A survey of circular RNAs in complex diseases: databases, tools and computational methods

Gastrin-related circRNA_0017065 promotes the proliferation and metastasis of colorectal cancer through the miR-3174/RBFOX2 axis

Gastrin is a gastrointestinal peptide hormone that plays an important role in the progression of colorectal cancer (CRC). However, the molecular mechanism remains unclear. In this study, we identified gastrin-related circRNAs via high-throughput sequencing and selected circRNA_0017065 as the research focus. We further studied its specific role and molecular mechanism in the progression of CRC. Knockdown and overexpression of circRNA_0017065 were performed, and the biological function of circRNA_0017065 in CRC progression was studied via in vitro and in vivo functional experiments. The potential downstream target genes were subsequently identified via screening of databases and gene chip data. The expression of circRNA_0017065 in tumour tissues was significantly upregulated compared with that in adjacent normal tissues. In vitro and in vivo functional experiments revealed that the proliferation and migration of CRC cells were significantly suppressed after circRNA_0017065 knockdown, while apoptosis was promoted. After overexpression of circRNA_0017065, the proliferation and migration of CRC cells were significantly promoted, while apoptosis was inhibited. Mechanistic studies revealed that circRNA_0017065 can act as a sponge for miR-3174 and promote CRC progression via the miR-3174/RBFOX2 axis. In general, gastrin-related circRNA_0017065 plays a key role in the occurrence and development of CRC and is expected to be a potential molecular target for the treatment of CRC metastasis.

Exploring potential circRNA biomarkers for cancers based on double-line heterogeneous graph representation learning

BACKGROUND

Compared with the time-consuming and labor-intensive for biological validation in vitro or in vivo, the computational models can provide high-quality and purposeful candidates in an instant. Existing computational models face limitations in effectively utilizing sparse local structural information for accurate predictions in circRNA-disease associations. This study addresses this challenge with a proposed method, CDA-DGRL (Prediction of CircRNA-Disease Association based on Double-line Graph Representation Learning), which employs a deep learning framework leveraging graph networks and a dual-line representation model integrating graph node features.

METHOD

CDA-DGRL comprises several key steps: initially, the integration of diverse biological information to compute integrated similarities among circRNAs and diseases, leading to the construction of a heterogeneous network specific to circRNA-disease associations. Subsequently, circRNA and disease node features are derived using sparse autoencoders. Thirdly, a graph convolutional neural network is employed to capture the local graph network structure by inputting the circRNA-disease heterogeneous network alongside node features. Fourthly, the utilization of node2vec facilitates depth-first sampling of the circRNA-disease heterogeneous network to grasp the global graph network structure, addressing issues associated with sparse raw data. Finally, the fusion of local and global graph network structures is inputted into an extra trees classifier to identify potential circRNA-disease associations.

RESULTS

The results, obtained through a rigorous five-fold cross-validation on the circR2Disease dataset, demonstrate the superiority of CDA-DGRL with an AUC value of 0.9866 and an AUPR value of 0.9897 compared to existing state-of-the-art models. Notably, the hyper-random tree classifier employed in this model outperforms other machine learning classifiers.

CONCLUSION

Thus, CDA-DGRL stands as a promising methodology for reliably identifying circRNA-disease associations, offering potential avenues to alleviate the necessity for extensive traditional biological experiments. The source code and data for this study are available at https://github.com/zywait/CDA-DGRL .

Predicting Drug-Protein Interactions through Branch-Chain Mining and multi-dimensional attention network

Identifying drug-protein interactions (DPIs) is crucial in drug discovery and repurposing. Computational methods for precise DPI identification can expedite development timelines and reduce expenses compared with conventional experimental methods. Lately, deep learning techniques have been employed for predicting DPIs, enhancing these processes. Nevertheless, the limitations observed in prior studies, where many extract features from complete drug and protein entities, overlooking the crucial theoretical foundation that pharmacological responses are often correlated with specific substructures, can lead to poor predictive performance. Furthermore, certain substructure-focused research confines its exploration to a solitary fragment category, such as a functional group. In this study, addressing these constraints, we present an end-to-end framework termed BCMMDA for predicting DPIs. The framework considers various substructure types, including branch chains, common substructures, and specific fragments. We designed a specific feature learning module by combining our proposed multi-dimensional attention mechanism with convolutional neural networks (CNNs). Deep CNNs assist in capturing the synergistic effects among these fragment sets, enabling the extraction of relevant features of drugs and proteins. Meanwhile, the multi-dimensional attention mechanism refines the relationship between drug and protein features by assigning attention vectors to each drug compound and amino acid. This mechanism empowers the model to further concentrate on pivotal substructures and elements, thereby improving its ability to identify essential interactions in DPI prediction. We evaluated the performance of BCMMDA on four well-known benchmark datasets. The results indicated that BCMMDA outperformed state-of-the-art baseline models, demonstrating significant improvement in performance.

Deep learning models for disease-associated circRNA prediction: a review

Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep learning with a more powerful ability for representation learning enables it to be a promising technology for predicting disease-associated circRNAs. In this review, we mainly introduce the most popular databases related to circRNA, and summarize three types of deep learning-based circRNA-disease associations prediction methods: feature-generation-based, type-discrimination and hybrid-based methods. We further evaluate seven representative models on benchmark with ground truth for both balance and imbalance classification tasks. In addition, we discuss the advantages and limitations of each type of method and highlight suggested applications for future research.

Comprehensive elaboration of circular RNA in multiple myeloma

Circular RNAs (circRNAs), a novel category of endogenous non-coding RNAs, are usually well conserved across different species with a covalent closed-loop structure. Existing and emerging evidence confirms that circRNAs can function as regulators of alternative splicing, microRNA and RNA-binding protein sponges and translation, as well as gene transcription. In consideration of their multi-faceted functions, circRNAs are critically involved in hematological malignancies including multiple myeloma (MM). In particular, circRNAs have been found to play vital roles in tumor microenvironment and drug resistance, which may grant them potential roles as biomarkers for MM diagnosis and targeted therapy. In this review, we comprehensively elaborate the current state-of-the-art knowledge of circRNAs in MM, and then focus on their potential as biomarkers in diagnosis and therapy of MM.

Role of circular RNAs in disease progression and diagnosis of cancers: An overview of recent advanced insights

Tumor microenvironment (TME) is a crucial regulator of tumor progression and cells in the TME release a number of molecules that are responsible for anaplasticity, invasion, metastasis of tumor, establishing stem cell niches, up-regulation and down-regulation of various pathways in cancer cells, interfering with immune surveillance and immune escape. Moreover, they can serve as diagnostic markers, and determine effective therapies. Among them, CircRNAs have gained special attention due to their involvement in mutated pathways in cancers. By functioning as a molecular sponge for miRNAs, binding with proteins, and directing selective splicing. CircRNAs modify the immunological environment of cancers to promote their growth. Besides of critical role in tumor growth, circRNAs are emerging as potential candidates as biomarkers for diagnosis cancer therapy. Also, circRNAs vaccination even offers a novel approach to tumor immunotherapy. Over the recent years, studies are advocating that circRNAs have tissue specific tumor specific expression patterns, which indicates their potential clinical utility. Especially, circRNAs have emerged as potential predictive and prognostic biomarkers. Although, there has been significant progress in deciphering the role of circRNA in cancers, literature lacks comprehensive overview on this topic. Keeping in view of these significant discoveries, this review systematically discusses circRNA and their role in the tumor in different dimensions.

Using Graph Attention Network and Graph Convolutional Network to Explore Human CircRNA-Disease Associations Based on Multi-Source Data

Cumulative research studies have verified that multiple circRNAs are closely associated with the pathogenic mechanism and cellular level. Exploring human circRNA-disease relationships is significant to decipher pathogenic mechanisms and provide treatment plans. At present, several computational models are designed to infer potential relationships between diseases and circRNAs. However, the majority of existing approaches could not effectively utilize the multisource data and achieve poor performance in sparse networks. In this study, we develop an advanced method, GATGCN, using graph attention network (GAT) and graph convolutional network (GCN) to detect potential circRNA-disease relationships. First, several sources of biomedical information are fused via the centered kernel alignment model (CKA), which calculates the corresponding weight of different kernels. Second, we adopt the graph attention network to learn latent representation of diseases and circRNAs. Third, the graph convolutional network is deployed to effectively extract features of associations by aggregating feature vectors of neighbors. Meanwhile, GATGCN achieves the prominent AUC of 0.951 under leave-one-out cross-validation and AUC of 0.932 under 5-fold cross-validation. Furthermore, case studies on lung cancer, diabetes retinopathy, and prostate cancer verify the reliability of GATGCN for detecting latent circRNA-disease pairs.