DNA methylation of noncoding RNAs: new insights into osteogenesis and common bone diseases

Yu, Liyuan; Xia, Kai; Cen, Xiao; Huang, Xinqi; Sun, Weihao; Zhao, Zhihe; Liu, Jun

doi:10.1186/s13287-020-01625-7

Cited by 22 publications

(21 citation statements)

References 78 publications

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“…Therefore, exploring the link between miRNAs and histone modifications may improve our understanding of HLF/OLF pathogenesis. On the other hand, DNA methylation of miRNAs has been proven to hold particular regulation function in osteogenic differentiation (Allas et al, 2019;Yu et al, 2020). For example, Li et al found that hypermethylation of miR-149 influenced the osteogenic differentiation of MSCs through SDF-1/CXCR4 signaling (Li et al, 2019).…”

Section: Microrna-based Molecular Mechanismsmentioning

confidence: 99%

Dysregulation of MicroRNAs in Hypertrophy and Ossification of Ligamentum Flavum: New Advances, Challenges, and Potential Directions

et al. 2021

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Pathological changes in the ligamentum flavum (LF) can be defined as a process of chronic progressive aberrations in the nature and structure of ligamentous tissues characterized by increased thickness, reduced elasticity, local calcification, or aggravated ossification, which may cause severe myelopathy, radiculopathy, or both. Hypertrophy of ligamentum flavum (HLF) and ossification of ligamentum flavum (OLF) are clinically common entities. Though accumulated evidence has indicated both genetic and environmental factors could contribute to the initiation and progression of HLF/OLF, the definite pathogenesis remains fully unclear. MicroRNAs (miRNAs), one of the important epigenetic modifications, are short single-stranded RNA molecules that regulate protein-coding gene expression at posttranscriptional level, which can disclose the mechanism underlying diseases, identify valuable biomarkers, and explore potential therapeutic targets. Considering that miRNAs play a central role in regulating gene expression, we summarized current studies from the point of view of miRNA-related molecular regulation networks in HLF/OLF. Exploratory studies revealed a variety of miRNA expression profiles and identified a battery of upregulated and downregulated miRNAs in OLF/HLF patients through microarray datasets or transcriptome sequencing. Experimental studies validated the roles of specific miRNAs (e.g., miR-132-3p, miR-199b-5p in OLF, miR-155, and miR-21 in HLF) in regulating fibrosis or osteogenesis differentiation of LF cells and related target genes or molecular signaling pathways. Finally, we discussed the perspectives and challenges of miRNA-based molecular mechanism, diagnostic biomarkers, and therapeutic targets of HLF/OLF.

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Section: Microrna-based Molecular Mechanismsmentioning

confidence: 99%

Dysregulation of MicroRNAs in Hypertrophy and Ossification of Ligamentum Flavum: New Advances, Challenges, and Potential Directions

et al. 2021

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“…However, screening biomarkers using changes in mRNAs only are insufficient as miRNAs, lncRNAs and the interaction between these three all play important roles in the growth and differentiation of cells and occurrence of cancers. 25,26 ceRNA hypothesis provides an in-depth understanding of the mechanism of carcinogenesis and new evidence for cancer diagnosis and treatment, chemotherapy efficacy prediction and cancer risk prediction. 6,27,28 Data mining using TCGA database is an effective approach to identify changes in RNA expression that are relevant to clinical outcomes and to screen new therapeutic targets.…”

Section: Discussionmentioning

confidence: 99%

“…Sequencing technology and bioinformatics allow the screening of the entire DNA mutation profile and contribute to the understanding of EAC origin and characteristics. However, screening biomarkers using changes in mRNAs only are insufficient as miRNAs, lncRNAs and the interaction between these three all play important roles in the growth and differentiation of cells and occurrence of cancers 25,26 . ceRNA hypothesis provides an in‐depth understanding of the mechanism of carcinogenesis and new evidence for cancer diagnosis and treatment, chemotherapy efficacy prediction and cancer risk prediction 6,27,28 …”

Section: Discussionmentioning

confidence: 99%

Multivariate gene expression‐based survival predictor model in esophageal adenocarcinoma

et al. 2020

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Background: Despite the recent development of molecular-targeted treatment and immunotherapy, survival of patients with esophageal adenocarcinoma (EAC) with poor prognosis is still poor due to lack of an effective biomarker. In this study, we aimed to explore the ceRNA and construct a multivariate gene expression predictor model using data from The Cancer Genome Atlas (TCGA) to predict the prognosis of EAC patients. Methods: We conducted differential expression analysis using mRNA, miRNA and lncRNA transciptome data from EAC and normal patients as well as corresponding clinical information from TCGA database, and gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of those unique differentially expressed mRNAs using the Integrate Discovery Database (DAVID) database. We then constructed the lncRNA-miRNA-mRNA competing endogenous RNA (ceRNA) network of EAC and used Cox proportional hazard analysis to generate a multivariate gene expression predictor model. We finally performed survival analysis to determine the effect of differentially expressed mRNA on patients' overall survival and discover the hub gene. Results: We identified a total of 488 lncRNAs, 33 miRNAs, and 1207 mRNAs with differentially expressed profiles. Cox proportional hazard analysis and survival analysis using the ceRNA network revealed four genes (IL-11, PDGFD, NPTX1, ITPR1) as potential biomarkers of EAC prognosis in our predictor model, and IL-11 was identified as an independent prognostic factor. Conclusions: In conclusion, we identified differences in the ceRNA regulatory networks and constructed a four-gene expression-based survival predictor model, which could be referential for future clinical research.

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“…DNA methylation, a type of central epigenetic modi cation, is involved in diverse biological activities, including organism development, genome regulation, aging, and disease [7,8] . Methylation generally occurs on cytosine residues in cytosine-phosphate-guanine (CpG) dinucleotides [9,10] . Typically, these CpG sites are located and enriched in large clusters forming CpG islands, found mostly at the promoter regions of genes [10] .…”

Section: Introductionmentioning

confidence: 99%

“…Methylation generally occurs on cytosine residues in cytosine-phosphate-guanine (CpG) dinucleotides [9,10] . Typically, these CpG sites are located and enriched in large clusters forming CpG islands, found mostly at the promoter regions of genes [10] . The methylation of CpG islands can recruit repressive methyl-binding proteins, impair transcription factor binding, and condense chromatin structure [11,12] .…”

Section: Introductionmentioning

confidence: 99%

Comprehensive analysis of DNA methylation and gene expression profiles in rectal cancer

Zhang¹,

Meng²,

Chao³

et al. 2020

Preprint

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BackgroundAbnormal hypomethylation of oncogenes and hypermethylation of tumor suppressor genes play important roles in human tumorigenesis and cancer progression, including those of rectal cancer (RC). However, conjoint analysis of RC involving both gene expression and methylation profiling datasets remains rare. This study aimed to identify methylation-regulated differentially expressed genes (MeDEGs) and to evaluate their prognostic value in RC through bioinformatics analysis.MethodsGene expression (GSE20842 and GSE68204) and gene methylation (GSE75546) profiling datasets were obtained from the Gene Expression Omnibus database. GEO2R was adopted to identify differentially expressed genes (DEGs) and differentially methylated genes (DMGs). MeDEGs were obtained by overlapping the DEGs and DMGs and then subjected to protein–protein interaction (PPI) network analysis using STRING. Modules and hub genes within the network were identified using MCODE and CytoHubba, respectively. Prognostic MeDEGs were selected by univariate Cox regression. Finally, our findings were validated based on The Cancer Genome Atlas (TCGA) database.ResultsIn total, 243 upregulated-hypomethylated and 51 downregulated-hypermethylated genes were identified as MeDEGs. A PPI network of MeDEGs was constructed with 290 nodes and 578 edges. Three modules and three hub genes—COL3A1, FPR1, and PLK1—within the network were identified. Three MeDEGs—NFE2, COMP, and LAMA1—were found to be survival-related. Furthermore, the expression and methylation status of two hub genes (excluding FPR1) and the three prognostic MeDEGs were also significantly altered in TCGA and were consistent with our findings.ConclusionsWe identified novel MeDEGs and explored their relationship with survival in RC. Our methodology may provide an effective bioinformatics basis for further understanding of the methylation-mediated regulatory mechanisms in RC.

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DNA methylation of noncoding RNAs: new insights into osteogenesis and common bone diseases

Cited by 22 publications

References 78 publications

Dysregulation of MicroRNAs in Hypertrophy and Ossification of Ligamentum Flavum: New Advances, Challenges, and Potential Directions

Dysregulation of MicroRNAs in Hypertrophy and Ossification of Ligamentum Flavum: New Advances, Challenges, and Potential Directions

Multivariate gene expression‐based survival predictor model in esophageal adenocarcinoma

Comprehensive analysis of DNA methylation and gene expression profiles in rectal cancer

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