Non-Coding RNA最新文献

{"title":"Single-Cell Transcriptomic Approaches for Decoding Non-Coding RNA Mechanisms in Colorectal Cancer.","authors":"Mahnoor Naseer Gondal, Hafiz Muhammad Umer Farooqi","doi":"10.3390/ncrna11020024","DOIUrl":"10.3390/ncrna11020024","url":null,"abstract":"<p><p>Non-coding RNAs (ncRNAs) play crucial roles in colorectal cancer (CRC) development and progression. Recent developments in single-cell transcriptome profiling methods have revealed surprising levels of expression variability among seemingly homogeneous cells, suggesting the existence of many more cell types than previously estimated. This review synthesizes recent advances in ncRNA research in CRC, emphasizing single-cell bioinformatics approaches for their analysis. We explore computational methods and tools used for ncRNA identification, characterization, and functional prediction in CRC, with a focus on single-cell RNA sequencing (scRNA-seq) data. The review highlights key bioinformatics strategies, including sequence-based and structure-based approaches, machine learning applications, and multi-omics data integration. We discuss how these computational techniques can be applied to analyze differential expression, perform functional enrichment, and construct regulatory networks involving ncRNAs in CRC. Additionally, we examine the role of bioinformatics in leveraging ncRNAs as diagnostic and prognostic biomarkers for CRC. We also discuss recent scRNA-seq studies revealing ncRNA heterogeneity in CRC. This review aims to provide a comprehensive overview of the current state of single-cell bioinformatics in ncRNA CRC research and outline future directions in this rapidly evolving field, emphasizing the integration of computational approaches with experimental validation to advance our understanding of ncRNA biology in CRC.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932299/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Multi-Input Neural Network Model for Accurate MicroRNA Target Site Detection.","authors":"Mohammad Mohebbi, Amirhossein Manzourolajdad, Ethan Bennett, Phillip Williams","doi":"10.3390/ncrna11020023","DOIUrl":"10.3390/ncrna11020023","url":null,"abstract":"<p><p>(1) Background: MicroRNAs are non-coding RNA sequences that regulate cellular functions by targeting messenger RNAs and inhibiting protein synthesis. Identifying their target sites is vital to understanding their roles. However, it is challenging due to the high cost and time demands of experimental methods and the high false-positive rates of computational approaches. (2) Methods: We introduce a Multi-Input Neural Network (MINN) algorithm that integrates diverse biologically relevant features, including the microRNA duplex structure, substructures, minimum free energy, and base-pairing probabilities. For each feature derived from a microRNA target-site duplex, we create a corresponding image. These images are processed in parallel by the MINN algorithm, allowing it to learn a comprehensive and precise representation of the underlying biological mechanisms. (3) Results: Our method, on an experimentally validated test set, detects target sites with an AUPRC of 0.9373, Precision of 0.8725, and Recall of 0.8703 and outperforms several commonly used computational methods of microRNA target-site predictions. (4) Conclusions: Incorporating diverse biologically explainable features, such as duplex structure, substructures, their MFEs, and binding probabilities, enables our model to perform well on experimentally validated test data. These features, rather than nucleotide sequences, enhance our model to generalize beyond specific sequence contexts and perform well on sequentially distant samples.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932204/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692753","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Mechanism of Action of circRNA/miRNA Network in DLBCL.","authors":"Elena Golovina, Cory Eaton, Virginia Cox, Jozef Andel, Karina Savvulidi Vargova","doi":"10.3390/ncrna11020022","DOIUrl":"10.3390/ncrna11020022","url":null,"abstract":"<p><p>Circular RNAs (circRNAs) make up approximately 10% of the human transcriptome. CircRNAs belong to the broad group of non-coding RNAs and characteristically are formed by backsplicing into a stable circular loop. Their main role is to regulate transcription through the inhibition of miRNAs' expression, termed miRNA sponging. CircRNAs promote tumorigenesis/lymphomagenesis by competitively binding to miRNAs at miRNA binding sites. In diffuse large B-cell lymphoma (DLBCL), several circRNAs have been identified and their expression is related to both progression and response to therapy. DLBCL is the most prevalent and aggressive subtype of B-cell lymphomas and accounts for about 25% to 30% of all non-Hodgkin lymphomas. DLBCL displays great heterogeneity concerning histopathology, biology, and genetics. Patients who have relapsed or have refractory disease after first-line therapy have a very poor prognosis, demonstrating an important unmet need for new treatment options. As more circRNAs are identified in the future, we will better understand their biological roles and potential use in treating cancer, including DLBCL. For example, circAmotl1 promotes nuclear translocation of <i>MYC</i> and upregulation of translational targets of MYC, thus enhancing lymphomagenesis. Another example is circAPC, which is significantly downregulated in DLBCL and correlates with disease aggressiveness and poor prognosis. CircAPC increases expression of the host gene adenomatous polyposis coli (APC), and in doing so inactivates the canonical Wnt/β-catenin signaling and restrains DLBCL growth. MiRNAs belong to the non-coding regulatory molecules that significantly contribute to lymphomagenesis through their target mRNAs. In DLBCL, among the highly expressed miRNAs, are miR-155-5p and miR-21-5p, which regulate NF-ĸB and PI3K/AKT signaling pathways. The aim of this review is to describe the function and mechanism of regulation of circRNAs on miRNAs' expression in DLBCL. This will help us to better understand the regulatory network of circRNA/miRNA/mRNA, and to propose novel therapeutic targets to treat DLBCL.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692769","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MicroRNAs: A Novel Approach for Monitoring Treatment Response in Major Depressive Disorder?","authors":"Cristina-Sorina Cătană, Monica Mihaela Marta, Daniel Ungureanu, Cătălina-Angela Crișan","doi":"10.3390/ncrna11020021","DOIUrl":"10.3390/ncrna11020021","url":null,"abstract":"<p><p>Major depressive disorder (MDD) is one of the most prevalent psychiatric disorders, with an increasing incidence each year and an important socioeconomic burden. Although new treatments are continuously being developed, there is no effective monitoring method to determine the suitability of treatment and ensure positive outcomes. Therefore, patients often struggle with ineffective antidepressants and their potential adverse effects, which halts any future progress in managing the disorder. Considering the potential of microRNAs (miRNAs) as biomarkers for various pathologies and the increasing evidence of the modulation of several genes involved in MDD, this minireview aimed to evaluate the literature data on the impact of miRNAs in MDD and their usefulness in monitoring treatment response. The correlations between antidepressants and the expression of several miRNAs support the existence of a common epigenetic mechanism of antidepressants and explain the epigenetic differences influencing treatment efficacy in MDD.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932203/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692772","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Emerging Applications of Artificial MicroRNA-Mediated Gene Silencing in Plant Biotechnology.","authors":"Luis Alberto Bravo-Vázquez, Ana Marta Castro-Pacheco, Rodrigo Pérez-Vargas, Joceline Fernanda Velázquez-Jiménez, Sujay Paul","doi":"10.3390/ncrna11020019","DOIUrl":"10.3390/ncrna11020019","url":null,"abstract":"<p><p>Improving crop yield potential is crucial to meet the increasing demands of a rapidly expanding global population in an ever-changing and challenging environment. Therefore, different technological approaches have been proposed over the last decades to accelerate plant breeding. Among them, artificial microRNAs (amiRNAs) represent an innovative tool with remarkable potential to assist plant improvement. MicroRNAs (miRNAs) are a group of endogenous, small (20-24 nucleotides), non-coding RNA molecules that play a crucial role in gene regulation. They are associated with most biological processes of a plant, including reproduction, development, cell differentiation, biotic and abiotic stress responses, metabolism, and plant architecture. In this context, amiRNAs are synthetic molecules engineered to mimic the structure and function of endogenous miRNAs, allowing for the targeted silencing of specific nucleic acids. The current review explores the diverse applications of amiRNAs in plant biology and agriculture, such as the management of infectious agents and pests, the engineering of plant metabolism, and the enhancement of plant resilience to abiotic stress. Moreover, we address future perspectives on plant amiRNA-based gene silencing strategies, highlighting the need for further research to fully comprehend the potential of this technology and to translate its scope toward the widespread adoption of amiRNA-based strategies for plant breeding.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932238/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692800","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Unpaved Road of Non-Coding RNA Structure-Function Relationships: Current Knowledge, Available Methodologies, and Future Trends.","authors":"Ana Lúcia Leitão, Francisco J Enguita","doi":"10.3390/ncrna11020020","DOIUrl":"10.3390/ncrna11020020","url":null,"abstract":"<p><p>The genomes from complex eukaryotes are enriched in non-coding genes whose transcription products (non-coding RNAs) are involved in the regulation of genomic output at different levels. Non-coding RNA action is predominantly driven by sequence and structural motifs that interact with specific functional partners. Despite the exponential growth in primary RNA sequence data facilitated by next-generation sequencing studies, the availability of tridimensional RNA data is comparatively more limited. The subjacent reasons for this relative lack of information regarding RNA structure are related to the specific chemical nature of RNA molecules and the limitations of the currently available methods for structural characterization of biomolecules. In this review, we describe and analyze the different structural motifs involved in non-coding RNA function and the wet-lab and computational methods used to characterize their structure-function relationships, highlighting the current need for detailed structural studies to explore the molecular determinants of non-coding RNA function.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-03-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932211/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Secondary-Structure-Informed RNA Inverse Design via Relational Graph Neural Networks.","authors":"Amirhossein Manzourolajdad, Mohammad Mohebbi","doi":"10.3390/ncrna11020018","DOIUrl":"10.3390/ncrna11020018","url":null,"abstract":"<p><p>RNA inverse design is an essential part of many RNA therapeutic strategies. To date, there have been great advances in computationally driven RNA design. The current machine learning approaches can predict the sequence of an RNA given its 3D structure with acceptable accuracy and at tremendous speed. The design and engineering of RNA regulators such as riboswitches, however, is often more difficult, partly due to their inherent conformational switching abilities. Although recent state-of-the-art models do incorporate information about the multiple structures that a sequence can fold into, there is great room for improvement in modeling structural switching. In this work, a relational geometric graph neural network is proposed that explicitly incorporates alternative structures to predict an RNA sequence. Converting the RNA structure into a geometric graph, the proposed model uses edge types to distinguish between the primary structure, secondary structure, and spatial positioning of the nucleotides in representing structures. The results show higher native sequence recovery rates over those of gRNAde across different test sets (eg. 72% vs. 66%) and a benchmark from the literature (60% vs. 57%). Secondary-structure edge types had a more significant impact on the sequence recovery than the spatial edge types as defined in this work. Overall, these results suggest the need for more complex and case-specific characterization of RNA for successful inverse design.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 2","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11932209/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143692776","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Combinatorial Analysis of miRNAs and tRNA Fragments as Potential Biomarkers for Cancer Patients in Liquid Biopsies.","authors":"Ilias Glogovitis, Silvia D'Ambrosi, Mafalda Antunes-Ferreira, Monica Chiogna, Galina Yahubyan, Vesselin Baev, Thomas Wurdinger, Danijela Koppers-Lalic","doi":"10.3390/ncrna11010017","DOIUrl":"10.3390/ncrna11010017","url":null,"abstract":"<p><p><b>Background:</b> Liquid biopsy has gained significant attention as a non-invasive method for cancer detection and monitoring. IsomiRs and tRNA-derived fragments (tRFs) are small non-coding RNAs that arise from non-canonical microRNA (miRNAs) processing and the cleavage of tRNAs, respectively. These small non-coding RNAs have emerged as pro-mising cancer biomarkers, and their distinct expression patterns highlight the need for further exploration of their roles in cancer research. <b>Methods:</b> In this study, we investigated the differential expression profiles of miRNAs, isomiRs, and tRFs in plasma extracellular vesicles (EVs) from colorectal and prostate cancer patients compared to healthy controls. Subsequently, a combinatorial analysis using the CombiROC package was performed to identify a panel of biomarkers with optimal diagnostic accuracy. <b>Results:</b> Our results demonstrate that a combination of miRNAs, isomiRs, and tRFs can effectively di- stinguish cancer patients from healthy controls, achieving accuracy and an area under the curve (AUC) of approximately 80%. <b>Conclusions:</b> These findings highlight the potential of a combinatorial approach to small RNA analysis in liquid biopsies for improved cancer diagnosis and management.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11858735/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493119","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Psoriasis Treatments: Emerging Roles and Future Prospects of MicroRNAs.","authors":"Li Tian Keane Teo, Nerissa Juantuah-Kusi, Gowtham Subramanian, Prabha Sampath","doi":"10.3390/ncrna11010016","DOIUrl":"10.3390/ncrna11010016","url":null,"abstract":"<p><p>Psoriasis, a widespread and chronic inflammatory skin disorder, is marked by its persistence and the lack of a definitive cure. The pathogenesis of psoriasis is increasingly understood, with ongoing research highlighting the intricate interplay of genetic, immunological, and environmental factors. Recent advancements have illuminated the pivotal role of microRNAs in orchestrating complex processes in psoriasis and other hyperproliferative skin diseases. This narrative review highlights the emerging significance of miRNAs as key regulators in psoriasis pathogenesis and examines their potential as therapeutic targets. We discuss current treatment approaches and the promising future of miRNAs as next-generation therapeutic agents for this condition.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11858470/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493126","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Small Non-Coding RNA Profile of Human and Mouse Sperm.","authors":"Yoon Sing Yap, Pasquale Patrizio, Luisa Cimmino, Konstantinos Sdrimas, Aristeidis G Telonis","doi":"10.3390/ncrna11010015","DOIUrl":"10.3390/ncrna11010015","url":null,"abstract":"<p><p>Small non-coding RNAs constitute a dynamic epigenetic layer in mature spermatozoa that can exert transgenerational regulatory functions. Here, we review recent advances in the field of small RNAs in spermatozoa, how their profiles change in response to lifestyle or environmental factors, and their impact on offsprings' physiology. The profile of these RNAs changes dramatically during spermatozoa maturation. The majority of intracellular small RNAs during early spermatogenesis are miRNAs and piRNAs, but, in mature spermatozoa, tRNA- and rRNA-derived fragments (tRFs and rRFs, respectively) are the predominant forms, primarily delivered from the epididymis via extracellular vesicles. Diet, exercise, and environmental exposures have a direct effect on small RNA levels in spermatozoa, and this differential abundance can reprogram the development of the embryo. Offsprings of fathers with different lifestyles can have different phenotypes, including altered metabolism or behavior. Therefore, small RNAs in spermatozoa are emerging as an important epigenetic layer in development and transgenerational inheritance.</p>","PeriodicalId":19271,"journal":{"name":"Non-Coding RNA","volume":"11 1","pages":""},"PeriodicalIF":3.6,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11858474/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143493129","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}