Dataset Information

Identification of aberrant tRNA-halves expression patterns in clear cell renal cell carcinoma.

ABSTRACT: Small non-coding RNAs (sncRNA; <200?nt) regulate various cellular processes and modify gene expression. Under nutritional, biological or physiochemical stress some mature sncRNAs (e.g. tRNAs) are cleaved into halves (30-50?nt) and smaller fragments (18-22?nt); the significance and functional role of these tRNA fragments is unknown, but their existence has been linked to carcinogenesis. We used small RNA sequencing to determine the expression of sncRNAs. Subsequently the findings were validated for miR-122-5p, miR-142-3p and 5'tRNA4-Val-AAC using qPCR. We identified differential expression of 132 miRNAs (upregulated: 61, downregulated: 71) and 32 tRNAs (upregulated: 13, downregulated: 19). Read length analysis showed that miRNAs mapped in the 20-24?nt fraction, whereas tRNA reads mapped in the 30-36?nt fraction instead the expected size of 73-95?nt thereby indicating cleavage of tRNAs. Overexpression of miR-122-5p and miR-142-3p as well as downregulation of 5'tRNA4-Val-AAC was validated in an independent cohort of 118 ccRCC and 74 normal renal tissues. Furthermore, staging and grading was inversely correlated with the 5'tRNA4-Val-AAC expression. Serum levels of miR-122-5p, miR-142-3p and 5'tRNA4-Val-AAC did not differ in ccRCC and control subjects. In conclusion, 5' cleavage of tRNAs occurs in ccRCC, but the exact functional implication of tRNA-halve deregulation remains to be clarified.

SUBMITTER: Nientiedt M

PROVIDER: S-EPMC5121638 | biostudies-literature | 2016 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Identification of aberrant tRNA-halves expression patterns in clear cell renal cell carcinoma.

Nientiedt Malin M Deng Mario M Schmidt Doris D Perner Sven S Müller Stefan C SC Ellinger Jörg J

Scientific reports 20161124

Small non-coding RNAs (sncRNA; <200 nt) regulate various cellular processes and modify gene expression. Under nutritional, biological or physiochemical stress some mature sncRNAs (e.g. tRNAs) are cleaved into halves (30-50 nt) and smaller fragments (18-22 nt); the significance and functional role of these tRNA fragments is unknown, but their existence has been linked to carcinogenesis. We used small RNA sequencing to determine the expression of sncRNAs. Subsequently the findings were validated f ...[more]

PMID: 27883021

Similar Datasets

Project description:BackgroundTumorigenesis is highly heterogeneous, and using clinicopathological signatures only is not enough to effectively distinguish clear cell renal cell carcinoma (ccRCC) and improve risk stratification of patients. DNA methylation (DNAm) with the stability and reversibility often occurs in the early stage of tumorigenesis. Disorders of transcription and metabolism are also an important molecular mechanisms of tumorigenesis. Therefore, it is necessary to identify effective biomarkers involved in tumorigenesis through multi-omics analysis, and these biomarkers also provide new potential therapeutic targets.MethodThe discovery stage involved 160 pairs of ccRCC and matched normal tissues for investigation of DNAm and biomarkers as well as 318 cases of ccRCC including clinical signatures. Correlation analysis of epigenetic, transcriptomic and metabolomic data revealed the connection and discordance among multi-omics and the deregulated functional modules. Diagnostic or prognostic biomarkers were obtained by the correlation analysis, the Least Absolute Shrinkage and Selection Operator (LASSO) and the LASSO-Cox methods. Two classifiers were established based on random forest (RF) and LASSO-Cox algorithms in training datasets. Seven independent datasets were used to evaluate robustness and universality. The molecular biological function of biomarkers were investigated using DAVID and GeneMANIA.ResultsBased on multi-omics analysis, the epigenetic measurements uniquely identified DNAm dysregulation of cellular mechanisms resulting in transcriptomic alterations, including cell proliferation, immune response and inflammation. Combination of the gene co-expression network and metabolic network identified 134 CpG sites (CpGs) as potential biomarkers. Based on the LASSO and RF algorithms, five CpGs were obtained to build a diagnostic classifierwith better classification performance (AUC > 99%). A eight-CpG-based prognostic classifier was obtained to improve risk stratification (hazard ratio (HR) > 4; log-rank test, p-value < 0.01). Based on independent datasets and seven additional cancers, the diagnostic and prognostic classifiers also had better robustness and stability. The molecular biological function of genes with abnormal methylation were significantly associated with glycolysis/gluconeogenesis and signal transduction.ConclusionThe present study provides a comprehensive analysis of ccRCC using multi-omics data. These findings indicated that multi-omics analysis could identify some novel epigenetic factors, which were the most important causes of advanced cancer and poor clinical prognosis. Diagnostic and prognostic biomarkers were identified, which provided a promising avenue to develop effective therapies for ccRCC.

Project description:The purpose of the present study was to screen potential pathogenic biomarkers of clear cell renal cell carcinoma (ccRCC) via microarray analysis. The mRNA and microRNA (miRNA) expression profiles of GSE96574 and GSE71302 were downloaded from the Gene Expression Omnibus (GEO) database, as well as the methylation profile of GSE61441. A total of 5 ccRCC tissue samples and 5 normal kidney tissue samples were contained in each profile of GSE96574 and GSE71302, and 46 ccRCC tissue samples and 46 normal kidney tissue samples were involved in GSE61441. The differentially expressed genes (DEGs) and the differentially expressed miRNAs (DEMs) were obtained via limma package in ccRCC tissues compared with normal kidney tissues. The Two Sample t-test and the Beta distribution test were used to identify the differentially methylated sites (DMSs). The Database for Annotation, Visualization and Integrated Discovery (DAVID) was used to perform the Gene Ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses of the DEGs. The targets of the DEMs were screened with the miRWalk database, and the further combination analyses of DEGs, DEMs and DMSs were conducted. Additionally, reverse transcription PCR (RT-PCR) and methylation-specific PCR (MS-PCR) were performed to detect the mRNA level and methylation status of HAPLN1. The mRNA levels of hsa-miR-204 and hsa-miR-218 were tested by RT-PCR. A total of 2,172 DEGs, 202 DEMs and 2,172 DMSs were identified in RCC samples compared with normal samples. The DEGs were enriched in 1,015 GO terms and 69 KEGG pathways. A total of 10,601 miRNA-gene pairs were identified in at least 5 algorithms of the miRWalk database. A total of 143 overlaps were identified between the DEGs and the differentially methylated genes. Furthermore, the DEGs were involved in 851 miRNA-gene pairs, including 127 pairs in which the target genes were negatively associated with their corresponding DEMs and DMSs. HAPLN1 was lowly expressed and highly methylated in ccRCC tissues, while hsa-miR-204 and hsa-miR-218 were highly expressed. The results of the present study indicated that HAPLN1, hsa-miR-204 and hsa-miR-218 may be involved in the pathogenesis of ccRCC.

Dataset Information

Identification of aberrant tRNA-halves expression patterns in clear cell renal cell carcinoma.

Publications

Identification of aberrant tRNA-halves expression patterns in clear cell renal cell carcinoma.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets