Dataset Information

Expression pattern of long non-coding RNAs in renal cell carcinoma revealed by microarray.

ABSTRACT:

Background

Recent large-scale transcriptome analyses have found large numbers of transcripts, including that of long non-coding RNAs (lncRNAs), which are aberrant in various diseases, especially cancers. However, it is not clear whether lncRNAs are involved specifically in renal cell carcinoma (RCC). We investigated the expression patterns of lncRNAs in five RCC tumor samples (T) relative to those of matched adjacent non-tumor tissues (N) via microarray.

Methods

A microarray with 33,045 lncRNA probes and 30,215 mRNA probes was used to identify deregulated lncRNAs in five RCC patients. Furthermore, we confirmed the relative expression levels of AK096725 and ENST00000453068 in 70 paired samples by quantitative reverse transcription polymerase chain reaction (qRT-PCR).

Results

The lncRNA microarray revealed 27,279 lncRNAs in RCC samples, of which 480 were significantly upregulated (P<0.05; T/N>1.5) and 417 were significantly downregulated (P<0.05; N/T>1.5) compared with the matched non-tumor samples. In addition, 19,995 mRNAs were detected, of which 458 were significantly upregulated (P<0.05; T/N>1.5) and 413 were significantly downregulated (P<0.05; N/T>1.5). The expression level changes of AK096725 (P?=?0.043) and ENST00000453068 (P<0.001) in 70 paired samples were in accord with the microarray data.

Conclusions

The study uncovered expression patterns of lncRNAs in 5 RCC patients, as well as a number of aberrant lncRNAs and mRNAs in tumor samples compared with the non-tumor tissues. The revelation of an association between AK096725 expression and RCC is especially noteworthy. These findings may help to find new biomarkers in RCC.

SUBMITTER: Qin C

PROVIDER: S-EPMC4048223 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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Publications

Expression pattern of long non-coding RNAs in renal cell carcinoma revealed by microarray.

Qin Chao C Han Zhijian Z Qian Jian J Bao Meiling M Li Pu P Ju Xiaobing X Zhang Shaobo S Zhang Lei L Li Shuang S Cao Qiang Q Lu Qiang Q Li Jie J Shao Pengfei P Meng Xiaoxin X Zhang Wei W Yin Changjun C

PloS one 20140606 6

<h4>Background</h4>Recent large-scale transcriptome analyses have found large numbers of transcripts, including that of long non-coding RNAs (lncRNAs), which are aberrant in various diseases, especially cancers. However, it is not clear whether lncRNAs are involved specifically in renal cell carcinoma (RCC). We investigated the expression patterns of lncRNAs in five RCC tumor samples (T) relative to those of matched adjacent non-tumor tissues (N) via microarray.<h4>Methods</h4>A microarray with ...[more]

PMID: 24905231

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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.

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.

Dataset Information

Expression pattern of long non-coding RNAs in renal cell carcinoma revealed by microarray.

Background

Methods

Results

Conclusions

Publications

Expression pattern of long non-coding RNAs in renal cell carcinoma revealed by microarray.

Similar Datasets

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