Graph convolutional network approach to discovering disease-related circRNA-miRNA-mRNA axes

CoupleMDA: Metapath-Induced Structural-Semantic Coupling Network for miRNA-Disease Association Prediction

The prediction of microRNA-disease associations (MDAs) is crucial for understanding disease mechanisms and biomarker discovery. While graph neural networks have emerged as promising tools for MDA prediction, existing methods face critical limitations: (1) data leakage caused by improper use of Gaussian interaction profile (GIP) kernel similarity during feature construction, (2) self-validation loops in calculating miRNA functional similarity using known MDA data, and (3) information bottlenecks in conventional graph neural network (GNN) architectures that flatten heterogeneous relationships and employ over-simplified decoders. To address these challenges, we propose CoupleMDA, a metapath-guided heterogeneous graph learning framework coupling structural and semantic features. The model constructs a biological heterogeneous network using independent data sources to eliminate feature-target space coupling. Our framework implements a two-stage encoding strategy: (1) relational graph convolutional networks (RGCN) for pre-encoding and (2) metapath-guided semantic aggregation for secondary encoding. During decoding, common metapaths between node pairs structurally guide feature pooling, mitigating information bottlenecks. The comprehensive evaluation shows that CoupleMDA achieves a 2-5% performance improvement over the current state-of-the-art baseline methods in the heterogeneous graph link prediction task. Ablation studies confirm the necessity of each proposed component, while case analyses reveal the framework's capability to recover cancer-related miRNA-disease associations through biologically interpretable metapaths.

Mitigating off-target effects of small RNAs: conventional approaches, network theory and artificial intelligence

Three types of highly promising small RNA therapeutics, namely, small interfering RNAs (siRNAs), microRNAs (miRNAs) and the RNA subtype of antisense oligonucleotides (ASOs), offer advantages over small-molecule drugs. These small RNAs can target any gene product, opening up new avenues of effective and safe therapeutic approaches for a wide range of diseases. In preclinical research, synthetic small RNAs play an essential role in the investigation of physiological and pathological pathways as silencers of specific genes, facilitating discovery and validation of drug targets in different conditions. Off-target effects of small RNAs, however, could make it difficult to interpret experimental results in the preclinical phase and may contribute to adverse events of small RNA therapeutics. Out of the two major types of off-target effects we focused on the hybridization-dependent, especially on the miRNA-like off-target effects. Our main aim was to discuss several approaches, including sequence design, chemical modifications and target prediction, to reduce hybridization-dependent off-target effects that should be considered even at the early development phase of small RNA therapy. Because there is no standard way of predicting hybridization-dependent off-target effects, this review provides an overview of all major state-of-the-art computational methods and proposes new approaches, such as the possible inclusion of network theory and artificial intelligence (AI) in the prediction workflows. Case studies and a concise survey of experimental methods for validating in silico predictions are also presented. These methods could contribute to interpret experimental results, to minimize off-target effects and hopefully to avoid off-target-related adverse events of small RNA therapeutics. LINKED ARTICLES: This article is part of a themed issue Non-coding RNA Therapeutics. To view the other articles in this section visit http://onlinelibrary.wiley.com/doi/10.1111/bph.v182.2/issuetoc.

Circ_0003789 Knockdown Inhibits Tumor Progression by miR-429/ZFP36L2 Axis in Gastric Cancer

An increasing number of circRNAs have been found to be involved in the development of gastric cancer. However, the function of circ_0003789 in regulating gastric cancer progression is unclear. Here, we aimed to investigate the expression, function and molecular mechanism of circ_0003789 in gastric cancer pathogenesis. Circ_0003789, miR-429 and ZFP36 ring finger protein like 2 (ZFP36L2) mRNA were quantified by quantitative real-time polymerase chain reaction (qRT-PCR). Cell proliferation was illustrated by 5-Ethynyl-2'-deoxyuridine (Edu), cell counting kit-8 (CCK-8) and colony formation assays. Apoptosis was determined by flow cytometry. Protein level was detected by Western blotting assay. Xenograft assays were used for functional analysis of circ_0003789 in vivo. The relationship between miR-429 and circ_0003789 or ZFP36L2 was predicted by starbase3.0 online database and identified by dual luciferase reporter assay. The expression levels of circ_0003789 and ZFP36L2 were significantly upregulated in gastric cancer tissues and cells, while the expression of miR-429 was downregulated. Downregulation of circ_0003789 inhibited gastric cancer cell growth and invasion and promoted apoptosis in vitro. Circ_0003789 acted as a sponge of miR-429. Moreover, miR-429 silencing by miR-429 inhibitors attenuated the effects of circ_0003789 interference on cell growth, apoptosis and invasion. ZFP36L2 was targeted by miR-429, and the effects of miR-429 on cell growth, invasion and apoptosis were attenuated by ZFP36L2 overexpression. Circ_0003789 could enhance ZFP36L2 expression by interacting with miR-429. Silencing of circ_0003789 inhibited tumor growth in vivo. Circ_0003789 regulates tumor progression in gastric cancer through miR-429/ZFP36L2 axis. This finding implies that circ_0003789 may be a therapeutic target for gastric cancer.

A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation

BACKGROUND

Circular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significance for studying etiopathogenesis and treating diseases. To this end, based on the graph Markov neural network algorithm (GMNN) constructed in our previous work GMNN2CD, we further considered the multisource biological data that affects the association between circRNA and disease and developed an updated web server CircDA and based on the human hepatocellular carcinoma (HCC) tissue data to verify the prediction results of CircDA.

RESULTS

CircDA is built on a Tumarkov-based deep learning framework. The algorithm regards biomolecules as nodes and the interactions between molecules as edges, reasonably abstracts multiomics data, and models them as a heterogeneous biomolecular association network, which can reflect the complex relationship between different biomolecules. Case studies using literature data from HCC, cervical, and gastric cancers demonstrate that the CircDA predictor can identify missing associations between known circRNAs and diseases, and using the quantitative real-time PCR (RT-qPCR) experiment of HCC in human tissue samples, it was found that five circRNAs were significantly differentially expressed, which proved that CircDA can predict diseases related to new circRNAs.

CONCLUSIONS

This efficient computational prediction and case analysis with sufficient feedback allows us to identify circRNA-associated diseases and disease-associated circRNAs. Our work provides a method to predict circRNA-associated diseases and can provide guidance for the association of diseases with certain circRNAs. For ease of use, an online prediction server ( http://server.malab.cn/CircDA ) is provided, and the code is open-sourced ( https://github.com/nmt315320/CircDA.git ) for the convenience of algorithm improvement.

Cross-view contrastive representation learning approach to predicting DTIs via integrating multi-source information

Drug-target interaction (DTI) prediction serves as the foundation of new drug findings and drug repositioning. For drugs/targets, the sequence data contains the biological structural information, while the heterogeneous network contains the biochemical functional information. These two types of information describe different aspects of drugs and targets. Due to the complexity of DTI machinery, it is necessary to learn the representation from multiple perspectives. We hereby try to design a way to leverage information from multi-source data to the maximum extent and find a strategy to fuse them. To address the above challenges, we propose a model, named MOVE (short for integrating multi-source information for predicting DTI via cross-view contrastive learning), for learning comprehensive representations of each drug and target from multi-source data. MOVE extracts information from the sequence view and the network view, then utilizes a fusion module with auxiliary contrastive learning to facilitate the fusion of representations. Experimental results on the benchmark dataset demonstrate that MOVE is effective in DTI prediction.

The malignancy of chordomas is enhanced via a circTLK1/miR-16-5p/Smad3 positive feedback axis

CircRNAs play crucial roles in various malignancies via an increasing number of reported regulatory mechanisms, including the classic sponging mechanism between circRNAs and micro RNAs (miRNAs). We performed bioinformatic analyses and identified circTLK1 as a regulator of malignant chordoma progression. Moreover, we observed that circTLK1 showed high expression in chordoma cells and tissues, while circTLK1 interference suppressed chordoma cell proliferation and invasion. In addition, circTLK1 directly interacted with miR-16-5p, which has previously been shown to repress chordoma, and circTLK1 knockdown suppressed Smad3 expression. Chromatin immunoprecipitation sequencing further demonstrated that Smad3 acts as a positive regulator by interacting with TLK1, thereby mediating the circTLK1/miR-16-5p/Smad3 positive feedback axis. Taken together, our findings suggested that the disruption of the circTLK1/miR-16-5p/Smad3 positive feedback pathway, particularly via the Smad3 inhibitor SIS3, could be a promising therapeutic strategy.

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective

Molecular and sequencing technologies have been successfully used in decoding biological mechanisms of various diseases. As revealed by many novel discoveries, the role of non-coding RNAs (ncRNAs) in understanding disease mechanisms is becoming increasingly important. Since ncRNAs primarily act as regulators of transcription, associating ncRNAs with diseases involves multiple inference steps. Leveraging the fast-accumulating high-throughput screening results, a number of computational models predicting ncRNA-disease associations have been developed. These tools suggest novel disease-related biomarkers or therapeutic targetable ncRNAs, contributing to the realization of precision medicine. In this survey, we first introduce the biological roles of different ncRNAs and summarize the databases containing ncRNA-disease associations. Then, we suggest a new trend in recent computational prediction of ncRNA-disease association, which is the mode of action (MoA) network perspective. This perspective includes integrating ncRNAs with mRNA, pathway and phenotype information. In the next section, we describe computational methodologies widely used in this research domain. Existing computational studies are then summarized in terms of their coverage of the MoA network. Lastly, we discuss the potential applications and future roles of the MoA network in terms of integrating biological mechanisms for ncRNA-disease associations.

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.

HPE-GCN: predicting efficacy of tonic formulae via graph convolutional networks integrating traditionally defined herbal properties

Chinese herbal formulae are the heritage of traditional Chinese medicine (TCM) in treating diseases through thousands of years. The formula function is not just a simple herbal efficacy addition, but produces complex and nonlinear relationships between different herbs and their overall efficacy, which brings challenges to the formula efficacy analysis. In our study, we proposed a model called HPE-GCN that combines graph convolutional networks (GCN) with TCM-defined herbal properties (TCM-HPs) to predict formulae efficacy. In addition, to process the unstructured natural language in the formula text, we proposed a weighting calculation method related to herb frequency and the number of herbs in a formula called Formula-Herb dependence degree (FHDD), to assess the dependency degree of a formula with its herbs. In our research, 214 classic tonic formulae from ancient TCM books such as Synopsis of the Golden Chamber, Jingyue's Complete Works and the Golden Mirror of Medicin were collected as datasets. The performance of HPE-GCN on multi-classification of tonic formulae reached the best result compared with classic machine learning models, such as support vector machine, naive Bayes, logistic regression, gradient boosting decision tree, and K-nearest neighbors. The evaluated index Macro-Precision, Macro-Recall, Macro-F1 of HPE-GCN on the test set were 87.70%, 84.08% and 83.51% respectively, increased by 7.27%, 7.41% and 7.30% respectively from second best compared models. GCN has the advantage of low-dimensional feature expression for herbs and formulae, and is an effective analysis tool for TCM research. HPE-GCN integrates TCM-HPs and fits the complex nonlinear mapping relationship between TCM-HPs and formulae efficacy, which provides new ideas for related research.