Serum mitochondrial tsRNA serves as a novel biomarker for hepatocarcinoma diagnosis / 医学前沿

Hepatocellular carcinoma (HCC), which makes up the majority of liver cancer, is induced by the infection of hepatitis B/C virus. Biomarkers are needed to facilitate the early detection of HCC, which is often diagnosed too late for effective therapy. The tRNA-derived small RNAs (tsRNAs) play vital roles in tumorigenesis and are stable in circulation. However, the diagnostic values and biological functions of circulating tsRNAs, especially for HCC, are still unknown. In this study, we first utilized RNA sequencing followed by quantitative reverse-transcription PCR to analyze tsRNA signatures in HCC serum. We identified tRF-Gln-TTG-006, which was remarkably upregulated in HCC serum (training cohort: 24 HCC patients vs. 24 healthy controls). In the validation stage, we found that tRF-Gln-TTG-006 signature could distinguish HCC cases from healthy subjects with high sensitivity (80.4%) and specificity (79.4%) even in the early stage (Stage I: sensitivity, 79.0%; specificity, 74.8%; 155 healthy controls vs. 153 HCC patients from two cohorts). Moreover, in vitro studies indicated that circulating tRF-Gln-TTG-006 was released from tumor cells, and its biological function was predicted by bioinformatics assay and validated by colony formation and apoptosis assays. In summary, our study demonstrated that serum tsRNA signature may serve as a novel biomarker of HCC.

SPORTS1.0: A Tool for Annotating and Profiling Non-coding RNAs Optimized for rRNA- and tRNA-derived Small RNAs / 基因组蛋白质组与生物信息学报·英文版

High-throughput RNA-seq has revolutionized the process of small RNA (sRNA) discovery, leading to a rapid expansion of sRNA categories. In addition to the previously well-characterized sRNAs such as microRNAs (miRNAs), piwi-interacting RNAs (piRNAs), and small nucleolar RNA (snoRNAs), recent emerging studies have spotlighted on tRNA-derived sRNAs (tsRNAs) and rRNA-derived sRNAs (rsRNAs) as new categories of sRNAs that bear versatile functions. Since existing software and pipelines for sRNA annotation are mostly focused on analyzing miRNAs or piRNAs, here we developed the sRNA annotation pipelineoptimized for rRNA- and tRNA-derived sRNAs (SPORTS1.0). SPORTS1.0 is optimized for analyzing tsRNAs and rsRNAs from sRNA-seq data, in addition to its capacity to annotate canonical sRNAs such as miRNAs and piRNAs. Moreover, SPORTS1.0 can predict potential RNA modification sites based on nucleotide mismatches within sRNAs. SPORTS1.0 is precompiled to annotate sRNAs for a wide range of 68 species across bacteria, yeast, plant, and animal kingdoms, while additional species for analyses could be readily expanded upon end users' input. For demonstration, by analyzing sRNA datasets using SPORTS1.0, we reveal that distinct signatures are present in tsRNAs and rsRNAs from different mouse cell types. We also find that compared to other sRNA species, tsRNAs bear the highest mismatch rate, which is consistent with their highly modified nature. SPORTS1.0 is an open-source software and can be publically accessed at https://github.com/junchaoshi/sports1.0.