Dataset Information

Race Disparities in the Contribution of miRNA Isoforms and tRNA-Derived Fragments to Triple-Negative Breast Cancer.

ABSTRACT: Triple-negative breast cancer (TNBC) is a breast cancer subtype characterized by marked differences between White and Black/African-American women. We performed a systems-level analysis on datasets from The Cancer Genome Atlas to elucidate how the expression patterns of mRNAs are shaped by regulatory noncoding RNAs (ncRNA). Specifically, we studied isomiRs, that is, isoforms of miRNAs, and tRNA-derived fragments (tRF). In normal breast tissue, we observed a marked cohesiveness in both the ncRNA and mRNA layers and the associations between them. This cohesiveness was widely disrupted in TNBC. Many mRNAs become either differentially expressed or differentially wired between normal breast and TNBC in tandem with isomiR or tRF dysregulation. The affected pathways included energy metabolism, cell signaling, and immune responses. Within TNBC, the wiring of the affected pathways with isomiRs and tRFs differed in each race. Multiple isomiRs and tRFs arising from specific miRNA loci (e.g., miR-200c, miR-21, the miR-17/92 cluster, the miR-183/96/182 cluster) and from specific tRNA loci (e.g., the nuclear tRNAGly and tRNALeu, the mitochondrial tRNAVal and tRNAPro) were strongly associated with the observed race disparities in TNBC. We highlight the race-specific aspects of transcriptome wiring by discussing in detail the metastasis-related MAPK and the Wnt/?-catenin signaling pathways, two of the many key pathways that were found differentially wired. In conclusion, by employing a data- and knowledge-driven approach, we comprehensively analyzed the normal and cancer transcriptomes to uncover novel key contributors to the race-based disparities of TNBC.Significance: This big data-driven study comparing normal and cancer transcriptomes uncovers RNA expression differences between Caucasian and African-American patients with triple-negative breast cancer that might help explain disparities in incidence and aggressive character. Cancer Res; 78(5); 1140-54. ©2017 AACR.

SUBMITTER: Telonis AG

PROVIDER: S-EPMC5935570 | biostudies-literature | 2018 Mar

REPOSITORIES: biostudies-literature

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Race Disparities in the Contribution of miRNA Isoforms and tRNA-Derived Fragments to Triple-Negative Breast Cancer.

Telonis Aristeidis G AG Rigoutsos Isidore I

Cancer research 20171211 5

Triple-negative breast cancer (TNBC) is a breast cancer subtype characterized by marked differences between White and Black/African-American women. We performed a systems-level analysis on datasets from The Cancer Genome Atlas to elucidate how the expression patterns of mRNAs are shaped by regulatory noncoding RNAs (ncRNA). Specifically, we studied isomiRs, that is, isoforms of miRNAs, and tRNA-derived fragments (tRF). In normal breast tissue, we observed a marked cohesiveness in both the ncRNA ...[more]

PMID: 29229607

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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.

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'.

Dataset Information

Race Disparities in the Contribution of miRNA Isoforms and tRNA-Derived Fragments to Triple-Negative Breast Cancer.

Publications

Race Disparities in the Contribution of miRNA Isoforms and tRNA-Derived Fragments to Triple-Negative Breast Cancer.

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