The Roles of Noncoding RNAs in Systemic Sclerosis

Liu, Yongmei; Cheng, Linlin; Zhan, Haoting; Li, Haolong; Li, Xiaomeng; Huang, Yuan; Li, Yongzhe

doi:10.3389/fimmu.2022.856036

Cited by 10 publications

(7 citation statements)

References 121 publications

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“…Long noncoding RNAs (lncRNAs) are RNAs with a length of more than 200 nt that can regulate transcription and translation, participate in intracellular signaling pathways, and regulate substance metabolism [10,11]. LncRNAs play an important regulatory role in various diseases, especially infammatory, viral, and cancer diseases [12][13][14].…”

Section: Introductionmentioning

confidence: 99%

Construction of a Prognostic Model Based on Cuproptosis-Related lncRNA Signatures in Pancreatic Cancer

Jiang

Zhang

et al. 2022

Canadian Journal of Gastroenterology and Hepatology

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Aim. The aim of this study is to identify cuproptosis-related lncRNAs and construct a prognostic model for pancreatic cancer patients for clinical use. Methods. The expression profile of lncRNAs was downloaded from The Cancer Genome Atlas database, and cuproptosis-related lncRNAs were identified. The prognostic cuproptosis-related lncRNAs were obtained and used to establish and validate a prognostic risk score model in pancreatic cancer. Results. In total, 181 cuproptosis-related lncRNAs were obtained. The prognostic risk score model was constructed based on five lncRNAs (AC025257.1, TRAM2-AS1, AC091057.1, LINC01963, and MALAT1). Patients were assigned to two groups according to the median risk score. Kaplan–Meier survival curves showed that the difference in the prognosis between the high- and low-risk groups was statistically significant. Multivariate Cox analysis showed that our risk score was an independent risk factor for pancreatic cancer patients. Receiver operator characteristic curves revealed that the cuproptosis-related lncRNA model can effectively predict the prognosis of pancreatic cancer. The principal component analysis showed a difference between the high- and low-risk groups intuitively. Functional enrichment analysis showed that different genes were involved in cancer-related pathways in patients in the high- and low-risk groups. Conclusion. The risk model based on five prognostic cuproptosis-related lncRNAs can well predict the prognosis of pancreatic cancer patients. Cuproptosis-related lncRNAs could be potential biomarkers for pancreatic cancer diagnosis and treatment.

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Section: Introductionmentioning

confidence: 99%

Construction of a Prognostic Model Based on Cuproptosis-Related lncRNA Signatures in Pancreatic Cancer

Jiang

Zhang

et al. 2022

Canadian Journal of Gastroenterology and Hepatology

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“…The higher amount of ENG observed also in SSc fibroblasts seems to be more pronounced at the protein, rather than mRNA, level and the suggestion is that post-transcriptional mechanisms could enhance protein expression in SSc fibroblasts ( 43 ). Our search did not provide any result regarding either this process, or an altered expression of miRNAs in SSc regulating ENG ( 63 ), making this an interesting field of future investigation.…”

Section: Discussionmentioning

confidence: 91%

Endoglin and Systemic Sclerosis: A PRISMA-driven systematic review

et al. 2022

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BackgroundSystemic Sclerosis (SSc) is a rare autoimmune disease whose pathogenesis is still poorly understood. The Transforming Growth Factor β superfamily is considered pivotal and a crucial role has been suggested for the type III receptor, Endoglin (ENG). The aim of this systematic review is to investigate and combine the current clinical and molecular available data, to suggest novel hints for further studies.MethodsWe followed PRISMA guidelines; the search was performed on three databases (MEDLINE, Web of Science, Embase) in date November 2nd, 2021. Subsequent to the exclusion of duplicates, we applied as inclusion criteria: 1. focus on the relationship between ENG and SSc; 2. English language. As exclusion criteria: 1. ENG exclusively as a cellular biomarker; 2. no focus on ENG-SSc relationship; 3. review articles and 4. abstracts that did not add novel data. Eligibility was assessed independently by each author to reduce biases. We divided records into clinical and molecular works and subgrouped them by their study features and aim.ResultsWe selected 25 original papers and 10 conference abstracts. Molecular studies included 6 articles and 4 abstracts, whereas clinical studies included 17 articles and 6 abstracts; 2 articles presented both characteristics. Molecular studies were focussed on ENG expression in different cell types, showing an altered ENG expression in SSc-affected cells. Clinical studies mainly suggested that different disease phenotypes can be related to peculiar disregulations in soluble ENG concentrations.DiscussionConcerning the possible limits of our search, boolean operators in our strings might have been uneffective. However, the use of different strings in different databases should have reduced this issue at a minimum. Another bias can be represented by the selection step, in which we excluded many articles based on the role of Endoglin as a histological vascular marker rather than a signaling receptor. We tried to reduce this risk by performing the selection independently by each author and discussing disagreements. Our systematic review pointed out that ENG has a pivotal role in activating different TGFβ-stimulated pathways that can be crucial in SSc pathogenesis and progression.

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“…Increasing studies have investigated non-invasive biomarkers to diagnose, evaluate, and treat SSc; however, few of these biomarkers are used clinically ( 8 ). Compared to circulating non-coding RNAs in the serum or plasma, the abnormal expression of non-coding RNAs carried by cirexos in SSc have the advantages of stability and practicability as novel biomarkers and therapeutic targets for the diagnosis and treatment of SSc ( 13 , 74 , 75 ).…”

Section: Discussionmentioning

confidence: 99%

Identification of lncRNA–miRNA–mRNA networks in circulating exosomes as potential biomarkers for systemic sclerosis

et al. 2023

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ObjectiveThis study aimed to analyze potential biomarkers for systemic sclerosis (SSc) by constructing lncRNA–miRNA–mRNA networks in circulating exosomes (cirexos).Materials and methodsDifferentially expressed mRNAs (DEmRNAs) and lncRNAs (DElncRNAs) in SSc cirexos were screened using high-throughput sequencing and detected with real-time quantitative PCR (RT-qPCR). Differentially expressed genes (DEGs) were analyzed using the DisGeNET, GeneCards, GSEA4.2.3, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. Receiver operating characteristic (ROC) curves, correlation analyses, and a double-luciferase reporter gene detection assay were used to analyze competing endogenous RNA (ceRNA) networks and clinical data.ResultsIn this study, 286 DEmRNAs and 192 DElncRNAs were screened, of which 18 DEGs were the same as the SSc-related genes. The main SSc-related pathways included extracellular matrix (ECM) receptor interaction, local adhesion, platelet activation, and IgA production by the intestinal immune network. A hub gene, COL1A1, was obtained by a protein–protein interaction (PPI) network. Four ceRNA networks were predicted through Cytoscape. The relative expression levels of COL1A1, ENST0000313807, and NON-HSAT194388.1 were significantly higher in SSc, while the relative expression levels of hsa-miR-29a-3p, hsa-miR-29b-3p, and hsa-miR-29c-3p were significantly lower in SSc (P < 0.05). The ROC curve showed that the ENST00000313807-hsa-miR-29a-3p-COL1A1 network as a combined biomarker of SSc is more valuable than independent diagnosis, and that it is correlated with high-resolution CT (HRCT), Scl-70, C-reactive protein (CRP), Ro-52, IL-10, IgM, lymphocyte percentage, neutrophil percentage, albumin divided by globulin, urea, and RDW-SD (P < 0.05). Double-luciferase reporter gene detection showed that ENST00000313807 interacts with hsa-miR-29a-3p, which interacts with COL1A1.ConclusionThe ENST00000313807-hsa-miR-29a-3p-COL1A1 network in plasma cirexos represents a potential combined biomarker for the clinical diagnosis and treatment of SSc.

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The Roles of Noncoding RNAs in Systemic Sclerosis

Cited by 10 publications

References 121 publications

Construction of a Prognostic Model Based on Cuproptosis-Related lncRNA Signatures in Pancreatic Cancer

Construction of a Prognostic Model Based on Cuproptosis-Related lncRNA Signatures in Pancreatic Cancer

Endoglin and Systemic Sclerosis: A PRISMA-driven systematic review

Identification of lncRNA–miRNA–mRNA networks in circulating exosomes as potential biomarkers for systemic sclerosis

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