Dataset Information

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

ABSTRACT: Multiple sclerosis (MS) is a chronic fatal central nervous system (CNS) disease involving in complex immunity dysfunction. Recently, long noncoding RNAs (lncRNAs) were discovered as the important regulatory factors for the pathogenesis of MS. However, these findings often cannot be repeated and confirmed by the subsequent studies. We considered that the small-scale samples or the heterogeneity among various tissues may result in the divergence of the results. Currently, RNA-seq has become a powerful approach to quantify the abundances of lncRNA transcripts. Therefore, we comprehensively collected the MS-related RNA-seq data from a variety of previous studies, and integrated these data using an expression-based meta-analysis to identify the differentially expressed lncRNA between MS patients and controls in whole samples and subgroups. Then, we performed the Jensen-Shannon (JS) divergence and cluster analysis to explore the heterogeneity and expression specificity among various tissues. Finally, we investigated the potential function of identified lncRNAs for MS using weighted gene co-expression network analysis (WGCNA) and gene set enrichment analysis (GSEA), and 5,420 MS-related lncRNAs specifically expressed in the brain tissue were identified. The subgroup analysis found a small heterogeneity of the lncRNA expression profiles between brain and blood tissues. The results of WGCNA and GSEA showed that a potential important function of lncRNAs in MS may be involved in the regulation of ribonucleoproteins and tumor necrosis factor cytokines receptors. In summary, this study provided a strategy to explore disease-related lncRNAs on genome-wide scale, and our findings will be benefit to improve the understanding of MS pathogenesis.

SUBMITTER: Han Z

PROVIDER: S-EPMC6861379 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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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.

Han Zhijie Z Hua Jiao J Xue Weiwei W Zhu Feng F

Frontiers in genetics 20191112

Multiple sclerosis (MS) is a chronic fatal central nervous system (CNS) disease involving in complex immunity dysfunction. Recently, long noncoding RNAs (lncRNAs) were discovered as the important regulatory factors for the pathogenesis of MS. However, these findings often cannot be repeated and confirmed by the subsequent studies. We considered that the small-scale samples or the heterogeneity among various tissues may result in the divergence of the results. Currently, RNA-seq has become a powe ...[more]

PMID: 31781177

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Project description:BackgroundDue to their inherent role in cell function, long non-coding ribonucleic acids (lncRNAs) mediate changes in the microenvironment, and thereby participate in the development of cellular senescence.AimsThis study aimed to identify cellular senescence-related lncRNAs that could predict the prognosis of liver cancer.Methods and resultsGene expression and clinical data were downloaded from the UCSC Xena platform, ICGC, and TCGA databases. Cox regression and LASSO regression were used to establish a cellular senescence-related lncRNA model. ROC curves and Kaplan-Meier survival curves were then constructed to predict patient prognosis. Cox regression analysis and clinical characteristics were used to evaluate the capability of the model. Tumor mutational burden and tumor-infiltrating immune cell analyses were subsequently performed in the risk subgroups and the samples in the entire cohort were reclustered. Finally, potential small molecule immune-targeted drugs were identified based on the model. The cellular senescence-related prognostic model that was constructed based on AGAP11 and FAM182B. Along with the results of Cox regression and Lasso regression, the risk score was found to be an independent factor for predicting overall survival in cohorts. In the subgroup analysis, the prognosis of the low-risk group in each cohort was significantly higher than that of the high-risk group; the area under temporal ROC curves and clinical ROC curves were all greater than 0.65, respectively. C-index shows that the risk scores are greater than 0.6, showing the stability of the model. The high-risk group demonstrated lower tumor microenvironment and higher tumor mutational burden scores, further verifying the reliability of the model grouping results. Analysis of tumor-infiltrating immune cells indicated that CD8+ and γδ T cells were more abundant among patients in the low-risk group; cluster reorganization indicated that the two groups had different prognoses and proportions of immune cells. The p value of potential drugs predicted based on the expression of model lncRNAs were all less than .05, demonstrating the potential of model lncRNAs as therapeutic targets to some extent.ConclusionA prognostic model based on cellular senescence-associated lncRNAs was established and this may be used as a potential biomarker for the prognosis assessment of liver cancer patients.

Dataset Information

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

Publications

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

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