TRNA-derived small RNA (tsRNA) is a novel regulatory small non-coding RNA and participates in diverse physiological and pathological processes. However, the presence of tsRNAs in exosome and their diagnostic potential remain unclear. In this study, we took advantage of small RNA-seq technology to profile exosomal tsRNAs from cell culture medium and plasma, and found ubiquitous presence of tsRNAs in exosome. To explore the potential value of tsRNA for cancer diagnosis, we compared exosomal tsRNA levels between liver cancer patients and healthy donors, revealing that tsRNAs were dramatically increased in plasma exosomes of liver cancer patients. Importantly, patients with liver cancer exhibited significantly higher levels of four tsRNAs (tRNA-ValTAC-3, tRNA-GlyTCC-5, tRNA-ValAAC-5 and tRNA-GluCTC-5) in plasma exosome, demonstrating that plasma exosomal tsRNA could serve as a novel diagnostic biomarker. Taken together, our results not only expand non-coding RNA species in exosome, but also highlight the potential of tsRNAs as a promising biomarker for cancer diagnosis.

Gliomas are complex and heterogeneous brain tumors with poor prognosis. Glioma cells can communicate with their surroundings to create a tumor-permissive microenvironment. Exosomes represent a new means of intercellular communication by delivering various bioactive molecules, including proteins, lipids and nucleic acids, and participate in tumor initiation and progression. Noncoding RNAs (ncRNAs) including microRNA, long-noncoding RNA, and circular RNA, account for a large portion of human transcriptome and play important roles in various pathophysiological processes, especially in cancers. In addition, ncRNAs can be selectively packaged, secreted and transferred between cells in exosomes and modulate numerous hallmarks of glioma, such as proliferation, invasion, angiogenesis, immune-escape, and treatment resistance. Hence, the strategies of specifically targeting exosomal ncRNAs could be attractive therapeutic options. Exosomes are able to cross the blood brain barrier (BBB), and are readily accessible in nearly all types of human biofluids, which make them the promising biomarkers for gliomas. Additionally, given the biocompatibility of exosomes, they can be engineered to deliver therapeutic factors, such as RNA, proteins and drugs, to target cells for therapeutic applications. Here, we reviewed current research on the roles of exosomal ncRNAs in glioma progression. We also discussed their potential clinical applications as novel biomarkers and therapeutics.

The choroid plexus (ChP) serves as the principal origin of cerebrospinal fluid (CSF). CSF hypersecretion due to ChP inflammation has emerged as an important pathogenesis of hydrocephalus recently. Nevertheless, the precise mechanisms of ChP inflammation and the ensuing CSF hypersecretion in hydrocephalus remain ill-defined. In the present study, we elucidate the critical role of macrophages in the pathogenesis of ChP inflammation. Specifically, we identify the chemokine CCL2, released by ChP epithelial cells, recruits CCR2⁺ monocytes to the ChP thereby inciting hydrocephalus pathogenesis. The accumulated ChP macrophages increase the inflammation in ChP epithelial cells through TNF-α/TNFR1/NF-κB signaling cascade, thereby leading to CSF hypersecretion. Strikingly, augmentation of ChP‒CCL2 using an adeno-associated viral approach (AAV) exacerbates macrophage recruitment, activation, and ventriculomegaly in rat PHH models. Systemic application of Bindarit, a specific CCL2 inhibitor, significantly inhibits ChP macrophage infiltration and activation and reduces CSF secretion rate. Furthermore, the administration of CCR2 antagonist (INCB 3284) reduces ChP macrophage accumulation and ventriculomegaly. This study not only unveils the ChP CCL2‒CCR2 signaling in the pathophysiology of hydrocephalus but also unveils Bindarit as a promising therapeutic choice for the management of posthemorrhagic hydrocephalus.

TRNA-derived small RNAs (tsRNAs) represent a novel category of small non-coding RNAs and serve as a new regulator of gene expression at both transcriptional and post-transcriptional levels. Growing evidence indicates that tsRNAs can be induced by diverse stimuli and regulate stress-responsive target genes, allowing plants to adapt to unfavorable environments. Here, we discuss the latest developments about the biogenesis and classification of tsRNAs and highlight the expression regulation and potential function of tsRNAs in plant biotic and abiotic stress responses. Of note, we also collect useful bioinformatics tools and resources for tsRNAs study in plants. Finally, we propose current limitations and future directions for plant tsRNAs research. These recent discoveries have refined our understanding of whether and how tsRNAs enhance plant stress tolerance.

Background: This study aims to evaluate the efficacy of chemotherapy and optimize treatment strategies for patients with advanced ovarian cancer. Methods: Based on The Cancer Genome Atlas (TCGA) transcriptome data, we conducted correlation and Bayesian network analyses to identify key genes strongly associated with chemotherapy prognosis. Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify the expression of these key genes. The Chemotherapy Benefit Index (CBI) was developed using these genes via multivariable Cox regression analysis, and validated using both internal and external validation sets (GSE32062 and GSE30161) with a random forest model. Subsequently, we analyzed distinct molecular characteristics and explored additional immunotherapy in CBI-high and CBI-low subgroups. Results: Based on the network and machine learning analyses, CBI was developed from the following ten genes: COL6A3, SPI1, HSF1, CD3E, PIK3R4, MZB1, FERMT3, GZMA, PSMB9 and RSF1. Significant differences in overall survival were observed among the CBI-high, medium, and low subgroups (P < 0.001), which were consistent with the two external validation sets (P < 0.001 and P = 0.003). The AUC of internal validation and two external validation cohorts were 0.87, 0.71 and 0.70, respectively. Molecular function analysis indicated that the CBI-low subgroup is characterized by the activation of cancer-related signaling pathways, immune-related biological processes, higher TP53 mutation rate, particularly with a better response to immune checkpoint blockade (ICB) treatment, while the CBI-high subgroup is characterized by inhibition of cell cycle, less response to ICB treatment, and potential therapeutic targets. Conclusions: This study provided a novel CBI for patients with advanced ovarian cancer through network analyses and machine learning. CBI could serve as a prognostic prediction tool for patients with advanced ovarian cancer, and also as a potential indicator for immunotherapy.