KGDCMI: A New Approach for Predicting circRNA–miRNA Interactions From Multi-Source Information Extraction and Deep Learning

Circular RNAs in Cell Cycle Regulation of Cancers

Cancer has been one of the most problematic health issues globally. Typically, all cancers share a common characteristic or cancer hallmark, such as sustaining cell proliferation, evading growth suppressors, and enabling replicative immortality. Indeed, cell cycle regulation in cancer is often found to be dysregulated, leading to an increase in aggressiveness. These dysregulations are partly due to the aberrant cellular signaling pathway. In recent years, circular RNAs (circRNAs) have been widely studied and classified as one of the regulators in various cancers. Numerous studies have reported that circRNAs antagonize or promote cancer progression through the modulation of cell cycle regulators or their associated signaling pathways, directly or indirectly. Mostly, circRNAs are known to act as microRNA (miRNA) sponges. However, they also hold additional mechanisms for regulating cellular activity, including protein binding, RNA-binding protein (RBP) recruitment, and protein translation. This review will discuss the current knowledge of how circRNAs regulate cell cycle-related proteins through the abovementioned mechanisms in different cancers.

BJLD-CMI: a predictive circRNA-miRNA interactions model combining multi-angle feature information

Increasing research findings suggest that circular RNA (circRNA) exerts a crucial function in the pathogenesis of complex human diseases by binding to miRNA. Identifying their potential interactions is of paramount importance for the diagnosis and treatment of diseases. However, long cycles, small scales, and time-consuming processes characterize previous biological wet experiments. Consequently, the use of an efficient computational model to forecast the interactions between circRNA and miRNA is gradually becoming mainstream. In this study, we present a new prediction model named BJLD-CMI. The model extracts circRNA sequence features and miRNA sequence features by applying Jaccard and Bert's method and organically integrates them to obtain CMI attribute features, and then uses the graph embedding method Line to extract CMI behavioral features based on the known circRNA-miRNA correlation graph information. And then we predict the potential circRNA-miRNA interactions by fusing the multi-angle feature information such as attribute and behavior through Autoencoder in Autoencoder Networks. BJLD-CMI attained 94.95% and 90.69% of the area under the ROC curve on the CMI-9589 and CMI-9905 datasets. When compared with existing models, the results indicate that BJLD-CMI exhibits the best overall competence. During the case study experiment, we conducted a PubMed literature search to confirm that out of the top 10 predicted CMIs, seven pairs did indeed exist. These results suggest that BJLD-CMI is an effective method for predicting interactions between circRNAs and miRNAs. It provides a valuable candidate for biological wet experiments and can reduce the burden of researchers.

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

Advances in applications of artificial intelligence algorithms for cancer-related miRNA research

MiRNAs are a class of small non-coding RNAs, which regulate gene expression post-transcriptionally by partial complementary base pairing. Aberrant miRNA expressions have been reported in tumor tissues and peripheral blood of cancer patients. In recent years, artificial intelligence algorithms such as machine learning and deep learning have been widely used in bioinformatic research. Compared to traditional bioinformatic tools, miRNA target prediction tools based on artificial intelligence algorithms have higher accuracy, and can successfully predict subcellular localization and redistribution of miRNAs to deepen our understanding. Additionally, the construction of clinical models based on artificial intelligence algorithms could significantly improve the mining efficiency of miRNA used as biomarkers. In this article, we summarize recent development of bioinformatic miRNA tools based on artificial intelligence algorithms, focusing on the potential of machine learning and deep learning in cancer-related miRNA research.

Altered circular RNA expressions in extracellular vesicles from bronchoalveolar lavage fluids in mice after bacterial infections

Circular RNAs (circRNAs) are a class of transcripts that often are generated by back-splicing that covalently connects the 3'end of the exon to the 5'end. CircRNAs are more resistant to nuclease and more stable than their linear counterparts. One of the well-recognized roles of circRNAs is the miRNA sponging effects that potentially lead to the regulation of downstream proteins. Despite that circRNAs have been reported to be involved in a wide range of human diseases, including cancers, cardiovascular, and neurological diseases, they have not been studied in inflammatory lung responses. Here, we analyzed the circRNA profiles detected in extracellular vesicles (EVs) obtained from the broncho-alveolar lavage fluids (BALF) in response to LPS or acid instillation in mice. Next, we validated two specific circRNAs in the BALF-EVs and BALF cells in response to endotoxin by RT-qPCR, using specific primers targeting the circular form of RNAs rather than the linear host RNAs. The expression of these selected circRNAs in the BALF inflammatory cells, alveolar macrophages (AMs), neutrophils, and lung tissue were analyzed. We further predicted the potential miRNAs that interact with these circRNAs. Our study is the first report to show that circRNAs are detectable in BALF EVs obtained from mice. The EV-cargo circRNAs are significantly altered by the noxious stimuli. The circRNAs identified using microarrays may be validated by RT-qPCR using primers specific to the circular but not the linear form. Future studies to investigate circRNA expression and function including miRNA sponging in lung inflammation potentially uncover novel strategies to develop diagnostic/therapeutic targets.

Dysregulated Expression Patterns of Circular RNAs in Cancer: Uncovering Molecular Mechanisms and Biomarker Potential

Circular RNAs (circRNAs) are stable, enclosed, non-coding RNA molecules with dynamic regulatory propensity. Their biogenesis involves a back-splicing process, forming a highly stable and operational RNA molecule. Dysregulated circRNA expression can drive carcinogenic and tumorigenic transformation through the orchestration of epigenetic modifications via extensive RNA and protein-binding domains. These multi-ranged functional capabilities have unveiled extensive identification of previously unknown molecular and cellular patterns of cancer cells. Reliable circRNA expression patterns can aid in early disease detection and provide criteria for genome-specific personalized medicine. Studies described in this review have revealed the novelty of circRNAs and their biological ss as prognostic and diagnostic biomarkers.

Predicting abiotic stress-responsive miRNA in plants based on multi-source features fusion and graph neural network

BACKGROUND

More and more studies show that miRNA plays a crucial role in plants' response to different abiotic stresses. However, traditional experimental methods are often expensive and inefficient, so it is important to develop efficient and economical computational methods. Although researchers have developed machine learning-based method, the information of miRNAs and abiotic stresses has not been fully exploited. Therefore, we propose a novel approach based on graph neural networks for predicting potential miRNA-abiotic stress associations.

RESULTS

In this study, we fully considered the multi-source feature information from miRNAs and abiotic stresses, and calculated and integrated the similarity network of miRNA and abiotic stress from different feature perspectives using multiple similarity measures. Then, the above multi-source similarity network and association information between miRNAs and abiotic stresses are effectively fused through heterogeneous networks. Subsequently, the Restart Random Walk (RWR) algorithm is employed to extract global structural information from heterogeneous networks, providing feature vectors for miRNA and abiotic stress. After that, we utilized the graph autoencoder based on GIN (Graph Isomorphism Networks) to learn and reconstruct a miRNA-abiotic stress association matrix to obtain potential miRNA-abiotic stress associations. The experimental results show that our model is superior to all known methods in predicting potential miRNA-abiotic stress associations, and the AUPR and AUC metrics of our model achieve 98.24% and 97.43%, respectively, under five-fold cross-validation.

CONCLUSIONS

The robustness and effectiveness of our proposed model position it as a valuable approach for advancing the field of miRNA-abiotic stress association prediction.

Biolinguistic graph fusion model for circRNA-miRNA association prediction

Emerging clinical evidence suggests that sophisticated associations with circular ribonucleic acids (RNAs) (circRNAs) and microRNAs (miRNAs) are a critical regulatory factor of various pathological processes and play a critical role in most intricate human diseases. Nonetheless, the above correlations via wet experiments are error-prone and labor-intensive, and the underlying novel circRNA-miRNA association (CMA) has been validated by numerous existing computational methods that rely only on single correlation data. Considering the inadequacy of existing machine learning models, we propose a new model named BGF-CMAP, which combines the gradient boosting decision tree with natural language processing and graph embedding methods to infer associations between circRNAs and miRNAs. Specifically, BGF-CMAP extracts sequence attribute features and interaction behavior features by Word2vec and two homogeneous graph embedding algorithms, large-scale information network embedding and graph factorization, respectively. Multitudinous comprehensive experimental analysis revealed that BGF-CMAP successfully predicted the complex relationship between circRNAs and miRNAs with an accuracy of 82.90% and an area under receiver operating characteristic of 0.9075. Furthermore, 23 of the top 30 miRNA-associated circRNAs of the studies on data were confirmed in relevant experiences, showing that the BGF-CMAP model is superior to others. BGF-CMAP can serve as a helpful model to provide a scientific theoretical basis for the study of CMA prediction.

PCDA-HNMP: Predicting circRNA-disease association using heterogeneous network and meta-path

Increasing amounts of experimental studies have shown that circular RNAs (circRNAs) play important regulatory roles in human diseases through interactions with related microRNAs (miRNAs). CircRNAs have become new potential disease biomarkers and therapeutic targets. Predicting circRNA-disease association (CDA) is of great significance for exploring the pathogenesis of complex diseases, which can improve the diagnosis level of diseases and promote the targeted therapy of diseases. However, determination of CDAs through traditional clinical trials is usually time-consuming and expensive. Computational methods are now alternative ways to predict CDAs. In this study, a new computational method, named PCDA-HNMP, was designed. For obtaining informative features of circRNAs and diseases, a heterogeneous network was first constructed, which defined circRNAs, mRNAs, miRNAs and diseases as nodes and associations between them as edges. Then, a deep analysis was conducted on the heterogeneous network by extracting meta-paths connecting to circRNAs (diseases), thereby mining hidden associations between various circRNAs (diseases). These associations constituted the meta-path-induced networks for circRNAs and diseases. The features of circRNAs and diseases were derived from the aforementioned networks via mashup. On the other hand, miRNA-disease associations (mDAs) were employed to improve the model's performance. miRNA features were yielded from the meta-path-induced networks on miRNAs and circRNAs, which were constructed from the meta-paths connecting miRNAs and circRNAs in the heterogeneous network. A concatenation operation was adopted to build the features of CDAs and mDAs. Such representations of CDAs and mDAs were fed into XGBoost to set up the model. The five-fold cross-validation yielded an area under the curve (AUC) of 0.9846, which was better than those of some existing state-of-the-art methods. The employment of mDAs can really enhance the model's performance and the importance analysis on meta-path-induced networks shown that networks produced by the meta-paths containing validated CDAs provided the most important contributions.

MicroRNAs: Small but Key Players in Viral Infections and Immune Responses to Viral Pathogens

Since the discovery of microRNAs (miRNAs) in C. elegans in 1993, the field of miRNA research has grown steeply. These single-stranded non-coding RNA molecules canonically work at the post-transcriptional phase to regulate protein expression. miRNAs are known to regulate viral infection and the ensuing host immune response. Evolving research suggests miRNAs are assets in the discovery and investigation of therapeutics and diagnostics. In this review, we succinctly summarize the latest findings in (i) mechanisms underpinning miRNA regulation of viral infection, (ii) miRNA regulation of host immune response to viral pathogens, (iii) miRNA-based diagnostics and therapeutics targeting viral pathogens and challenges, and (iv) miRNA patents and the market landscape. Our findings show the differential expression of miRNA may serve as a prognostic biomarker for viral infections in regard to predicting the severity or adverse health effects associated with viral diseases. While there is huge market potential for miRNA technology, the novel approach of using miRNA mimics to enhance antiviral activity or antagonists to inhibit pro-viral miRNAs has been an ongoing research endeavor. Significant hurdles remain in terms of miRNA delivery, stability, efficacy, safety/tolerability, and specificity. Addressing these challenges may pave a path for harnessing the full potential of miRNAs in modern medicine.

BCMCMI: A Fusion Model for Predicting circRNA-miRNA Interactions Combining Semantic and Meta-path

More and more evidence suggests that circRNA plays a vital role in generating and treating diseases by interacting with miRNA. Therefore, accurate prediction of potential circRNA-miRNA interaction (CMI) has become urgent. However, traditional wet experiments are time-consuming and costly, and the results will be affected by objective factors. In this paper, we propose a computational model BCMCMI, which combines three features to predict CMI. Specifically, BCMCMI utilizes the bidirectional encoding capability of the BERT algorithm to extract sequence features from the semantic information of circRNA and miRNA. Then, a heterogeneous network is constructed based on cosine similarity and known CMI information. The Metapath2vec is employed to conduct random walks following meta-paths in the network to capture topological features, including similarity features. Finally, potential CMIs are predicted using the XGBoost classifier. BCMCMI achieves superior results compared to other state-of-the-art models on two benchmark datasets for CMI prediction. We also utilize t-SNE to visually observe the distribution of the extracted features on a randomly selected dataset. The remarkable prediction results show that BCMCMI can serve as a valuable complement to the wet experiment process.

Inferring pseudogene-MiRNA associations based on an ensemble learning framework with similarity kernel fusion

Accumulating evidence shows that pseudogenes can function as microRNAs (miRNAs) sponges and regulate gene expression. Mining potential interactions between pseudogenes and miRNAs will facilitate the clinical diagnosis and treatment of complex diseases. However, identifying their interactions through biological experiments is time-consuming and labor intensive. In this study, an ensemble learning framework with similarity kernel fusion is proposed to predict pseudogene-miRNA associations, named ELPMA. First, four pseudogene similarity profiles and five miRNA similarity profiles are measured based on the biological and topology properties. Subsequently, similarity kernel fusion method is used to integrate the similarity profiles. Then, the feature representation for pseudogenes and miRNAs is obtained by combining the pseudogene-pseudogene similarities, miRNA-miRNA similarities. Lastly, individual learners are performed on each training subset, and the soft voting is used to yield final decision based on the prediction results of individual learners. The k-fold cross validation is implemented to evaluate the prediction performance of ELPMA method. Besides, case studies are conducted on three investigated pseudogenes to validate the predict performance of ELPMA method for predicting pseudogene-miRNA interactions. Therefore, all experiment results show that ELPMA model is a feasible and effective tool to predict interactions between pseudogenes and miRNAs.

Identifying circRNA-miRNA interaction based on multi-biological interaction fusion

CircRNA is a new type of non-coding RNA with a closed loop structure. More and more biological experiments show that circRNA plays important roles in many diseases by regulating the target genes of miRNA. Therefore, correct identification of the potential interaction between circRNA and miRNA not only helps to understand the mechanism of the disease, but also contributes to the diagnosis, treatment, and prognosis of the disease. In this study, we propose a model (IIMCCMA) by using network embedding and matrix completion to predict the potential interaction of circRNA-miRNA. Firstly, the corresponding adjacency matrix is constructed based on the experimentally verified circRNA-miRNA interaction, circRNA-cancer interaction, and miRNA-cancer interaction. Then, the Gaussian kernel function and the cosine function are used to calculate the circRNA Gaussian interaction profile kernel similarity, circRNA functional similarity, miRNA Gaussian interaction profile kernel similarity, and miRNA functional similarity. In order to reduce the influence of noise and redundant information in known interactions, this model uses network embedding to extract the potential feature vectors of circRNA and miRNA, respectively. Finally, an improved inductive matrix completion algorithm based on the feature vectors of circRNA and miRNA is used to identify potential interactions between circRNAs and miRNAs. The 10-fold cross-validation experiment is utilized to prove the predictive ability of the IIMCCMA. The experimental results show that the AUC value and AUPR value of the IIMCCMA model are higher than other state-of-the-art algorithms. In addition, case studies show that the IIMCCMA model can correctly identify the potential interactions between circRNAs and miRNAs.