scholarly journals Long non‐coding RNA MIR31HG as a prognostic predictor for malignant cancers: A meta‐ and bioinformatics analysis

2021 ◽  
Author(s):  
Yuanfeng Wei ◽  
Yingjie Zhai ◽  
Xiaoang Liu ◽  
Shan Jin ◽  
Lu Zhang ◽  
...  
Keyword(s):  
Bioinformatics Analysis ◽  
Prognostic Predictor ◽  
Non Coding Rna ◽  
Long Non Coding Rna

scholarly journals Identification of Potential Prognostic Long Non-Coding RNA Biomarkers for Predicting Survival in Patients with Hepatocellular Carcinoma

2018 ◽  
Vol 48 (5) ◽  
pp. 1854-1869 ◽  
Author(s):  
Xiwen Liao ◽  
Chengkun Yang ◽  
Rui Huang ◽  
Chuangye Han ◽  
Tingdong Yu ◽  
...  
Keyword(s):  
Hepatocellular Carcinoma ◽  
Functional Assessment ◽  
Bioinformatics Analysis ◽  
Survival Prediction ◽  
Prognostic Signature ◽  
Characteristic Analysis ◽  
Risk Of Death ◽  
Non Coding Rna ◽  
Cox Analysis ◽  
Long Non Coding Rna

Background/Aims: The aim of the current study was to identify potential prognostic long non-coding RNA (lncRNA) biomarkers for predicting survival in patients with hepatocellular carcinoma (HCC) using The Cancer Genome Atlas (TCGA) dataset and bioinformatics analysis. Methods: RNA sequencing and clinical data of HCC patients from TCGA were used for prognostic association assessment by univariate Cox analysis. A prognostic signature was built using stepwise multivariable Cox analysis, and a comprehensive analysis was performed to evaluate its prognostic value. The prognostic signature was further evaluated by functional assessment and bioinformatics analysis. Results: Thirteen differentially expressed lncRNAs (DELs) were identified and used to construct a single prognostic signature. Patients with high risk scores showed a significantly increased risk of death (adjusted P < 0.0001, adjusted hazard ratio = 3.522, 95% confidence interval = 2.307–5.376). In the time-dependent receiver operating characteristic analysis, the prognostic signature performed well for HCC survival prediction with an area under curve of 0.809, 0.782 and 0.79 for 1-, 3- and 5-year survival, respectively. Comprehensive survival analysis of the 13-DEL prognostic signature suggested that it serves as an independent factor in HCC, showing a better performance for prognosis prediction than traditional clinical indicators. Functional assessment and bioinformatics analysis suggested that the prognostic signature was associated with the cell cycle and peroxisome proliferator-activated receptor signaling pathway. Conclusions: The novel lncRNA expression signature identified in the present study may be a potential biomarker for predicting the prognosis of HCC patients.

scholarly journals Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview

2021 ◽  
Vol 12 ◽  
Author(s):  
Yuxin Gong ◽  
Wen Zhu ◽  
Meili Sun ◽  
Lei Shi
Keyword(s):  
Small Rnas ◽  
Bioinformatics Analysis ◽  
Regulation Of Gene Expression ◽  
Research Progress ◽  
Biological Processes ◽  
Existing Problems ◽  
Non Coding Rna ◽  
Non Coding Rnas ◽  
Cancer Côlon ◽  
Long Non Coding Rna

Long non-coding RNAs (lncRNAs) are usually located in the nucleus and cytoplasm of cells. The transcripts of lncRNAs are &gt;200 nucleotides in length and do not encode proteins. Compared with small RNAs, lncRNAs have longer sequences, more complex spatial structures, and more diverse and complex mechanisms involved in the regulation of gene expression. LncRNAs are widely involved in the biological processes of cells, and in the occurrence and development of many human diseases. Many studies have shown that lncRNAs can induce the occurrence of diseases, and some lncRNAs undergo specific changes in tumor cells. Research into the roles of lncRNAs has covered the diagnosis of, for example, cardiovascular, cerebrovascular, and central nervous system diseases. The bioinformatics of lncRNAs has gradually become a research hotspot and has led to the discovery of a large number of lncRNAs and associated biological functions, and lncRNA databases and recognition models have been developed. In this review, the research progress of lncRNAs is discussed, and lncRNA-related databases and the mechanisms and modes of action of lncRNAs are described. In addition, disease-related lncRNA methods and the relationships between lncRNAs and human lung adenocarcinoma, rectal cancer, colon cancer, heart disease, and diabetes are discussed. Finally, the significance and existing problems of lncRNA research are considered.

scholarly journals Long Non-coding RNA MEG3 Promotes Renal Tubular Epithelial Cell Pyroptosis by Regulating the miR-18a-3p/GSDMD Pathway in Lipopolysaccharide-Induced Acute Kidney Injury

2021 ◽  
Vol 12 ◽  
Author(s):  
Junhui Deng ◽  
Wei Tan ◽  
Qinglin Luo ◽  
Lirong Lin ◽  
Luquan Zheng ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Rna Binding ◽  
Bioinformatics Analysis ◽  
Kidney Injury ◽  
Luciferase Reporter ◽  
Cell Models ◽  
Caspase 1 ◽  
Non Coding Rna ◽  
Renal Tubular ◽  
Long Non Coding Rna

Background and Objective: Acute kidney injury (AKI) is a complication of sepsis. Pyroptosis of gasdermin D (GSDMD)-mediated tubular epithelial cells (TECs) play important roles in pathogenesis of sepsis-associated AKI. Long non-coding RNA (lncRNA) maternally expressed gene 3 (MEG3), an imprinted gene involved in tumorigenesis, is implicated in pyroptosis occurring in multiple organs. Herein, we investigated the role and mechanisms of MEG3 in regulation of TEC pyroptosis in lipopolysaccharide (LPS)-induced AKI.Materials and Methods: Male C57BL/6 mice and primary human TECs were treated with LPS for 24 h to establish the animal and cell models, respectively, of sepsis-induced AKI. Renal function was assessed by evaluation of serum creatinine and urea levels. Renal tubule injury score was assessed by Periodic acid-Schiff staining. Renal pyroptosis was assessed by evaluating expression of caspase-1, GSDMD, and inflammatory factors IL-1β and IL-18. Cellular pyroptosis was assessed by analyzing the release rate of LDH, expression of IL-1β, IL-18, caspase-1, and GSDMD, and using EtBr and EthD2 staining. MEG3 expression in renal tissues and cells was detected using RT-qPCR. The molecular mechanisms of MEG3 in LPS-induced AKI were assessed through bioinformatics analysis, RNA-binding protein immunoprecipitation, dual luciferase reporter gene assays, and a rescue experiment.Results: Pyroptosis was detected in both LPS-induced animal and cell models, and the expression of MEG3 in these models was significantly up-regulated. MEG3-knockdown TECs treated with LPS showed a decreased number of pyroptotic cells, down-regulated secretion of LDH, IL-1β, and IL-18, and decreased expression of GSDMD, compared with those of controls; however, there was no difference in the expression of caspase-1 between MEG3 knockdown cells and controls. Bioinformatics analysis screened out miR-18a-3P, and further experiments demonstrated that MEG3 controls GSDMD expression by acting as a ceRNA for miR-18a-3P to promote TECs pyroptosis.Conclusion: Our study demonstrates that lncRNA MEG3 promoted renal tubular epithelial pyroptosis by regulating the miR-18a-3p/GSDMD pathway in LPS-induced AKI.

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