bims-lorfki Biomed News
on Long non-coding RNA functions in the kidney
Issue of 2020‒12‒20
six papers selected by
Nikita Dewani
Max Delbrück Centre for Molecular Medicine

  1. Methods Mol Biol. 2021 ;2254 195-218
      Long noncoding RNAs (lncRNAs) contain >200 nucleotides and act as regulatory molecules in transcription and translation processes in both normal and pathological conditions. LncRNAs have been reported to localize in nuclei, cytoplasm, and, more recently, extracellular vesicles such as exosomes. Exosomal lncRNAs have gained much attention as exosomes secreted from one cell type can transfer their cargo (e.g., protein, RNA species, and lipids) to recipient cells and mediate phenotypic changes in the recipient cell. In recent years, many exosomal lncRNAs have been discovered and annotated and are attracting much attention as potential markers for disease diagnosis and prognosis. It is expected that many exosomal lncRNAs are yet to be identified. However, characterization of unannotated exosomal RNAs with non-protein-coding sequences from massive RNA sequencing data is technically challenging. Here, we describe a method for the discovery of annotated and unannotated exosomal lncRNA. This method includes a large-scale isolation and purification strategy for exosome subtypes, using the human colorectal cancer cell line (LIM1863) as a model. The method inputs RNA sequencing clean reads and performs transcript assembly to identify annotated and unannotated exosomal lncRNAs. Cutoffs (length, number of exon, classification code, and human protein-coding probability) are used to identify potentially novel exosomal lncRNAs. Raw read count calculation and differential expression analysis are also introduced for downstream analysis and candidate selection. Exosomal lncRNA candidates are validated using RT-qPCR. This method provides a template for exosomal lncRNA discovery and analysis from next-generation RNA sequencing.
    Keywords:  Bioinformatics; Exosomes; Long noncoding RNAs; Next-generation RNA sequencing; Transcriptomics
  2. Front Oncol. 2020 ;10 591254
      Epithelial-mesenchymal transition (EMT), a reversible cellular program, is critically important in tumor progression and is regulated by a family of transcription factors, induction factors, and an array of signaling pathway genes. The prognostic role and biological functions of EMT-related lncRNAs in ccRCC are largely unknown. In the present study, we analyzed the gene expression data and clinical information retrieved from The Cancer Genome Atlas (TCGA) database (N=512) and International Cancer Genome Consortium (ICGC) database (N=90) which served as training and external validation dataset, respectively. Then, we constructed an EMT-related lncRNA risk signature based on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information. The Kaplan-Meier curve analysis revealed that patients in the low-risk and high-risk groups exhibited significant divergence in the overall survival (OS) and disease-free survival (DFS) of ccRCC, as was confirmed in the validation dataset. The Cox regression analysis of the clinical factors and risk signature in the OS and DFS demonstrated that the risk signature can be utilized as an independent prognostic predictor. Moreover, we developed an individualized prognosis prediction model relying on the nomogram and receive operator curve (ROC) analysis based on the independent factors. The Gene Set Enrichment Analysis (GSEA) indicated that patients in the low-risk group were associated with adherens junction, focal adhesion, MAPK signaling pathway, pathways in cancer, and renal cell carcinoma pathway. In addition, we identified three robust subtypes (named C1, C2 and C3) of ccRCC with distinct clinical characteristics and prognostic role in the TCGA dataset and ICGC dataset. Among them, C1 was associated with a better survival outcome, whereas C2 and C3 was associated with a worse survival outcome and have more advanced-stage patients. Moreover, C2 was more likely to respond to immunotherapy and was sensitive to chemo drugs, this may provide insights to clinicians to develop an individualized treatment. Collectively, this work developed a reliable EMT-related lncRNA risk signature that can independently predict the OS and DFS of ccRCC. Besides, we identified three stable molecular subtypes based on the EMT-related lncRNA expression, which may comprehensively be vital in elucidating the underlying molecular mechanism of ccRCC.
    Keywords:  clear cell renal cell carcinoma; epithelial–mesenchymal transition; lncRNA; molecular subtype; nomogram; prognostic model
  3. Arch Physiol Biochem. 2020 Dec 17. 1-8
      Diabetic nephropathy (DN) is one of the most important complications of diabetes mellitus. Thus, it is urgent to develop a novel diagnosis or therapeutic strategy that could suspend DN progression. Moreover, there is increasing evidence demonstrating that long non-coding RNA (lncRNA) acts as critical players in regulating autophagy and are involved in DN. We demonstrated that lncRNA X-inactive specific transcript (XIST) was downregulated in high glucose (HG) treated podocytes, accompanied by increased apoptosis of podocytes. Overexpression of XIST significantly reduced the apoptosis and promoted the number of viable cells of podocyte under HG treatment. Prediction by Targets can and dual-luciferase reporter assay revealed the interaction between miR-30 and XIST and AVEN. Further WB (Western Blot), MTT (3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide), and flow cytometry confirmed that XIST could reverse the expression of AVEN and ameliorate HG-induced apoptosis. In conclusion, our research revealed that XIST plays a protective effect on podocyte injury induced by HG through miR-30/AVEN axis.
    Keywords:  Diabetic nephropathy; MiR-30; XIST; high glucose; podocyte
  4. Front Oncol. 2020 ;10 559730
      Objective: The roles of long non-coding RNAs (lncRNAs) in the diagnosis of clear cell renal cell carcinoma (ccRCC) are still not well-defined. We aimed to identify differentially expressed lncRNAs and mRNAs in plasma of ccRCC patients and health controls systematically. Methods: Expression profile of plasma lncRNAs and mRNAs in ccRCC patients and healthy controls was analyzed based on microarray assay. Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway-based approaches were used to investigate biological function and signaling pathways mediated by the differentially expressed mRNAs. SOCS2-AS1 was selected for validation using Real-Time PCR. The differentially expressed lncRNAs and mRNAs were further compared with E-MTAB-1830 datasets using Venn and the NetworkAnalyst website. The GEPIA and ULCAN websites were utilized for the evaluation of the expression level of differentially expressed mRNA and their association with overall survival (OS). Results: A total of 3,664 differentially expressed lncRNAs were identified in the plasma of ccRCC patients, including 1,511 up-regulated and 2,153 down-regulated lncRNAs (fold change ≥2 and P < 0.05), respectively. There were 2,268 differentially expressed mRNAs, including 932 up-regulated mRNAs and 1,336 down-regulated mRNAs, respectively (fold change ≥2 and P < 0.05). Pathway analysis based on deregulated mRNAs was mainly involved in melanogenesis and Hippo signaling pathway (P < 0.05). In line with the lncRNA microarray findings, the SOCS2-AS1 was down-regulated in ccRCC plasma and tissues, as well as in cell lines. Compared with the E-MTAB-1830 gene expression profiles, we identified 18 lncRNAs and 87 mRNAs differently expressed in both plasma and neoplastic tissues of ccRCC. The expression of 10 mRNAs (EPB41L4B, CCND1, GGT1, CGNL1, CYSLTR1, PLAUR, UGT3A1, PROM2, MUC12, and PCK1) was correlated with the overall survival (OS) rate in ccRCC patients based on the GEPIA and ULCAN websites. Conclusions: We firstly reported differentially expressed lncRNAs in ccRCC patients and healthy controls systemically. Several differentially expressed lncRNAs and mRNAs were identified, which might serve as diagnostic or prognostic markers. The biological function of these lncRNAs and mRNAs should be further validated. Our study may contribute to the future treatment of ccRCC and provide novel insights into cancer biology.
    Keywords:  bioinformatics; clear cell renal cell carcinoma; differentially expressed gene; long non-coding RNA; mRNA; microarray
  5. Methods Mol Biol. 2021 ;2254 1-13
      There is accumulating evidence that long noncoding RNAs (lncRNAs) play crucial roles in biological processes and diseases. In recent years, computational models have been widely used to predict potential lncRNA-disease relations. In this chapter, we systematically describe various computational algorithms and prediction tools that have been developed to elucidate the roles of lncRNAs in diseases, coding potential/functional characterization, or ascertaining their involvement in critical biological processes as well as provide a comprehensive summary of these applications.
    Keywords:  LncRNA Bioinformatics; LncRNA coding potential; LncRNA functional prediction; LncRNA–disease association; LncRNA–protein correlation
  6. Med Sci Monit. 2020 Dec 17. 26 e927725
      BACKGROUND Long non-coding RNA (lncRNA) can act as competing endogenous RNA (ceRNA) during tumor development. However, no study has elucidated the ceRNA network in pediatric rhabdoid tumor of the kidney (RTK) and its prognostic-related lncRNAs. The goal of the present study was to identify potential biomarkers of prognostic-related lncRNAs. MATERIAL AND METHODS RNA sequencing and clinical data were procured from the TARGET database. The "EdgeR" package was used to obtain differentially expressed lncRNA (DElncRNA), differentially expressed messenger RNAs (DEmRNA), and differentially expressed microRNAs (DEmiRNA). Cytoscape software was used to construct a ceRNA network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were conducted on the ceRNA network-related DEmRNA. The Kaplan-Meier method was used for predicting survival with ceRNA network-related DElncRNA. Univariate and multivariate Cox analyses were used to identify prognosis-related lncRNAs in the ceRNA network, and an RTK prognostic signature was constructed. RESULTS We identified 1109 DElncRNAs, 215 DEmiRNAs, and 3436 DEmRNAs; and 107 DElncRNAs, 21 DEmiRNAs, and 74 DEmRNAs were included in the ceRNA regulatory network. GO enrichment analysis and KEGG pathway enrichment indicated that the DEmRNAs were mainly related to the regulation of phospholipase C activity and the MAPK signaling pathway. Survival analysis showed that 9 of 107 DElncRNAs were correlated with prognosis (P<0.05). Univariate and multivariate Cox analysis identified 4 DElncRNAs (HNF1A-AS1, TPTEP1, SNHG6, and ZNF503-AS2) to establish a predictive model and can be used as independent prognostic biomarkers. CONCLUSIONS We constructed a ceRNA network that reveals potential lncRNA biomarkers for pediatric RTK.