The recent advancements and developments reached in the field of RNA, especially over the past year, have been exceptional and are the result of the great achievements made by researchers and scientists thus far. In order to explore this fast-growing field, Frontiers is launching a series of Research Topics focusing on the evolution of methods, processes, techniques and ways of thinking in RNA that led to new discoveries and research approaches. In this article collection, we seek contributions exploring the changing context and the rising new perspectives within RNA. The emphasis of this series is on the dynamics of change and evolution of the latest progresses made, as well as on the evolving methods and responses in research across the field of RNA. This article collection will inform, inspire and provide direction and guidance to researchers in the field.

Cold Spring Harbor Laboratory

Peking University

Guangxi University

Views

Article Views

Article Downloads

Frontiers in Genetics

Frontiers in Molecular Biosciences

The Evolution in RNA: 2023

With the development of Next-Generation Sequencing (NGS) technologies, more and more RNAs have been identified. They can be classified into coding RNA and non-coding RNA. In the past long time, non-coding RNA has been characterized as genomic &#x201c;junk.&#x201d; However, many studies have proved that it also plays key roles in many important biological processes. RNAs have a close relationship with the regulation, expression, and evolution of genetic information. To provide a platform for exhibiting the latest technology regarding RNA, we organized a Research Topic on &#x201c;The Evolution in RNA: 2023.&#x201d; This Research Topic presents four articles. We expect that these articles will promote more advanced studies in RNA research.RNA modification is emerging as an important new layer of post-transcriptional regulation of the transcriptome. More than a hundred types of RNA modifications have been discovered, but only a few of them allow transcriptome-wide profiling. Dihydrouridine (D) modification, a conserved oxidoreduction RNA modification type in tRNAs and a few mRNAs, is one of the latest RNA modifications that can be profiled at the transcriptome level. In this Research Topic, <a href="https://doi.org/10.3389/fgene.2023.1334132">Ren et al.</a> have comprehensively encoded the sequence features of D modification and established a multi-species prediction method for D modification. With this predictor, it will be possible to more accurately predict D modification based on RNA sequences alone.With the development of high-throughput techniques, the definition of non-coding RNAs has evolved. That is, more and more peptides and small proteins in the proteome are produced by non-coding RNAs. Among these non-coding RNAs, circular RNAs (circRNAs) are emerging as an interesting category with relatively extensive coding potential. In this Research Topic, <a href="https://doi.org/10.3389/fgene.2023.1264606">Shi et al.</a> have systematically reviewed the recent progress of protein-coding circRNAs since 2022. This review outlines the mechanisms, research progress, and prediction models of circRNA-coding proteins. They found that although oncogenesis and tumor suppression are major focuses, circRNAs also affect cardiovascular, muscular, and nervous system diseases. Some helpful perspectives for future research were also provided, including but not limited to the need for more in-depth validation through rescue assays and the urgent need for investigation beyond cancers.MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play critical roles in a variety of biological processes. In this Research Topic, <a href="https://doi.org/10.3389/fgene.2024.1375148">Li et al.</a> proposed a new miRNA set enrichment analysis method (MHIF-MSEA) based on multi-source heterogeneous information fusion. They constructed three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. In addition, the authors integrated these miRNA similarity networks into a single similarity network by using an averaging method. The random walk with restart algorithm was used to expand the original miRNA list based on the fused network Finally, the researchers performedenrichment analysis on the expanded list. This work will be useful for miRNA functional enrichment analysis.Glioblastoma is a type of aggressive brain tumor with a very poor survival prognosis due to its tumorigenic stem cells (GSCs). Long non-coding RNAs (lncRNAs) have attracted considerable interest due to their critical roles in normal physiological processes and cancer development. Extensive research has highlighted their significant influence in regulating the properties of GSCs. In this Research Topic, <a href="https://doi.org/10.3389/fgene.2024.1416772">Hazra et al.</a> systemically reviewed the role of long non-coding RNA in glioblastoma. First, they delved into some notable examples of lncRNAs related to glioblastoma stem cells, and elucidated their functional roles in driving glioblastoma progression. Then, they further explored the use of a 3D in vitro model to investigate GSC biology. In addition, they elucidated four primary methods for targeting lncRNAs as potential therapeutics in the management of glioblastoma.WL: Writing&#x2013;original draft. YZ: Writing&#x2013;original draft. RH: Writing&#x2013;review and editing.The author(s) declare that financial support was received for the research, authorship, and/or publication of this article. This work was partially supported by the National Natural Science Foundation of China (No. 62072124), the Natural Science Foundation of Guangxi (No. 2023JJG170006), the CAAI-Huawei MindSpore Open Fund (No. CAAIXSJLJJ-2022-022A), the Natural Science and Technology Innovation Development Foundation of Guangxi University (No. 2022BZRC009), the Guangxi Key Laboratory of Eye Health (No. GXYJK-202407), the Guangxi Health Commission eye and related diseases artificial intelligence screen technology key laboratory (No. GXYAI-202402).The Topic Editors thank all the authors and reviewers of the articles submitted to this Frontiers Research Topic.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Editorial: The evolution in RNA: 2023

Glioblastoma poses a formidable challenge among primary brain tumors: its tumorigenic stem cells, capable of self-renewal, proliferation, and differentiation, contribute substantially to tumor initiation and therapy resistance. These glioblastoma stem cells (GSCs), resembling conventional stem and progenitor cells, adopt pathways critical for tissue development and repair, promoting uninterrupted tumor expansion. Long non-coding RNAs (lncRNAs), a substantial component of the human transcriptome, have garnered considerable interest for their pivotal roles in normal physiological processes and cancer pathogenesis. They display cell- or tissue-specific expression patterns, and extensive investigations have highlighted their impact on regulating GSC properties and cellular differentiation, thus offering promising avenues for therapeutic interventions. Consequently, lncRNAs, with their ability to exert regulatory control over tumor initiation and progression, have emerged as promising targets for innovative glioblastoma therapies. This review explores notable examples of GSC-associated lncRNAs and elucidates their functional roles in driving glioblastoma progression. Additionally, we delved deeper into utilizing a 3D in vitro model for investigating GSC biology and elucidated four primary methodologies for targeting lncRNAs as potential therapeutics in managing glioblastoma.

Glioblastoma poses a formidable challenge among primary brain tumors: its tumorigenic stem cells, capable of self-renewal, proliferation, and differentiation, contribute substantially to tumor initiation and therapy resistance. These glioblastoma stem cells (GSCs), resembling conventional stem and progenitor cells, adopt pathways critical for tissue development and repair, promoting uninterrupted tumor expansion. Long non-coding RNAs (lncRNAs), a substantial component of the human transcriptome, have garnered considerable interest for their pivotal roles in normal physiological processes and cancer pathogenesis. They display cell- or tissue-specific expression patterns, and extensive investigations have highlighted their impact on regulating GSC properties and cellular differentiation, thus offering promising avenues for therapeutic interventions. Consequently, lncRNAs, with their ability to exert regulatory control over tumor initiation and progression, have emerged as promising targets for innovative glioblastoma therapies. This review explores notable examples of GSC-associated lncRNAs and elucidates their functional roles in driving glioblastoma progression. Additionally, we delved deeper into utilizing a 3D in vitro model for investigating GSC biology and elucidated four primary methodologies for targeting lncRNAs as potential therapeutics in managing glioblastoma.

Glioblastoma stem cell long non-coding RNAs: therapeutic perspectives and opportunities

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases.Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list.Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases.Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list.Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion

Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA.Methods: To identify D in epitranscriptome, we provided a prediction framework named “DPred_3S” based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time.Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae, Escherichia coli, and Schizosaccharomyces pombe, respectively. The performances of different machine learning algorithms were also compared in this study.Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.

Introduction: Dihydrouridine (D) is a conserved modification of tRNA among all three life domains. D modification enhances the flexibility of a single nucleotide base in the spatial structure and is disease- and evolution-associated. Recent studies have also suggested the presence of dihydrouridine on mRNA.Methods: To identify D in epitranscriptome, we provided a prediction framework named “DPred_3S” based on the machine learning approach for three species D epitranscriptome, which used epitranscriptome sequencing data as training data for the first time.Results: The optimal features were evaluated by the F-score and integration of different features; our model achieved area under the receiver operating characteristic curve (AUROC) scores 0.955, 0.946, and 0.905 for Saccharomyces cerevisiae, Escherichia coli, and Schizosaccharomyces pombe, respectively. The performances of different machine learning algorithms were also compared in this study.Discussion: The high performances of our model suggest the D sites can be distinguished based on their surrounding sequence, but the lower performance of cross-species prediction may be limited by technique preferences.

DPred_3S: identifying dihydrouridine (D) modification on three species epitranscriptome based on multiple sequence-derived features

Circular RNA (circRNA) is a special class of noncoding RNA molecules and the latest research hotspot in the field of RNA. CircRNA molecules have a closed loop structure, which is not affected by RNA exonuclease and has the characteristics of more stable expression. Previous studies have shown that circRNA molecules are rich in microRNA (miRNA) binding sites and act as miRNA sponges in cells. By interacting with miRNAs associated with tumors and other diseases, circRNAs play an important regulatory role. However, circRNAs have recently been found to have small open reading frames that enable them to encode peptides/proteins. These proteins have been reported to play an important role in the mechanism of regulation of a variety of diseases and have great potential in the diagnosis and treatment of diseases. In this review, we summarize the mechanism of action of the newly discovered circRNA-coding proteins since 2022 and briefly describe their research process. In addition, we also discuss the prediction model of the functional sites and encoded proteins of circRNAs, which provides a potential idea for future research on circRNAs.

Circular RNA (circRNA) is a special class of noncoding RNA molecules and the latest research hotspot in the field of RNA. CircRNA molecules have a closed loop structure, which is not affected by RNA exonuclease and has the characteristics of more stable expression. Previous studies have shown that circRNA molecules are rich in microRNA (miRNA) binding sites and act as miRNA sponges in cells. By interacting with miRNAs associated with tumors and other diseases, circRNAs play an important regulatory role. However, circRNAs have recently been found to have small open reading frames that enable them to encode peptides/proteins. These proteins have been reported to play an important role in the mechanism of regulation of a variety of diseases and have great potential in the diagnosis and treatment of diseases. In this review, we summarize the mechanism of action of the newly discovered circRNA-coding proteins since 2022 and briefly describe their research process. In addition, we also discuss the prediction model of the functional sites and encoded proteins of circRNAs, which provides a potential idea for future research on circRNAs.