EMCMDA: predicting miRNA-disease associations via efficient matrix completion

Abundant researches have consistently illustrated the crucial role of microRNAs (miRNAs) in a wide array of essential biological processes. Furthermore, miRNAs have been validated as promising therapeutic targets for addressing complex diseases. Given the costly and time-consuming nature of traditional biological experimental validation methods, it is imperative to develop computational methods. In the work, we developed a novel approach named efficient matrix completion (EMCMDA) for predicting miRNA-disease associations. First, we calculated the similarities across multiple sources for miRNA/disease pairs and combined this information to create a holistic miRNA/disease similarity measure. Second, we utilized this biological information to create a heterogeneous network and established a target matrix derived from this network. Lastly, we framed the miRNA-disease association prediction issue as a low-rank matrix-complete issue that was addressed via minimizing matrix truncated schatten p-norm. Notably, we improved the conventional singular value contraction algorithm through using a weighted singular value contraction technique. This technique dynamically adjusts the degree of contraction based on the significance of each singular value, ensuring that the physical meaning of these singular values is fully considered. We evaluated the performance of EMCMDA by applying two distinct cross-validation experiments on two diverse databases, and the outcomes were statistically significant. In addition, we executed comprehensive case studies on two prevalent human diseases, namely lung cancer and breast cancer. Following prediction and multiple validations, it was evident that EMCMDA proficiently forecasts previously undisclosed disease-related miRNAs. These results underscore the robustness and efficacy of EMCMDA in miRNA-disease association prediction.

dCCA: detecting differential covariation patterns between two types of high-throughput omics data

MOTIVATION

The advent of multimodal omics data has provided an unprecedented opportunity to systematically investigate underlying biological mechanisms from distinct yet complementary angles. However, the joint analysis of multi-omics data remains challenging because it requires modeling interactions between multiple sets of high-throughput variables. Furthermore, these interaction patterns may vary across different clinical groups, reflecting disease-related biological processes.

RESULTS

We propose a novel approach called Differential Canonical Correlation Analysis (dCCA) to capture differential covariation patterns between two multivariate vectors across clinical groups. Unlike classical Canonical Correlation Analysis, which maximizes the correlation between two multivariate vectors, dCCA aims to maximally recover differentially expressed multivariate-to-multivariate covariation patterns between groups. We have developed computational algorithms and a toolkit to sparsely select paired subsets of variables from two sets of multivariate variables while maximizing the differential covariation. Extensive simulation analyses demonstrate the superior performance of dCCA in selecting variables of interest and recovering differential correlations. We applied dCCA to the Pan-Kidney cohort from the Cancer Genome Atlas Program database and identified differentially expressed covariations between noncoding RNAs and gene expressions.

AVAILABILITY AND IMPLEMENTATION

The R package that implements dCCA is available at https://github.com/hwiyoungstat/dCCA.

Topology and Dynamics of Transcriptome (Dys)Regulation

RNA transcripts play a crucial role as witnesses of gene expression health. Identifying disruptive short sequences in RNA transcription and regulation is essential for potentially treating diseases. Let us delve into the mathematical intricacies of these sequences. We have previously devised a mathematical approach for defining a "healthy" sequence. This sequence is characterized by having at most four distinct nucleotides (denoted as nt≤4). It serves as the generator of a group denoted as fp. The desired properties of this sequence are as follows: fp should be close to a free group of rank nt-1, it must be aperiodic, and fp should not have isolated singularities within its SL2(C) character variety (specifically within the corresponding Groebner basis). Now, let us explore the concept of singularities. There are cubic surfaces associated with the character variety of a four-punctured sphere denoted as S24. When we encounter these singularities, we find ourselves dealing with some algebraic solutions of a dynamical second-order differential (and transcendental) equation known as the Painlevé VI Equation. In certain cases, S24 degenerates, in the sense that two punctures collapse, resulting in a "wild" dynamics governed by the Painlevé equations of an index lower than VI. In our paper, we provide examples of these fascinating mathematical structures within the context of miRNAs. Specifically, we find a clear relationship between decorated character varieties of Painlevé equations and the character variety calculated from the seed of oncomirs. These findings should find many applications including cancer research and the investigation of neurodegenative diseases.

A Survey of Deep Learning for Detecting miRNA- Disease Associations: Databases, Computational Methods, Challenges, and Future Directions

MicroRNAs (miRNAs) are an important class of non-coding RNAs that play an essential role in the occurrence and development of various diseases. Identifying the potential miRNA-disease associations (MDAs) can be beneficial in understanding disease pathogenesis. Traditional laboratory experiments are expensive and time-consuming. Computational models have enabled systematic large-scale prediction of potential MDAs, greatly improving the research efficiency. With recent advances in deep learning, it has become an attractive and powerful technique for uncovering novel MDAs. Consequently, numerous MDA prediction methods based on deep learning have emerged. In this review, we first summarize publicly available databases related to miRNAs and diseases for MDA prediction. Next, we outline commonly used miRNA and disease similarity calculation and integration methods. Then, we comprehensively review the 48 existing deep learning-based MDA computation methods, categorizing them into classical deep learning and graph neural network-based techniques. Subsequently, we investigate the evaluation methods and metrics that are frequently used to assess MDA prediction performance. Finally, we discuss the performance trends of different computational methods, point out some problems in current research, and propose 9 potential future research directions. Data resources and recent advances in MDA prediction methods are summarized in the GitHub repository https://github.com/sheng-n/DL-miRNA-disease-association-methods.

Exosomes derived from pulmonary metastatic sites enhance osteosarcoma lung metastasis by transferring the miR-194/215 cluster targeting MARCKS

Osteosarcoma, a prevalent primary malignant bone tumor, often presents with lung metastases, severely impacting patient survival rates. Extracellular vesicles, particularly exosomes, play a pivotal role in the formation and progression of osteosarcoma-related pulmonary lesions. However, the communication between primary osteosarcoma and exosome-mediated pulmonary lesions remains obscure, with the potential impact of pulmonary metastatic foci on osteosarcoma progression largely unknown. This study unveils an innovative mechanism by which exosomes originating from osteosarcoma pulmonary metastatic sites transport the miR-194/215 cluster to the primary tumor site. This transportation enhances lung metastatic capability by downregulating myristoylated alanine-rich C-kinase substrate (MARCKS) expression. Addressing this phenomenon, in this study we employ cationic bovine serum albumin (CBSA) to form nanoparticles (CBSA-anta-194/215) via electrostatic interaction with antagomir-miR-194/215. These nanoparticles are loaded into nucleic acid-depleted exosomal membrane vesicles (anta-194/215@Exo) targeting osteosarcoma lung metastatic sites. Intervention with bioengineered exosome mimetics (anta-194/215@Exo) not only impedes osteosarcoma progression but also significantly prolongs the lifespan of tumor-bearing mice. These findings suggest that pulmonary metastatic foci-derived exosomes initiate primary osteosarcoma lung metastasis by transferring the miR-194/215 cluster targeting MARCKS, making the miR-194/215 cluster a promising therapeutic target for inhibiting the progression of patients with osteosarcoma lung metastases.

Promoter of lncRNA MORT is aberrantly methylated in colorectal cancer

Aberrant DNA methylation plays essential roles in the colorectal cancer (CRC) carcinogenesis and has been demonstrated as a promising marker for cancer early detection. In this project, methylation status of the MORT promoter was studied in CRC and their marginal tissues using qMSP assay. Furthermore, we investigated the molecular function of MORT in CRC progression using computational analysis. The results showed a high methylation level of MORT promoter in CRC tissues. By in silico analysis, we found that MORT downregulation could promote the proliferation of CRC cells via sponging of has-miR-574-5p and has-miR-31-5p, and alteration of their targets expression pattern such as MYOCD and FOXP2. In conclusion, based on our results, promoter hypermethylation of MORT might be considered as a potential biomarker for CRC detection.

Recent applications of artificial intelligence in RNA-targeted small molecule drug discovery

INTRODUCTION

Targeting RNAs with small molecules offers an alternative to the conventional protein-targeted drug discovery and can potentially address unmet and emerging medical needs. The recent rise of interest in the strategy has already resulted in large amounts of data on disease associated RNAs, as well as on small molecules that bind to such RNAs. Artificial intelligence (AI) approaches, including machine learning and deep learning, present an opportunity to speed up the discovery of RNA-targeted small molecules by improving decision-making efficiency and quality.

AREAS COVERED

The topics described in this review include the recent applications of AI in the identification of RNA targets, RNA structure determination, screening of chemical compound libraries, and hit-to-lead optimization. The impact and limitations of the recent AI applications are discussed, along with an outlook on the possible applications of next-generation AI tools for the discovery of novel RNA-targeted small molecule drugs.

EXPERT OPINION

Key areas for improvement include developing AI tools for understanding RNA dynamics and RNA - small molecule interactions. High-quality and comprehensive data still need to be generated especially on the biological activity of small molecules that target RNAs.

Pan-cancer analyses suggest kindlin-associated global mechanochemical alterations

Kindlins serve as mechanosensitive adapters, transducing extracellular mechanical cues to intracellular biochemical signals and thus, their perturbations potentially lead to cancer progressions. Despite the kindlin involvement in tumor development, understanding their genetic and mechanochemical characteristics across different cancers remains elusive. Here, we thoroughly examined genetic alterations in kindlins across more than 10,000 patients with 33 cancer types. Our findings reveal cancer-specific alterations, particularly prevalent in advanced tumor stage and during metastatic onset. We observed a significant co-alteration between kindlins and mechanochemical proteome in various tumors through the activation of cancer-related pathways and adverse survival outcomes. Leveraging normal mode analysis, we predicted structural consequences of cancer-specific kindlin mutations, highlighting potential impacts on stability and downstream signaling pathways. Our study unraveled alterations in epithelial-mesenchymal transition markers associated with kindlin activity. This comprehensive analysis provides a resource for guiding future mechanistic investigations and therapeutic strategies targeting the roles of kindlins in cancer treatment.

MUSCLE: multi-view and multi-scale attentional feature fusion for microRNA-disease associations prediction

MicroRNAs (miRNAs) synergize with various biomolecules in human cells resulting in diverse functions in regulating a wide range of biological processes. Predicting potential disease-associated miRNAs as valuable biomarkers contributes to the treatment of human diseases. However, few previous methods take a holistic perspective and only concentrate on isolated miRNA and disease objects, thereby ignoring that human cells are responsible for multiple relationships. In this work, we first constructed a multi-view graph based on the relationships between miRNAs and various biomolecules, and then utilized graph attention neural network to learn the graph topology features of miRNAs and diseases for each view. Next, we added an attention mechanism again, and developed a multi-scale feature fusion module, aiming to determine the optimal fusion results for the multi-view topology features of miRNAs and diseases. In addition, the prior attribute knowledge of miRNAs and diseases was simultaneously added to achieve better prediction results and solve the cold start problem. Finally, the learned miRNA and disease representations were then concatenated and fed into a multi-layer perceptron for end-to-end training and predicting potential miRNA-disease associations. To assess the efficacy of our model (called MUSCLE), we performed 5- and 10-fold cross-validation (CV), which got average the Area under ROC curves of 0.966${\pm }$0.0102 and 0.973${\pm }$0.0135, respectively, outperforming most current state-of-the-art models. We then examined the impact of crucial parameters on prediction performance and performed ablation experiments on the feature combination and model architecture. Furthermore, the case studies about colon cancer, lung cancer and breast cancer also fully demonstrate the good inductive capability of MUSCLE. Our data and code are free available at a public GitHub repository: https://github.com/zht-code/MUSCLE.git.

CMC: Cancer miRNA Census - a list of cancer-related miRNA genes

A growing body of evidence indicates an important role of miRNAs in cancer; however, there is no definitive, convenient-to-use list of cancer-related miRNAs or miRNA genes that may serve as a reference for analyses of miRNAs in cancer. To this end, we created a list of 165 cancer-related miRNA genes called the Cancer miRNA Census (CMC). The list is based on a score, built on various types of functional and genetic evidence for the role of particular miRNAs in cancer, e.g. miRNA-cancer associations reported in databases, associations of miRNAs with cancer hallmarks, or signals of positive selection of genetic alterations in cancer. The presence of well-recognized cancer-related miRNA genes, such as MIR21, MIR155, MIR15A, MIR17 or MIRLET7s, at the top of the CMC ranking directly confirms the accuracy and robustness of the list. Additionally, to verify and indicate the reliability of CMC, we performed a validation of criteria used to build CMC, comparison of CMC with various cancer data (publications and databases), and enrichment analyses of biological pathways and processes such as Gene Ontology or DisGeNET. All validation steps showed a strong association of CMC with cancer/cancer-related processes confirming its usefulness as a reference list of miRNA genes associated with cancer.

Complementary feature learning across multiple heterogeneous networks and multimodal attribute learning for predicting disease-related miRNAs

Inferring the latent disease-related miRNAs is helpful for providing a deep insight into observing the disease pathogenesis. We propose a method, CMMDA, to encode and integrate the context relationship among multiple heterogeneous networks, the complementary information across these networks, and the pairwise multimodal attributes. We first established multiple heterogeneous networks according to the diverse disease similarities. The feature representation embedding the context relationship is formulated for each miRNA (disease) node based on transformer. We designed a co-attention fusion mechanism to encode the complementary information among multiple networks. In terms of a pair of miRNA and disease nodes, the pairwise attributes from multiple networks form a multimodal attribute embedding. A module based on depthwise separable convolution is constructed to enhance the encoding of the specific features from each modality. The experimental results and the ablation studies show that CMMDA's superior performance and the effectiveness of its major innovations.

HMDD v4.0: a database for experimentally supported human microRNA-disease associations

MicroRNAs (miRNAs) are a class of important small non-coding RNAs with critical molecular functions in almost all biological processes, and thus, they play important roles in disease diagnosis and therapy. Human MicroRNA Disease Database (HMDD) represents an important and comprehensive resource for biomedical researchers in miRNA-related medicine. Here, we introduce HMDD v4.0, which curates 53530 miRNA-disease association entries from literatures. In comparison to HMDD v3.0 released five years ago, HMDD v4.0 contains 1.5 times more entries. In addition, some new categories have been curated, including exosomal miRNAs implicated in diseases, virus-encoded miRNAs involved in human diseases, and entries containing miRNA-circRNA interactions. We also curated sex-biased miRNAs in diseases. Furthermore, in a case study, disease similarity analysis successfully revealed that sex-biased miRNAs related to developmental anomalies are associated with a number of human diseases with sex bias. HMDD can be freely visited at http://www.cuilab.cn/hmdd.

MDformer: A transformer-based method for predicting miRNA-Disease associations using multi-source feature fusion and maximal meta-path instances encoding

There is a growing body of evidence suggesting that microRNAs (miRNAs), small biological molecules, play a crucial role in the diagnosis, treatment, and prognostic assessment of diseases. However, it is often inefficient to verify the association between miRNAs and diseases (MDA) through traditional experimental methods. Based on this situation, researchers have proposed various computational-based methods, but the existing methods often have many drawbacks in terms of predictive effectiveness and accuracy. Therefore, in order to improve the prediction performance of computational methods, we propose a transformer-based prediction model (MDformer) for multi-source feature information. Specifically, first, we consider multiple features of miRNAs and diseases from the molecular biology perspective and utilize them in a fusion. Then high-quality node feature embeddings were generated using a feature encoder based on the transformer architecture and meta-path instances. Finally, a deep neural network was built for MDA prediction. To evaluate the performance of our model, we performed multiple 5-fold cross-validations as well as comparison experiments on HMDD v3.2 and HMDD v2.0 databases, and the experimental results of the average ROC area under the curve (AUC) were higher than the comparative methods for both databases at 0.9506 and 0.9369. We conducted case studies on five highly lethal cancers (breast, lung, colorectal, gastric, and hepatocellular cancers), and the first 30 predictions for these five diseases achieved 97.3% accuracy. In conclusion, MDformer is a reliable and scientifically sound tool that can be used to accurately predict MDA. In addition, the source code is available at https://github.com/Linda908/MDformer.

Comprehensive analysis of miRNA-mRNA regulatory pairs associated with colorectal cancer and the role in tumor immunity

BACKGROUND

MicroRNA (miRNA) which can act as post-transcriptional regulators of mRNAs via base-pairing with complementary sequences within mRNAs is involved in processes of the complex interaction between immune system and tumors. In this research, we elucidated the profiles of miRNAs and target mRNAs expression and their associations with the phenotypic hallmarks of colorectal cancers (CRC) by integrating transcriptomic, immunophenotype, methylation, mutation and survival data.

RESULTS

We conducted the analysis of differential miRNA/mRNA expression profile by GEO, TCGA and GTEx databases and the correlation between miRNA and targeted mRNA by miRTarBase and TarBase. Then we detected using qRT-PCR and validated the diagnostic value of miRNA-mRNA regulator pairs by the ROC, calibration curve and DCA. Phenotypic hallmarks of regulatory pairs including tumor-infiltrating lymphocytes, tumor microenvironment, tumor mutation burden, global methylation and gene mutation were also described. The expression levels of miRNAs and target mRNAs were detected in 80 paired colon tissue samples. Ultimately, we picked up two pivotal regulatory pairs (miR-139-5p/ STC1 and miR-20a-5p/ FGL2) and verified the diagnostic value of the complex model which is the combination of 4 signatures above-mentioned in 3 testing GEO datasets and an external validation cohort.

CONCLUSIONS

We found that 2 miRNAs by targeting 2 metastasis-related mRNAs were correlated with tumor-infiltrating macrophages, HRAS, and BRAF gene mutation status. Our results established the diagnostic model containing 2 miRNAs and their respective targeted mRNAs to distinguish CRCs and normal controls and displayed their complex roles in CRC pathogenesis especially tumor immunity.

Identifying the potential miRNA biomarkers based on multi-view networks and reinforcement learning for diseases

MicroRNAs (miRNAs) play important roles in the occurrence and development of diseases. However, it is still challenging to identify the effective miRNA biomarkers for improving the disease diagnosis and prognosis. In this study, we proposed the miRNA data analysis method based on multi-view miRNA networks and reinforcement learning, miRMarker, to define the potential miRNA disease biomarkers. miRMarker constructs the cooperative regulation network and functional similarity network based on the expression data and known miRNA-disease relations, respectively. The cooperative regulation of miRNAs was evaluated by measuring the changes of relative expression. Natural language processing was introduced for calculating the miRNA functional similarity. Then, miRMarker integrates the multi-view miRNA networks and defines the informative miRNA modules through a reinforcement learning strategy. We compared miRMarker with eight efficient data analysis methods on nine transcriptomics datasets to show its superiority in disease sample discrimination. The comparison results suggested that miRMarker outperformed other data analysis methods in receiver operating characteristic analysis. Furthermore, the defined miRNA modules of miRMarker on colorectal cancer data not only show the excellent performance of cancer sample discrimination but also play significant roles in the cancer-related pathway disturbances. The experimental results indicate that miRMarker can build the robust miRNA interaction network by integrating the multi-view networks. Besides, exploring the miRNA interaction network using reinforcement learning favors defining the important miRNA modules. In summary, miRMarker can be a hopeful tool in biomarker identification for human diseases.

Identifying the driver miRNAs with somatic copy number alterations driving dysregulated ceRNA networks in cancers

BACKGROUND

MicroRNAs (miRNAs) play critical roles in cancer initiation and progression, which were critical components to maintain the dynamic balance of competing endogenous RNA (ceRNA) networks. Somatic copy number alterations (SCNAs) in the cancer genome could disturb the transcriptome level of miRNA to deregulate this balance. However, the driving effects of SCNAs of miRNAs were insufficiently understood.

METHODS

In this study, we proposed a method to dissect the functional roles of miRNAs under different copy number states and identify driver miRNAs by integrating miRNA SCNAs profile, miRNA-target relationships and expression profiles of miRNA, mRNA and lncRNA.

RESULTS

Applying our method to 813 TCGA breast cancer (BRCA) samples, we identified 29 driver miRNAs whose SCNAs significantly and concordantly regulated their own expression levels and further inversely dysregulated expression levels of their targets or disturbed the miRNA-target networks they directly involved. Based on miRNA-target networks, we further constructed dynamic ceRNA networks driven by driver SCNAs of miRNAs and identified three different patterns of SCNA interference in the miRNA-mediated dynamic ceRNA networks. Survival analysis of driver miRNAs showed that high-level amplifications of four driver miRNAs (including has-miR-30d-3p, has-mir-30b-5p, has-miR-30d-5p and has-miR-151a-3p) in 8q24 characterized a new BRCA subtype with poor prognosis and contributed to the dysfunction of cancer-associated hallmarks in a complementary way. The SCNAs of driver miRNAs across different cancer types contributed to the cancer development by dysregulating different components of the same cancer hallmarks, suggesting the cancer specificity of driver miRNA.

CONCLUSIONS

These results demonstrate the efficacy of our method in identifying driver miRNAs and elucidating their functional roles driven by endogenous SCNAs, which is useful for interpreting cancer genomes and pathogenic mechanisms.

BertNDA: A Model Based on Graph-Bert and Multi-Scale Information Fusion for ncRNA-Disease Association Prediction

Non-coding RNAs (ncRNAs) are a class of RNA molecules that lack the ability to encode proteins in human cells, but play crucial roles in various biological process. Understanding the interactions between different ncRNAs and their impact on diseases can significantly contribute to diagnosis, prevention, and treatment of diseases. However, predicting tertiary interactions between ncRNAs and diseases based on structural information in multiple scales remains a challenging task. To address this challenge, we propose a method called BertNDA, aiming to predict potential relationships between miRNAs, lncRNAs, and diseases. The framework identifies the local information through connectionless subgraph, which aggregate neighbor nodes' feature. And global information is extracted by leveraging Laplace transform of graph structures and WL (Weisfeiler-Lehman) absolute role coding. Additionally, an EMLP (Element-wise MLP) structure is designed to fuse pairwise global information. The transformer-encoder is employed as the backbone of our approach, followed by a prediction-layer to output the final correlation score. Extensive experiments demonstrate that BertNDA outperforms state-of-the-art methods in prediction assignment and exhibits significant potential for various biological applications. Moreover, we develop an online prediction platform that incorporates the prediction model, providing users with an intuitive and interactive experience. Overall, our model offers an efficient, accurate, and comprehensive tool for predicting tertiary associations between ncRNAs and diseases.

ESOMIR: a curated database of biomarker genes and miRNAs associated with esophageal cancer

'Esophageal cancer' (EC) is a highly aggressive and deadly complex disease. It comprises two types, esophageal adenocarcinoma (EAC) and esophageal squamous cell carcinoma (ESCC), with Barrett's esophagus (BE) being the only known precursor. Recent research has revealed that microRNAs (miRNAs) play a crucial role in the development, prognosis and treatment of EC and are involved in various human diseases. Biological databases have become essential for cancer research as they provide information on genes, proteins, pathways and their interactions. These databases collect, store and manage large amounts of molecular data, which can be used to identify patterns, predict outcomes and generate hypotheses. However, no comprehensive database exists for EC and miRNA relationships. To address this gap, we developed a dynamic database named 'ESOMIR (miRNA in esophageal cancer) (https://esomir.dqweilab-sjtu.com)', which includes information about targeted genes and miRNAs associated with EC. The database uses analysis and prediction methods, including experimentally endorsed miRNA(s) information. ESOMIR is a user-friendly interface that allows easy access to EC-associated data by searching for miRNAs, target genes, sequences, chromosomal positions and associated signaling pathways. The search modules are designed to provide specific data access to users based on their requirements. Additionally, the database provides information about network interactions, signaling pathways and region information of chromosomes associated with the 3'untranslated region (3'UTR) or 5'UTR and exon sites. Users can also access energy levels of specific miRNAs with targeted genes. A fuzzy term search is included in each module to enhance the ease of use for researchers. ESOMIR can be a valuable tool for researchers and clinicians to gain insight into EC, including identifying biomarkers and treatments for this aggressive tumor. Database URL https://esomir.dqweilab-sjtu.com.

A comprehensive review and evaluation of graph neural networks for non-coding RNA and complex disease associations

Non-coding RNAs (ncRNAs) play a critical role in the occurrence and development of numerous human diseases. Consequently, studying the associations between ncRNAs and diseases has garnered significant attention from researchers in recent years. Various computational methods have been proposed to explore ncRNA-disease relationships, with Graph Neural Network (GNN) emerging as a state-of-the-art approach for ncRNA-disease association prediction. In this survey, we present a comprehensive review of GNN-based models for ncRNA-disease associations. Firstly, we provide a detailed introduction to ncRNAs and GNNs. Next, we delve into the motivations behind adopting GNNs for predicting ncRNA-disease associations, focusing on data structure, high-order connectivity in graphs and sparse supervision signals. Subsequently, we analyze the challenges associated with using GNNs in predicting ncRNA-disease associations, covering graph construction, feature propagation and aggregation, and model optimization. We then present a detailed summary and performance evaluation of existing GNN-based models in the context of ncRNA-disease associations. Lastly, we explore potential future research directions in this rapidly evolving field. This survey serves as a valuable resource for researchers interested in leveraging GNNs to uncover the complex relationships between ncRNAs and diseases.

KGETCDA: an efficient representation learning framework based on knowledge graph encoder from transformer for predicting circRNA-disease associations

Recent studies have demonstrated the significant role that circRNA plays in the progression of human diseases. Identifying circRNA-disease associations (CDA) in an efficient manner can offer crucial insights into disease diagnosis. While traditional biological experiments can be time-consuming and labor-intensive, computational methods have emerged as a viable alternative in recent years. However, these methods are often limited by data sparsity and their inability to explore high-order information. In this paper, we introduce a novel method named Knowledge Graph Encoder from Transformer for predicting CDA (KGETCDA). Specifically, KGETCDA first integrates more than 10 databases to construct a large heterogeneous non-coding RNA dataset, which contains multiple relationships between circRNA, miRNA, lncRNA and disease. Then, a biological knowledge graph is created based on this dataset and Transformer-based knowledge representation learning and attentive propagation layers are applied to obtain high-quality embeddings with accurately captured high-order interaction information. Finally, multilayer perceptron is utilized to predict the matching scores of CDA based on their embeddings. Our empirical results demonstrate that KGETCDA significantly outperforms other state-of-the-art models. To enhance user experience, we have developed an interactive web-based platform named HNRBase that allows users to visualize, download data and make predictions using KGETCDA with ease. The code and datasets are publicly available at https://github.com/jinyangwu/KGETCDA.

Generative Adversarial Matrix Completion Network based on Multi-Source Data Fusion for miRNA-Disease Associations Prediction

Numerous biological studies have shown that considering disease-associated micro RNAs (miRNAs) as potential biomarkers or therapeutic targets offers new avenues for the diagnosis of complex diseases. Computational methods have gradually been introduced to reveal disease-related miRNAs. Considering that previous models have not fused sufficiently diverse similarities, that their inappropriate fusion methods may lead to poor quality of the comprehensive similarity network and that their results are often limited by insufficiently known associations, we propose a computational model called Generative Adversarial Matrix Completion Network based on Multi-source Data Fusion (GAMCNMDF) for miRNA-disease association prediction. We create a diverse network connecting miRNAs and diseases, which is then represented using a matrix. The main task of GAMCNMDF is to complete the matrix and obtain the predicted results. The main innovations of GAMCNMDF are reflected in two aspects: GAMCNMDF integrates diverse data sources and employs a nonlinear fusion approach to update the similarity networks of miRNAs and diseases. Also, some additional information is provided to GAMCNMDF in the form of a 'hint' so that GAMCNMDF can work successfully even when complete data are not available. Compared with other methods, the outcomes of 10-fold cross-validation on two distinct databases validate the superior performance of GAMCNMDF with statistically significant results. It is worth mentioning that we apply GAMCNMDF in the identification of underlying small molecule-related miRNAs, yielding outstanding performance results in this specific domain. In addition, two case studies about two important neoplasms show that GAMCNMDF is a promising prediction method.

DEJKMDR: miRNA-disease association prediction method based on graph convolutional network

Numerous studies have shown that miRNAs play a crucial role in the investigation of complex human diseases. Identifying the connection between miRNAs and diseases is crucial for advancing the treatment of complex diseases. However, traditional methods are frequently constrained by the small sample size and high cost, so computational simulations are urgently required to rapidly and accurately forecast the potential correlation between miRNA and disease. In this paper, the DEJKMDR, a graph convolutional network (GCN)-based miRNA-disease association prediction model is proposed. The novelty of this model lies in the fact that DEJKMDR integrates biomolecular information on miRNA and illness, including functional miRNA similarity, disease semantic similarity, and miRNA and disease similarity, according to their Gaussian interaction attribute. In order to minimize overfitting, some edges are randomly destroyed during the training phase after DropEdge has been used to regularize the edges. JK-Net, meanwhile, is employed to combine various domain scopes through the adaptive learning of nodes in various placements. The experimental results demonstrate that this strategy has superior accuracy and dependability than previous algorithms in terms of predicting an unknown miRNA-disease relationship. In a 10-fold cross-validation, the average AUC of DEJKMDR is determined to be 0.9772.

Cell cycle protein BORA is associated with colorectal cancer progression by AURORA-PLK1 cascades: a bioinformatics analysis

Colorectal cancer (CRC) is the third most diagnosed cancer in the world. A better understanding of the molecular mechanism of CRC is essential for making novel strategies for the CRC management and its prevention. The present study aims to explore the molecular mechanism through integrated bioinformatics analysis by analyzing genes and their co-expression pattern in normal and CRC states. GSE110223, GSE110224 and GSE113513 gene expression profiles were analyzed in this study. The co-expression networks for normal and tumor samples were constructed separately and analyzed to identify the modules, sub-networks and key genes. Gene regulatory network analysis was done to understand the regulatory mechanism of selected genes. Survival analysis was performed for the identified sub-networks and key genes to understand their role in CRC progression. A total of seven modules were detected and the KEGG pathway analysis revealed these modules were mainly enriched with cell cycle, metabolism and signaling-related pathways. E2F6 and ETV4 transcription factors regulating the activity of multiple genes of identified modules were found to be up-regulated in CRC. Six Sub-networks and seven key genes, BORA, CCT7, DTL, RUVBL1, RUVBL2, THEM6 and TMEM97 associated with the CRC progression were identified. Disease-gene association analysis identified a novel association of the BORA gene with CRC that activates and regulates the AURORA-PLK1 cascades in the cell cycle. Survival analysis indicates that the overexpressed BORA is associated with unfavourable overall survival in CRC. The mechanistic role of BORA in the regulation of cell cycle progression suggests that BORA might act as a potential therapeutic target for CRC.

Long noncoding RNA TUG1 promotes malignant progression of osteosarcoma by enhancing ZBTB7C expression

BACKGROUND

Dysregulation of long non-coding RNAs (lncRNAs) is an important component of tumorigenesis. Aberrant expression of lncRNA taurine upregulated gene 1 (lncTUG1) has been reported in various tumors; however, its precise role and key targets critically involved in osteosarcoma (OS) progression remain unclear.

METHODS

The expression profiles of lncRNAs and their regulated miRNAs related to OS progression were assessed by bioinformatics analysis and confirmed by qRT-PCR of OS cells. The miRNA targets were identified by transcriptome sequencing and verified by luciferase reporter and RNA pull-down assays. Several in vivo and in vitro approaches, including CCK8 assay, western blot, qRT-PCR, lentiviral transduction and OS cell xenograft mouse model were established to validate the effects of lncTUG1 regulation of miRNA and the downstream target genes on OS cell growth, apoptosis and progression.

RESULTS

We found that lncTUG1 and miR-26a-5p were inversely up or down-regulated in OS cells, and siRNA-mediated lncTUG1 knockdown reversed the miR-26a-5p down-regulation and suppressed proliferation and enhanced apoptosis of OS cells. Further, we identified that an oncoprotein ZBTB7C was also upregulated in OS cells that were subjected to lncTUG1/miR-26a-5p regulation. More importantly, ZBTB7C knockdown reduced the ZBTB7C upregulation and ZBTB7C overexpression diminished the anti-OS effects of lncTUG1 knockdown in the OS xenograft model.

CONCLUSIONS

Our data suggest that lncTUG1 acts as a miR-26a-5p sponge and promotes OS progression via up-regulating ZBTB7C, and targeting lncTUG1 might be an effective strategy to treat OS.

Identification of Disease-Associated MicroRNAs Via Locality-Constrained Linear Coding-Based Ensemble Learning

Clinical trials indicate that the dysregulation of microRNAs (miRNAs) is closely associated with the development of diseases. Therefore, predicting miRNA-disease associations is significant for studying the pathogenesis of diseases. Since traditional wet-lab methods are resource-intensive, cost-saving computational models can be an effective complementary tool in biological experiments. In this work, a locality-constrained linear coding is proposed to predict associations (ILLCEL). Among them, ILLCEL adopts miRNA sequence similarity, miRNA functional similarity, disease semantic similarity, and interaction profile similarity obtained by locality-constrained linear coding (LLC) as the priori information. Next, features and similarities extracted from multiperspectives are input to the ensemble learning framework to improve the comprehensiveness of the prediction. Significantly, the introduction of hypergraph-regular terms improves the accuracy of prediction by describing complex associations between samples. The results under fivefold cross validation indicate that ILLCEL achieves superior prediction performance. In case studies, known associations are accurately predicted and novel associations are verified in HMDD v3.2, miRCancer, and existing literature. It is concluded that ILLCEL can be served as a powerful tool for inferring potential associations.

Single cell RNA-seq analysis with a systems biology approach to recognize important differentially expressed genes in pancreatic ductal adenocarcinoma compared to adjacent non-cancerous samples by targeting pancreatic endothelial cells

Pancreatic ductal adenocarcinoma (PDAC) is a cancer that is usually diagnosed at late stages. This highly aggressive tumor is resistant to most therapeutic approaches, necessitating identification of differentially expressed genes to design new therapies. Herein, we have analyzed single cell RNA-seq data with a systems biology approach to identify important differentially expressed genes in PDAC samples compared to adjacent non-cancerous samples. Our approach revealed 1462 DEmRNAs, including 1389 downregulated DEmRNAs (like PRSS1 and CLPS) and 73 upregulated DEmRNAs (like HSPA1A and SOCS3), 27 DElncRNAs, including 26 downregulated DElncRNAs (like LINC00472 and SNHG7) and 1 upregulated DElncRNA (SNHG5). We also listed a number of dysregulated signaling pathways, abnormally expressed genes and aberrant cellular functions in PDAC which can be used as possible biomarkers and therapeutic targets in this type of cancer.

Improving the identification of miRNA-disease associations with multi-task learning on gene-disease networks

MicroRNAs (miRNAs) are a family of non-coding RNA molecules with vital roles in regulating gene expression. Although researchers have recognized the importance of miRNAs in the development of human diseases, it is very resource-consuming to use experimental methods for identifying which dysregulated miRNA is associated with a specific disease. To reduce the cost of human effort, a growing body of studies has leveraged computational methods for predicting the potential miRNA-disease associations. However, the extant computational methods usually ignore the crucial mediating role of genes and suffer from the data sparsity problem. To address this limitation, we introduce the multi-task learning technique and develop a new model called MTLMDA (Multi-Task Learning model for predicting potential MicroRNA-Disease Associations). Different from existing models that only learn from the miRNA-disease network, our MTLMDA model exploits both miRNA-disease and gene-disease networks for improving the identification of miRNA-disease associations. To evaluate model performance, we compare our model with competitive baselines on a real-world dataset of experimentally supported miRNA-disease associations. Empirical results show that our model performs best using various performance metrics. We also examine the effectiveness of model components via ablation study and further showcase the predictive power of our model for six types of common cancers. The data and source code are available from https://github.com/qwslle/MTLMDA.

Screening and introduction of key cell cycle microRNAs deregulated in colorectal cancer by integrated bioinformatics analysis

Colorectal cancer (CRC) is the second most common cancer in women and the third most common in men worldwide. Impaired cell cycle regulation leads to many cancers and is also approved in CRC. Therefore, cell cycle regulation is a critical therapeutic target for CRC. Furthermore, miRNAs have been discovered as regulators in a variety of cancer-related pathways. This study is designed to investigate how miRNAs and mRNAs interact to regulate the cell cycle in CRC patients. Utilizing the Kyoto Encyclopedia of Genes and Genomes (KEGG), Gene Expression Omnibus (GEO), and Therapeutic Target Database (TTD), cell cycle-associated genes were identified and evaluated. Seven of the 22 differentially expressed genes (DEGs) implicated in the cell cycle in three GSEs (GSE24514, GSE10950, and GSE74604) were identified as potential therapeutic targets. Then, using PyRx software, we performed docking proteins with selected drugs. The results demonstrated that these drugs are appropriate molecules for targeting cell cycle DEGs. Tarbase, miRTarbase, miRDIP, and miRCancer databases were used to find miRNAs that target the indicated genes. The ability of these six miRNAs to impact the cell cycle in colorectal cancer may be concluded. These miRNAs were found to be downregulated in SW480 cells when compared to the normal tissue. Our data imply that a precise selection of bioinformatics tools can facilitate the identification of miRNAs that impact mRNA translation at different stages of the cell cycle. The candidates can be investigated more as targets for cell cycle arrest in cancers.

Inducing apoptosis by using microRNA in radio-resistant prostate cancer: an in-silico study with an in-vitro validation

BACKGROUND

One of the problems with radiation therapy (RT) is that prostate tumor cells are often radio-resistant, which results in treatment failure. This study aimed to determine the procedure involved in radio-resistant prostate cancer apoptosis. For a deeper insight, we devoted a novel bioinformatics approach to analyze the targeting between microRNAs and radio-resistant prostate cancer genes.

METHOD

This study uses the Tarbase, and the Mirtarbase databases as validated experimental databases and mirDIP as a predicted database to identify microRNAs that target radio-resistant anti-apoptotic genes. These genes are used to construct the radio-resistant prostate cancer genes network using the online tool STRING. The validation of causing apoptosis by using microRNA was confirmed with flow cytometry of Annexin V.

RESULTS

The anti-apoptotic gene of radio-resistant prostate cancer included BCL-2, MCL1, XIAP, STAT3, NOTCH1, REL, REL B, BIRC3, and AKT1 genes. These genes were identified as anti-apoptotic genes for radio-resistant prostate cancer. The crucial microRNA that knockdown all of these genes was hsa-miR-7-5p. The highest rate of apoptotic cells in a cell transfected with hsa-miR-7-5p was (32.90 ± 1.49), plenti III (21.99 ± 3.72), and the control group (5.08 ± 0.88) in 0 Gy (P < 0.001); also, this rate was in miR-7-5p (47.01 ± 2.48), plenti III (33.79 ± 3.40), and the control group (16.98 ± 3.11) (P < 0.001) for 4 Gy.

CONCLUSION

The use of this new treatment such as gene therapy to suppress genes involved in apoptosis can help to improve the treatment results and increase the quality of life of patients with prostate cancer.

KATZNCP: a miRNA-disease association prediction model integrating KATZ algorithm and network consistency projection

BACKGROUND

Clinical studies have shown that miRNAs are closely related to human health. The study of potential associations between miRNAs and diseases will contribute to a profound understanding of the mechanism of disease development, as well as human disease prevention and treatment. MiRNA-disease associations predicted by computational methods are the best complement to biological experiments.

RESULTS

In this research, a federated computational model KATZNCP was proposed on the basis of the KATZ algorithm and network consistency projection to infer the potential miRNA-disease associations. In KATZNCP, a heterogeneous network was initially constructed by integrating the known miRNA-disease association, integrated miRNA similarities, and integrated disease similarities; then, the KATZ algorithm was implemented in the heterogeneous network to obtain the estimated miRNA-disease prediction scores. Finally, the precise scores were obtained by the network consistency projection method as the final prediction results. KATZNCP achieved the reliable predictive performance in leave-one-out cross-validation (LOOCV) with an AUC value of 0.9325, which was better than the state-of-the-art comparable algorithms. Furthermore, case studies of lung neoplasms and esophageal neoplasms demonstrated the excellent predictive performance of KATZNCP.

CONCLUSION

A new computational model KATZNCP was proposed for predicting potential miRNA-drug associations based on KATZ and network consistency projections, which can effectively predict the potential miRNA-disease interactions. Therefore, KATZNCP can be used to provide guidance for future experiments.

Integrative analysis of negatively regulated miRNA-mRNA axes for esophageal squamous cell carcinoma

BACKGROUND

MicroRNAs regulating mRNA expression by targeting at mRNAs is known constructive in tumor occurrence, immune escape, and metastasis.

OBJECTIVE

This research aims at finding negatively regulatory miRNA-mRNA pairs in esophageal squamous cell carcinoma (ESCC).

METHODS

GENE expression data of The Cancer Genome Atlas (TCGA) and GEO database were employed in differently expressed RNA and miRNA (DE-miRNAs/DE-mRNAs) screening. Function analysis was conducted with DAVID-mirPath. MiRNA-mRNA axes were identified by MiRTarBase and TarBase and verified in esophageal specimen by real-time reverse transcription polymerase chain reaction (RT-qPCR). Receiver operation characteristic (ROC) curve and Decision Curve Analysis (DCA) were applied in miRNA-mRNA pairs predictive value estimation. Interactions between miRNA-mRNA regulatory pairs and immune features were analyzed using CIBERSORT.

RESULTS

Combining TCGA database, 4 miRNA and 10 mRNA GEO datasets, totally 26 DE-miRNAs (13 up and 13 down) and 114 DE-mRNAs (64 up and 50 down) were considered significant. MiRTarBase and TarBase identified 37 reverse regulation miRNA-mRNA pairs, 14 of which had been observed in esophageal tissue or cell line. Through analysis of RT-qPCR outcome, miR-106b-5p/KIAA0232 signature was chosen as characteristic pair of ESCC. ROC and DCA verified the predictive value of model containing miRNA-mRNA axis in ESCC. Via affecting mast cells, miR-106b-5p/KIAA0232 may contribute to tumor microenvironment.

CONCLUSIONS

The diagnostic model of miRNA-mRNA pair in ESCC was established. Their complex role in ESCC pathogenesis especially tumor immunity was partly disclosed.

Small molecule-mediated targeting of microRNAs for drug discovery: Experiments, computational techniques, and disease implications

Small molecules have been providing medical breakthroughs for human diseases for more than a century. Recently, identifying small molecule inhibitors that target microRNAs (miRNAs) has gained importance, despite the challenges posed by labour-intensive screening experiments and the significant efforts required for medicinal chemistry optimization. Numerous experimentally-verified cases have demonstrated the potential of miRNA-targeted small molecule inhibitors for disease treatment. This new approach is grounded in their posttranscriptional regulation of the expression of disease-associated genes. Reversing dysregulated gene expression using this mechanism may help control dysfunctional pathways. Furthermore, the ongoing improvement of algorithms has allowed for the integration of computational strategies built on top of laboratory-based data, facilitating a more precise and rational design and discovery of lead compounds. To complement the use of extensive pharmacogenomics data in prioritising potential drugs, our previous work introduced a computational approach based on only molecular sequences. Moreover, various computational tools for predicting molecular interactions in biological networks using similarity-based inference techniques have been accumulated in established studies. However, there are a limited number of comprehensive reviews covering both computational and experimental drug discovery processes. In this review, we outline a cohesive overview of both biological and computational applications in miRNA-targeted drug discovery, along with their disease implications and clinical significance. Finally, utilizing drug-target interaction (DTIs) data from DrugBank, we showcase the effectiveness of deep learning for obtaining the physicochemical characterization of DTIs.

GKLOMLI: a link prediction model for inferring miRNA-lncRNA interactions by using Gaussian kernel-based method on network profile and linear optimization algorithm

BACKGROUND

The limited knowledge of miRNA-lncRNA interactions is considered as an obstruction of revealing the regulatory mechanism. Accumulating evidence on Human diseases indicates that the modulation of gene expression has a great relationship with the interactions between miRNAs and lncRNAs. However, such interaction validation via crosslinking-immunoprecipitation and high-throughput sequencing (CLIP-seq) experiments that inevitably costs too much money and time but with unsatisfactory results. Therefore, more and more computational prediction tools have been developed to offer many reliable candidates for a better design of further bio-experiments.

METHODS

In this work, we proposed a novel link prediction model based on Gaussian kernel-based method and linear optimization algorithm for inferring miRNA-lncRNA interactions (GKLOMLI). Given an observed miRNA-lncRNA interaction network, the Gaussian kernel-based method was employed to output two similarity matrixes of miRNAs and lncRNAs. Based on the integrated matrix combined with similarity matrixes and the observed interaction network, a linear optimization-based link prediction model was trained for inferring miRNA-lncRNA interactions.

RESULTS

To evaluate the performance of our proposed method, k-fold cross-validation (CV) and leave-one-out CV were implemented, in which each CV experiment was carried out 100 times on a training set generated randomly. The high area under the curves (AUCs) at 0.8623 ± 0.0027 (2-fold CV), 0.9053 ± 0.0017 (5-fold CV), 0.9151 ± 0.0013 (10-fold CV), and 0.9236 (LOO-CV), illustrated the precision and reliability of our proposed method.

CONCLUSION

GKLOMLI with high performance is anticipated to be used to reveal underlying interactions between miRNA and their target lncRNAs, and deciphers the potential mechanisms of the complex diseases.

A DNAzyme-enhanced nonlinear hybridization chain reaction for sensitive detection of microRNA

As a typical biomarker, the expression of microRNA is closely related to the occurrence of cancer. However, in recent years, the detection methods have had some limitations in the research and application of microRNAs. In this paper, an autocatalytic platform was constructed through the combination of a nonlinear hybridization chain reaction and DNAzyme to achieve efficient detection of microRNA-21. Fluorescently labeled fuel probes can form branched nanostructures and new DNAzyme under the action of the target, and the newly formed DNAzyme can trigger a new round of reactions, resulting in enhanced fluorescence signals. This platform is a simple, efficient, fast, low-cost, and selective method for the detection of microRNA-21, which can detect microRNA-21 at concentrations as low as 0.004 nM and can distinguish sequence differences by single-base differences. In tissue samples from liver cancer patients, the platform shows the same detection accuracy as real-time PCR but with better reproducibility. In addition, through the flexible design of the trigger chain, our method could be adapted to detect other nucleic acids biomarkers.

BMPMDA: Prediction of MiRNA-Disease Associations Using a Space Projection Model Based on Block Matrix

With the high-quality development of bioinformatics technology, miRNA-disease associations (MDAs) are gradually being uncovered. At present, convenient and efficient prediction methods, which solve the problem of resource-consuming in traditional wet experiments, need to be further put forward. In this study, a space projection model based on block matrix is presented for predicting MDAs (BMPMDA). Specifically, two block matrices are first composed of the known association matrix and similarity to increase comprehensiveness. For the integrity of information in the heterogeneous network, matrix completion (MC) is utilized to mine potential MDAs. Considering the neighborhood information of data points, linear neighborhood similarity (LNS) is regarded as a measure of similarity. Next, LNS is projected onto the corresponding completed association matrix to derive the projection score. Finally, the AUC and AUPR values for BMPMDA reach 0.9691 and 0.6231, respectively. Additionally, the majority of novel MDAs in three disease cases are identified in existing databases and literature. It suggests that BMPMDA can serve as a reliable prediction model for biological research.

Improving miRNA Disease Association Prediction Accuracy Using Integrated Similarity Information and Deep Autoencoders

MicroRNAs (miRNAs) are short endogenous non-encoding RNA molecules (22nt) that have a vital role in many biological and molecular processes inside the human body. Abnormal and dysregulated expressions of miRNAs are correlated with many complex disorders. Time-consuming wet-lab biological experiments are costly and labour-intensive. So, the situation demands feasible and efficient computational approaches for predicting promising miRNAs associated with diseases. Here a two-stage feature pruning approach based on miRNA feature similarity fusion that uses deep attention autoencoder and recursive feature elimination with cross-validation (RFECV) is proposed for predicting unknown miRNA-disease associations. In the first stage, an attention autoencoder captures highly influential features from the fused feature vector. For further pruning of features, RFECV is applied. The resultant features were given to a Random Forest classifier for association prediction. The Highest AUC of 94.41% is attained when all miRNA similarity measures are merged with disease similarities. Case studies were done on two diseases-lymphoma and leukaemia, to examine the reliability of the approach. Comparative analysis shows that the proposed approach outperforms recent methodologies for predicting miRNA-disease associations.

Bioinformatics and cheminformatics approaches to identify pathways, molecular mechanisms and drug substances related to genetic basis of cervical cancer

Cervical cancer (CC) is a global threat to women and our knowledge is frighteningly little about its underlying genomic contributors. Our research aimed to understand the underlying molecular and genetic mechanisms of CC by integrating bioinformatics and network-based study. Transcriptomic analyses of three microarray datasets identified 218 common differentially expressed genes (DEGs) within control samples and CC specimens. KEGG pathway analysis revealed pathways in cell cycle, drug metabolism, DNA replication and the significant GO terms were cornification, proteolysis, cell division and DNA replication. Protein-protein interaction (PPI) network analysis identified 20 hub genes and survival analyses validated CDC45, MCM2, PCNA and TOP2A as CC biomarkers. Subsequently, 10 transcriptional factors (TFs) and 10 post-transcriptional regulators were detected through TFs-DEGs and miRNAs-DEGs regulatory network assessment. Finally, the CC biomarkers were subjected to a drug-gene relationship analysis to find the best target inhibitors. Standard cheminformatics method including in silico ADMET and molecular docking study substantiated PD0325901 and Selumetinib as the most potent candidate-drug for CC treatment. Overall, this meticulous study holds promises for further in vitro and in vivo research on CC diagnosis, prognosis and therapies. Communicated by Ramaswamy H. Sarma.

Matrix reconstruction with reliable neighbors for predicting potential MiRNA-disease associations

Numerous experimental studies have indicated that alteration and dysregulation in mircroRNAs (miRNAs) are associated with serious diseases. Identifying disease-related miRNAs is therefore an essential and challenging task in bioinformatics research. Computational methods are an efficient and economical alternative to conventional biomedical studies and can reveal underlying miRNA-disease associations for subsequent experimental confirmation with reasonable confidence. Despite the success of existing computational approaches, most of them only rely on the known miRNA-disease associations to predict associations without adding other data to increase the prediction accuracy, and they are affected by issues of data sparsity. In this paper, we present MRRN, a model that combines matrix reconstruction with node reliability to predict probable miRNA-disease associations. In MRRN, the most reliable neighbors of miRNA and disease are used to update the original miRNA-disease association matrix, which significantly reduces data sparsity. Unknown miRNA-disease associations are reconstructed by aggregating the most reliable first-order neighbors to increase prediction accuracy by representing the local and global structure of the heterogeneous network. Five-fold cross-validation of MRRN produced an area under the curve (AUC) of 0.9355 and area under the precision-recall curve (AUPR) of 0.2646, values that were greater than those produced by comparable models. Two different types of case studies using three diseases were conducted to demonstrate the accuracy of MRRN, and all top 30 predicted miRNAs were verified.

DeepsmirUD: Prediction of Regulatory Effects on microRNA Expression Mediated by Small Molecules Using Deep Learning

Aberrant miRNA expression has been associated with a large number of human diseases. Therefore, targeting miRNAs to regulate their expression levels has become an important therapy against diseases that stem from the dysfunction of pathways regulated by miRNAs. In recent years, small molecules have demonstrated enormous potential as drugs to regulate miRNA expression (i.e., SM-miR). A clear understanding of the mechanism of action of small molecules on the upregulation and downregulation of miRNA expression allows precise diagnosis and treatment of oncogenic pathways. However, outside of a slow and costly process of experimental determination, computational strategies to assist this on an ad hoc basis have yet to be formulated. In this work, we developed, to the best of our knowledge, the first cross-platform prediction tool, DeepsmirUD, to infer small-molecule-mediated regulatory effects on miRNA expression (i.e., upregulation or downregulation). This method is powered by 12 cutting-edge deep-learning frameworks and achieved AUC values of 0.843/0.984 and AUCPR values of 0.866/0.992 on two independent test datasets. With a complementarily constructed network inference approach based on similarity, we report a significantly improved accuracy of 0.813 in determining the regulatory effects of nearly 650 associated SM-miR relations, each formed with either novel small molecule or novel miRNA. By further integrating miRNA-cancer relationships, we established a database of potential pharmaceutical drugs from 1343 small molecules for 107 cancer diseases to understand the drug mechanisms of action and offer novel insight into drug repositioning. Furthermore, we have employed DeepsmirUD to predict the regulatory effects of a large number of high-confidence associated SM-miR relations. Taken together, our method shows promise to accelerate the development of potential miRNA targets and small molecule drugs.

miRdisNET: Discovering microRNA biomarkers that are associated with diseases utilizing biological knowledge-based machine learning

During recent years, biological experiments and increasing evidence have shown that microRNAs play an important role in the diagnosis and treatment of human complex diseases. Therefore, to diagnose and treat human complex diseases, it is necessary to reveal the associations between a specific disease and related miRNAs. Although current computational models based on machine learning attempt to determine miRNA-disease associations, the accuracy of these models need to be improved, and candidate miRNA-disease relations need to be evaluated from a biological perspective. In this paper, we propose a computational model named miRdisNET to predict potential miRNA-disease associations. Specifically, miRdisNET requires two types of data, i.e., miRNA expression profiles and known disease-miRNA associations as input files. First, we generate subsets of specific diseases by applying the grouping component. These subsets contain miRNA expressions with class labels associated with each specific disease. Then, we assign an importance score to each group by using a machine learning method for classification. Finally, we apply a modeling component and obtain outputs. One of the most important outputs of miRdisNET is the performance of miRNA-disease prediction. Compared with the existing methods, miRdisNET obtained the highest AUC value of .9998. Another output of miRdisNET is a list of significant miRNAs for disease under study. The miRNAs identified by miRdisNET are validated via referring to the gold-standard databases which hold information on experimentally verified microRNA-disease associations. miRdisNET has been developed to predict candidate miRNAs for new diseases, where miRNA-disease relation is not yet known. In addition, miRdisNET presents candidate disease-disease associations based on shared miRNA knowledge. The miRdisNET tool and other supplementary files are publicly available at: https://github.com/malikyousef/miRdisNET.

Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets

Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease state is essential. A microscopic approach to the molecular field is a more coherent and accurate way to explore the mechanism, progression, and therapy with the introduction and evolution of technology than a macroscopic approach. Non-coding RNAs (ncRNAs) play increasingly important roles in detecting, developing, and treating all abnormalities related to physiology, pathology, genetics, epigenetics, cancer, and developmental diseases. Noncoding RNAs are becoming increasingly crucial as powerful, multipurpose regulators of all biological processes. Parallel to this, a rising amount of scientific information has revealed links between abnormal noncoding RNA expression and human disorders. Numerous non-coding transcripts with unknown functions have been found in addition to advancements in RNA-sequencing methods. Non-coding linear RNAs come in a variety of forms, including circular RNAs with a continuous closed loop (circRNA), long non-coding RNAs (lncRNA), and microRNAs (miRNA). This comprises specific information on their biogenesis, mode of action, physiological function, and significance concerning disease (such as cancer or cardiovascular diseases and others). This study review focuses on non-coding RNA as specific biomarkers and novel therapeutic targets.

MicroRNA-Mediated Mitochondrial Dysfunction Is Involved in the Anti-triple-Negative Breast Cancer Cell Activity of Phytosesquiterpene Lactones

Aims: Emerging evidence suggests that modulating redox homeostasis through targeting mitochondrial functions may be a useful strategy for suppressing triple-negative breast cancer (TNBC) activities. However, whether there are specific microRNAs (miRNAs) involved in regulating oxidative stress-associated mitochondrial functions that can act as therapeutic targets to suppress TNBC activities remains unclear. Here, we aimed to identify the role of redox-associated miRNAs in TNBC and investigated their potential as therapeutic targets. Results: We identified oxidative stress-responsive differentially expressed miRNAs (DEMs) regulated by phytosesquiterpene lactone deoxyelephantopin (DET) and its novel derivative DETD-35, which are known to inhibit TNBC growth and metastasis in vitro and in vivo, using comparative miRNA microarray analysis and reactive oxygen species (ROS) scavenging approaches. Mitochondrial dysfunction was identified as a major biological function regulated by a few specific DEMs. In particular, miR-4284 was identified to play a role in DET- and DETD-35-mediated ROS production, mitochondrial basal proton leak, and antiproliferation activity in TNBC cells. Moreover, DET- and DETD-35-induced mitochondrial DNA damage was observed in TNBC cells and xenograft tumors. miR-4284 was also identified to play a role in oxidative DNA damage in TNBC tumors. Innovation: We identified a novel role for miR-4284 in regulating mitochondrial basal proton leak in TNBC cells, and highlighted its significance in TNBC tumor oxidative DNA damage, and its direct correlation with TNBC patient survival. Conclusion: We used DET and DETD-35 as proof of concept to demonstrate that activities of anticancer agents can involve regulation of multiple miRNAs playing different roles in cancer progression. Antioxid. Redox Signal. 38, 198-214.

Microfluidics for Profiling miRNA Biomarker Panels in AI-Assisted Cancer Diagnosis and Prognosis

Early detection of cancers and their precise subtyping are essential to patient stratification and effective cancer management. Data-driven identification of expression biomarkers coupled with microfluidics-based detection shows promise to revolutionize cancer diagnosis and prognosis. MicroRNAs play key roles in cancers and afford detection in tissue and liquid biopsies. In this review, we focus on the microfluidics-based detection of miRNA biomarkers in AI-based models for early-stage cancer subtyping and prognosis. We describe various subclasses of miRNA biomarkers that could be useful in machine-based predictive modeling of cancer staging and progression. Strategies for optimizing the feature space of miRNA biomarkers are necessary to obtain a robust signature panel. This is followed by a discussion of the issues in model construction and validation towards producing Software-as-Medical-Devices (SaMDs). Microfluidic devices could facilitate the multiplexed detection of miRNA biomarker panels, and an overview of the different strategies for designing such microfluidic systems is presented here, with an outline of the detection principles used and the corresponding performance measures. Microfluidics-based profiling of miRNAs coupled with SaMD represent high-performance point-of-care solutions that would aid clinical decision-making and pave the way for accessible precision personalized medicine.

Epigenetic adaptations in drug-tolerant tumor cells

Traditional chemotherapy against cancer is often severely hampered by acquired resistance to the drug. Epigenetic alterations and other mechanisms like drug efflux, drug metabolism, and engagement of survival pathways are crucial in evading drug pressure. Herein, growing evidence suggests that a subpopulation of tumor cells can often tolerate drug onslaught by entering a "persister" state with minimal proliferation. The molecular features of these persister cells are gradually unraveling. Notably, the "persisters" act as a cache of cells that can eventually re-populate the tumor post-withdrawal drug pressure and contribute to acquiring stable drug-resistant features. This underlines the clinical significance of the tolerant cells. Accumulating evidence highlights the importance of modulation of the epigenome as a critical adaptive strategy for evading drug pressure. Chromatin remodeling, altered DNA methylation, and de-regulation of non-coding RNA expression and function contribute significantly to this persister state. No wonder targeting adaptive epigenetic modifications is increasingly recognized as an appropriate therapeutic strategy to sensitize them and restore drug sensitivity. Furthermore, manipulating the tumor microenvironment and "drug holiday" is also explored to maneuver the epigenome. However, heterogeneity in adaptive strategies and lack of targeted therapies have significantly hindered the translation of epigenetic therapy to the clinics. In this review, we comprehensively analyze the epigenetic alterations adapted by the drug-tolerant cells, the therapeutic strategies employed to date, and their limitations and future prospects.

Splicing signature database development to delineate cancer pathways using literature mining and transcriptome machine learning

Alternative splicing (AS) events modulate certain pathways and phenotypic plasticity in cancer. Although previous studies have computationally analyzed splicing events, it is still a challenge to uncover biological functions induced by reliable AS events from tremendous candidates. To provide essential splicing event signatures to assess pathway regulation, we developed a database by collecting two datasets: (i) reported literature and (ii) cancer transcriptome profile. The former includes knowledge-based splicing signatures collected from 63,229 PubMed abstracts using natural language processing, extracted for 202 pathways. The latter is the machine learning-based splicing signatures identified from pan-cancer transcriptome for 16 cancer types and 42 pathways. We established six different learning models to classify pathway activities from splicing profiles as a learning dataset. Top-ranked AS events by learning model feature importance became the signature for each pathway. To validate our learning results, we performed evaluations by (i) performance metrics, (ii) differential AS sets acquired from external datasets, and (iii) our knowledge-based signatures. The area under the receiver operating characteristic values of the learning models did not exhibit any drastic difference. However, random-forest distinctly presented the best performance to compare with the AS sets identified from external datasets and our knowledge-based signatures. Therefore, we used the signatures obtained from the random-forest model. Our database provided the clinical characteristics of the AS signatures, including survival test, molecular subtype, and tumor microenvironment. The regulation by splicing factors was additionally investigated. Our database for developed signatures supported retrieval and visualization system.

Analysis of aberrant miRNA-mRNA interaction networks in prostate cancer to conjecture its molecular mechanisms

BACKGROUND

MicroRNAs (miRNAs) capable of post-transcriptionally regulating mRNA expression are essential to tumor occurrence and progression.

OBJECTIVE

This study aims to find negatively regulatory miRNA-mRNA pairs in prostate adenocarcinoma (PRAD).

METHODS

Combining The Cancer Genome Atlas (TCGA) RNA-Seq data with Gene Expression Omnibus (GEO) mRNA/miRNA expression profiles, differently expressed miRNA/mRNA (DE-miRNAs/DE-mRNAs) were identified. MiRNA-mRNA pairs were screened by miRTarBase and TarBase, databases collecting experimentally confirmed miRNA-mRNA pairs, and verified in 30 paired prostate specimens by real-time reverse transcription polymerase chain reaction (RT-qPCR). The diagnostic values of miRNA-mRNA pairs were measured by receiver operation characteristic (ROC) curve and Decision Curve Analysis (DCA). DAVID-mirPath database and Connectivity Map were employed in GO/KEGG analysis and compounds research. Interactions between miRNA-mRNA pairs and phenotypic features were analyzed with correlation heatmap in hiplot.

RESULTS

Based on TCGA RNA-Seq data, 22 miRNA and 14 mRNA GEO datasets, 67 (20 down and 47 up) miRNAs and 351 (139 up and 212 down) mRNAs were selected. After screening from 2 databases, 8 miRNA (up)-mRNA (down) and 7 miRNA (down)-mRNA (up) pairs were identified with Pearson's correlation in TCGA. By external validation, miR-221-3p (down)/GALNT3 (up) and miR-20a-5p (up)/FRMD6 (down) were chosen. The model combing 4 signatures possessed better diagnostic value. These two miRNA-mRNA pairs were significantly connected with immune cells fraction and tumor immune microenvironment.

CONCLUSIONS

The diagnostic model containing 2 negatively regulatory miRNA-mRNA pairs was established to distinguish PRADs from normal controls.

In Silico and In Vivo Evaluation of microRNA-181c-5p's Role in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is a fatal disease, accounting for 75-85% of primary liver cancers. The conclusive research on miR-181c-5p's role in hepatocarcinogenesis, whether it has oncogenic effects or acts as a tumor repressor, is limited and fluctuating. Therefore, the current study aimed to elucidate the role of miR-181c-5p in HCC in silico and in vivo. The bioinformatics analysis of miR-181c-5p expression data in HCC using several databases strongly shed light on its involvement in HCC development, but also confirmed the fluctuating data around its role. miR-181c-5p was proven here to have an oncogenic role by increasing HepG2 cells' viability as confirmed by MTT analysis. In addition, miR-181c-5p was upregulated in the HCC positive control group and progressed the HCC development and malignant features by its forced expression in an HCC mouse model by targeted delivery using a LA-PAMAM polyplex. This is indicated by the cancerous gross and histological features, and the significant increase in liver function biomarkers. The functional enrichment bioinformatics analyses of miR-181c-5p-downregulated targets in HCC indicated that miR-181c-5p targets were significantly enriched in multiple pathways and biological processes involved in HCC development. Fbxl3, an example for miR-181c-5p potential targets, downregulation and its correlation with miR-181c-5p were validated by qPCR. In conclusion, miR-181c-5p is upregulated in HCC and has an oncogenic role enhancing HCC progression.

SGAEMDA: Predicting miRNA-Disease Associations Based on Stacked Graph Autoencoder

MicroRNA (miRNA)-disease association (MDA) prediction is critical for disease prevention, diagnosis, and treatment. Traditional MDA wet experiments, on the other hand, are inefficient and costly.Therefore, we proposed a multi-layer collaborative unsupervised training base model called SGAEMDA (Stacked Graph Autoencoder-Based Prediction of Potential miRNA-Disease Associations). First, from the original miRNA and disease data, we defined two types of initial features: similarity features and association features. Second, stacked graph autoencoder is then used to learn unsupervised low-dimensional representations of meaningful higher-order similarity features, and we concatenate the association features with the learned low-dimensional representations to obtain the final miRNA-disease pair features. Finally, we used a multilayer perceptron (MLP) to predict scores for unknown miRNA-disease associations. SGAEMDA achieved a mean area under the ROC curve of 0.9585 and 0.9516 in 5-fold and 10-fold cross-validation, which is significantly higher than the other baseline methods. Furthermore, case studies have shown that SGAEMDA can accurately predict candidate miRNAs for brain, breast, colon, and kidney neoplasms.

Cis-regulatory mutations associate with transcriptional and post-transcriptional deregulation of gene regulatory programs in cancers

Most cancer alterations occur in the noncoding portion of the human genome, where regulatory regions control gene expression. The discovery of noncoding mutations altering the cells' regulatory programs has been limited to few examples with high recurrence or high functional impact. Here, we show that transcription factor binding sites (TFBSs) have similar mutation loads to those in protein-coding exons. By combining cancer somatic mutations in TFBSs and expression data for protein-coding and miRNA genes, we evaluate the combined effects of transcriptional and post-transcriptional alterations on the regulatory programs in cancers. The analysis of seven TCGA cohorts culminates with the identification of protein-coding and miRNA genes linked to mutations at TFBSs that are associated with a cascading trans-effect deregulation on the cells' regulatory programs. Our analyses of cis-regulatory mutations associated with miRNAs recurrently predict 12 mature miRNAs (derived from 7 precursors) associated with the deregulation of their target gene networks. The predictions are enriched for cancer-associated protein-coding and miRNA genes and highlight cis-regulatory mutations associated with the dysregulation of key pathways associated with carcinogenesis. By combining transcriptional and post-transcriptional regulation of gene expression, our method predicts cis-regulatory mutations related to the dysregulation of key gene regulatory networks in cancer patients.