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Quantitative and qualitative detection of tRNAs, tRNA halves and tRFs in human cancer samples: Molecular grounds for biomarker development and clinical perspectives
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tRNA-derived fragments (tRFs) are an emerging category of small non-coding RNAs that are generated from cleavage of mature tRNAs or tRNA precursors. The advance in high-throughput sequencing has contributed to the identification of increasing number of tRFs with critical functions in distinct physiological and pathophysiological processes. tRFs can regulate cell viability, differentiation, and homeostasis through multiple mechanisms and are thus considered as critical regulators of human diseases including cancer. In addition, increasing evidence suggest the extracellular tRFs may be utilized as promising diagnostic and prognostic biomarkers for cancer liquid biopsy. In this review, we focus on the biogenesis, classification and modification of tRFs, and summarize the multifaceted functions of tRFs with an emphasis on the current research status and perspectives of tRFs in cancer.
Background
Despite advances in early detection and therapies, cancer is still one of the most common causes of death worldwide. Since each tumor is unique, there is a need to implement personalized care and develop robust tools for monitoring treatment response to assess drug efficacy and prevent disease relapse.
Main body
Recent developments in liquid biopsies have enabled real-time noninvasive monitoring of tumor burden through the detection of molecules shed by tumors in the blood. These molecules include circulating tumor nucleic acids (ctNAs), comprising cell-free DNA or RNA molecules passively and/or actively released from tumor cells. Often highlighted for their diagnostic, predictive, and prognostic potential, these biomarkers possess valuable information about tumor characteristics and evolution. While circulating tumor DNA (ctDNA) has been in the spotlight for the last decade, less is known about circulating tumor RNA (ctRNA). There are unanswered questions about why some tumors shed high amounts of ctNAs while others have undetectable levels. Also, there are gaps in our understanding of associations between tumor evolution and ctNA characteristics and shedding kinetics. In this review, we summarize current knowledge about ctNA biology and release mechanisms and put this information into the context of tumor evolution and clinical utility.
Conclusions
A deeper understanding of the biology of ctDNA and ctRNA may inform the use of liquid biopsies in personalized medicine to improve cancer patient outcomes.
Background:
tRNA-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.
Methods:
Small 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.
Results:
The 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.
Conclusions:
5'-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.
Insights into the eukaryotic gene regulation networks have improved due to the advent of diverse classes of non-coding RNAs. The transfer RNA (tRNA)-derived non-coding RNAs or tncRNAs is a novel class of non-coding RNAs, shown to regulate gene expression at transcription and translation levels. Here, we present a pipeline ‘tncRNA Toolkit’ for accurately identifying tncRNAs using small RNA sequencing (sRNA-seq) data. Previously, we identified tncRNA in six major angiosperms by utilizing our pipeline and highlighted the significant points regarding their generation and functions. The ‘tncRNA Toolkit’ is available at the URL: http://www.nipgr.ac.in/tncRNA. The scripts are written in bash and Python3 programming languages. The program can be efficiently run as a standalone command-line tool and installed in any Linux-based Operating System (OS). The user can run this program by providing the input of sRNA-seq data and genome file.
The various features of the ‘tncRNA Toolkit’ are as follows:
•Major tncRNA classes identified by this tool include tRF-5, tRF-3, tRF-1, 5’tRH, 3’tRH, and leader tRF. Also, it categorizes miscellaneous tncRNAs as other tRF.
•It provides the following information for each identified tncRNA viz. tncRNA class, raw and normalized read count (RPM), read length, progenitor tRNA information (amino acid, anticodon, locus, strand), tncRNA sequence, and tRNA modification sites.
•We hope to facilitate quick and reliable tncRNA identification, which will boost the exploration of this novel class of non-coding RNAs and their relevance in the living world, including plants.
(1) Transfer RNA (tRNA)-derived fragments (tRFs) are a new category of regulatory non-coding RNAs with distinct biological functions in cancer. They are produced from pre-tRNAs or mature tRNAs and their sequences are relatively short; thus, the amplification of tRFs, especially those in body fluids, is faced with certain technical difficulties. In this study, we established a quantitative method to detect plasma tRF-27-87R8WP9N1E5 (tRF-27) and used it to screen gastric cancer patients. (2) A specific stem-loop-structure reverse transcription primer, a TaqMan probe, and amplification primers for tRF-27 were prepared, and the absolute quantitative method was used to measure plasma tRF-27 levels. To determine the noninvasive diagnostic value of tRF-27 in gastric cancer, plasma tRF-27 levels in patients with benign and malignant lesions (120 healthy individuals, 48 patients with benign lesions, 48 patients with precancerous lesions, and 72 patients with early gastric cancer) were analyzed. Plasma tRF-27 levels were also analyzed in 106 preoperative gastric cancer patients, 106 postoperative gastric cancer patients, and 120 healthy individuals. Survival curves and Cox regression models were established and analyzed. (3) A new absolute quantitative method to determine the plasma tRF-27 copy number was established. Plasma tRF-27 levels were significantly increased in gastric cancer patients compared to healthy individuals, and the area under the receiver operating characteristic curve was 0.7767, when the cutoff value was 724,807 copies/mL, with sensitivity and specificity values of 0.6226 and 0.8917, respectively. The positive predictive and negative predictive values were 83.50% and 72.80%, respectively. Plasma tRF-27 levels in postoperative gastric cancer patients were significantly decreased compared to preoperative gastric cancer patients and tended to the levels of healthy individuals. Moreover, tRF-27 levels were closely related to tumor size and Ki67 expression in gastric cancer patients. Prognostic analysis showed that tRF-27 may be an independent predictor of overall survival. (4) This novel and non-invasive method of measuring plasma tRF-27 levels was valuable in the early diagnosis of gastric cancer.
tRNA molecules contain dense, abundant modifications that affect tRNA structure, stability, mRNA decoding and tsRNA formation. tRNA modifications and related enzymes are responsive to environmental cues and are associated with a range of physiological and pathological processes. However, there is a lack of resources that can be used to mine and analyse these dynamically changing tRNA modifications. In this study, we established tModBase (https://www.tmodbase.com/) for deciphering the landscape of tRNA modification profiles from epitranscriptome data. We analysed 103 datasets generated with second- and third-generation sequencing technologies and illustrated the misincorporation and termination signals of tRNA modification sites in ten species. We thus systematically demonstrate the modification profiles across different tissues/cell lines and summarize the characteristics of tRNA-associated human diseases. By integrating transcriptome data from 32 cancers, we developed novel tools for analysing the relationships between tRNA modifications and RNA modification enzymes, the expression of 1442 tRNA-derived small RNAs (tsRNAs), and 654 DNA variations. Our database will provide new insights into the features of tRNA modifications and the biological pathways in which they participate.
tRNA-derived fragments (tRFs) are a class of emerging post-transcriptional regulators of gene expression likely binding to the transcripts of target genes. However, only a few tRFs targets have been experimentally validated, making it hard to extrapolate the functions or binding mechanisms of tRFs. The paucity of resources supporting the identification of the targets of tRFs creates a bottleneck in the fast-developing field. We have previously analyzed chimeric reads in crosslinked Argonaute1-RNA complexes to help infer the guide-target pairs and binding mechanisms of multiple tRFs based on experimental data in human HEK293 cells. To efficiently disseminate these results to the research community, we designed a web-based database tatDB (targets of tRFs DataBase) populated with close to 250 000 experimentally determined guide-target pairs with ∼23 000 tRF isoforms. tatDB has a user-friendly interface with flexible query options/filters allowing one to obtain comprehensive information on given tRFs (or targets). Modes of interactions are supported by secondary structures of potential guide-target hybrids and binding motifs, essential for understanding the targeting mechanisms of tRFs. Further, we illustrate the value of the database on an example of hypothesis-building for a tRFs potentially involved in the lifecycle of the SARS-CoV-2 virus. tatDB is freely accessible at https://grigoriev-lab.camden.rutgers.edu/tatdb.
The role of tRNAs is best known as adapter components of translational machinery. According to the central dogma of molecular biology, DNA is transcribed to RNA and in turn is translated into proteins, in which tRNA outstands by its role of the cellular courier. Recent studies have led to the revision of the canonical function of transfer RNAs (tRNAs), which indicates that tRNAs also serve as a source for short non-coding RNAs called tRNA-derived small RNAs (tsRNAs). tsRNAs play key roles in cellular processes by modulating complicated regulatory networks beyond translation and are widely involved in multiple diseases. Herein, the biogenesis and classification of tsRNAs were firstly clarified. tsRNAs are generated from pre-tRNAs or mature tRNAs and are classified into tRNA-derived fragments (tRFs) and tRNA halves (tiRNA). The tRFs include five types according to the incision loci: tRF-1, tRF-2, tRF-3, tRF-5 and i-tRF which contain 3′ tiRNA and 5′ tiRNA. The functions of tsRNAs and their regulation mechanisms involved in disease processes are systematically summarized as well. The mechanisms can elaborate on the specific regulation of tsRNAs. In conclusion, the current research suggests that tsRNAs are promising targets for modulating pathological processes, such as breast cancer, ischemic stroke, respiratory syncytial virus, osteoporosis and so on, and maintain vital clinical implications in diagnosis and therapeutics of various diseases.
tRNA‐derived fragments (tRFs), non‐coding RNAs that regulate protein expression after transcription, have recently been identified as potential biomarkers. We identified differentially expressed tRFs in gastric cancer (GC) and the biological properties of tRFs in predicting the malignancy status of GCs as possible biomarkers. Until 15 February 2022, two independent reviewers did a thorough search in electronic databases of Scopus, EMBASE and PubMed. The QUADAS scale was used for quality assessment of the included studies. Ten articles investigating the clinical significance of tRFs, including 928 patients, were analysed. In 10 GC studies, seven tRFs were considerably upregulated and five tRFs were significantly downregulated when compared to controls. Risk of bias was rated low for index test, and flow as well as timing domains in relation to the review question. The applicability of the index test, flow and timing and patient selection for 10 studies was deemed low. In this study, we review the advances in the study of tRFs in GC and describe their functions in gene expression regulation, such as suppression of translation, cell differentiation, proliferation and the related signal transduction pathways associated with them. Our findings may offer researchers new ideas for cancer treatment as well as potential biomarkers for further research in GC.
A transfer RNA (tRNA)-derived fragment (tRF) was found to be a new possible biological marker and target in carcinoma therapy. However, the effect exerted by tRFs on cervical carcinoma remains unclear. In the present study, the potential tumor suppressor gene tRF-Glu49 was identified in cervical carcinoma through tRF and tiRNA microarray investigation. A reverse transcription-quantitative PCR assay then demonstrated that tRF-Glu49 was downregulated in the cervical carcinoma tissue. Further clinicopathological analysis proved that tRF-Glu49 was associated with less aggressive clinical features and improved prognosis. Cell Counting Kit-8 tests, Transwell and Matrigel tests, and xCELLigence system tests revealed that tRF-Glu49 inhibited cervical cell proliferation, migration and invasion processes. Mechanistic investigation revealed that tRF-Glu49 directly regulated the oncogene, fibrinogen-like protein-1 (FGL1). In general, according to the result achieved in the present study, tRF-Glu49 can modulate cervical cell proliferation, migration, and invasion processes through the target process for FGL1, and tRF-Glu49 is likely to be a possible prognostic biological marker in patients with cervical carcinoma.
Transfer RNA (tRNA)-derived fragment (tRDF) is a novel small non-coding RNA that presents in different types of cancer. The comprehensive understanding of tRDFs in non-small cell lung cancer remains largely unknown. In this study, 1,550 patient samples of non-small cell lung cancer (NSCLC) were included, and 52 tRDFs with four subtypes were identified. Six tRDFs were picked as diagnostic signatures based on the tRDFs expression patterns, and area under the curve (AUC) in independent validations is up to 0.90. Two signatures were validated successfully in plasma samples, and six signatures confirmed the consistency of distinguished expression in NSCLC cell lines. Ten tRDFs along with independent risk scores can be used to predict survival outcomes by stages; 5a_tRF-Ile-AAT/GAT can be a prognosis biomarker for early stage. Association analysis of tRDFs-signatures-correlated mRNAs and microRNA (miRNA) were targeted to the cell cycle and oocyte meiosis signaling pathways. Five tRDFs were assessed to associate with PD-L1 immune checkpoint and correlated with the genes that target in PD-L1 checkpoint signaling pathway. Our study is the first to provide a comprehensive analysis of tRDFs in lung cancer, including four subtypes of tRDFs, investigating the diagnostic and prognostic values, and demonstrated their biological function and transcriptional role as well as potential immune therapeutic value.
Recent high-throughput sequencing protocols have facilitated increased accuracy in measurements of transfer tRNAs (tRNAs) and tRNA-derived RNAs (tDRs) from biological samples. However, commonly used RNA-seq analysis pipelines overlook special considerations given the unique features of tRNA metabolism. We present tRAX (tRNA Analysis of eXpression), a user-friendly analytic package for streamlined processing and graphic presentation of small-RNA sequencing data. Here, we apply it to both tRNAs and tDRs from mouse tissues to illustrate the extensive analysis and visualization features. Biologically compelling results demonstrate tRAX as an effective and accessible tool for in-depth characterization of tRNA and tDR transcriptomes.
The modulation of the function and expression of epigenetic regulators of RNA modification has gradually become the hotspot of cancer research. Studies have shown that alteration of epigenetic modifications can promote the development and metastasis of breast cancer. This review highlights the progress in characterization of the link between RNA modification and the prognosis, carcinogenesis and treatment of breast cancer, which may provide a new theoretical basis for development of effective strategies for monitoring of breast cancer based on epigenetics.
Transfer RNA (tRNA) is a central component of protein synthesis and plays important roles in epigenetic regulation of gene expression in tumors. tRNAs are also involved in many cell processes including cell proliferation, cell signaling pathways and stress response, implicating a role in tumorigenesis and cancer progression. The complex role of tRNA in cell regulation implies that an understanding of tRNA function and dysregulation can be used to develop treatments for many cancers including breast cancer, colon cancer, and glioblastoma. Moreover, tRNA modifications including methylation are necessary for tRNA folding, stability, and function. In response to certain stress conditions, tRNAs can be cleaved in half to form tiRNAs, or even shorter tRNA fragments (tRF). tRNA structure and modifications, tiRNA induction of stress granule formation, and tRF regulation of gene expression through the repression of translation can all impact a cell’s fate. This review focuses on how these functions of tRNAs, tiRNA, and tRFs can lead to tumor development and progression. Further studies focusing on the specific pathways of tRNA regulation could help identify tRNA biomarkers and therapeutic targets, which might prevent and treat cancers.
Objectives: tRNA-derived small non-coding RNAs (tsncRNAs) are one of mysterious small non-coding RNAs. Dysregulated tsncRNAs can led to all kinds of cancers. Recently, tsncRNAs were postulated to be potentially useful biomarkers for tumor diagnosis and prognosis. However, there were no systematic reviews of prognostic and diagnostic tsncRNAs in neoplasms. The study aimed to decipher the relationships between tsncRNAs expression, diagnostic and prognostic outcome in tumors. Methods: This study systematically searched Google Scholar, MEDLINE, Scopus, PubMed, Embase, ScienceDirect, Ovid-Medline, Chinese National Knowledge Infrastructure, WanFang and SinoMed databases for relevant articles published before September 21, 2020. Results: The study is registered in PROSPERO (CRD42020213863). Fourteen relevant studies were included in the meta-analysis: 12 on diagnosis and 5 on prognosis. The pooled add ratio, 95% confidence intervals (Cl) and hazard ratios (HR) of the studies were used to investigate the clinical parameters and overall survival (OS) of cancer patients. The area under the curve (AUC), sensitivity, and specificity was 0.79, 72%, and 73% in tumors, respectively. Though abnormally expressed tsncRNAs were associated with poor and unfavorable impacts on the OS time of cancer patients, the oncogenic tsncRNA may be a favorable impact on overall survival (OS: HR = 0.67, 95% Cl: 0.48-0.94, P = 0.02), and tumor-suppressor tsncRNA might have an unfavorable impact on overall survival (OS: HR = 1.41, 95% Cl: 0.84-2.37, P = 0.19). Conclusion: These results strongly suggested that tsncRNAs were potential novel prognostic and diagnostic indicators in tumors.
Background
Recent deep sequencing technologies have proven to be valuable resources to gain insights into the expression profiles of diverse tRNAs. However, despite these technologies, the association of tRNAs with diverse diseases has not been explored in depth because analytical tools are lacking.
Results
We developed a user-friendly tool, tRNA Expression Analysis Software Utilizing R for Easy use (tReasure), to analyze differentially expressed tRNAs (DEtRNAs) from deep sequencing data of small RNAs using R packages. tReasure can quantify individual mature tRNAs, isodecoders, and isoacceptors. By adopting stringent mapping strategies, tReasure supports the precise measurement of mature tRNA read counts. The whole analysis workflow for determining DEtRNAs (uploading FASTQ files, removing adapter sequences and poor-quality reads, mapping and quantifying tRNAs, filtering out low count tRNAs, determining DEtRNAs, and visualizing statistical analysis) can be performed with the tReasure package.
Conclusions
tReasure is an open-source software available for download at https://treasure.pmrc.re.kr and will be indispensable for users who have little experience with command-line software to explore the biological implication of tRNA expression.
The R programming language is approaching its 30th birthday, and in the last three decades it has achieved a prominent role in statistics, bioinformatics, and data science in general. It currently ranks among the top 10 most popular languages worldwide, and its community has produced tens of thousands of extensions and packages, with scopes ranging from machine learning to transcriptome data analysis. In this review, we provide an historical chronicle of how R became what it is today, describing all its current features and capabilities. We also illustrate the major tools of R, such as the current R editors and integrated development environments (IDEs), the R Shiny web server, the R methods for machine learning, and its relationship with other programming languages. We also discuss the role of R in science in general as a driver for reproducibility. Overall, we hope to provide both a complete snapshot of R today and a practical compendium of the major features and applications of this programming language.
Background
The prevalence of lung adenocarcinoma (LUAD) has increased, thus novel biomarkers for its early diagnosis is becoming more important than ever. tRNA-derived small RNA (tsRNA) is a new class of non-coding RNA which has important regulatory roles in cancer biology. This study was designed to identify novel predictive and prognostic tsRNA biomarkers.
Methods
tsRNAs were identified and performed differential expression analysis from 10 plasma samples (6 LUAD and 4 normal, SRP266333) and 96 tissue samples (48 LUAD and 48 normal, SRP133217). Then a tsRNA-mRNA regulatory network was constructed to find hub tsRNAs. Functional enrichment analysis was performed to infer the potential pathways associated with tsRNAs. Afterwards, a Support Vector Machine (SVM) algorithm was used to explore the potential biomarkers for diagnosing LUAD. Lastly, the function of tRF-21-RK9P4P9L0 was explored in A549 and H1299 cell lines.
Results
A significant difference of read distribution was observed between normal people and LUAD patients whether in plasma or tissue. A tsRNA-mRNA regulatory network consisting of 155 DEtsRNAs (differential expression tsRNAs) and 406 DEmRNAs (differential expression mRNAs) was established. Three tsRNAs (tRF-16-L85J3KE, tRF-21-RK9P4P9L0 and tRF-16-PSQP4PE) were identified as hub genes with degree > 100. We found Co-DEmRNAs (intersection of DEtsRNAs target mRNAs and differentially expressed mRNAs in LUAD) were engaged in a number of cancer pathways. The AUC of the three hub tsRNAs’ expression for diagnosing LUAD reached 0.92. Furthermore, the qPCR validation of the three hub tsRNAs in 37 paired normal and LUAD tissues was consistent with the RNA-Seq results. In addition, tRF-21-RK9P4P9L0 was negatively associated with LUAD prognosis. Inhibition of tRF-21-RK9P4P9L0 expression reduced the proliferation, migration and invasion ability of A549 and H1299 cell lines.
Conclusion
These findings will help us further understand the molecular mechanisms of LUAD and contribute to novel diagnostic biomarkers and therapeutic target discovery.
Objectives. The epithelial-to-mesenchymal transition (EMT) is one key step for the invasion and metastasis of colorectal cancer (CRC). Up until now, the underlying mechanism of EMT in CRC is still unpromising. Thus, it is essential to have a better understanding of its carcinogenesis. The transfer RNA-derived small fragments (tsRNAs) are a new group of small noncoding RNAs (sncRNAs), including tRNA-derived stress-induced RNAs (tiRNAs) and tRNA-derived fragments (tRFs), which have been observed to play an important role in many cancers. However, the relationship between tRFs and EMT in CRC is still unknown. Herein, we aimed to investigate the involvement of tRFs in EMT and its contribution to CRC development. Methods. We identified the differentially expressed tsRNAs in colorectal cancer cell line HT29 treated with TGF-β compared with control cells by using high-throughput sequencing and quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR). QRT-PCR was conducted to validate the differentially expressed fragments in 68 CRC tumor samples (22 women and 46 men) and adjacent nontumor samples. The association of the expression of tRFs with CRC metastasis and clinical stage was analyzed. Meanwhile, the correlation between tRF expression and overall survival (OS) was also analyzed. TargetScan and miRanda and multiple bioinformatic approaches were used to predict the possible target genes of tsRNAs and analyze possible functions of the tRFs. Results. A series of differentially expressed tsRNAs were identified in TGF-β-treated HT29 cells compared with control cells. tRF-phe-GAA-031 and tRF-VAL-TCA-002 were found to be significantly upregulated in CRC tissues compared to adjacent nontumor tissues. They were significantly correlated with distant metastasis and clinical stage. We compared the differences between tumor samples and nontumor tissues from the ROC curves. The area under the ROC curve (AUC) was up to 0.7554 (95% confidence interval: 0.6739 to 0.8369, ) for tRF-Phe-GAA-031 and up to 0.7313 (95% confidence interval: 0.6474 to 0.8151, ) for tRF-VAL-TCA-002. For OS analysis, higher tRF-phe-GAA-031 and tRF-VAL-TCA-002 expressions were associated with shorter survival for CRC patients. Conclusion. A series of differentially expressed tsRNAs are identified in the EMT process of CRC. And tRF-phe-GAA-031 and tRF-VAL-TCA-002 are higher expressed in CRC tissues, and they might play an important role in the metastasis of CRC. Meanwhile, they may be potential biomarkers and intervention targets in the clinical treatment of CRC.
1. Introduction
The development of high-throughput RNA sequencing technologies has allowed us to progressively discover thousands of small noncoding RNA (sncRNA), including endogenous siRNAs (endo siRNAs), microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), ribosomal RNAs (rRNAs), transfer RNAs (tRNAs), and transfer RNA-derived small fragments (tsRNAs) [1, 2]. The tsRNAs are derived from the mature or primary tRNAs. Two subtypes of tsRNAs have been reported: tRNA-derived stress-induced RNA (tiRNA) and tRNA-derived fragment (tRF) [3–5]. tiRNAs are generated by a specific cleavage in the middle of mature tRNAs. They can be classified into tiRNA-5 and tiRNA-3 [5]. tRFs are generated through precise processing of both mature and precursor tRNAs (pre-tRNAs) and then classified into four types including the tRF-5, tRF-3, tRF-1, and tRF-2 series [3]. Recently, accumulating evidence has revealed remarkable functions of tsRNAs, tiRNAs, and tRFs, in many physiological and pathological processes, such as protein synthesis, ribosome biogenesis, and cancer transcriptome [4–8].
Recently, a growing number of studies have shown tRFs as novel regulatory molecules implicated in the pathogenesis of cancers [9]. Lee et al. have reported that one tRF-1, namely, tRF-1001, derived from pre-tRNASer, is highly expressed in several cancer cell lines and is required for proliferation of prostate cancer cells [10]; another tRF, the tRF-3Thr, has been demonstrated to be significantly downregulated in primary breast cancer and metastatic tumors, and overexpression of tRF-3Thr in breast cancer cells remarkably inhibited cell invasiveness and migration [11]. tRF-3019a was upregulated in gastric cancer (GC) tissues and cell lines, and tRF-3019a overexpression enhanced GC cell proliferation, migration, and invasion [12]. tRF-Leu-CAG promoted cell proliferation and cell cycle in non-small-cell lung cancer [13]. tRF-03357 promoted cell proliferation, migration, and invasion in high-grade serous ovarian cancer [14].
Colorectal cancer (CRC) is one most common malignance and is one leading cause of cancer-related deaths worldwide [15]. Despite the development of treatments such as surgery and chemotherapy, the prognosis of this disease is still unpromising in many patients due to the presence with metastasis at the time of diagnosis or distant recurrence after therapy [16]. Therefore, it is important to understand the mechanisms by which CRC cells become metastatic. One key step for CRC metastasis is the epithelial-to-mesenchymal transition (EMT), during which epithelial tumor cells lose their polarized organization and cell-cell junctions [17–19]. Intricate positive and negative regulatory processes can regulate EMT. Generally, oncogenic signaling pathways can induce EMT, while tumor-suppressive genes inhibit it. EMT can be induced by a large variety of stimuli, such as transforming growth factor-β (TGF-β). TGF-β belongs to the TGF superfamily, which has been demonstrated to regulate many biological processes, including cell survival, differentiation, and apoptosis [20, 21]. TGF-β is also one most important inducer of EMT process. Previous studies have shown that CRC cells presented an enhanced migration capacity after being directly treated with TGF-β which might derive from the effect of EMT, since EMT is an important biological process induced by TGF-β [20, 21]. Meanwhile, Numbers of miRNAs, lncRNAs, and other types of ncRNAs have been reported to be implicated in the EMT regulatory networks [22–28].
In this study, we aimed to identify dysregulated tsRNAs in TGF-β-induced EMT process in CRC cell line HT29, which might provide new perspectives on the mechanisms of EMT, and probably help to better understand the pathogenesis of CRC. We also demonstrated that tRF-phe-GAA-031 and tRF-VAL-TCA-002 were significantly upregulated in CRC tissues when compared with adjacent nontumor tissues. And tRF-phe-GAA-031 and tRF-VAL-TCA-002 were significantly correlated with distant metastasis and clinical stage. Higher levels of tRF-phe-GAA-031 and tRF-VAL-TCA-002 expression were associated with shorter survival. Thus, tRF-phe-GAA-031 and tRF-VAL-TCA-002 may be potential biomarkers and intervention targets in the clinical treatment of CRC. Furthermore, we also used bioinformatic tools to predict possible target genes of the two tRFs and discussed the regulatory mechanisms of them in EMT of CRC. This study will be helpful to screen out potential biomarkers for diagnosis or therapeutic target for CRC.
2. Materials and Methods
2.1. Tumor Samples
68 CRC tissue samples and matched adjacent normal tissues were obtained from patients who had undergone surgical resection in the Second Xiangya Hospital of Central South University (Changsha, China), between January 2015 and December 2018. Tumor samples were diagnosed according to the World Health Organization (WHO) system, by two pathologists who were unaware of patient data. No radiotherapy or chemotherapy was administered before surgery. The studies involving human participants were reviewed and approved by the ethics committee of the Second Xiangya Hospital of Central South University. All the patients in this study provided their written informed consent.
2.2. Cell Culture and Reagents
The human CRC cell line HT29 was cultured in RPMI 1640 medium supplemented with 10% fetal calf serum (FBS) at 37°C with 5% CO2. To induce EMT, HT29 cells were seeded into six-well plates at 25% confluence and maintained in a standard medium for 18 h. Then, the cells were starved in 0.5% FBS for 8 h. After starvation, cells were stimulated with TGF-β (10 ng/mL, R&D, Minneapolis, MN) in a 0.5% FBS medium for another 48 h to establish the cellular model of EMT.
2.3. RNA Isolation and Quantitative Real-Time PCR for the mRNA Expression
Total RNA was extracted from cells by using Trizol reagent (Life Technologies, USA) following the manufacturer’s instructions. A total of 1 μg RNA was reverse transcribed to cDNA by using a reverse transcription kit (Fermentas, Glen Burnie, MD, USA). The mRNA expression was assessed using SYBR Premix Ex Taq (Takara, Dalian, China). And GAPDH was used as an internal control. The quantitative real-time PCR assays were performed on the Roche Detection System (Roche Applied Science). The mRNA expression was quantified by a comparative threshold cycle (CT) method and then converted to fold changes. All the experiments were performed at least for three times. The primer sequences used for mRNA expression detection are as follows: Vimentin-F AGATGGCCCTTGACATTGAG, Vimentin-R TGGAAGAGGCAGAGAAATCC; Fibronectin-F GGTGACACTTATGAGCGTCCTAAA, Fibronectin-R AACATGTAACCACCAGTCTCATGTG; MMP-2-F CTGCGGTTTTCTCGAATCCA, MMP-2-R GGGTATCCATCGCCATGCT; ZEB1-F GCACAACCAAGTGCAGAGA, ZEB1-R GCC TGGTTCAGGAGAAGATG; ZEB2-F CAAGAGGCGCAAACAAGCC, ZEB2-R GGTTGGCAATAC CGTCATCC; Slug-F TTCCGATCAGCCTGCCTTTAGA, Slug-R TTTGCCTTGCACAAAGACCAA A; SNAIL-F GCTGCAGGACTCTAATCCAGAGTT, SNAIL-R GACAGAGTCCCAGATGAGCA TTG; CD44-F CTGCCGCTTTGCAGGTGTA, CD44-R CATTGTGGGCAAGGTGCTATT; Twist-F GCCGACGACAGCCTGAGCAACA, Twist-R CGCCACAGCCCGCAGACTTCTT.
2.4. RNA Sequence Processing and Data Analysis
The RNA samples were outsourced for library construction and sequenced on the Illumina NextSeq500 System (KangChen Bio-tech, Shanghai, China). Briefly, the RNA samples were pretreated to remove some RNA modifications and were sequentially ligated to 3 and 5 small RNA adapters. The RNA was then reversed and amplified. Consequently, ~134-160 bp PCR amplified fragments were purified and used for the preparation of sequencing libraries. And finally, the libraries were sequenced by Illumina deep sequencing. The tRNA sequences of cytoplasmic were downloaded from GtRNAdb, and tRNA sequences of mitochondrial were predicted with tRNA scan-SE software. To generate the mature tRNA libraries, the predicted intronic sequences were moved. Meanwhile, we added an additional 3-terminal “CCA” to each tRNA. We also included 40 nucleotides of flanking genomic sequence on either side of the original tRNA sequence in order to generate the precursor tRNA libraries. The generated adjusted values lower than 0.05 were considered significant.
2.5. Quantitative Real-Time PCR for tRFs
Total RNA extracted from cells and clinical samples were treated with an rtStar™ tRF&tiRNA Pretreatment Kit (Arraystar, USA) to remove RNA modifications that interfere with qRT-PCR assays. Then, the RNA was reversely transcribed by using a Bulge-Loop miRNA qRT-PCR Starter Kit (Ribo, China) according to the manufacturer’s protocols. Then, the qPCR assays were performed by using SYBR Green Mix. U6 were used as an internal control. The expression of tRFs was quantified by measuring cycle threshold (Ct) values and normalized using the 2-ΔΔCt method relative to U6.
2.6. Bioinformatic Analyses
The possible target genes of tsRNAs were predicted by miRanda and TargetScan. The tsRNA-mRNA network was then constructed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis of these genes were employed for the prediction of biological functions and pathways involved in EMT process of CRC.
2.7. Statistical Analysis
Statistical analysis was performed using SPSS17.0 and GraphPad Prism 5. The differences of mRNAs or tRF expression were determined with ANOVA. The associations between dysregulated tRFs and clinicopathological parameters were determined by the chi-square test. The survival curves were estimated by the Kaplan-Meier method. values lower than 0.05 were considered statistically significant.
3. Results
3.1. TGF-β Induces EMT in CRC Cells
To systematically identify tsRNAs that were differentially expressed in EMT process, CRC cell line HT29 was stimulated with TGF-β (10 ng/mL) for 48 h to induce EMT. Briefly, HT29 cells were seeded into six-well plates at 25% confluence and grown in a standard medium for 18 h. The cells were starved in 0.5% FBS and then stimulated with TGF-β in a 0.5% FBS medium for another 48 h. We observed that TGF-β was capable of inducing EMT in HT29 cells, as evidenced by changes in cell morphologies (Figure 1(a)) and changed expression levels of Vimentin, Fibronectin, MMP2, ZEB1, ZEB2, Slug, CD44, Snail, and Twist1 (Figure 1(b)). As expected, TGF-β treatment induced an increase in the CRC EMT phenotype.
(a)
Simple Summary
In the precision medicine era, epithelial ovarian cancer (EOC) is characterized by a high death-to-incidence rate and poor 5-year survival. The identification of novel molecular markers is of utmost importance to guide personalized prognosis. The objective of the present study has been to evaluate, for the first time, the prognostic utility of tRNA-derived fragments (tRFs) in ovarian carcinomas. In this context, we have performed in silico analysis and expression profiling, utilizing a TCGA-OV database, GEO datasets and our two institutionally-independent cohorts. The analysis highlighted the internal tRF derived from tRNAGlyGCC (i-tRF-GlyGCC) as a novel molecular predictor of EOC prognosis. More precisely, elevated i-tRF-GlyGCC levels were correlated with an aggressive phenotype of ovarian tumor and linked to adverse survival outcomes and early progression following debulking surgery and platinum-based chemotherapy. Interestingly, i-tRF-GlyGCC integration in multivariate strategies benefits prognostication and achieves superior patient risk-stratification, supporting precision medicine decisions.
Abstract
Epithelial ovarian cancer (EOC) remains a highly-lethal gynecological malignancy, characterized by frequent recurrence, chemotherapy resistance and poor 5-year survival. Identifying novel predictive molecular markers remains an overdue challenge in the disease’s clinical management. Herein, in silico analysis of TCGA-OV highlighted the tRNA-derived internal fragment (i-tRF-GlyGCC) among the most abundant tRFs in ovarian tumors, while target prediction and gene ontology (GO) enrichment analysis predicted its implication in key biological processes. Thereafter, i-tRF-GlyGCC levels were quantified in a screening EOC (n = 98) and an institutionally-independent serous ovarian cancer (SOC) validation cohort (n = 100, OVCAD multicenter study). Disease progression and patient death were used as clinical endpoints for the survival analysis. Internal validation was performed by bootstrap analysis and the clinical net benefit was estimated by decision curve analysis. The analysis highlighted the significant association of i-tRF-GlyGCC with advanced FIGO stages, suboptimal debulking and most importantly, with early progression and poor overall survival of EOC patients. The OVCAD validation cohort corroborated the unfavorable predictive value of i-tRF-GlyGCC in EOC. Ultimately, evaluation of i-tRF-GlyGCC with the established/clinically used prognostic markers offered superior patient risk-stratification and enhanced clinical benefit in EOC prognosis. In conclusion, i-tRF-GlyGCC assessment could aid towards personalized prognosis and support precision medicine decisions in EOC.
The ~22 mitochondrial and ~45 cytosolic tRNAs contain several dozen different posttranscriptional modified nucleotides such that each carries a unique constellation that complements its function. Many tRNA modifications are linked to altered gene expression and their deficiencies due to mutations in tRNA modification enzymes (TMEs) are responsible for numerous diseases. Easily accessible methods to detect tRNA hypomodifications can facilitate progress in advancing such molecular studies. Our lab developed a northern blot method that can quantify relative levels of base modifications on multiple specific tRNAs ~10 years ago which has been used to characterize four different TME deficiencies and is likely further extendable. The assay method depends on differential annealing efficiency of an DNA-oligo probe to the modified versus unmodified tRNA. The signal of this probe is then normalized by a second probe elsewhere on the same tRNA. This positive hybridization in the absence of modification (PHAM) assay has proven useful for i6A37, t6A37, m3C32 and m2,2G26 in multiple laboratories. Yet, over the years we have observed idiosyncratic inconsistency and variability in the assay. Here we document these for some tRNAs and probes and illustrate principles and practices for improved reliability and uniformity in performance. We provide an overview of the method and illustrate benefits of the improved conditions. This is followed by data that demonstrate quantitative validation of PHAM using a TME deletion control, and that nearby modifications can falsely alter the calculated apparent modification efficiency. Finally, we include a calculator tool for matching probe and hybridization conditions.
The MODOMICS database has been, since 2006, a manually curated and centralized resource, storing and distributing comprehensive information about modified ribonucleosides. Originally, it only contained data on the chemical structures of modified ribonucleosides, their biosynthetic pathways, the location of modified residues in RNA sequences, and RNA-modifying enzymes. Over the years, prompted by the accumulation of new knowledge and new types of data, it has been updated with new information and functionalities. In this new release, we have created a catalog of RNA modifications linked to human diseases, e.g., due to mutations in genes encoding modification enzymes. MODOMICS has been linked extensively to RCSB Protein Data Bank, and sequences of experimentally determined RNA structures with modified residues have been added. This expansion was accompanied by including nucleotide 5-monophosphate residues. We redesigned the web interface and upgraded the database back-end. In addition, a search engine for chemically similar modified residues has been included that can be queried by SMILES codes or by drawing chemical molecules. Finally, previously available datasets of modified residues, biosynthetic pathways, and RNA-modifying enzymes have been updated. Overall, we provide users with a new, enhanced, and restyled tool for research on RNA modification. MODOMICS is available at https://iimcb.genesilico.pl/modomics/.
Ensembl (https://www.ensembl.org) is unique in its flexible infrastructure for access to genomic data and annotation. It has been designed to efficiently deliver annotation at scale for all eukaryotic life, and it also provides deep comprehensive annotation for key species. Genomes representing a greater diversity of species are increasingly being sequenced. In response, we have focussed our recent efforts on expediting the annotation of new assemblies. Here, we report the release of the greatest annual number of newly annotated genomes in the history of Ensembl via our dedicated Ensembl Rapid Release platform (http://rapid.ensembl.org). We have also developed a new method to generate comparative analyses at scale for these assemblies and, for the first time, we have annotated non-vertebrate eukaryotes. Meanwhile, we continually improve, extend and update the annotation for our high-value reference vertebrate genomes and report the details here. We have a range of specific software tools for specific tasks, such as the Ensembl Variant Effect Predictor (VEP) and the newly developed interface for the Variant Recoder. All Ensembl data, software and tools are freely available for download and are accessible programmatically.
tRNA-derived small RNA (tsRNA), a novel type of regulatory small noncoding RNA, plays an important role in physiological and pathological processes. However, the understanding of the functional mechanism of tsRNAs in cells and their role in the occurrence and development of diseases is limited. Here, we integrated multiomics data such as transcriptome, epitranscriptome, and targetome data, and developed novel computer tools to establish tsRFun, a comprehensive platform to facilitate tsRNA research (http://rna.sysu.edu.cn/tsRFun/ or http://biomed.nscc-gz.cn/DB/tsRFun/). tsRFun evaluated tsRNA expression profiles and the prognostic value of tsRNAs across 32 types of cancers, identified tsRNA target molecules utilizing high-throughput CLASH/CLEAR or CLIP sequencing data, and constructed the interaction networks among tsRNAs, microRNAs, and mRNAs. In addition to its data presentation capabilities, tsRFun offers multiple real-time online tools for tsRNA identification, target prediction, and functional enrichment analysis. In summary, tsRFun provides a valuable data resource and multiple analysis tools for tsRNA investigation.
Significance
Retrotransposons are noninfectious, mobile genetic elements that proliferate in host genomes via an RNA intermediate that is copied into DNA by a reverse transcriptase (RT) enzyme. RTs are important for biotechnological applications involving information capture from RNA since RNA is first converted into complementary DNA for detection or sequencing. Here, we biochemically characterized RTs from two retroelements and uncovered several activities that allowed us to design a streamlined, efficient workflow for determining the inventory of RNA sequences in processed RNA pools. The unique properties of nonretroviral RT activities obviate many technical issues associated with current methods of RNA sequence analysis, with wide applications in research, biotechnology, and diagnostics.
We describe a method for direct tRNA sequencing using the Oxford Nanopore MinION. The principal technical advance is custom adapters that facilitate end-to-end sequencing of individual transfer RNA (tRNA) molecules at subnanometer precision. A second advance is a nanopore sequencing pipeline optimized for tRNA. We tested this method using purified E. coli tRNAfMet, tRNALys, and tRNAPhe samples. 76–92% of individual aligned tRNA sequence reads were full length. As a proof of concept, we showed that nanopore sequencing detected all 43 expected isoacceptors in total E. coli MRE600 tRNA as well as isodecoders that further define that tRNA population. Alignment-based comparisons between the three purified tRNAs and their synthetic controls revealed systematic nucleotide miscalls that were diagnostic of known modifications. Systematic miscalls were also observed proximal to known modifications in total E. coli tRNA alignments, including a highly conserved pseudouridine in the T loop. This work highlights the potential of nanopore direct tRNA sequencing as well as improvements needed to implement tRNA sequencing for human healthcare applications.
tRNAscan-SE has been widely used for transfer RNA (tRNA) gene prediction for over twenty years, developed just as the first genomes were decoded. With the massive increase in quantity and phylogenetic diversity of genomes, the accurate detection and functional prediction of tRNAs has become more challenging. Utilizing a vastly larger training set, we created nearly one hundred specialized isotype- and clade-specific models, greatly improving tRNAscan-SE’s ability to identify and classify both typical and atypical tRNAs. We employ a new comparative multi-model strategy where predicted tRNAs are scored against a full set of isotype-specific covariance models, allowing functional prediction based on both the anticodon and the highest-scoring isotype model. Comparative model scoring has also enhanced the program's ability to detect tRNA-derived SINEs and other likely pseudogenes. For the first time, tRNAscan-SE also includes fast and highly accurate detection of mitochondrial tRNAs using newly developed models. Overall, tRNA detection sensitivity and specificity is improved for all isotypes, particularly those utilizing specialized models for selenocysteine and the three subtypes of tRNA genes encoding a CAU anticodon. These enhancements will provide researchers with more accurate and detailed tRNA annotation for a wider variety of tRNAs, and may direct attention to tRNAs with novel traits.
Colorectal cancer often presents as a highly variable disease with myriad forms that are at times difficult to detect in early screenings with sufficient accuracy, for which novel diagnostic methods are an attractive and valuable area of improvement. To improve colorectal cancer diagnosis and prognosis, new biomarkers that can be assembled into a diagnostic panel must be identified, and tRNA-derived small RNAs (tsRNAs) are a particularly interesting and increasingly visible new class of molecules to examine. In this study, small RNA-seq data were profiled for the expression of 104 human tsRNAs in tumor tissue and adjacent normal tissue samples, and a diagnostic model was built based on four differentially expressed tsRNAs: tRF-22-WB86Q3P92, tRF-22-WE8SPOX52, tRF-22-WE8S68L52, tRF-18-8R1546D2. Furthermore, the diagnostic model was validated by two independent validation datasets (AUC was 0.97 and 0.99), and a LASSO model was applied to develop a seven-tsRNA-based risk score model for colorectal cancer prognosis. Finally, a tsRNA-mRNA interaction network was established according to potential mRNA targets predicted by bioinformatic methods. In conclusion, the results suggest that abnormal expression of tsRNA in colorectal cancer may have a functional effect on tumor action and moreover, that some of the tsRNAs identified in this study with diagnostic and prognostic potential could be of clinical significance.
Background:
Therapeutic outcomes of osteosarcoma treatment have not significantly improved in several decades. Therefore, strong prognostic biomarkers are urgently needed.
Methods:
We first extracted the tRNA-derived small RNA (tsRNA) expression profiles of osteosarcoma from the GEO database. Then, we performed a unique module analysis and use the LASSO-Cox model to select survival-associated tsRNAs. Model effectiveness was further verified using an independent validation dataset. Target genes with selected tsRNAs were predicted using RNAhybrid.
Results:
A LASSO-Cox model was established to select six prognostic tsRNA biomarkers: tRF-33-6SXMSL73VL4YDN, tRF-32-6SXMSL73VL4YK, tRF-32-M1M3WD8S746D2, tRF-35-RPM830MMUKLY5Z, tRF-33-K768WP9N1EWJDW, and tRF-32-MIF91SS2P46I3. We developed a prognostic panel for osteosarcoma patients concerning their overall survival by high-low risk. Patients with a low-risk profile had improved survival rates in training and validation dataset.
Conclusions:
The suggested prognostic panel can be utilized as a reliable biomarker to predict osteosarcoma patient survival rates.
Measurements of cellular tRNA abundance are hampered by pervasive blocks to cDNA synthesis at modified nucleosides and the extensive similarity among tRNA genes. We overcome these limitations with modification-induced misincorporation tRNA sequencing (mim-tRNAseq), which combines a workflow for full-length cDNA library construction from endogenously modified tRNA with a comprehensive and user-friendly computational analysis toolkit. Our method accurately captures tRNA abundance and modification status in yeast, fly, and human cells and is applicable to any organism with a known genome. We applied mim-tRNAseq to discover a dramatic heterogeneity of tRNA isodecoder pools among diverse human cell lines and a surprising interdependence of modifications at distinct sites within the same tRNA transcript.
Background
tRNA-derived small RNAs (tDRs), which are widely distributed in human tissues including blood and urine, play an important role in the progression of cancer. However, the expression of tDRs in colorectal cancer (CRC) plasma and their potential diagnostic values have not been systematically explored.
Methods
The expression profiles of tDRs in plasma of CRC and health controls (HCs) are investigated by small RNA sequencing. The level and diagnostic value of 5′-tRF-GlyGCC are evaluated by quantitative PCR in plasma samples from 105 CRC patients and 90 HCs. The mechanisms responsible for biogenesis of 5′-tRF-GlyGCC are checked by in vitro and in vivo models.
Results
5′-tRF-GlyGCC is dramatically increased in plasma of CRC patients compared to that of HCs. The area under curve (AUC) for 5′-tRF-GlyGCC in CRC group is 0.882. The combination of carcinoembryonic antigen (CEA) and carbohydrate antigen 199 (CA199) with 5′-tRF-GlyGCC improves the AUC to 0.926. Consistently, the expression levels of 5′-tRF-GlyGCC in CRC cells and xenograft tissues are significantly greater than that in their corresponding controls. Blood cells co-cultured with CRC cells or mice xenografted with CRC tumors show increased levels of 5′-tRF-GlyGCC. In addition, we find that the increased expression of 5′-tRF-GlyGCC is dependent on the upregulation of AlkB homolog 3 (ALKBH3), a tRNA demethylase which can promote tRNA cleaving to generate tDRs.
Conclusions
The level of 5′-tRF-GlyGCC in plasma is a promising diagnostic biomarker for CRC diagnosis.
Liquid biopsy is noninvasive and convenient to detect cancer-derived materials in blood or other body fluids. The aim of this study was to identify tRNA-derived small RNAs (tsRNAs) in plasma that could distinguish patients with breast cancer (BC) from healthy controls. Basing on high-throughput sequencing, 15 significantly upregulated tsRNAs were selected and assessed in cell supernatants and cell lines. 6 tsRNAs were identified and verified in a large cohort of 120 patients with BC and 112 healthy controls. tRF-Arg-CCT-017, tRF-Gly-CCC-001, and tiRNA-Phe-GAA-003 could serve as novel diagnostic biomarkers. Meanwhile, tRF-Arg-CCT-017 and tiRNA-Phe-GAA-003 could also act as prognostic biomarkers. Target genes of these tsRNAs were related to the development of cancers. These results suggested that specific tsRNAs in plasma might serve as diagnostic and prognostic biomarkers of BC.
Head and neck cancer (HNC), the seventh most common form of cancer worldwide, is a group of epithelial malignancies affecting sites in the upper aerodigestive tract. The 5-year overall survival for patients with HNC has stayed around 40–50% for decades, with mortality being attributable mainly to late diagnosis and recurrence. Recently, non-coding RNAs, including tRNA halves, YRNA fragments, microRNAs (miRNAs), and long non-coding RNAs (lncRNAs), have been identified in the blood and saliva of patients diagnosed with HNC. These observations have recently fueled the study of their potential use in early detection, diagnosis, and risk assessment. The present review focuses on recent insights and the potential impact that circulating non-coding RNA evaluation may have on clinical decision-making in the management of HNC.
Cellular RNAs are subject to a myriad of different chemical modifications that play important roles in controlling RNA expression and function. Dysregulation of certain RNA modifications, the so-called ‘epitranscriptome’, contributes to human disease. One limitation in studying the functional, physiological, and pathological roles of the epitranscriptome is the availability of methods for the precise mapping of individual RNA modifications throughout the transcriptome. 3-Methylcytidine (m3C) modification of certain tRNAs is well established and was also recently detected in mRNA. However, methods for the specific mapping of m3C throughout the transcriptome are lacking. Here, we developed a m3C-specific technique, Hydrazine-Aniline Cleavage sequencing (HAC-seq), to profile the m3C methylome at single-nucleotide resolution. We applied HAC-seq to analyze ribosomal RNA (rRNA)-depleted total RNAs in human cells. We found that tRNAs are the predominant m3C-modified RNA species, with 17 m3C modification sites on 11 cytoplasmic and 2 mitochondrial tRNA isoacceptors in MCF7 cells. We found no evidence for m3C-modification of mRNA or other non-coding RNAs at comparable levels to tRNAs in these cells. HAC-seq provides a novel method for the unbiased, transcriptome-wide identification of m3C RNA modification at single-nucleotide resolution, and could be widely applied to reveal the m3C methylome in different cells and tissues.
Simple Summary
Bladder cancer (BlCa) management relies on lifelong surveillance strategies with invasive interventions that adversely affect patients’ quality-of-life and lead to a high economic burden for healthcare systems. Exploitation of bladder tumors’ molecular background could lead to modern precision medicine. tRNA-derived fragments (tRFs), rather than degradation debris, are novel functional small ncRNAs that have emerged as key regulators of cellular homeostasis. This is the first study of the clinical utility of tRFs in BlCa. Using in silico analysis of the TCGA-BLCA project, we identified 5′-tRF-LysCTT (5′-tRF of tRNALysCTT) to be significantly deregulated in BlCa, and we have studied its clinical value in our cohort of 230 BlCa patients. Elevated 5′-tRF-LysCTT levels were significantly associated with aggressive tumor phenotype as well as early disease progression and poor treatment outcome. Integration of 5′-tRF-LysCTT with established disease markers resulted in superior prediction of patients’ prognosis, supporting personalized treatment and monitoring decisions.
Abstract
The heterogeneity of bladder cancer (BlCa) prognosis and treatment outcome requires the elucidation of tumors’ molecular background towards personalized patients’ management. tRNA-derived fragments (tRFs), although originally considered as degradation debris, represent a novel class of powerful regulatory non-coding RNAs. In silico analysis of the TCGA-BLCA project highlighted 5′-tRF-LysCTT to be significantly deregulated in bladder tumors, and 5′-tRF-LysCTT levels were further quantified in our screening cohort of 230 BlCa patients. Recurrence and progression for non-muscle invasive (NMIBC) patients, as well as progression and patient’s death for muscle-invasive (MIBC) patients, were used as clinical endpoint events. TCGA-BLCA were used as validation cohort. Bootstrap analysis was performed for internal validation and the clinical net benefit of 5′-tRF-LysCTT on disease prognosis was assessed by decision curve analysis. Elevated 5′-tRF-LysCTT was associated with unfavorable disease features, and significant higher risk for early progression (multivariate Cox: HR = 2.368; p = 0.033) and poor survival (multivariate Cox: HR = 2.151; p = 0.032) of NMIBC and MIBC patients, respectively. Multivariate models integrating 5′-tRF-LysCTT with disease established markers resulted in superior risk-stratification specificity and positive prediction of patients’ progression. In conclusion, increased 5′-tRF-LysCTT levels were strongly associated with adverse disease outcome and improved BlCa patients’ prognostication.
The tRNA-derived fragments (tRFs) and tRNA halves (tiRNAs) are newly discovered noncoding RNAs in recent years. They are derived from specific cleavage of mature and pre-tRNAs and expressed in various cancers. They enhance cell proliferation and metastasis or inhibit cancer progression. Many studies have investigated their roles in the diagnosis, progression, metastasis, and prognosis of various cancers, but the mechanisms through which they are involved in resistance to cancer treatment are unclear. This review outlines the classification of tRFs and tiRNAs and their mechanisms in cancer drug resistance, thus providing new ideas for cancer treatment.
Transfer RNA-derived fragments (tRFs) are a new class of small non-coding RNAs and play important roles in biological and physiological processes. Prediction of tRF target genes and binding sites is crucial in understanding the biological functions of tRFs in the molecular mechanisms of human diseases. We developed a publicly accessible web-based database, tRFtarget (http://trftarget.net), for tRF target prediction. It contains the computationally predicted interactions between tRFs and mRNA transcripts using the two state-of-the-art prediction tools RNAhybrid and IntaRNA, including location of the binding sites on the target, the binding region, and free energy of the binding stability with graphic illustration. tRFtarget covers 936 tRFs and 135 thousand predicted targets in eight species. It allows researchers to search either target genes by tRF IDs or tRFs by gene symbols/transcript names. We also integrated the manually curated experimental evidence of the predicted interactions into the database. Furthermore, we provided a convenient link to the DAVID® web server to perform downstream functional pathway analysis and gene ontology annotation on the predicted target genes. This database provides useful information for the scientific community to experimentally validate tRF target genes and facilitate the investigation of the molecular functions and mechanisms of tRFs.
Transfer RNA-derived small RNA (tsRNA)s are novel non-coding RNAs, expressed in a variety of tissues and organs. Two subtypes of tsRNAs have been reported: tRNA-derived stress-induced RNA (tiRNA)s and tRNA-derived fragment (tRF)s. tsRNAs have been reported to play essential roles and possess different biological functions in a variety of physiological activities. Recently, tsRNAs have been implicated in a large number of diseases, such as cancers (including breast cancer, ovarian cancer, lung cancer, prostate cancer, colorectal cancer, etc.), neurological disorders, viral infections, metabolic diseases and angiogenesis-related diseases. Although the biological functions of tsRNAs are still poorly understood, correlations between dysregulated tsRNA expression and disease development have been recently reported. Additionally, their capabilities as potential biomarkers for disease diagnosis and prognosis have been revealed in clinical studies. In this review, we summarize the current knowledge of tsRNAs, and discuss their potential clinical applications as biomarkers in different diseases. Although the regulation of tsRNAs is similar to miRNAs in regards to the related physiological and pathological processes, the higher stability and expression levels of tsRNAs place them as ideal biomarkers for the diagnosis and prognosis in cancer and other diseases. Therefore, it is worth to verify the possibility and reliability of these reported tsRNAs as potential biomarkers for clinical applications in disease diagnosis and prognosis.
Non-coding RNAs (ncRNAs) have been the focus of many studies over the last few decades, and their fundamental roles in human diseases have been well established. Transfer RNAs (tRNAs) are housekeeping ncRNAs that deliver amino acids to ribosomes during protein biosynthesis. tRNA fragments (tRFs) are a novel class of small ncRNAs produced through enzymatic cleavage of tRNAs and have been shown to play key regulatory roles similar to microRNAs. Development and application of high-throughput sequencing technologies has provided accumulating evidence of dysregulated tRFs in cancer. Aberrant expression of tRFs has been found to participate in cell proliferation, invasive metastasis, and progression in several human malignancies. These newly identified functional tRFs also have great potential as new biomarkers and therapeutic targets for cancer treatment. In this review, we focus on the major biological functions of tRFs including RNA silencing, translation regulation, and epigenetic regulation; summarize recent research on the roles of tRFs in different types of cancer; and discuss the potential of using tRFs as clinical biomarkers for cancer diagnosis and prognosis and as therapeutic targets for cancer treatment.
The fragments that derive from transfer RNAs (tRNAs) are an emerging category of regulatory RNAs. Known as tRFs, these fragments were reported for the first time only a decade ago, making them a relatively recent addition to the ever-expanding pantheon of non-coding RNAs. tRFs are short, 16-35 nucleotides (nts) in length, and produced through cleavage of mature and precursor tRNAs at various positions. Both cleavage positions and relative tRF abundance depend strongly on context, including the tissue type, tissue state, and disease, as well as the sex, population of origin, and race/ethnicity of an individual. These dependencies increase the urgency to understand the regulatory roles of tRFs. Such efforts are gaining momentum, and comprise experimental and computational approaches. System-level studies across many tissues and thousands of samples have produced strong evidence that tRFs have important and multi-faceted roles. Here, we review the relevant literature on tRF biology in higher organisms, single cell eukaryotes, and prokaryotes.
Transfer RNAs (tRNAs) are small adaptor RNAs essential for mRNA translation. Alterations in the cellular tRNA population can directly affect mRNA decoding rates and translational efficiency during cancer development and progression. To evaluate changes in the composition of the tRNA pool, multiple sequencing approaches have been developed to overcome reverse transcription blocks caused by the stable structures of these molecules and their numerous base modifications. However, it remains unclear whether current sequencing protocols faithfully capture tRNAs existing in cells or tissues. This is specifically challenging for clinical tissue samples that often present variable RNA qualities. For this reason, we developed ALL-tRNAseq, which combines the highly processive MarathonRT and RNA demethylation for the robust assessment of tRNA expression, together with a randomized adapter ligation strategy prior to reverse transcription to assess tRNA fragmentation levels in both cell lines and tissues. Incorporation of tRNA fragments not only informed on sample integrity but also significantly improved tRNA profiling of tissue samples. Our data showed that our profiling strategy effectively improves classification of oncogenic signatures in glioblastoma and diffuse large B-cell lymphoma tissues, particularly for samples presenting higher levels of RNA fragmentation, further highlighting the utility of ALL-tRNAseq for translational research.
tRNAs are essential modulators that recognize mRNA codons and bridge amino acids for mRNA translation. The tRNAs are heavily modified, which is essential for forming a complex secondary structure that facilitates codon recognition and mRNA translation. In recent years, studies have identified the regulatory roles of tRNA modifications in mRNA translation networks. Misregulation of tRNA modifications is closely related to the progression of developmental diseases and cancers. In this review, we summarize the tRNA biogenesis process and then discuss the effects and mechanisms of tRNA modifications on tRNA processing and mRNA translation. Finally, we provide a comprehensive overview of tRNA modifications’ physiological and pathological functions, focusing on diseases including cancers.
m1A58 is a common tRNA modification that can influence protein translational efficiency. Here Liu et al. reveal that tRNA methylation controls in vivo T cell activation by enhancing translation of the key transcription factor Myc.
Although non-coding RNAs have long been considered as non-functional “junk” RNAs, accumulating evidence in the past decade indicates that they play a critical role in pathogenesis of various cancers. In addition to their biological significance, the recognition that their expression levels are frequently dysregulated in multiple cancers have fueled the interest for exploiting their clinical potential as cancer biomarkers. In particular, microRNAs (miRNAs), a subclass of small non-coding RNAs that epigenetically modulate gene-transcription, have become one of the most well-studied substrates for the development of liquid biopsy biomarkers for cancer patients. The emergence of high-throughput sequencing technologies has enabled comprehensive molecular characterisation of various non-coding RNA expression profiles in multiple cancers. Furthermore, technological advances for quantifying lowly expressed RNAs in the circulation have facilitated robust identification of previously unrecognised and undetectable biomarkers in cancer patients. Here we summarise the latest progress on the utilisation of non-coding RNAs as non-invasive cancer biomarkers. We evaluated the suitability of multiple non-coding RNA types as blood-based cancer biomarkers and examined the impact of recent technological breakthroughs on the development of non-invasive molecular biomarkers in cancer.
A T cell sleeper agent against stress
Considerable changes in cellular metabolism occur when T cells transition from a resting to an activated state. One side effect of this process is an increase in reactive oxygen species (ROS). These molecules potentiate T cell receptor (TCR) signaling but can also result in detrimental oxidative stress (see the Perspective by Su and Dutta). Yue et al. describe one mechanism by which T cells can resolve this contradiction. Using mice with a T cell-specific deficiency in Schlafen 2 (SLFN2), they found that this protein binds to and protects transfer RNAs from oxidative stress-induced cleavage by the ribonuclease angiogenin. This process is downstream of ROS generation, which allows activated T cells to maintain protein synthesis despite the ROS that would otherwise inhibit translation.
Science , this issue p. eaba4220 ; see also p. 683
Increased proliferation and protein synthesis are characteristics of transformed and tumor cells. Although the components of the translation machinery are often dysregulated in cancer, the role of tRNAs in cancer cells has not been well studied. Nevertheless, the number of related studies has recently started increasing. With the development of high throughput technologies such as next-generation sequencing, genome-wide differential tRNA expression patterns in breast cancer–derived cell lines and breast tumors have been investigated. The genome-wide transcriptomics analyses have been linked with many studies for functional and phenotypic characterization, whereby tRNAs or tRNA-related fragments have been shown to play important roles in breast cancer regulation and as promising prognostic biomarkers. Here, we review their expression patterns, functions, prognostic value, and potential therapeutic use as well as related technologies.
Small non-coding RNAs (sncRNAs) play critical roles in multiple regulatory processes, including transcription, post-transcription, and translation. Emerging evidence reveals the critical roles of sncRNAs in cancer development and their potential role as biomarkers and/or therapeutic targets. In this paper, we review recent research on four sncRNA species with functional significance in cancer: small nucleolar RNAs, transfer RNA, small nuclear RNAs, and piwi-interacting RNAs. We introduce their functional roles in tumorigenesis and discuss the potential utility of sncRNAs as prognostic and diagnostic biomarkers and therapeutic targets. We further summarize approaches to characterize sncRNAs in a high-throughput manner, including the specific library construction and computational framework. Our review provides a perspective of the functions, clinical utility, and characterization of sncRNAs in cancer.
Aim:
Noncoding RNAs are a cluster of RNAs that do not encode functional proteins. Instead, they are incorporated into DNA structure and regulate gene expression. Of these two classes, transfer RNAs (tRNAs) belong to the former, and small RNAs (sRNAs) belong to the latter. Recently, tRNA-derived small RNAs (tsRNAs/tDRs) were discovered among small noncoding RNAs, as the newly discovered regulatory small RNA. They play a role in pathological and physiological processes, in which gene expression is frequently dysregulated. TsRNAs can be bound to Argonaute proteins and Piwi proteins, such as miRNAs and piRNAs sequentially.
Methods:
In initial searches, 2,744 articles were identified with the following literature databases, up to February 25, 2020: PubMed, Embase, Web of Science, Scopus, and Google Scholar. Finally, after full-text assessment, 48 articles were identified that are related to gene expression profiling of tsRNA in cancer.
Results:
The development of cancer biomarkers based on noncoding RNAs is a thriving area of biomedical research that has expanded significantly. Currently, several groups of tsRNA/tDR biomarkers should be considered in updating the latest findings.
Conclusion:
In this systematic review, we summarized the most recent findings related to the expression of tsRNAs in 17 cancer types. We suggested that use of tsRNAs in the cancer field attracted researcher focus and facilitated diagnostic and therapeutic approaches.
tRNA-derived fragments (tRFs), which by definition are cleaved from tRNAs, comprise a novel class of regulatory small non-coding RNAs. Recent evidence has revealed that tRFs can be loaded onto Argonaute (AGO) family proteins to perform post-transcriptional regulations via substantial tRF-target gene interactions (TGIs). However, there is no resource that systematically profiles potential AGO-mediated TGIs. To this end, we performed a systemic computational screening of potential AGO-mediated TGIs by a re-analysis of 146 crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) datasets in which 920,690 TGIs between 12,102 tRFs and 5,688 target genes were identified. The predicted TGIs have superior signal-to-noise ratio and good consistency with TGIs identified from an orthogonal technique. AGO-bound tRFs are not evenly distributed, where the 5′-tRF and 3′-tRF are enriched and some commonly expressed tRFs are also overrepresented. The tRFs tend to target conserved regions of transcripts and co-express with their target genes. Filtering TGIs with consistent co-expression with target genes results in a set of regulatory TGIs that contains 25,281 tRF-target pairs. Together, our results unveiled the extensive regulatory interactions between tRFs and target genes. Finally, the CLIP-derived TGIs were incorporated in a user-friendly online platform termed as tRFTar, where various functions like custom searching, co-expressed TGI filtering, genome browser and TGI-based tRF functional enrichment analysis are enabled to help users to investigate the functions of tRFs. The tRFTar is freely available at http://www.rnanut.net/tRFTar/.