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Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments.

ABSTRACT: tRNA-derived fragments (tRFs) constitute a novel class of small non-coding RNA cleaved from tRNAs. In recent years, researches have shown the regulatory roles of a few tRFs in cancers, illuminating a new direction for tRF-centric cancer researches. Nonetheless, more specific screening of tRFs related to oncogenesis pathways, cancer progression stages and cancer prognosis is continuously demanded to reveal the landscape of the cancer-associated tRFs. In this work, by combining the clinical information recorded in The Cancer Genome Atlas (TCGA) and the tRF expression profiles curated by MINTbase v2.0, we systematically screened 1,516 cancer-associated tRFs (ca-tRFs) across seven cancer types. The ca-tRF set collectively combined the differentially expressed tRFs between cancer samples and control samples, the tRFs significantly correlated with tumor stage and the tRFs significantly correlated with patient survival. By incorporating our previous tRF-target dataset, we found the ca-tRFs tend to target cancer-associated genes and onco-pathways like ATF6-mediated unfolded protein response, angiogenesis, cell cycle process regulation, focal adhesion, PI3K-Akt signaling pathway, cellular senescence and FoxO signaling pathway across multiple cancer types. And cell composition analysis implies that the expressions of ca-tRFs are more likely to be correlated with T-cell infiltration. We also found the ca-tRF expression pattern is informative to prognosis, suggesting plausible tRF-based cancer subtypes. Together, our systematic analysis demonstrates the potentially extensive involvements of tRFs in cancers, and provides a reasonable list of cancer-associated tRFs for further investigations.

SUBMITTER: Zhou Y

PROVIDER: S-EPMC8795687 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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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Screening and Comprehensive Analysis of Cancer-Associated tRNA-Derived Fragments.

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