tRNA-derived small RNAs (tsRNAs), including tRNA-derived fragments (tRFs) and tRNA halves (tiRNAs), are small regulatory RNAs processed from mature tRNAs or precursor tRNAs. tRFs and tiRNAs play biological roles through a variety of mechanisms by interacting with proteins or mRNA, inhibiting translation, and regulating gene expression, the cell cycle, and chromatin and epigenetic modifications. The establishment and application of research technologies are important in understanding the biological roles of tRFs and tiRNAs. To study the molecular mechanisms of tRFs and tiRNAs, researchers have used a variety of bioinformatics and molecular biology methods, such as microarray analysis, real-time quantitative reverse transcription-polymerase chain reaction (qRT-PCR); Northern blotting; RNA sequencing (RNA-seq); cross-linking, ligation and sequencing of hybrids (CLASH); and photoactivatable-ribonucleoside-enhanced cross-linking and immunoprecipitation (PAR-CLIP). This paper summarizes the classification, action mechanisms, and roles of tRFs and tiRNAs in human diseases and the related signal transduction pathways, targeted therapies, databases, and research methods associated with them.

tRNA-derived fragments (tRFs) are a new category of regulatory noncoding RNAs with distinct biological functions in cancers and stress-induced diseases. Herein, we first summarize the classification and biogenesis of tRFs. tRFs are produced from pre-tRNAs or mature tRNAs. Based on the incision loci, tRFs are classified into several types: tRF-1, tRF-2, tRF-3, tRF-5, and i-tRF. Some tRFs participate in posttranscriptional regulation through microRNA-like actions or by displacing RNA binding proteins and regulating protein translation by promoting ribosome biogenesis or interfering with translation initiation. Other tRFs prevent cell apoptosis by binding to cytochrome c or promoting virus replication. More importantly, the dysregulation of tRFs has important clinical implications. They are potential diagnostic and prognostic biomarkers of gastric cancer, liver cancer, breast cancer, prostate cancer, and chronic lymphocytic leukemia. tRFs may become new therapeutic targets for the treatment of diseases such as hepatocellular carcinoma and respiratory syncytial virus infection. Finally, we point out the existing problems and future research directions associated with tRFs. In conclusion, the current progress in the research of tRFs reveals that they have important clinical implications and may constitute novel molecular therapeutic targets for modulating pathological processes.

Transfer RNA (tRNA)-derived small RNAs (tsRNAs) have been found to play important roles in the occurrence and development of cancers. However, the tsRNA profile in gastric cancer is unknown. In this study, we aimed to identify the global tsRNA profile in plasma from gastric cancer patients and elucidate the role of tRF-33-P4R8YP9LON4VDP in gastric cancer. Differentially expressed tsRNAs in the plasma of gastric cancer patients and healthy controls were investigated using RNA sequencing. The expression levels of tRF-33-P4R8YP9LON4VDP in the plasma of gastric cancer patients, healthy controls and gastric cancer cell lines were first detected by quantitative reverse transcription-polymerase chain reaction. The effects of tRF-33-P4R8YP9LON4VDP overexpression or downregulation in gastric cancer cells on proliferation, migration, apoptosis, and cell cycle were analyzed using the Cell Counting Kit‐8, scratch assay, Transwell assay, and flow cytometry, respectively. There were 21 upregulated and 46 downregulated tsRNAs found in plasma from gastric cancer patients. The significantly upregulated tsRNAs included tRF-18-S3M83004, tRF-31-PNR8YP9LON4VD, tRF-19-3L7L73JD, tRF-33-P4R8YP9LON4VDP, tRF-31-PER8YP9LON4VD, tRF-18-MBQ4NKDJ, and tRF-31-PIR8YP9LON4VD. The significantly downregulated tsRNAs included tRF-41-YDLBRY73W0K5KKOVD, tRF-18-07QSNHD2, tRF-28-86J8WPMN1E0J, tRF-29-86V8WPMN1EJ3, tRF-31-6978WPRLXN4VE, tRF-30-MIF91SS2P46I, tRF-26-MI7O3B1NR8E, tRF-30-RRJ89O9NF5W8, tRF-26-XIP2801MK8E, and tRF-35-V0J8O9YEKPRS93, In vitro studies showed that tRF-33-P4R8YP9LON4VDP inhibited proliferation of gastric cancer cells. In conclusion, tsRNAs such as tRF-33-P4R8YP9LON4VDP could serve as a novel diagnostic biomarker and target for gastric cancer therapeutics.

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Currently, there are no robust models for predicting the outcome of acute-on-chronic hepatitis B liver failure (ACHBLF). We aimed to establish and validate a new prognostic scoring system, named ALPH-Q, that integrates electrocardiography parameters that may be used to predict short-term mortality of patients with ACHBLF.Two hundred fourteen patients were included in this study. The APLH-Q score was constructed by Cox proportional hazard regression analysis and was validated in an independent patient cohort. The area under the receiver-operating characteristic curve was used to compare the performance of different models, including APLH-Q, Child–Pugh score (CPS), model of end-stage liver disease (MELD), and a previously reported logistic regression model (LRM).The APLH-Q score was constructed with 5 independent risk factors, including age (HR = 1.034, 95% CI: 1.007–1.061), liver cirrhosis (HR = 2.753, 95% CI: 1.366–5.548), prothrombin time (HR = 1.031, 95% CI: 1.002–1.062), hepatic encephalopathy (HR = 2.703, 95% CI: 1.630–4.480), and QTc (HR = 1.008, 95% CI: 1.001–1.016). The performance of the ALPH-Q score was significantly better than that of MELD and CPS in both the training (0.896 vs 0.712, 0.896 vs 0.738, respectively, both P < 0.05) and validation cohorts (0.837 vs 0.689, 0.837 vs 0.585, respectively, both P < 0.05). Compared with LRM, APLH-Q also showed a better performance (0.896 vs 0.825, 0.837 vs 0.818, respectively).We have developed a novel APLH-Q score with greater performance than CPS, MELD, and LRM for predicting short-term mortality of patients with ACHBLF.