Integrating the Ribonucleic Acid Sequencing Data From Various Studies for Exploring the Multiple Sclerosis-Related Long Noncoding Ribonucleic Acids and Their Functions

Han, Zhijie; Hua, Jing; Xue, Weiwei; Zhu, Feng

doi:10.3389/fgene.2019.01136

Cited by 8 publications

(5 citation statements)

References 85 publications

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“…Some co-expressed protein coding genes are involved in immune response by leukocytes and interleukin. One important lncRNA resulting from that analysis is NONHSAG049754.2 , which targets TNFRSF10A , a gene that encodes for the receptor of tumor necrosis factor cytokines, susceptible to developing MS. From the just mentioned analysis, it was seen that a key mechanism of lncRNA involved in MS is associated with the regulation of TNF cytokine receptors and ribonucleoprotein [ 51 ].…”

Section: Resultsmentioning

confidence: 99%

A Comprehensive Exploration of the Transcriptomic Landscape in Multiple Sclerosis: A Systematic Review

Chiricosta

Blando

D’Angiolini

et al. 2023

IJMS

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Multiple Sclerosis (MS) is, to date, an incurable disease of the nervous system characterized by demyelination. Several genetic mutations are associated with the disease but they are not able to explain all the diagnosticated cases. Thus, it is suggested that altered gene expression may play a role in human pathologies. In this review, we explored the role of the transcriptomic profile in MS to investigate the main altered biological processes and pathways involved in the disease. Herein, we focused our attention on RNA-seq methods that in recent years are producing a huge amount of data rapidly replacing microarrays, both with bulk and single-cells. The studies evidenced that different MS stages have specific molecular signatures and non-coding RNAs may play a key role in the disease. Sex-dependence was observed before and after treatments used to alleviate symptomatology activating different biological processes in a drug-dependent manner. New pathways, such as neddylation, were found deregulated in MS and inflammation was linked to neuron degeneration areas through spatial transcriptomics. It is evident that the use of RNA-seq in the study of complex pathologies, such as MS, is a valid strategy to shed light on new involved mechanisms.

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Section: Resultsmentioning

confidence: 99%

A Comprehensive Exploration of the Transcriptomic Landscape in Multiple Sclerosis: A Systematic Review

Chiricosta

Blando

D’Angiolini

et al. 2023

IJMS

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“…We also identified hub genes and evaluated their usefulness for the prognosis of MS. Compared with previous reports [22,23], our study set different criteria for dataset selection and employed new bioinformatics approaches. Briefly, (1) we concentrated on the spinal cord, which is extensively involved in MS but has not been thoroughly investigated in neuropathological studies, and (2) we sought to explore the underlying mechanism by creating a ceRNA network in the key module identified to be significantly correlated with MS.…”

Section: Discussionmentioning

confidence: 99%

Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network

Hao

et al. 2022

Oxidative Medicine and Cellular Longevity

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Objective. Multiple sclerosis (MS) is an autoimmune disease of the central nervous system characterized by chronic inflammation and demyelination. This study is aimed at identifying crucial genes and molecular pathways involved in MS pathogenesis. Methods. Raw data in GSE52139 were collected from the Gene Expression Omnibus. The top 50% expression variants were subjected to weighted gene coexpression network analysis (WGCNA), and the key module associated with MS occurrence was identified. A long noncoding RNA- (lncRNA-) associated competing endogenous RNA (ceRNA) network was constructed in the key module. The hub gene candidates were subsequently verified in an individual database. Results. Of the 18 modules obtained, the cyan module was designated as the key module. The established ceRNA network was composed of seven lncRNAs, 45 mRNAs, and 21 microRNAs (miRNAs), and the FAM13A-AS1 was the lncRNA with the highest centrality. Functional assessments indicated that the genes in the cyan module primarily gathered in ribosome-related functional terms. Interestingly, the targeted mRNAs of the ceRNA network enriched in diverse categories. Moreover, highly expressed CYBRD1, GNG12, and SMAD1, which were identified as hub genes, may be associated with “valine leucine and isoleucine degradation,” “base excision repair,” and “fatty acid metabolism,” respectively, according to the results of single gene-based genomes and gene set enrichment analysis (GSEA). Conclusions. Combined with the WGCNA and ceRNA network, our findings provide novel insights into the pathogenesis of MS. The hub genes discovered herein might also serve as novel biomarkers that correlate with the development and management of MS.

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“…The sequencing data of the selected studies was downloaded by Prefetch and converted into FASTQ files using the fastq-dump tool of the SRA Toolkit software v2.11.0 [ 22 , 23 ]. Then, the reference sequences of lncRNA and protein-coding transcripts were downloaded from the most complete annotated non-coding RNA databases, NONCODE (v6; [ 24 ]), for lncRNAs and Ensembl for PCGs (release 104; [ 25 ]), respectively.…”

Section: Methodsmentioning

confidence: 99%

“…To identify the significantly differentially expressed lncRNAs and PCGs in glioblastoma tissues, the individual results of each study were integrated by meta-analysis using the MetaDE module of MetaOmics for the four selected studies. The normalization process used in this meta-analysis was performed using a random-effects model (REM) for lncRNAs/PCGs with count ≥ 10 [ 23 , 27 , 28 ]. Differentially expressed lncRNAs (DElncRNA) and differentially expressed PCGs (DEPCGs) were then identified by selecting for lncRNAs/PCGs differentially expressed in at least three studies (out of four), having valid Ensembl ID with FDR < 0.05 and having a z-value of ≥|4|.…”

Section: Methodsmentioning

confidence: 99%

Meta-Analysis of RNA-Seq Datasets Identifies Novel Players in Glioblastoma

Sallam

Mysara

Baatout

et al. 2022

Cancers

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Glioblastoma is a devastating grade IV glioma with poor prognosis. Identification of predictive molecular biomarkers of disease progression would substantially contribute to better disease management. In the current study, we performed a meta-analysis of different RNA-seq datasets to identify differentially expressed protein-coding genes (PCGs) and long non-coding RNAs (lncRNAs). This meta-analysis aimed to improve power and reproducibility of the individual studies while identifying overlapping disease-relevant pathways. We supplemented the meta-analysis with small RNA-seq on glioblastoma tissue samples to provide an overall transcriptomic view of glioblastoma. Co-expression correlation of filtered differentially expressed PCGs and lncRNAs identified a functionally relevant sub-cluster containing DANCR and SNHG6, with two novel lncRNAs and two novel PCGs. Small RNA-seq of glioblastoma tissues identified five differentially expressed microRNAs of which three interacted with the functionally relevant sub-cluster. Pathway analysis of this sub-cluster identified several glioblastoma-linked pathways, which were also previously associated with the novel cell death pathway, ferroptosis. In conclusion, the current meta-analysis strengthens evidence of an overarching involvement of ferroptosis in glioblastoma pathogenesis and also suggests some candidates for further analyses.

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Integrating the Ribonucleic Acid Sequencing Data From Various Studies for Exploring the Multiple Sclerosis-Related Long Noncoding Ribonucleic Acids and Their Functions

Cited by 8 publications

References 85 publications

A Comprehensive Exploration of the Transcriptomic Landscape in Multiple Sclerosis: A Systematic Review

A Comprehensive Exploration of the Transcriptomic Landscape in Multiple Sclerosis: A Systematic Review

Identification of Novel Key Genes and Pathways in Multiple Sclerosis Based on Weighted Gene Coexpression Network Analysis and Long Noncoding RNA-Associated Competing Endogenous RNA Network

Meta-Analysis of RNA-Seq Datasets Identifies Novel Players in Glioblastoma

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