expression in the blood of NMOSD patients and controls. Besides, the expression level of an imprinted gene, namely maternally expressed gene 3 (MEG3) that is involved in the immune system and the MS development 24 is assessed in the present study. MEG3 plays important roles in various cancers as a tumor suppressor 25 .…”

Section: Introductionmentioning

confidence: 99%

Significant up-regulation of lncRNAs in neuromyelitis optica spectrum disorder

Taheri,

Sadeghi,

Gharebaghi

et al. 2023

Sci Rep

View full text Add to dashboard Cite

Neuromyelitis optica spectrum disorder (NMOSD) is an immune-related demyelinating defect. Long non-coding RNAs (lncRNAs) might influence the pathobiology and progression of NMOSD. The current study assessed expression level of NEAT1, PANDAR, MEG3 and TUG1 lncRNAs in the peripheral blood of NMOSD patients compared with healthy individuals. All mentioned lncRNAs were shown to be over-expressed in total NMOSD cases, male NMOSD cases and female NMOSD cases compared with the matching control subgroups. MEG3 had the most robust over-expression in patients subgroups compared with normal subjects. There was no noteworthy difference in the expression of any of lncRNAs between female and male patients. MEG3 had an ideal performance in the differentiation of NMOSD cases from healthy persons (Sensitivity and specificity values = 100%). Other lncRNAs could also efficiently separate NMOSD cases from control subjects (AUC values = 0.97, 0.89 and 0.88 for PANDAR, NEAT1 and TUG1, respectively). Cumulatively, NEAT1, PANDAR, MEG3 and TUG1 lncRNAs can be considered as appropriate disease markers for NMOSD.

show abstract

“…They observed that the FER1L4 gene promoter was significantly methylated in RA FLSs, which led to a reduction in the FER1L4 level [ 31 ]. This in turn corresponded to an increased level of NLRC5 [ 32 ]. NLRC5 is a key regulator of the adaptive immune response stimulating the production of inflammatory cytokines, and its deregulation has also been linked to the pathogenesis of rheumatoid arthritis [ 31 , 33 ].…”

Section: Autoimmune Disordersmentioning

confidence: 99%

“…In multiple sclerosis , analyses of blood samples from RRMS patients revealed downregulated expression of MEG3 as well, and its level was negatively correlated with the EDSS score and with the level of its putative target, NLRC5, which stimulates the production of inflammatory cytokines [ 32 , 158 ]. Increased level of NLRC5 and reduced level of MEG3 were also reported in complete Freund’s adjuvant (CFA)-induced synovial tissues and in FLSs from a rat model of RA [ 33 ].…”

Section: Autoimmune and Neurodegenerative Disorder Pathomechanisms Ca...mentioning

confidence: 99%

Long Intergenic Noncoding RNAs Affect Biological Pathways Underlying Autoimmune and Neurodegenerative Disorders

Plewka

Raczyńska

2022

Mol Neurobiol

View full text Add to dashboard Cite

Long intergenic noncoding RNAs (lincRNAs) are a class of independently transcribed molecules longer than 200 nucleotides that do not overlap known protein-coding genes. LincRNAs have diverse roles in gene expression and participate in a spectrum of biological processes. Dysregulation of lincRNA expression can abrogate cellular homeostasis, cell differentiation, and development and can also deregulate the immune and nervous systems. A growing body of literature indicates their important and multifaceted roles in the pathogenesis of several different diseases. Furthermore, certain lincRNAs can be considered potential therapeutic targets and valuable diagnostic or prognostic biomarkers capable of predicting the onset of a disease, its degree of activity, or the progression phase. In this review, we discuss possible mechanisms and molecular functions of lincRNAs in the pathogenesis of selected autoimmune and neurodegenerative disorders: multiple sclerosis, rheumatoid arthritis, systemic lupus erythematosus, Sjögren’s syndrome, Huntington’s disease, Parkinson’s disease, Alzheimer’s disease, and amyotrophic lateral sclerosis. This summary can provide new ideas for future research, diagnosis, and treatment of these highly prevalent and devastating diseases.

show abstract

Dysregulation of long noncoding RNA MEG3 and NLRC5 expressions in patients with relapsing-remitting multiple sclerosis: is there any correlation?

Cited by 8 publications

References 36 publications

Bioinformatics and next generation sequencing data analysis to identify key genes and pathways influencing in Parkinson’s disease

Bioinformatics and next generation sequencing data analysis to identify key genes and pathways influencing in Parkinson’s disease

Significant up-regulation of lncRNAs in neuromyelitis optica spectrum disorder

Long Intergenic Noncoding RNAs Affect Biological Pathways Underlying Autoimmune and Neurodegenerative Disorders

Contact Info

Product

Resources

About