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Background: Recent advances in the functional analyses of endogenous non-coding RNA (ncRNA) molecules, including long non-coding RNAs (LncRNAs), have provided a new perspective on the crucial roles of RNA in gene regulation. Consequently, LncRNA deregulation is a key factor in various diseases, including pulmonary disorders like Cystic Fibrosis (CF). CF is the most common life limiting recessive disease in the U.S., and is due to mutations in the CFTR gene. CF mutations, of which the most common is F508del-CFTR, prevents correct folding, trafficking and function of the mutant CFTR protein and is further manifested by the hyper-expression of pro-inflammatory cytokines and chemokines into the airway lumen leading to bronchiectasis and culminating in lung destruction.Methods: Here we report a distinct LncRNA signature and corresponding mRNAs that distinguishes CF lung (airway and parenchyma) tissues from matched non-CF controls (n = 4 each group), generated by microarray specific for LncRNAs which includes corresponding mRNA expressions. In silico analyses of the cellular processes that are impacted by these LncRNAs was performed using Gene Ontology (GO). A selected subset of LncRNAs were validated by quantitative real-time PCR.Results: We have identified 636 LncRNAs differentially expressed in CF airway epithelium and 1974 in CF lung parenchyma compared to matched non-CF controls (fold change ≥2, p < 0.05), majority of which (> 50%) are intergenic. Interestingly, 15 of these differentially expressed LncRNAs and 9 coding mRNAs are common to airway and parenchyma tissues. GO analyses indicates that signaling pathways and cell membrane functions are significantly affected by the alteration in LncRNA expressions in CF lung tissues. Seven of the differentially expressed LncRNAs, exhibit similar expression trends in CFBE41o- compared to control cells.Conclusion: Understanding the mechanisms by which these LncRNAs regulate CF disease phenotype will help develop novel therapeutic targets for CF and related pulmonary diseases, such as COPD and Asthma. [ABSTRACT FROM AUTHOR]

Background: Long non-coding RNAs (lncRNAs) can be used as prognostic biomarkers in many types of cancer., Objective: We sought to establish an lncRNA signature to improve postoperative risk stratification for patients with localized clear cell renal cell carcinoma (ccRCC)., Design, Setting, and Participants: Based on the RNA-seq data of 444 stage I-III ccRCC tumours from The Cancer Genome Atlas project, we built a four-lncRNA-based classifier using the least absolute shrinkage and selection operation (LASSO) Cox regression model in 222 randomly selected samples (training set) and validated the classifier in the remaining 222 samples (internal validation set). We confirmed this classifier in an external validation set of 88 patients with stage I-III ccRCC from a Japan cohort and using quantitative reverse transcription polymerase chain reaction (RT-PCR) in another three independent sets that included 1869 patients from China with stage I-III ccRCC., Outcome Measurements and Statistical Analysis: Univariable and multivariable Cox regression, Harrell's concordance index (c-index), and time-dependent receiver operating characteristic curves were used to evaluate the association of the classifier with overall survival, disease-specific survival, and disease-free survival., Results and Limitations: Using the LASSO Cox regression model, we built a classifier named RCClnc4 based on four lncRNAs: ENSG00000255774, ENSG00000248323, ENSG00000260911, and ENSG00000231666. In the RNA-seq and RT-PCR data sets, the RCClnc4 signature significantly stratified patients into high-risk versus low-risk groups in terms of clinical outcome across and within subpopulations and remained as an independent prognostic factor in multivariate analyses (hazard ratio range, 1.34 [95% confidence interval {CI}: 1.03-1.75; p=0.028] to 1.89 [95% CI, 1.55-2.31; p<0.001]) after adjusting for clinical and pathologic factors. The RCClnc4 signature achieved a higher accuracy (mean c-index, 0.72) than clinical staging systems such as TNM (mean c-index, 0.62) and the stage, size, grade, and necrosis (SSIGN) score (mean c-index, 0.64), currently reported prognostic signatures and biomarkers for the estimation of survival. When integrated with clinical characteristics, the composite clinical and lncRNA signature showed improved prognostic accuracy in all data sets (TNM + RCClnc4 mean c-index, 0.75; SSIGN + RCClnc4 score mean c-index, 0.75). The RCClnc4 classifier was able to identify a clinically significant number of both high-risk stage I and low-risk stage II-III patients., Conclusions: The RCClnc4 classifier is a promising and potential prognostic tool in predicting the survival of patients with stage I-III ccRCC. Combining the lncRNA classifier with clinical and pathological parameters allows for accurate risk assessment in guiding clinical management., Patient Summary: The RCClnc4 classifier could facilitate patient management and treatment decisions., (Copyright © 2018 European Association of Urology. Published by Elsevier B.V. All rights reserved.)