CMAGN: circRNA–miRNA association prediction based on graph attention auto-encoder and network consistency projection

Circular RNA (circRNA) is a new type of non-coding RNA. It is mainly composed of exons, and prevalent and stable presence among living organisms. Research has revealed that circRNA has many functions such as microRNA (miRNA) sponges, and regulation of alternative splicing and gene expression. Recent evidence suggests that circRNA plays an important role in many diseases. This review summarizes our current understanding of theclassifications, characteristics, functions, and the relationships of circRNA with diseases, in order to aid diagnosis, treatment, and prevention of clinical related diseases. Key words: Circular RNA; Non-coding RNA; MicroRNA sponges

A growing body of evidence indicates that circular RNAs (circRNAs) play a pivotal role in various biological processes and have a close association with the initiation and progression of diseases. Moreover, circRNAs are considered as promising biomarkers for disease diagnosis owing to their characteristics of conservation, stability and universality. Inferring disease–circRNA relationships will contribute to the understanding of disease pathology. However, it is costly and laborious to discover novel disease–circRNA interactions by wet-lab experiments, and few computational methods have been devoted to predicting potential circRNAs for diseases. Here, we advance a computational method (NCPCDA) to identify novel circRNA–disease associations based on network consistency projection. For starters, we make use of multi-view similarity data, including circRNA functional similarity, disease semantic similarity, and association profile similarity, to construct the integrated circRNA similarity and disease similarity. Then, we project circRNA space and disease space on the circRNA–disease interaction network, respectively. Finally, we can obtain the predicted circRNA–disease association score matrix by combining the above two space projection scores. Simulation results show that NCPCDA can efficiently infer disease–circRNA relationships with high accuracy, obtaining AUCs of 0.9541 and 0.9201 in leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, case studies also suggest that NCPCDA is promising for discovering new disease–circRNA interactions. The NCPCDA dataset and code, as well as the detailed readme file for our code, can be downloaded from Github (https://github.com/ghli16/NNCPCD).

Circular RNAs, including circular exonic RNAs (circRNA), circular intronic RNAs (ciRNA) and exon-intron circRNAs (EIciRNAs), are a new type of noncoding RNAs. Growing shoots of moso bamboo (Phyllostachys edulis) represent an excellent model of fast growth and their circular RNAs have not been studied yet. To understand the potential regulation of circular RNAs, we systematically characterized circular RNAs from eight different developmental stages of rapidly growing shoots. Here, we identified 895 circular RNAs including a subset of mutually inclusive circRNA. These circular RNAs were generated from 759 corresponding parental coding genes involved in cellulose, hemicellulose and lignin biosynthetic process. Gene co-expression analysis revealed that hub genes, such as DEFECTIVE IN RNA-DIRECTED DNA METHYLATION 1 (DRD1), MAINTENANCE OF METHYLATION (MOM), dicer-like 3 (DCL3) and ARGONAUTE 1 (AGO1), were significantly enriched giving rise to circular RNAs. The expression level of these circular RNAs presented correlation with its linear counterpart according to transcriptome sequencing. Further protoplast transformation experiments indicated that overexpressing circ-bHLH93 generating from transcription factor decreased its linear transcript. Finally, the expression profiles suggested that circular RNAs may have interplay with miRNAs to regulate their cognate linear mRNAs, which was further supported by overexpressing miRNA156 decreasing the transcript of circ-TRF-1 and linear transcripts of TRF-1. Taken together, the overall profile of circular RNAs provided new insight into an unexplored category of long noncoding RNA regulation in moso bamboo.

Noncoding circular RNAs are widespread in the tree of life. Particularly, intron-containing circular RNAs which apparently upregulate their parental gene expression. Entamoeba histolytica, the causative agent of dysentery and liver abscesses in humans, codes for several noncoding RNAs, including circular ribosomal RNAs, but no intron containing circular RNAs have been described to date. Divergent RT-PCR and diverse molecular approaches, allowed us to detect bona fide full-length intronic circular RNA (flicRNA) molecules. Self-splicing reactions, RNA polymerase II inhibition with Actinomycin D, and second step of splicing-inhibition with boric acid showed that the production of flicRX13 (one of the flicRNAs found in this work, and our test model) depends on mRNA synthesis and pre-mRNA processing instead of self-splicing. To explore the cues and factors involved in flicRX13 biogenesis in vivo, splicing assays were carried out in amoeba transformants where splicing factors and Dbr1 (intron lariat debranching enzyme 1) were silenced or overexpressed, or where Rabx13 wild-type and mutant 5′ss (splice site) and branch site minigene constructs were overexpressed. Whereas SF1 (splicing factor 1) is not involved, the U2 auxiliary splicing factor, Dbr1, and the GU-rich 5′ss are involved in postsplicing flicRX13 biogenesis, probably by Dbr1 stalling, in a similar fashion to the formation of ciRNAs (circular intronic RNAs), but with distinctive 5′-3′ss ligation points. Different from the reported functions of ciRNAs, the 5′ss GU-rich element of flicRX13 possibly interacts with transcription machinery to silence its own gene in cis. Furthermore, introns of E. histolytica virulence-related genes are also processed as flicRNAs.

CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs. We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA-disease associations. Firstly, the circRNA-circRNA similarity network and disease-disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA-disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA-disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.

A growing number of clinical studies have provided substantial evidence of a close relationship between the microbe and the disease. Thus, it is necessary to infer potential microbe-disease associations. But traditional approaches use experiments to validate these associations that often spend a lot of materials and time. Hence, more reliable computational methods are expected to be applied to predict disease-associated microbes. In this article, an innovative mean for predicting microbe-disease associations is proposed, which is based on network consistency projection and label propagation (NCPLP). Given that most existing algorithms use the Gaussian interaction profile (GIP) kernel similarity as the similarity criterion between microbe pairs and disease pairs, in this model, Medical Subject Headings descriptors are considered to calculate disease semantic similarity. In addition, 16S rRNA gene sequences are borrowed for the calculation of microbe functional similarity. In view of the gene-based sequence information, we use two conventional methods (BLAST+ and MEGA7) to assess the similarity between each pair of microbes from different perspectives. Especially, network consistency projection is added to obtain network projection scores from the microbe space and the disease space. Ultimately, label propagation is utilized to reliably predict microbes related to diseases. NCPLP achieves better performance in various evaluation indicators and discovers a greater number of potential associations between microbes and diseases. Also, case studies further confirm the reliable prediction performance of NCPLP. To conclude, our algorithm NCPLP has the ability to discover these underlying microbe-disease associations and can provide help for biological study.

Circular RNA forms had been described in all domains of life. Such RNAs were shown to have diverse biological functions, including roles in the life cycle of viral and viroid genomes, and in maturation of permuted tRNA genes. Despite their potentially important biological roles, discovery of circular RNAs has so far been mostly serendipitous. We have developed circRNA-seq, a combined experimental/computational approach that enriches for circular RNAs and allows profiling their prevalence in a whole-genome, unbiased manner. Application of this approach to the archaeon Sulfolobus solfataricus P2 revealed multiple circular transcripts, a subset of which was further validated independently. The identified circular RNAs included expected forms, such as excised tRNA introns and rRNA processing intermediates, but were also enriched with non-coding RNAs, including C/D box RNAs and RNase P, as well as circular RNAs of unknown function. Many of the identified circles were conserved in Sulfolobus acidocaldarius, further supporting their functional significance. Our results suggest that circular RNAs, and particularly circular non-coding RNAs, are more prevalent in archaea than previously recognized, and might have yet unidentified biological roles. Our study establishes a specific and sensitive approach for identification of circular RNAs using RNA-seq, and can readily be applied to other organisms.

Introduction: Glioblastomas are a high-grade primary brain cancer of astrocytes, a glial cell. The average survival upon diagnosis is 15 months. Researchers have been continuously attempting to develop new drugs and therapies that would exhibit greater efficiency against this cancer. However, tumor resistance is often observed in glioblastomas. Circular RNA is a relatively novel finding of non-coding RNA that is highly stable compared to linear RNA. Recent studies have found a variation in circular RNA expression levels in resistant versus non-resistant cancer cell lines. Thus, researchers have been exploring this endogenous entity to understand the role it may play in chemo- and radioresistant glioblastomas. Methods: A comprehensive look into the most recent findings on this topic was done using Google Scholar and PubMed search engines. Key words used include “circRNA,” “glioblastoma,” and “chemo- and radio-resistance”. This review mainly stems from primary research published between 2017 and 2024. Results: Only a few specific circular RNAs have been found to be implicated in chemoresistance. Research has focused on understanding resistance to temozolomide. However, variation in certain circular RNA expression has also been noted in cisplatin and histone deacytlase inhibitor therapies for glioblastoma treatment. These circular RNAs inhibit the cytotoxic effects of chemotherapeutics primarily though microRNA sponging. Additionally, similar results were found in radioresistant glioblastoma tumors, whereby specific circular RNAs block apoptosis by altering various protein mechanisms through microRNA sponging and inhibiting translocation. Discussion: Developing methods that inhibit this tumor’s ability to resist otherwise effective therapies may be an ideal method of treatment. Many studies have attempted to modify cellular pathways to facilitate apoptosis of cancer cells. However, due to complex interactions and possible off-target effects, these methods are inefficient. Circular RNAs may serve as improved therapeutic targets due to their tissue specificity and increased stability. Conclusion: The newly identified circular RNAs involved in glioblastoma therapy resistance may be used to increase cancer cell susceptibility to treatments. Knock-out and overexpression analysis indicate these molecules to be sufficient in altering resistance abilities of these cells. Highly specific targeting techniques may therefore be a viable solution for chemo- and radioresistance.

Machine learning-based identification of endogenous cellular microRNA sponges against viral microRNAs.

Circular RNAs, which are a novel subclass of noncoding RNAs, are reported to be involved in various biological processes. Aberrant expression of circular RNAs may promote cancer progression. The function of circular GOLPH3 RNA (circGOLPH3) in oral squamous cell carcinoma (OSCC) is unclear. In this study, the circGOLPH3 levels in OSCC cell lines were determined using quantitative real-time polymerase chain reaction (qRT-PCR). Gain-of-function and loss-of-function experiments were performed to evaluate the roles of circGOLPH3 in OSCC. Cell counting kit 8, migration, and invasion assays were performed to determine the functions of circGOLPH3. The mechanism of circGOLPH3 in OSCC was investigated using qRT-PCR, western blotting, luciferase activity, and RNA pull-down analyses. Furthermore, the function of circGOLPH3 in vivo was evaluated. circGOLPH3 derived from GOLPH3 was mainly localized to the cytoplasm and exhibited high stability. The expression of circGOLPH3 was upregulated in OSCC cells. circGOLPH3 promoted the growth of OSCC in vitro and in vivo. Additionally, circGOLPH3 upregulated OSCC cell migration and invasion. Mechanistically, circGOLPH3 functioned as a microRNA sponge and downregulated miR-1299 expression. miR-1299 downregulated the expression of LIF by targeting its 3’-untranslated region. Inhibition of the circGOLPH3/miR-1299/LIF axis suppressed the growth, migration, and invasion of OSCC cells. These findings indicate that the circGOLPH3/miR-1299/LIF axis promotes OSCC cell growth, migration, and invasion and that this axis is a potential therapeutic target for OSCC.

Circular RNAs (circRNAs) are a type of long noncoding RNA that are highly abundant and highly conserved throughout evolution and exhibit differential expression patterns in various tissue types in multiple diseases, including amyotrophic lateral sclerosis (ALS). The most well-known function of circRNAs is their ability to act as microRNA (miRNA) sponges. This entails circRNA binding to miRNA, which would otherwise target and degrade messenger RNA, thus affecting gene expression. This study analyzed ALS patient samples from three spinal cord regions to investigate circular transcriptome perturbation and circular RNA-microRNA-mRNA interactions. Using stringent statistical parameters, we identified 92 differentially expressed circRNAs across the spinal cord tissues with the aim of identifying specific circRNAs with biomarker potential. We also found evidence for differential expression of 37 linear RNAs possibly due to miRNA sequestration by circRNAs, thus revealing their potential as novel biomarkers and therapeutic candidates for ALS.

Circular RNAs and cancer

BackgroundLung cancer remains a leading cause of cancer-related mortality worldwide. Its progression is intricately associated with the dynamic regulation of autophagy and RNA-binding proteins (RBPs), which play crucial roles in mRNA stability, alternative splicing, and cellular stress responses.ObjectivesThis review aims to systematically analyze the mechanisms through which RBPs and autophagy contribute to lung cancer progression and explore potential therapeutic strategies targeting these pathways. Methods: We reviewed recent studies on the molecular mechanisms by which RBPs regulate tumor proliferation, metabolic adaptation, and their interaction with autophagy. The review also examines the dual roles of autophagy in lung cancer, highlighting its context-dependent effects on cell survival and death.ResultsThe interactions and regulatory networks between RBPs and autophagy involve multiple levels of regulation. RBPs can directly influence autophagy processes and act as microRNA (miRNA) sponges to regulate mRNA stability. The modulation of RBPs affects the expression of autophagy-related genes (ATGs) and autophagosome formation. Additionally, RBPs participate in complex regulatory interactions with non-coding RNAs (ncRNAs), including long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and other proteins.ConclusionsThis review proposes innovative therapeutic strategies that combine RBP-targeting approaches (e.g., small molecule inhibitors, CRISPR gene editing) with autophagy modulators (e.g., mTOR inhibitors, chloroquine) to enhance treatment efficacy. Nanoparticle drug delivery systems and epigenetic regulation offer further opportunities for targeted interventions. This review lays a theoretical foundation for advancing lung cancer research and provides novel insights into synergistic therapies that target both RBPs and autophagy to improve treatment outcomes for lung cancer.Graphical

Exploring the potential long noncoding RNA (lncRNA)-disease associations (LDAs) plays a critical role for understanding disease etiology and pathogenesis. Given the high cost of biological experiments, developing a computational method is a practical necessity to effectively accelerate experimental screening process of candidate LDAs. However, under the high sparsity of LDA dataset, many computational models hardly exploit enough knowledge to learn comprehensive patterns of node representations. Moreover, although the metapath-based GNN has been recently introduced into LDA prediction, it discards intermediate nodes along the meta-path and results in information loss. This paper presents a new multi-view contrastive heterogeneous graph attention network (GAT) for lncRNA-disease association prediction, MCHNLDA for brevity. Specifically, MCHNLDA firstly leverages rich biological data sources of lncRNA, gene and disease to construct two-view graphs, feature structural graph of feature schema view and lncRNA-gene-disease heterogeneous graph of network topology view. Then, we design a cross-contrastive learning task to collaboratively guide graph embeddings of the two views without relying on any labels. In this way, we can pull closer the nodes of similar features and network topology, and push other nodes away. Furthermore, we propose a heterogeneous contextual GAT, where long short-term memory network is incorporated into attention mechanism to effectively capture sequential structure information along the meta-path. Extensive experimental comparisons against several state-of-the-art methods show the effectiveness of proposed framework.The code and data of proposed framework is freely available athttps://github.com/zhaoxs686/MCHNLDA.

Colorectal cancer (CRC) remains a major global health burden, with rising incidence and mortality, particularly in developing countries. The tumor microenvironment (TME) plays a critical role in CRC progression by facilitating angiogenesis, immune evasion, and metastasis through complex intercellular communication. Among the key mediators of this communication are exosomes-nano-sized extracellular vesicles-that transport a variety of bioactive molecules, including competing endogenous RNAs (ceRNAs). These ceRNAs, such as long non-coding RNAs (lncRNAs), circular RNAs (circRNAs), and mRNAs, act as molecular sponges for microRNAs (miRNAs), thereby regulating gene expression and influencing cancer-related pathways like Wnt/β-catenin and PI3K/Akt. This review explores the emerging role of exosomal ceRNAs derived from the TME in CRC progression, emphasizing their involvement in promoting tumor cell proliferation, invasion, metastasis, and resistance to therapy. By elucidating the intricate crosstalk between exosomal ceRNAs and the TME, we highlight their potential as novel biomarkers and therapeutic targets, offering new avenues for personalized treatment strategies in colorectal malignancies. Despite growing interest in exosomes and non-coding RNAs in colorectal cancer (CRC), limited attention has been given to the specific role of exosomal ceRNAs derived from the tumor microenvironment (TME). Existing reviews have predominantly addressed broader aspects of exosomal non-coding RNAs (e.g., miRNAs, lncRNAs, and circRNAs in general CRC progression, metastasis, or as biomarkers) or focused on exosomal communication in other malignancies. However, the intricate mechanisms by which TME-derived exosomal ceRNAs establish regulatory networks to drive CRC pathology-particularly through miRNA sponging in stromal-tumor crosstalk-remain underexplored and insufficiently synthesized. This review addresses these critical gaps by uniquely emphasizing exosomal ceRNAs sourced from the TME (including CAFs, macrophages, and other stromal components), elucidating their convergent roles in orchestrating proliferation, invasion, immune evasion, and chemoresistance. By integrating recent evidence into a TME-centric framework, we provide novel insights into potential diagnostic and therapeutic applications, advancing beyond general exosomal RNA overviews toward targeted strategies for CRC.