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A comprehensive repertoire of tRNA-derived fragments in prostate cancer.

ABSTRACT: Prostate cancer (PCa) is the most common cancer among men in developed countries. Although its genetic background is thoroughly investigated, rather little is known about the role of small non-coding RNAs (sncRNA) in this disease. tRNA-derived fragments (tRFs) represent a new class of sncRNAs, which are present in a broad range of species and have been reported to play a role in several cellular processes. Here, we analyzed the expression of tRFs in fresh frozen patient samples derived from normal adjacent prostate and different stages of PCa by RNA-sequencing. We identified 598 unique tRFs, many of which are deregulated in cancer samples when compared to normal adjacent tissue. Most of the identified tRFs are derived from the 5'- and 3'-ends of mature cytosolic tRNAs, but we also found tRFs produced from other parts of tRNAs, including pre-tRNA trailers and leaders, as well as tRFs from mitochondrial tRNAs. The 5'-derived tRFs comprise the most abundant class of tRFs in general and represent the major class among upregulated tRFs. The 3'-derived tRFs types are dominant among downregulated tRFs in PCa. We validated the expression of three tRFs using qPCR. The ratio of tRFs derived from tRNALysCTT and tRNAPheGAA emerged as a good indicator of progression-free survival and a candidate prognostic marker. This study provides a systematic catalogue of tRFs and their dysregulation in PCa and can serve as the basis for further research on the biomarker potential and functional roles of tRFs in this disease.

SUBMITTER: Olvedy M

PROVIDER: S-EPMC5029740 | biostudies-literature | 2016 Apr

REPOSITORIES: biostudies-literature

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A comprehensive repertoire of tRNA-derived fragments in prostate cancer.

Olvedy Michael M Scaravilli Mauro M Hoogstrate Youri Y Visakorpi Tapio T Jenster Guido G Martens-Uzunova Elena S ES

Oncotarget 20160401 17

Prostate cancer (PCa) is the most common cancer among men in developed countries. Although its genetic background is thoroughly investigated, rather little is known about the role of small non-coding RNAs (sncRNA) in this disease. tRNA-derived fragments (tRFs) represent a new class of sncRNAs, which are present in a broad range of species and have been reported to play a role in several cellular processes. Here, we analyzed the expression of tRFs in fresh frozen patient samples derived from norm ...[more]

PMID: 27015120

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Project description:BackgroundtRNA-derived RNA fragments (tRFs) are a novel class of small ncRNA that are derived from precursor or mature tRNAs. Recently, the general relevance of their roles and clinical values in tumorigenesis, metastasis, and recurrence have been increasingly highlighted. However, there has been no specific systematic study to elucidate any potential clinical significance for these tRFs in prostate adenocarcinoma (PRAD), one of the most common and malignant cancers that threatens male health worldwide. Here, we investigate the clinical value of 5'-tRFs in PRAD.MethodsSmall RNA sequencing data were analyzed to discover new 5'-tRFs biomarkers for PRAD. Machine learning algorithms were used to identify 5'-tRF classifiers to distinguish PRAD tumors from normal tissues. LASSO and Cox regression analyses were used to construct 5'-tRF prognostic predictive models. NMF and consensus clustering analyses were performed on 5'-tRF profiles to identify molecular subtypes of PRAD.ResultsThe overall levels of 5'-tRFs were significantly upregulated in the PRAD tumor samples compared to their adjacent normal samples. tRF classifiers composed of 13 5'-tRFs achieved AUC values as high as 0.963, showing high sensitivity and specificity in distinguishing PRAD tumors from normal samples. Multiple 5'-tRFs were identified as being associated with the PRAD prognosis. The tRF score, defined by a set of eight 5'-tRFs, was highly predictive of survival in PRAD patients. The combination of tRF and Gleason scores showed a significantly better performance than the Gleason score alone, suggesting that 5'-tRFs can offer PRAD patients additional and improved prognostic information. Four molecular subtypes of the PRAD tumor were identified based on their 5'-tRF expression profiles. Genetically, these 5'-tRFs PRAD tumor subtypes exhibited distinct genomic landscapes in tumor cells. Clinically, they showed marked differences in survival and clinicopathological features.Conclusions5'-tRFs are potential clinical biomarkers for the diagnosis, prognosis, and classification of tumor subtypes on a molecular level. These can help clinicians formulate personalized treatment plans for PRAD patients and may have similar potential applications for other disease types.

Project description:Pancreatic cancer is a deadly disease, the deadliest of all the solid tumors; the 5-year survival rate of patients with this disease is ~8%. Previously, high-throughput sequencing has led to the discovery of novel small non-coding RNAs, also known as transfer RNA-derived fragments (tRFs). Studies have suggested that tRFs may be novel biomarkers for certain diseases. However, the expression of tRFs in pancreatic cancer has yet to be characterized. In the present study, the expression levels of tRFs observed in clinical pancreatic cancer samples were analyzed, quantitative PCR (qPCR) was performed to validate the tRFs expression levels and bioinformatics predictions were analyzed. The results revealed that the pancreatic cancer samples screened out a total of 48 tRFs and transfer RNA halves (tiRNAs). There were four tRFs and tiRNAs selected for qPCR validation; the findings were consistent with the sequencing results. Bioinformatic predictions revealed that AS-tDR-000064 was predicted to have 2,450 target genes; AS-tDR-000069 was predicted 445 target genes; AS-tDR-000102 was predicted 746 target genes; and AS-tDR-001391 was predicted 216 target genes. Gene Ontology (GO) analyses demonstrated that the target genes of AS-tDR-000064 were mostly enriched in 'the regulation of cellular processes' (Biological Process), 'the synapses' (Cellular Component) and 'enzyme binding' (Molecular Function). The target genes of AS-tDR-000069 were mostly enriched in 'signaling' (Biological Process), 'the plasma membrane' (Cellular Component) and 'phosphatidylinositol 3-kinase (PI3K) binding'(Molecular Function), the target genes of AS-tDR-000102 were mostly enriched in 'axon development' (Biological Process), 'the synapse' (Cellular Component) and 'sequence-specific DNA binding' (Molecular Function) and the target genes of AS-tDR-001391 were mostly enriched in 'the neuromuscular processes' (Biological Process), the neurons' (Cellular Component) and 'PDZ domain binding' (Molecular Function). The Kyoto Encyclopedia of Genes and Genomes pathway analyses revealed that the target genes of AS-tDR-000064 were mostly enriched in 'the Ras signaling pathway', the target genes of AS-tDR-000069 were mostly enriched in 'the cancer pathways', the target genes of AS-tDR-000102 were mostly enriched in 'axon guidance' and the target genes of AS-tDR-001391 were mostly enriched in 'the PI3K/protein kinase-B signaling pathway'.

Project description:BackgroundtRNA-derived fragments (tRFs) are 14-40-nucleotide-long, small non-coding RNAs derived from specific tRNA cleavage events with key regulatory functions in many biological processes. Many studies have shown that tRFs are associated with Argonaute (AGO) complexes and inhibit gene expression in the same manner as miRNAs. However, there are currently no tools for accurately predicting tRF target genes.MethodsWe used tRF-mRNA pairs identified by crosslinking, ligation, and sequencing of hybrids (CLASH) and covalent ligation of endogenous AGO-bound RNAs (CLEAR)-CLIP to assess features that may participate in tRF targeting, including the sequence context of each site and tRF-mRNA interactions. We applied genetic algorithm (GA) to select key features and support vector machine (SVM) to construct tRF prediction models.ResultsWe first identified features that globally influenced tRF targeting. Among these features, the most significant were the minimum free folding energy (MFE), position 8 match, number of bases paired in the tRF-mRNA duplex, and length of the tRF, which were consistent with previous findings. Our constructed model yielded an area under the receiver operating characteristic (ROC) curve (AUC) = 0.980 (0.977-0.983) in the training process and an AUC = 0.847 (0.83-0.861) in the test process. The model was applied to all the sites with perfect Watson-Crick complementarity to the seed in the 3' untranslated region (3'-UTR) of the human genome. Seven of nine target/nontarget genes of tRFs confirmed by reporter assay were predicted. We also validated the predictions via quantitative real-time PCR (qRT-PCR). Thirteen potential target genes from the top of the predictions were significantly down-regulated at the mRNA levels by overexpression of the tRFs (tRF-3001a, tRF-3003a or tRF-3009a).ConclusionsPredictions can be obtained online, tRFTars, freely available at http://trftars.cmuzhenninglab.org:3838/tar/ , which is the first tool to predict targets of tRFs in humans with a user-friendly interface.

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A comprehensive repertoire of tRNA-derived fragments in prostate cancer.

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A comprehensive repertoire of tRNA-derived fragments in prostate cancer.

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