Dataset Information

Novel Non-coding RNA Analysis in Multiple Myeloma Identified Through High-Throughput Sequencing.

ABSTRACT: This study aimed to explore the potential effects of novel non-coding ribose nucleic acids (ncRNAs) in patients with multiple myeloma (MM). The gene expression profile of plasma cells was used for sequence analysis to explore the expression pattern of ncRNAs in MM. The expression patterns of non-coding RNAs in MM were analyzed by RNA sequencing (whole-transcriptome-specific RNA sequencing). Next, the expression of the selected ncRNAs was verified by quantitative real-time polymerase chain reaction. Further, the lncRNA-associated competitive endogenous RNA network in MM was elucidated using deep RNA-seq. Differentially expressed (DE) ncRNAs were significantly regulated in patients with MM. DE target lncRNAs were analyzed by cis and trans targeting prediction. Two new lncRNAs were shown to be related to MM oncogenes. MSTRG.155519 played a carcinogenic role in myeloma by targeting CEACAM1; MSTRG.13132 was related to FAM46C. Finally, the network of lncRNA-mRNA-miRNA in MM was constructed in this study. The expression of non-coding RNAs through sequence and functional analyses might be helpful for further studies on the pathogenesis of MM and the development of new MM-targeted therapy for non-coding RNAs.

SUBMITTER: Lu M

PROVIDER: S-EPMC8181418 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Novel Non-coding RNA Analysis in Multiple Myeloma Identified Through High-Throughput Sequencing.

Lu Minqiu M Wu Yin Y Gao Wen W Tian Ying Y Wang Guorong G Liu Aijun A Chen Wenming W

Frontiers in genetics 20210524

This study aimed to explore the potential effects of novel non-coding ribose nucleic acids (ncRNAs) in patients with multiple myeloma (MM). The gene expression profile of plasma cells was used for sequence analysis to explore the expression pattern of ncRNAs in MM. The expression patterns of non-coding RNAs in MM were analyzed by RNA sequencing (whole-transcriptome-specific RNA sequencing). Next, the expression of the selected ncRNAs was verified by quantitative real-time polymerase chain reacti ...[more]

PMID: 34108986

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Project description:The continuously prolonged human lifespan is accompanied by increase in neurodegenerative diseases incidence, calling for the development of inexpensive blood-based diagnostics. Analyzing blood cell transcripts by RNA-Seq is a robust means to identify novel biomarkers that rapidly becomes a commonplace. However, there is lack of tools to discover novel exons, junctions and splicing events and to precisely and sensitively assess differential splicing through RNA-Seq data analysis and across RNA-Seq platforms. Here, we present a new and comprehensive computational workflow for whole-transcriptome RNA-Seq analysis, using an updated version of the software AltAnalyze, to identify both known and novel high-confidence alternative splicing events, and to integrate them with both protein-domains and microRNA binding annotations. We applied the novel workflow on RNA-Seq data from Parkinson's disease (PD) patients' leukocytes pre- and post- Deep Brain Stimulation (DBS) treatment and compared to healthy controls. Disease-mediated changes included decreased usage of alternative promoters and N-termini, 5'-end variations and mutually-exclusive exons. The PD regulated FUS and HNRNP A/B included prion-like domains regulated regions. We also present here a workflow to identify and analyze long non-coding RNAs (lncRNAs) via RNA-Seq data. We identified reduced lncRNA expression and selective PD-induced changes in 13 of over 6,000 detected leukocyte lncRNAs, four of which were inversely altered post-DBS. These included the U1 spliceosomal lncRNA and RP11-462G22.1, each entailing sequence complementarity to numerous microRNAs. Analysis of RNA-Seq from PD and unaffected controls brains revealed over 7,000 brain-expressed lncRNAs, of which 3,495 were co-expressed in the leukocytes including U1, which showed both leukocyte and brain increases. Furthermore, qRT-PCR validations confirmed these co-increases in PD leukocytes and two brain regions, the amygdala and substantia-nigra, compared to controls. This novel workflow allows deep multi-level inspection of RNA-Seq datasets and provides a comprehensive new resource for understanding disease transcriptome modifications in PD and other neurodegenerative diseases.

Dataset Information

Novel Non-coding RNA Analysis in Multiple Myeloma Identified Through High-Throughput Sequencing.

Publications

Novel Non-coding RNA Analysis in Multiple Myeloma Identified Through High-Throughput Sequencing.

Similar Datasets

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