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Current Concepts of Non-Coding RNAs in the Pathogenesis of Non-Clear Cell Renal Cell Carcinoma.

ABSTRACT: Renal cell carcinoma (RCC) is a relatively rare malignancy of the urinary tract system. RCC is a heterogenous disease in terms of underlying histology and its associated underlying pathobiology, prognosis and treatment schedule. The most prevalent histological RCC subtype is clear-cell renal cell carcinoma (ccRCC), accounting for about 70-80% of all RCCs. Though the pathobiology and treatment schedule for ccRCC are well-established, non-ccRCC subtypes account for 20%-30% of RCC altogether, and their underlying molecular biology and treatment options are poorly defined. The class of non-coding RNAs-molecules that are generally not translated into proteins-are new cancer drivers and suppressors in all types of cancer. Of these, small non-coding microRNAs (miRNAs) contribute to carcinogenesis by regulating posttranscriptional gene silencing. Additionally, a growing body of evidence supports the role of long non-coding RNAs (lncRNAs) in cancer development and progression. Most studies on non-coding RNAs in RCC focus on clear-cell histology, and there is a relatively limited number of studies on non-ccRCC subtypes. The aim of this review is to give an overview of the current knowledge regarding the role of non-coding RNAs (including short and long non-coding RNAs) in non-ccRCC and to highlight possible implications as diagnostic, prognostic and predictive biomarkers.

SUBMITTER: Barth DA

PROVIDER: S-EPMC6826455 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Current Concepts of Non-Coding RNAs in the Pathogenesis of Non-Clear Cell Renal Cell Carcinoma.

Barth Dominik A DA Slaby Ondrej O Klec Christiane C Juracek Jaroslav J Drula Rares R Calin George A GA Pichler Martin M

Cancers 20191017 10

Renal cell carcinoma (RCC) is a relatively rare malignancy of the urinary tract system. RCC is a heterogenous disease in terms of underlying histology and its associated underlying pathobiology, prognosis and treatment schedule. The most prevalent histological RCC subtype is clear-cell renal cell carcinoma (ccRCC), accounting for about 70-80% of all RCCs. Though the pathobiology and treatment schedule for ccRCC are well-established, non-ccRCC subtypes account for 20%-30% of RCC altogether, and t ...[more]

PMID: 31627266

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Project description:BackgroundRenal cell carcinoma (RCC) is one of the most common malignant tumors of the urinary system, of which the clear cell renal cell carcinoma (ccRCC) accounts for the most subtypes. The increasing discoveries of abundant autophagy-related long non-coding RNAs (ARLNRs) lead to a resurgent interest in evaluating their potential on prognosis prediction. Based on a large number of ccRCC gene samples from TCGA and clinics, ARLNRs analysis will provide a novel perspective into this field.MethodsWe calculated the autophagy scores of each sample according to the expression levels of autophagy-related genes (ARGs) and screened the survival-related ARLNRs (sARLNRs) of ccRCC patients by Cox regression analysis. The high-risk group and the low-risk group were distinguished by the median score of the autophagy-related risk score (ARRS) model. The functional annotations were detected by gene set enrichment analysis (GSEA) and principal component analysis (PCA). The expression levels of two kinds of sARLNRs in the renal tumor and adjacent normal tissues and cell lines were verified.ResultsThere were 146 ARLNRs selected by Pearson analysis. A total of 30 sARLNRs were remarkably correlated with the clinical outcomes of ccRCC patients. Eleven sARLNRs (AC002553.1, AC092611.2, AL360181.2, AP002807.1, AC098484.1, AL513218.1, AC008735.2, MHENCR, AC020907.4, AC011462.4, and AC008870.2) with the highest prognosis value were recruited to establish the ARRS in which the overall survival (OS) in the high-risk group was shorter than that in the low-risk group. ARRS could be treated as an independent prognostic factor and has significant correlations with OS. The distributions of autophagy genes were different between the high-risk group and the low-risk group. In addition, we also found that the expression levels of AC098484.1 in ccRCC cell lines and tumor tissues were lower than those in HK-2 and adjacent normal tissues, but AL513218.1 showed the inverse level. Furthermore, the AC098484.1 expressed decreasingly with the more advanced T-stages, but AL513218.1 gradually increased.ConclusionOur study identified and verified some sARLNRs with clinical significances and revealed their potential values on predicting prognoses of ccRCC patients, which may provide a novel perspective for autophagy-related research and clinical decisions.

Project description: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.

Project description:The aim of the present study was to identify long non-coding RNA (lncRNA)-based prognostic biomarkers in papillary renal cell carcinoma (pRCC). lncRNA expression data and corresponding clinical data from patients with pRCC were obtained from The Cancer Genome Atlas. R software and packages were used for data analysis. Univariate Cox regression analysis and least absolute shrinkage and selection operator regression were performed to identify key lncRNAs, which were then used to construct a prognostic model using multivariate Cox regression analysis. Patients were divided into high- and low-risk groups, and Kaplan-Meier (KM) survival curves and time-dependent receiver operating characteristic (ROC) curves were plotted. The C-index was calculated to estimate the model's prognostic power. The hazard ratio (HR), 95% confidence interval (CI), and statistical significance of each key lncRNA were also calculated by multivariate Cox regression. Based on the result of the multivariate Cox regression analysis, KM survival plots were plotted for each significantly associated lncRNA. The subcellular locations of the prognostic biomarkers were predicted using lncRNAMap and lncLocator. A total of 17 lncRNA signatures were identified as key lncRNAs. Overall survival rate was significantly higher in the low-risk group compared with the high-risk group. The areas under the ROC curve were 0.93 (3-year ROC) and 0.902 (5-year ROC), and the C-index was 0.915. A forest plot was used to illustrate the HR and 95% CI of key lncRNAs. KM survival analysis revealed the prognostic significance of two protective biomarkers, AC024022.1 and GAS6-AS1, and three adverse biomarkers, AC087379.2, AL352984.1, and AL499627.1. It was predicted that AC024022.1 and AC087379.2 may be located in the cytoplasm and GAS6-AS1 may be located in the cytosol. The present study may contribute to the management of pRCC and serve as a foundation for further investigations into the underlying mechanism of tumorigenesis and progression of pRCC.

Dataset Information

Current Concepts of Non-Coding RNAs in the Pathogenesis of Non-Clear Cell Renal Cell Carcinoma.

Publications

Current Concepts of Non-Coding RNAs in the Pathogenesis of Non-Clear Cell Renal Cell Carcinoma.

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