bims-lorfki Biomed News
on Long non-coding RNA functions in the kidney
Issue of 2021‒08‒15
four papers selected by
Nikita Dewani
Max Delbrück Centre for Molecular Medicine

  1. Front Oncol. 2021 ;11 713346
      To identify novel hypoxia-associated long non-coding RNAs (lncRNAs) as potential biomarkers, we developed a risk stratification signature and constructed a prognosis prediction nomogram of clear cell renal cell carcinoma (ccRCC). Hypoxia-related lncRNAs were identified through Pearson correlation analysis between the expression profiles of hypoxia-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset. Then, a signature of four key lncRNAs (COMETT, EMX2OS, AC026462.3, and HAGLR) was developed. The four lncRNAs were downregulated in high-grade, advanced stage, and high-risk ccRCC. The signature had an independent and long-standing prognosis prediction ability up to a 10-year follow-up. Notably, the risk score was significantly positively correlated with the infiltration abundances of six immune cells from the Tumor IMmune Estimation Resource (TIMER). The gene set enrichment analysis (GSEA) also suggested that the signature was involved in metabolism and tumorigenesis, which were closely related to the hypoxic tumor microenvironment. Ultimately, a nomogram of signature, age, stage, and grade, was built to predict the individual long-term survival possibility. Finally, the expressions of four lncRNAs were validated by quantitative real-time PCR (qRT-PCR). Our study identified a four-lncRNA signature and established a prognostic nomogram that reliably predicts survival in ccRCC. The findings may be beneficial to therapeutic customization and medical decision-making.
    Keywords:  The Cancer Genome Atlas (TCGA); clear cell renal cell carcinoma; hypoxia; lncRNA signature; nomogram
  2. Front Genet. 2021 ;12 682904
      Renal fibrosis (RF) is a pathological process that culminates in terminal renal failure in chronic kidney disease (CKD). Fibrosis contributes to progressive and irreversible decline in renal function. However, the molecular mechanisms involved in RF are complex and remain poorly understood. Long non-coding RNAs (lncRNAs) are a major type of non-coding RNAs, which significantly affect various disease processes, cellular homeostasis, and development through multiple mechanisms. Recent investigations have implicated aberrantly expressed lncRNA in RF development and progression, suggesting that lncRNAs play a crucial role in determining the clinical manifestation of RF. In this review, we comprehensively evaluated the recently published articles on lncRNAs in RF, discussed the potential application of lncRNAs as diagnostic and/or prognostic biomarkers, proposed therapeutic targets for treating RF-associated diseases and subsequent CKD transition, and highlight future research directions in the context of the role of lncRNAs in the development and treatment of RF.
    Keywords:  diabetic nephropathy; fibrosis; kidney disease; long non-coding RNA; renal fibrosis
  3. J Immunol Res. 2021 ;2021 9921466
      Background: Clear cell renal cell carcinoma (ccRCC) is the most common subtype of kidney cancer whose incidence and mortality rate are increasing. Identifying immune-related lncRNAs and constructing a model would probably provide new insights into biomarkers and immunotherapy for ccRCC and aid in the prognosis prediction.Methods: The transcription profile and clinical information were obtained from The Cancer Genome Atlas (TCGA). Immune-related gene sets and transcription factor genes were downloaded from GSEA website and Cistrome database, respectively. Tumor samples were divided into the training set and the testing set. Immune-related differentially expressed lncRNAs (IDElncRNAs) were identified from the whole set. Univariate Cox regression, LASSO, and stepwise multivariate Cox regression were performed to screen out ideal prognostic IDElncRNAs (PIDElncRNAs) from the training set and develop a multi-lncRNA signature.
    Results: Consequently, AC012236.1, AC078778.1, AC078950.1, AC087318.1, and AC092535.4 were screened to be significantly related to the prognosis of ccRCC patients, which were used to establish the five-lncRNA signature. Its wide diagnostic capacity was revealed in different subgroups of clinical parameters. Then AJCC-stage, Fuhrman-grade, pharmaceutical, age, and risk score regarded as independent prognostic factors were integrated to construct a nomogram, whose good performance in predicting 3-, 5-, and 7-year overall survival of ccRCC patients was revealed by time-dependent ROC curves and verified by the testing sets and ICGC dataset. The calibration plots showed great agreement of the nomogram between predicted and observed outcomes. Functional enrichment analysis showed the signature and each lncRNA were mainly enriched in pathways associated with regulation of immune response. Several kinds of tumor-infiltrating immune cells like regulatory T cells, T follicular helper cells, CD8+ T cells, resting mast cells, and naïve B cells were significantly correlated with the signature.
    Conclusion: Therefore, we constructed a five-lncRNA model integrating clinical parameters to help predict the prognosis of ccRCC patients. The five immune-related lncRNAs could potentially be therapeutic targets for immunotherapy in ccRCC in the future.
  4. Ren Fail. 2021 Dec;43(1): 1188-1197
      BACKGROUND: Atherosclerotic renal artery stenosis (ARAS) is frequently related to ischemic nephropathy, secondary hypertension, and end-stage renal failure. Thus, this study aimed to explore whether certain circulating long noncoding RNAs (lncRNAs) may be used as potential specific ARAS biomarkers.METHODS: In the present study, a microarray analysis was performed to screen for lncRNAs in renal artery tissue from four ARAS patients and four non-ARAS individuals. To identify specific lncRNAs as candidate potential biomarkers of ARAS, we used the following criteria: the fold change was set to >3.0 (compared with non-ARAS tissues), and p value cutoff was set at .05. According to these criteria, six lncRNAs were identified from 1150 lncRNAs. After validation by quantitative PCR (qPCR), these lncRNAs were independently validated in blood from groups of 18 ARAS patients, 18 non-ARAS individuals, and 18 healthy volunteers, furthermore, the predictive value of lncRNA PR11-387H17.6 was further assessed using blood from groups of 99 ARAS patients, 49 non-ARAS individuals, and 50 healthy volunteers. A receiver operating characteristic (ROC) curve analysis was performed to assess the performance of these lncRNAs as biomarkers.
    RESULTS: In the ROC analysis, the area under the curve (AUC) of PR11-387H17.6 was 0.733, with 52.5% sensitivity and 84.8% specificity in predicting the occurrence of ARAS. After considering the risk factors, the AUC of PR11-387H17.6 was 0.844, and the optimal sensitivity increased from 52.5% to 74.5%, although the specificity decreased from 84.8% to 81.9%. In the multivariable logistic analysis, PR11-387H17.6 was an independent predictor of major adverse events (OR: 3.039; 95% CI: 1.388-6.654; p= .006).
    CONCLUSIONS: PR11-387H17.6 is a potential diagnostic biomarker of ARAS. The lncRNA levels in blood cells are regulated in ARAS. Thus, further investigations of the role of lncRNAs in ARAS are warranted.
    Keywords:  Long noncoding RNAs; atherosclerotic renal artery stenosis; biomarker; risk factor