Extracellular RNA as a kind of communication molecule and emerging cancer biomarker

Deciphering the roles of cellular and extracellular non-coding RNAs in chemotherapy-induced cardiotoxicity

Chemotherapy-induced cardiotoxicity is a major adverse effect, driven by multiple factors in its pathogenesis. Notably, RNAs have emerged as significant contributors in both cancer and heart failure (HF). RNAs carry genetic and metabolic information that mirrors the current state of cells, making them valuable as potential biomarkers and therapeutic tools for diagnosing, predicting, and treating a range of diseases, including cardiotoxicity. Over 97% of the genome is transcribed into non-coding RNAs (ncRNAs), including ribosomal RNA (rRNAs), transfer RNAs (tRNAs), and newly identified microRNAs (miRNAs), circular RNAs (circRNAs), and long non-coding RNAs (lncRNAs). NcRNAs function not only within their originating cells but also in recipient cells by being transported through extracellular compartments, referred to as extracellular RNAs (exRNAs). Since ncRNAs were identified as key regulators of gene expression, numerous studies have highlighted their significance in both cancer and cardiovascular diseases. Nevertheless, the role of ncRNAs in cardiotoxicity remains not fully elucidated. The study aims to review the existing knowledge on ncRNAs in Cardio-Oncology and explore the potential of ncRNA-based biomarkers and therapies. These investigations could advance the clinical application of ncRNA research, improving early detection and mitigating of chemotherapy-induced cardiotoxicity.

Nanomolecular machines: Pioneering precision medicine for neoplastic diseases through advanced diagnosis and treatment

Tumors pose a major threat to human health, accounting for nearly one-sixth of global deaths annually. The primary treatments include surgery, radiotherapy, chemotherapy, and immunotherapy, each associated with significant side effects. This has driven the search for new therapies with fewer side effects and greater specificity. Nanotechnology has emerged as a promising field in this regard, particularly nanomolecular machines at the nanoscale. Nanomolecular machines are typically constructed from biological macromolecules like proteins, DNA, and RNA. These machines can be programmed to perform specialized tasks with precise instructions. Recent research highlights their potential in tumor diagnostics-identifying susceptibility genes, detecting viruses, and pinpointing tumor markers. Nanomolecular machines also offer advancements in tumor therapy. They can reduce traditional treatment side effects by delivering chemotherapy drugs and enhancing immunotherapy, and they support innovative treatments like sonodynamic and phototherapy. Additionally, they can starve tumors by blocking blood vessels, and eliminate tumors by disrupting cell membranes or lysosomes. This review categorizes and explains the latest achievements in molecular machine research, explores their models, and practical clinical uses in tumor diagnosis and treatment. It aims to broaden the research perspective and accelerate the clinical adoption of these technologies.

Extracellular vesicles-derived long noncoding RNAs participated in benzene hematotoxicity by mediating apoptosis and autophagy

Benzene is a common contaminant in the workplace and wider environment, which induces hematotoxicity. Our previous study has implicated that lncRNAs mediated apoptosis and autophagy induced by benzene. Nevertheless, the roles of extracellular vesicle(EVs)-derived lncRNAs in benzene toxicity are unknown. However, the role of EVs and EVs-derived lncRNAs in benzene-induced toxicity remains unclear. In this research, we explored the function of EVs and EVs-derived lncRNAs in cell-cell communication through benzene-induced apoptosis and autophagy. Our findings demonstrated that EVs derived from 1,4-BQ-treated cells treated cells and coculture with 1,4-BQ-treated cells enhanced apoptosis and autophagy via regulating the pathways of PI3K-AKT-mTOR and chaperone-mediated autophagy. Treating with GW4869 in 1,4-BQ-treated cells significantly inhibited EV secretion, which reduced apoptosis and autophagy. Furthermore, we identified a set of differentially expressed autophagy- and apoptosis-related lncRNAs using EVs-derived lncRNA sequencing. Among them, 8 candidate lncRNAs were upregulated in EVs derived from 1,4-BQ-treated cells, as determined by lncRNA sequencing and qRT-PCR. Importantly, these lncRNAs were also increased in the serum EVs of benzene-exposed workers. 1,4-BQ-treated cells released EVs that transfer differentially expressed lncRNAs, thereby inducing apoptosis and autophagy in the recipient cells. The above results support the hypothesis that EVs-derived lncRNAs participate in intercellular communication during benzene-induced hematotoxicity and function as potential biomarkers for risk assessment of benzene-exposed workers.

Pioneering role of RNA in the early evolution of life

The catalytic, regulatory and structural properties of RNA, combined with their extraordinary ubiquity in cellular processes, are consistent with the proposal that this molecule played a much more conspicuous role in heredity and metabolism during the early stages of biological evolution. This review explores the pivotal role of RNA in the earliest life forms and its relevance in modern biological systems. It examines current models that study the early evolution of life, providing insights into the primordial RNA world and its legacy in contemporary biology.

Increased levels of circulating cell-free double-stranded nucleic acids in the plasma of glioblastoma patients

Circulating cell-free nucleic acids are considered a promising source of biomarkers for diseases and cancer. Liquid biopsy biomarkers for brain tumours represent a major, still unmet, clinical need. In plasma, nucleic acids can be free or be associated with extracellular vesicles (EVs). Here we report an easy and reproducible method to analyse cell-free nucleic acids in plasma and EVs by conventional flow cytometry easy to translate into the clinics. Nucleic acids associated with the EVs or present in plasma samples are stained by Pyronin Y, which is a fluorescent dye that is preferably binding double-stranded nucleic acids. Fluorescent staining of EVs isolated from cell-conditioned media is suitable for DNA and RNA detection by flow cytometry. The nucleic acids are partially protected from degradation by the EVs' membrane. Additionally, DNA and RNA can be stained in plasma samples and plasma-derived EVs. Remarkably, analysis of plasma from patients and healthy individuals reveals a difference in their nucleic acid profiles. Taken together, our results indicate that the proposed methodology, which is based on conventional direct flow cytometry, is a promising easy tool for plasma nucleic acid analysis.

Revealing the grammar of small RNA secretion using interpretable machine learning

Small non-coding RNAs can be secreted through a variety of mechanisms, including exosomal sorting, in small extracellular vesicles, and within lipoprotein complexes. However, the mechanisms that govern their sorting and secretion are not well understood. Here, we present ExoGRU, a machine learning model that predicts small RNA secretion probabilities from primary RNA sequences. We experimentally validated the performance of this model through ExoGRU-guided mutagenesis and synthetic RNA sequence analysis. Additionally, we used ExoGRU to reveal cis and trans factors that underlie small RNA secretion, including known and novel RNA-binding proteins (RBPs), e.g., YBX1, HNRNPA2B1, and RBM24. We also developed a novel technique called exoCLIP, which reveals the RNA interactome of RBPs within the cell-free space. Together, our results demonstrate the power of machine learning in revealing novel biological mechanisms. In addition to providing deeper insight into small RNA secretion, this knowledge can be leveraged in therapeutic and synthetic biology applications.

The intricate dance of tumor evolution: Exploring immune escape, tumor migration, drug resistance, and treatment strategies

Recent research has unveiled fascinating insights into the intricate mechanisms governing tumor evolution. These studies have illuminated how tumors adapt and proliferate by exploiting various factors, including immune evasion, resistance to therapeutic drugs, genetic mutations, and their ability to adapt to different environments. Furthermore, investigations into tumor heterogeneity and chromosomal aberrations have revealed the profound complexity that underlies the evolution of cancer. Emerging findings have also underscored the role of viral influences in the development and progression of cancer, introducing an additional layer of complexity to the field of oncology. Tumor evolution is a dynamic and complex process influenced by various factors, including immune evasion, drug resistance, tumor heterogeneity, and viral influences. Understanding these elements is indispensable for developing more effective treatments and advancing cancer therapies. A holistic approach to studying and addressing tumor evolution is crucial in the ongoing battle against cancer. The main goal of this comprehensive review is to explore the intricate relationship between tumor evolution and critical aspects of cancer biology. By delving into this complex interplay, we aim to provide a profound understanding of how tumors evolve, adapt, and respond to treatment strategies. This review underscores the pivotal importance of comprehending tumor evolution in shaping effective approaches to cancer treatment.

Leveraging a Genomic Instability-Derived Signature to Predict the Prognosis and Therapy Sensitivity of Clear Cell Renal Cell Carcinoma

BACKGROUND

Kidney cancer is a significant health concern with growing treatment resistance, often linked to genomic instability. This study used datasets from 72 renal and 952 clear cell renal cell carcinoma samples to identify genomic instability-derived lncRNAs and develop a prognostic index (GILPI).

METHODS

The study involved differential expression analysis, weighted gene co-expression network analysis, Cox analyses to construct GILPI, and its validation through survival analysis. SNP, TMB, and MSI data were integrated, and GSEA analysis explored associated pathways. A predictive nomogram was created, and immune cell infiltration was assessed. Targeted treatments for low-GILPI patients were identified through molecular docking and network pharmacology.

RESULTS

GILPI proved reliable in predicting prognosis (P<0.001, AUC=0.68) and in combination with other factors. GSEA revealed distinct pathway enrichments for different GILPI subgroups. The nomogram exhibited strong predictive performance (AUC=0.902). Immune cell differences suggest potential for immunotherapy in high-GILPI patients and targeted treatment in low-GILPI patients. Lapatinib and nilotinib were identified as effective drugs for low-GILPI patients.

CONCLUSION

This study identified a GILPI for kidney cancer prognosis, integrating various factors for a comprehensive assessment. It highlighted potential treatment strategies based on GILPI subgroups, enhancing personalized treatment approaches.

DAMPs and radiation injury

The heightened risk of ionizing radiation exposure, stemming from radiation accidents and potential acts of terrorism, has spurred growing interests in devising effective countermeasures against radiation injury. High-dose ionizing radiation exposure triggers acute radiation syndrome (ARS), manifesting as hematopoietic, gastrointestinal, and neurovascular ARS. Hematopoietic ARS typically presents with neutropenia and thrombocytopenia, while gastrointestinal ARS results in intestinal mucosal injury, often culminating in lethal sepsis and gastrointestinal bleeding. This deleterious impact can be attributed to radiation-induced DNA damage and oxidative stress, leading to various forms of cell death, such as apoptosis, necrosis and ferroptosis. Damage-associated molecular patterns (DAMPs) are intrinsic molecules released by cells undergoing injury or in the process of dying, either through passive or active pathways. These molecules then interact with pattern recognition receptors, triggering inflammatory responses. Such a cascade of events ultimately results in further tissue and organ damage, contributing to the elevated mortality rate. Notably, infection and sepsis often develop in ARS cases, further increasing the release of DAMPs. Given that lethal sepsis stands as a major contributor to the mortality in ARS, DAMPs hold the potential to function as mediators, exacerbating radiation-induced organ injury and consequently worsening overall survival. This review describes the intricate mechanisms underlying radiation-induced release of DAMPs. Furthermore, it discusses the detrimental effects of DAMPs on the immune system and explores potential DAMP-targeting therapeutic strategies to alleviate radiation-induced injury.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Dynamic RNA synthetic biology: new principles, practices and potential

An increased appreciation of the role of RNA dynamics in governing RNA function is ushering in a new wave of dynamic RNA synthetic biology. Here, we review recent advances in engineering dynamic RNA systems across the molecular, circuit and cellular scales for important societal-scale applications in environmental and human health, and bioproduction. For each scale, we introduce the core concepts of dynamic RNA folding and function at that scale, and then discuss technologies incorporating these concepts, covering new approaches to engineering riboswitches, ribozymes, RNA origami, RNA strand displacement circuits, biomaterials, biomolecular condensates, extracellular vesicles and synthetic cells. Considering the dynamic nature of RNA within the engineering design process promises to spark the next wave of innovation that will expand the scope and impact of RNA biotechnologies.