PDMDA: predicting deep-level miRNA-disease associations with graph neural networks and sequence features

MOTIVATION

Many studies have shown that microRNAs (miRNAs) play a key role in human diseases. Meanwhile, traditional experimental methods for miRNA-disease association identification are extremely costly, time-consuming and challenging. Therefore, many computational methods have been developed to predict potential associations between miRNAs and diseases. However, those methods mainly predict the existence of miRNA-disease associations, and they cannot predict the deep-level miRNA-disease association types.

RESULTS

In this study, we propose a new end-to-end deep learning method (called PDMDA) to predict deep-level miRNA-disease associations with graph neural networks (GNNs) and miRNA sequence features. Based on the sequence and structural features of miRNAs, PDMDA extracts the miRNA feature representations by a fully connected network (FCN). The disease feature representations are extracted from the disease-gene network and gene-gene interaction network by GNN model. Finally, a multilayer with three fully connected layers and a softmax layer is designed to predict the final miRNA-disease association scores based on the concatenated feature representations of miRNAs and diseases. Note that PDMDA does not take the miRNA-disease association matrix as input to compute the Gaussian interaction profile similarity. We conduct three experiments based on six association type samples (including circulations, epigenetics, target, genetics, known association of which their types are unknown and unknown association samples). We conduct fivefold cross-validation validation to assess the prediction performance of PDMDA. The area under the receiver operating characteristic curve scores is used as metric. The experiment results show that PDMDA can accurately predict the deep-level miRNA-disease associations.

AVAILABILITY AND IMPLEMENTATION

Data and source codes are available at https://github.com/27167199/PDMDA.

MLRDFM: a multi-view Laplacian regularized DeepFM model for predicting miRNA-disease associations

MOTIVATION

MicroRNAs (miRNAs), as critical regulators, are involved in various fundamental and vital biological processes, and their abnormalities are closely related to human diseases. Predicting disease-related miRNAs is beneficial to uncovering new biomarkers for the prevention, detection, prognosis, diagnosis and treatment of complex diseases.

RESULTS

In this study, we propose a multi-view Laplacian regularized deep factorization machine (DeepFM) model, MLRDFM, to predict novel miRNA-disease associations while improving the standard DeepFM. Specifically, MLRDFM improves DeepFM from two aspects: first, MLRDFM takes the relationships among items into consideration by regularizing their embedding features via their similarity-based Laplacians. In this study, miRNA Laplacian regularization integrates four types of miRNA similarity, while disease Laplacian regularization integrates two types of disease similarity. Second, to judiciously train our model, Laplacian eigenmaps are utilized to initialize the weights in the dense embedding layer. The experimental results on the latest HMDD v3.2 dataset show that MLRDFM improves the performance and reduces the overfitting phenomenon of DeepFM. Besides, MLRDFM is greatly superior to the state-of-the-art models in miRNA-disease association prediction in terms of different evaluation metrics with the 5-fold cross-validation. Furthermore, case studies further demonstrate the effectiveness of MLRDFM.

Production and Stabilization of Specific Upregulated Long Noncoding RNA HOXD-AS2 in Glioblastomas Are Mediated by TFE3 and miR-661, Respectively

Differential expression of long noncoding RNAs (lncRNA) plays a key role in the development of gliomas. Because gliomas are the most common primary central nervous system tumor and glioblastomas have poor prognosis, it is urgent to develop new diagnostic methods. We have previously reported that lncRNA HOXD-AS2, which is specifically up-regulated in gliomas, can activate cell cycle and promote the development of gliomas. It is expected to be a new marker for molecular diagnosis of gliomas, but little is known about HOXD-AS2. Here, we demonstrate that TFE3 and miR-661 maintain the high expression level of HOXD-AS2 by regulating its production and degradation. We found that TFE3 acted as a transcription factor binding to the HOXD-AS2 promoter region and raised H3K27ac to activate HOXD-AS2. As the cytoplasmic-located lncRNA, HOXD-AS2 could be degraded by miR-661. This process was inhibited in gliomas due to the low expression of miR-661. Our study explains why HOXD-AS2 was specifically up-regulated in gliomas, helps to understand the molecular characteristics of gliomas, and provids insights for the search for specific markers in gliomas.

Integrated multi-omics analysis reveals miR-20a as a regulator for metabolic colorectal cancer

Single-driver molecular events specific to the metabolic colorectal cancer (CRC) have not been clearly elucidated. Herein, we identified 12 functional miRNAs linked to activated metabolism by integrating multi-omics features in metabolic CRC. These miRNAs exhibited significantly enriched CRC driver miRNAs, significant impacts on CRC cell growth and significantly correlated metabolites. Importantly, miR-20a is minimally expressed in normal colorectal tissues but highly expressed in metabolic CRC, suggesting the potential therapeutic target. Bioinformatics analyses further revealed miR-20a as the most powerful determinant that regulates a cascade of dysregulated events, including Wnt signaling pathway, core enzymes involved in FA metabolism program and triacylglycerol abundances. In vitro assays demonstrated that elevated miR-20a up-regulated FA synthesis enzymes via Wnt/β-catenin signaling, and finally promoted proliferative and migration of metabolic CRC cells. Overall, our study revealed that miR-20a promoted progression of metabolic CRC by regulating FA metabolism and served as a potential target for preventing tumor metastasis.

A miRNA-Disease Association Identification Method Based on Reliable Negative Sample Selection and Improved Single-Hidden Layer Feedforward Neural Network

miRNAs are a category of important endogenous non-coding small RNAs and are ubiquitous in eukaryotes. They are widely involved in the regulatory process of post-transcriptional gene expression and play a critical part in the development of human diseases. By utilizing recent advancements in big data technology, using bioinformatics methods to identify causative miRNA becomes a hot spot. In this paper, a method called RNSSLFN is proposed to identify the miRNA-disease associations by reliable negative sample selection and an improved single-hidden layer feedforward neural network (SLFN). It involves, firstly, obtaining integrated similarity for miRNAs and diseases; next, selecting reliable negative samples from unknown miRNA-disease associations via distinguishing up-regulated or down-regulated miRNAs; then, introducing an improved SLFN to solve the prediction task. The experimental results on the latest data sets HMDD v3.2 and the framework of 5-fold cross-validation (CV) show that the average AUC and AUPR of RNSSLFN achieve 0.9316 and 0.9065 m, respectively, which are superior to the other three state-of-the-art methods. Furthermore, in the case studies of 10 common cancers, more than 70% of the top 30 predicted miRNA-disease association pairs are verified in the databases, which further confirms the reliability and effectiveness of the RNSSLFN model. Generally, RNSSLFN in predicting miRNA-disease associations has prodigious potential and extensive foreground.

Investigation of miRNA expression profiles using cohort samples reveals potential early detectability of colorectal cancers by serum miR-26a-5p before clinical diagnosis

Previous studies have investigated the usefulness of microRNA (miRNA/miR) expression data for the early detection of colorectal cancer (CRC). However, limited data are available regarding miRNAs that detect CRC before clinical diagnoses. Accordingly, the present study investigated the early detectability of CRC by miRNAs using the preserved serum samples of the cohort participants affected with CRC within 2 years of study enrollment. First, the significant miRNAs were revealed using clinical CRC samples for a (seven early CRCs and seven controls) microarray analysis based on significance analysis of microarrays. Next, replicability was verified by reverse transcription-quantitative (RT-q)PCR (eight early CRCs and eight controls, together with 12 CRCs and 12 controls). Finally, early detectability was tested using the cohort samples of Japan Multi-Institutional Collaborative Cohort Study (17 CRCs and 17 controls) to reveal how a certain number of patients developed CRC within 2 years after participation. In the discovery phase, miRNA expression measurements were conducted using a 3D-Gene Human miRNA Oligo Chip for 2,555 miRNAs, and RT-qPCR analyses were performed to validate the replicability. In the first validation set with eight CRCs with early clinical stage and eight age- and gender-matched controls, miR-26a-5p and miR-223-3p demonstrated the highest diagnostic accuracy of area under the curve (AUC)=1.000 (sensitivity and specificity 100%). In an examination of the predictability of CRC incidence using pre-clinical cohort samples, miR-26a-5p demonstrated good predictability of advanced CRC incidence with an AUC of 0.840. Overall, the present study revealed serum miR-26a-5p as a potential early detection marker for CRC.

Biomedical data, computational methods and tools for evaluating disease-disease associations

In recent decades, exploring potential relationships between diseases has been an active research field. With the rapid accumulation of disease-related biomedical data, a lot of computational methods and tools/platforms have been developed to reveal intrinsic relationship between diseases, which can provide useful insights to the study of complex diseases, e.g. understanding molecular mechanisms of diseases and discovering new treatment of diseases. Human complex diseases involve both external phenotypic abnormalities and complex internal molecular mechanisms in organisms. Computational methods with different types of biomedical data from phenotype to genotype can evaluate disease-disease associations at different levels, providing a comprehensive perspective for understanding diseases. In this review, available biomedical data and databases for evaluating disease-disease associations are first summarized. Then, existing computational methods for disease-disease associations are reviewed and classified into five groups in terms of the usages of biomedical data, including disease semantic-based, phenotype-based, function-based, representation learning-based and text mining-based methods. Further, we summarize software tools/platforms for computation and analysis of disease-disease associations. Finally, we give a discussion and summary on the research of disease-disease associations. This review provides a systematic overview for current disease association research, which could promote the development and applications of computational methods and tools/platforms for disease-disease associations.

Screening Differential CircRNAs Expression Profiles Reveals the Regulatory Role of the has_circTPT1_003-has-miR-218-5p-CCNE2/SMC4 Signaling Axis in Bladder Carcinoma Progression

Circular RNAs (circRNAs) are a class of noncoding RNAs closely related to the development and progression of various human cancers. However, it is unclear whether circRNAs play an important role in the development of bladder cancer. We utilized human circRNA array V2 microarrays to screen circRNA expression profiles in bladder cancer tissues. Bioinformatic tools including circBank, dbDEMC 2.0, miRCancer, TarBase v7.0, miRtarbase, TCGA-BLCA, Cytoscape-MCODE, String, ENCORI, and Venny 2.1 were then employed to construct the circRNA-miRNA-mRNA regulatory networks. In total, 105 upregulated circRNAs and 167 downregulated circRNAs (fold change >2 and p < 0.001) were filtered out. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis of filtered dysregulated circRNAs disclosed that the circRNAs regulatory network was closely related with mRNA processing and cell cycle, etc. Further excavation analysis showed that seven differentially overexpressed circRNAs including hsa_circ_0000133, hsa_circ_0023610, hsa_circ_0005615, hsa_circ_0030162, hsa_circ_0077007, hsa_circ_0001140, and hsa_circ_0107031 were associated with bladder cancer invasiveness, and the cell cycle signal axis. has_circTPT1_003-has-miR-218-5p-CCNE2/SMC4 was finally clarified as a possible mechanism for bladder cancer progression. Based on results derived from multiple approaches, we identified that has_circTPT1_003-has-miR-218-5p-CCNE2/SMC4 signal axis may be involved in the invasion process of bladder cancer.

Comprehensive bioinformatics analysis of functional molecules in colorectal cancer

BACKGROUND

Colorectal cancer (CRC) is the 3rd most common cancer and the 2nd leading cause of cancer-related death. Numerous studies have found that aberrations in cellular molecules play an important role in the development of tumors. Studying and determining the interactions between these molecules can contribute to the diagnosis, treatment, and prognosis of tumors.

METHODS

The GSE151021, GSE156720, and GSE156719 data sets were analyzed to screen the differentially expressed messenger RNAs (DEmRNAs), long non-coding RNAs (DElncRNAs), and microRNAs (DEmiRNAs) in CRC. Database for Annotation, Visualization and Integrated Discovery (DAVID) and the Search Tool for the Retrieval of Interacting Genes/Proteins software were used to examine gene enrichment and the hub genes. Gene Expression Profiling Interactive Analysis 2 (GEPIA2) and UALCAN was used to verify the expression of the hub genes. To analyze the overall survival (OS) of the hub genes, Kaplan-Meier plotter (KM plotter) was performed. Finally, the miRCancer database, TargetScan, and GSE156719 were used to identify the targets of the identified miRNAs. To predict the lncRNA-miRNA interactions, we used DIANA-LncBase v2 and GSE156720. Finally, the visualization protein‑protein interaction (PPI), competitive endogenous RNA (ceRNA) network was constructed using Cytoscape v3.1.

RESULTS

By analyzing GSE151021 and GSE156720, 23 upregulated mRNAs and 10 downregulated mRNAs were identified as sharing the differentially expressed genes (DEGs) between CRC and adjacent tissues. Furthermore, nucleolar protein 14 (NOP14), the sonic hedgehog (SHH) signaling molecule, phorbol-12-myristate-13-acetate-induced protein 1 (PMAIP1), the BCL2 apoptosis regulator (BCL2), and zinc finger E-box binding homeobox 2 (ZEB2) were considered hub genes. The constructed lncRNA-miRNA-mRNA network revealed 7 intersecting miRNAs (4 upregulated and 3 downregulated), 79 lncRNAs (40 upregulated and 39 downregulated), and 5 mRNAs (3 upregulated and 2 downregulated). Finally, we determined that the dysregulation of lncRNAs, such as HCG16, CASC9, SNHG16, HAND2-AS1, and NR2F1-AS1, secluded altered the expression of several miRNAs, such as hsa-miR-193a-5p, hsa-miR-485-5p, hsa-miR-17-5p, and hsa-miR-92a-3p, and affected the occurrence and development of CRC.

CONCLUSIONS

We identified a series of DElncRNAs, DEmRNAs, and DEmiRNAs in CRC that might be considered potential biomarkers in understanding the complex molecular pathways leading to CRC development.

MIMRDA: A Method Incorporating the miRNA and mRNA Expression Profiles for Predicting miRNA-Disease Associations to Identify Key miRNAs (microRNAs)

Identifying cancer-related miRNAs (or microRNAs) that precisely target mRNAs is important for diagnosis and treatment of cancer. Creating novel methods to identify candidate miRNAs becomes an imminent Frontier of researches in the field. One major obstacle lies in the integration of the state-of-the-art databases. Here, we introduce a novel method, MIMRDA, which incorporates the miRNA and mRNA expression profiles for predicting miRNA-disease associations to identify key miRNAs. As a proof-of-principle study, we use the MIMRDA method to analyze TCGA datasets of 20 types (BLCA, BRCA, CESE, CHOL, COAD, ESCA, HNSC, KICH, KIRC, KIRP, LIHC, LUAD, LUSC, PAAD, PRAD, READ, SKCM, STAD, THCA and UCEC) of cancer, which identified hundreds of top-ranked miRNAs. Some (as Category 1) of them are endorsed by public databases including TCGA, miRTarBase, miR2Disease, HMDD, MISIM, ncDR and mTD; others (as Category 2) are supported by literature evidences. miR-21 (representing Category 1) and miR-1258 (representing Category 2) display the excellent characteristics of biomarkers in multi-dimensional assessments focusing on the function similarity analysis, overall survival analysis, and anti-cancer drugs’ sensitivity or resistance analysis. We compare the performance of the MIMRDA method over the Limma and SPIA packages, and estimate the accuracy of the MIMRDA method in classifying top-ranked miRNAs via the Random Forest simulation test. Our results indicate the superiority and effectiveness of the MIMRDA method, and recommend some top-ranked key miRNAs be potential biomarkers that warrant experimental validations.

MiRNA-disease association prediction based on meta-paths

Since miRNAs can participate in the posttranscriptional regulation of gene expression, they may provide ideas for the development of new drugs or become new biomarkers for drug targets or disease diagnosis. In this work, we propose an miRNA-disease association prediction method based on meta-paths (MDPBMP). First, an miRNA-disease-gene heterogeneous information network was constructed, and seven symmetrical meta-paths were defined according to different semantics. After constructing the initial feature vector for the node, the vector information carried by all nodes on the meta-path instance is extracted and aggregated to update the feature vector of the starting node. Then, the vector information obtained by the nodes on different meta-paths is aggregated. Finally, miRNA and disease embedding feature vectors are used to calculate their associated scores. Compared with the other methods, MDPBMP obtained the highest AUC value of 0.9214. Among the top 50 predicted miRNAs for lung neoplasms, esophageal neoplasms, colon neoplasms and breast neoplasms, 49, 48, 49 and 50 have been verified. Furthermore, for breast neoplasms, we deleted all the known associations between breast neoplasms and miRNAs from the training set. These results also show that for new diseases without known related miRNA information, our model can predict their potential miRNAs. Code and data are available at https://github.com/LiangYu-Xidian/MDPBMP.

Role of BCAR4 in prostate cancer cell autophagy

Background

Increased autophagy of prostate cancer (PC) cells contributes to their resistance to chemotherapy. Recently, we reported that a long non-coding RNA (lncRNA)-breast-cancer anti-estrogen resistance 4 (BCAR4)-is highly expressed in PC and contributes to castration resistance through activation of GLI2 signaling. However, the role of BCAR4 in the regulation of PC cell autophagy is unknown and is the subject of the current study.

Methods

BCAR4 and Beclin-1 levels and the alteration in autophagy pathway genes were assessed in PC using a public database and in our own clinical specimens. The correlation between BCAR4 and Beclin-1 levels in PC and PC cell lines was determined and their regulatory relationship was assessed by overexpression and knockout assay. The final effect on autophagy was measured by microtubule-associated protein 1A/1B-light chain 3 (LC3) levels. The mechanism that underlies the control of Beclin-1 by BCAR4 was analyzed by cancer database and gain-of-function and loss-of-function approaches.

Results

BCAR4 and Beclin-1 were both upregulated in PC and were positively correlated. BCAR4 directly activated Beclin-1 at transcriptional level, which subsequently increased the ratio of LC3 II to LC3I to augment PC cell autophagy. Beclin-1 did not control levels of BCAR4. Mechanically, BCAR4 and Beclin-1 shared several targeting microRNAs, among which miR-15 and miR-146 appeared to be the mediators of the effects of BACR4 on Beclin-1.

Conclusions

BCAR4 may enhance PC cell autophagy through altering miRNA-regulated Beclin-1 expression in PC.

MicroRNA Targeting

MicroRNAs (miRNAs) are small noncoding elements that play essential roles in the posttranscriptional regulation of biochemical processes. miRNAs recognize and target multiple mRNAs; therefore, investigating miRNA dysregulation is an indispensable strategy to understand pathological conditions and to design innovative drugs. Targeting miRNAs in diseases improve outcomes of several therapeutic strategies thus, this present study highlights miRNA targeting methods through experimental assays and bioinformatics tools. The first part of this review focuses on experimental miRNA targeting approaches for elucidating key biochemical pathways. A growing body of evidence about the miRNA world reveals the fact that it is not possible to uncover these molecules' structural and functional characteristics related to the biological processes with a deterministic approach. Instead, a systemic point of view is needed to truly understand the facts behind the natural complexity of interactions and regulations that miRNA regulations present. This task heavily depends both on computational and experimental capabilities. Fortunately, several miRNA bioinformatics tools catering to nonexperts are available as complementary wet-lab approaches. For this purpose, this work provides recent research and information about computational tools for miRNA targeting research.

Higher-Order Proximity-Based MiRNA-Disease Associations Prediction

MiRNA-disease association prediction plays an important role in identifying human disease-related miRNAs. This approach is helpful not only to formulate individualized diagnosis schemes, but also to understand the pathogenesis of diseases. Many studies have focused on enhancing the prediction performance using explicit side information, such as miRNA functional similarity and disease semantic similarity. The existing approaches, however, often ignore the higher-order implicit proximity among miRNAs and diseases. To this end, in this paper, we first propose a novel approach HOP_MDA (Higher-Order Proximity based MiRNA and Disease Association Prediction) for predicting potential association between miRNA and disease. Both explicit interaction information and implicit higher-order proximity information between miRNA and disease are encoded with different order proximity matrices which are weightily combined into a parameterized prediction matrix. A supervised learning approach based on the known miRNAs-disease associations is proposed to determine the optimal weight parameters. The prediction matrix is then used to achieve effective prediction. Additionally, a higher-order proximity approximation technique (HOPA_MDA) is presented to make more efficient predictions. 5-fold cross validation is used to evaluate the performance of our proposed method. The average AUC values of HOPA_MDA for two real datasets are 0.921+/-0.002 and 0.944+/-0.0015, respectively. Our method can also predict potential miRNAs specific to new diseases with no known related miRNAs.

Artificial Intelligence and Machine Learning in Cancer Research: A Systematic and Thematic Analysis of the Top 100 Cited Articles Indexed in Scopus Database

INTRODUCTION

Cancer is a major public health problem and a global leading cause of death where the screening, diagnosis, prediction, survival estimation, and treatment of cancer and control measures are still a major challenge. The rise of Artificial Intelligence (AI) and Machine Learning (ML) techniques and their applications in various fields have brought immense value in providing insights into advancement in support of cancer control.

METHODS

A systematic and thematic analysis was performed on the Scopus database to identify the top 100 cited articles in cancer research. Data were analyzed using RStudio and VOSviewer.Var1.6.6.

RESULTS

The top 100 articles in AI and ML in cancer received a 33 920 citation score with a range of 108 to 5758 times. Doi Kunio from the USA was the most cited author with total number of citations (TNC = 663). Out of 43 contributed countries, 30% of the top 100 cited articles originated from the USA, and 10% originated from China. Among the 57 peer-reviewed journals, the "Expert Systems with Application" published 8% of the total articles. The results were presented in highlight technological advancement through AI and ML via the widespread use of Artificial Neural Network (ANNs), Deep Learning or machine learning techniques, Mammography-based Model, Convolutional Neural Networks (SC-CNN), and text mining techniques in the prediction, diagnosis, and prevention of various types of cancers towards cancer control.

CONCLUSIONS

This bibliometric study provides detailed overview of the most cited empirical evidence in AI and ML adoption in cancer research that could efficiently help in designing future research. The innovations guarantee greater speed by using AI and ML in the detection and control of cancer to improve patient experience.

Network-based cancer genomic data integration for pattern discovery

BACKGROUND

Since genes involved in the same biological modules usually present correlated expression profiles, lots of computational methods have been proposed to identify gene functional modules based on the expression profiles data. Recently, Sparse Singular Value Decomposition (SSVD) method has been proposed to bicluster gene expression data to identify gene modules. However, this model can only handle the gene expression data where no gene interaction information is integrated. Ignoring the prior gene interaction information may produce the identified gene modules hard to be biologically interpreted.

RESULTS

In this paper, we develop a Sparse Network-regularized SVD (SNSVD) method that integrates a prior gene interaction network from a protein protein interaction network and gene expression data to identify underlying gene functional modules. The results on a set of simulated data show that SNSVD is more effective than the traditional SVD-based methods. The further experiment results on real cancer genomic data show that most co-expressed modules are not only significantly enriched on GO/KEGG pathways, but also correspond to dense sub-networks in the prior gene interaction network. Besides, we also use our method to identify ten differentially co-expressed miRNA-gene modules by integrating matched miRNA and mRNA expression data of breast cancer from The Cancer Genome Atlas (TCGA). Several important breast cancer related miRNA-gene modules are discovered.

CONCLUSIONS

All the results demonstrate that SNSVD can overcome the drawbacks of SSVD and capture more biologically relevant functional modules by incorporating a prior gene interaction network. These identified functional modules may provide a new perspective to understand the diagnostics, occurrence and progression of cancer.

TSMDA: Target and symptom-based computational model for miRNA-disease-association prediction

The emergence of high-throughput sequencing techniques has revealed a primary role of microRNAs (miRNAs) in a wide range of diseases, including cancers and neurodegenerative disorders. Understanding novel relationships between miRNAs and diseases can potentially unveil complex pathogenesis mechanisms, leading to effective diagnosis and treatment. The investigation of novel miRNA-disease associations, however, is currently costly and time consuming. Over the years, several computational models have been proposed to prioritize potential miRNA-disease associations, but with limited usability or predictive capability. In order to fill this gap, we introduce TSMDA, a novel machine-learning method that leverages target and symptom information and negative sample selection to predict miRNA-disease association. TSMDA significantly outperforms similar methods, achieving an area under the receiver operating characteristic (ROC) curve (AUC) of 0.989 and 0.982 under 5-fold cross-validation and blind test, respectively. We also demonstrate the capability of the method to uncover potential miRNA-disease associations in breast, prostate, and lung cancers, as case studies. We believe TSMDA will be an invaluable tool for the community to explore and prioritize potentially new miRNA-disease associations for further experimental characterization. The method was made available as a freely accessible and user-friendly web interface at http://biosig.unimelb.edu.au/tsmda/.

Data Integration Using Tensor Decomposition for The Prediction of miRNA-Disease Associations

Dysfunction of miRNAs has an important relationship with diseases by impacting their target genes. Identifying disease-related miRNAs is of great significance to prevent and treat diseases. Integrating information of genes related miRNAs and/or diseases in calculational methods for miRNA-disease association studies is meaningful because of the complexity of biological mechanisms. Therefore, in this study, we propose a novel method based on tensor decomposition, termed TDMDA, to integrate multi-type data for identifying pathogenic miRNAs. First, we construct a three-order association tensor to express the associations of miRNA-disease pairs, the associations of miRNA-gene pairs, and the associations of gene-disease pairs simultaneously. Then, a tensor decomposition-based method with auxiliary information is applied to reconstruct the association tensor for predicting miRNA-disease associations, and the auxiliary information includes biological similarity information and adjacency information. The performance of TDMDA is compared with other advanced methods under 5-fold cross-validations. The experimental results indicate the TDMDA is a competitive method.

Human microRNA similarity in breast cancer

MicroRNAs (miRNAs) play important roles in a variety of human diseases, including breast cancer. A number of miRNAs are up- and down-regulated in breast cancer. However, little is known about miRNA similarity and similarity network in breast cancer. Here, a collection of 272 breast cancer-associated miRNA precursors (pre-miRNAs) were utilized to calculate similarities of sequences, target genes, pathways and functions and construct a combined similarity network. Well-characterized miRNAs and their similarity network were highlighted. Interestingly, miRNA sequence-dependent similarity networks were not identified in spite of sequence–target gene association. Similarity networks with minimum and maximum number of miRNAs originate from pathway and mature sequence, respectively. The breast cancer-associated miRNAs were divided into seven functional classes (classes I–VII) followed by disease enrichment analysis and novel miRNA-based disease similarities were found. The finding would provide insight into miRNA similarity, similarity network and disease heterogeneity in breast cancer.

Identifying Complex lncRNA/Pseudogene-miRNA-mRNA Crosstalk in Hormone-Dependent Cancers

Simple Summary

Competing endogenous RNAs (ceRNAs) have gained attention in cancer research owing to their involvement in microRNA-mediated gene regulation. Here, we identified a shared ceRNA network across five hormone-dependent (HD) cancers (prostate, breast, colon, rectal, and endometrial), that contain two long non-coding RNAs, nine mRNAs, and seventy-four microRNAs. Among them, two mRNAs and forty-one microRNAs were associated with at least one HD cancer survival. A similar analytical approach can be applied to identify shared ceRNAs across a group of related cancers, which will significantly contribute to understanding their shared disease biology.

Abstract

The discovery of microRNAs (miRNAs) has fundamentally transformed our understanding of gene regulation. The competing endogenous RNA (ceRNA) hypothesis postulates that messenger RNAs and other RNA transcripts, such as long non-coding RNAs and pseudogenes, can act as natural miRNA sponges. These RNAs influence each other’s expression levels by competing for the same pool of miRNAs through miRNA response elements on their target transcripts, thereby modulating gene expression and protein activity. In recent years, these ceRNA regulatory networks have gained considerable attention in cancer research. Several studies have identified cancer-specific ceRNA networks. Nevertheless, prior bioinformatic analyses have focused on long-non-coding RNA-associated ceRNA networks. Here, we identify an extended ceRNA network (including both long non-coding RNAs and pseudogenes) shared across a group of five hormone-dependent (HD) cancers, i.e., prostate, breast, colon, rectal, and endometrial cancers, using data from The Cancer Genome Atlas (TCGA). We performed a functional enrichment analysis for differentially expressed genes in the shared ceRNA network of HD cancers, followed by a survival analysis to determine their prognostic ability. We identified two long non-coding RNAs, nine genes, and seventy-four miRNAs in the shared ceRNA network across five HD cancers. Among them, two genes and forty-one miRNAs were associated with at least one HD cancer survival. This study is the first to investigate pseudogene-associated ceRNAs across a group of related cancers and highlights the value of this approach to understanding the shared molecular pathogenesis in a group of related diseases.

Integration of pairwise neighbor topologies and miRNA family and cluster attributes for miRNA-disease association prediction

Identifying disease-related microRNAs (miRNAs) assists the understanding of disease pathogenesis. Existing research methods integrate multiple kinds of data related to miRNAs and diseases to infer candidate disease-related miRNAs. The attributes of miRNA nodes including their family and cluster belonging information, however, have not been deeply integrated. Besides, the learning of neighbor topology representation of a pair of miRNA and disease is a challenging issue. We present a disease-related miRNA prediction method by encoding and integrating multiple representations of miRNA and disease nodes learnt from the generative and adversarial perspective. We firstly construct a bilayer heterogeneous network of miRNA and disease nodes, and it contains multiple types of connections among these nodes, which reflect neighbor topology of miRNA-disease pairs, and the attributes of miRNA nodes, especially miRNA-related families and clusters. To learn enhanced pairwise neighbor topology, we propose a generative and adversarial model with a convolutional autoencoder-based generator to encode the low-dimensional topological representation of the miRNA-disease pair and multi-layer convolutional neural network-based discriminator to discriminate between the true and false neighbor topology embeddings. Besides, we design a novel feature category-level attention mechanism to learn the various importance of different features for final adaptive fusion and prediction. Comparison results with five miRNA-disease association methods demonstrated the superior performance of our model and technical contributions in terms of area under the receiver operating characteristic curve and area under the precision-recall curve. The results of recall rates confirmed that our model can find more actual miRNA-disease associations among top-ranked candidates. Case studies on three cancers further proved the ability to detect potential candidate miRNAs.

In silico identification of variations in microRNAs with a potential impact on dairy traits using whole ruminant genome SNP datasets

MicroRNAs are small noncoding RNAs that have important roles in the lactation process and milk biosynthesis. Some polymorphisms have been studied in various livestock species from the perspective of pathology or production traits. To target variants that could be the causal variants of dairy traits, genetic variants of microRNAs expressed in the mammary gland or present in milk and localized in dairy quantitative trait loci (QTLs) were investigated in bovine, caprine, and ovine species. In this study, a total of 59,124 (out of 28 millions), 13,427 (out of 87 millions), and 4761 (out of 38 millions) genetic variants in microRNAs expressed in the mammary gland or present in milk were identified in bovine, caprine, and ovine species, respectively. A total of 4679 of these detected bovine genetic variants are located in dairy QTLs. In caprine species, 127 genetic variants are localized in dairy QTLs. In ovine species, no genetic variant was identified in dairy QTLs. This study leads to the detection of microRNA genetic variants of interest in the context of dairy production, taking advantage of whole genome data to identify microRNA genetic variants expressed in the mammary gland and localized in dairy QTLs.

A comprehensive review of online resources for microRNA-diseases associations: the state of the art

MicroRNAs (miRNAs) as small 19- to 24-nucleotide noncoding RNAs regulate several mRNA targets and signaling pathways. Therefore, miRNAs are considered key regulators in cellular pathways as well as various pathologies. There is substantial interest in the relationship between disease and miRNAs, which made that one of the important research topics. Interestingly, miRNAs emerged as an attractive approach for clinical application, not only as biomarkers for diagnosis and prognosis or in the prediction of therapy response but also as therapeutic tools. For these purposes, the identification of crucial miRNAs in disease is very important. Databases provided valuable experimental and computational miRNAs-disease information in an accessible and comprehensive manner, such as miRNA target genes, miRNA related in signaling pathways and miRNA involvement in various diseases. In this review, we summarized miRNAs-disease databases in two main categories based on the general or specific diseases. In these databases, researchers could search diseases to identify critical miRNAs and developed that for clinical applications. In another way, by searching particular miRNAs, they could recognize in which disease these miRNAs would be dysregulated. Despite the significant development that has been done in these databases, there are still some limitations, such as not being updated and not providing uniform and detailed information that should be resolved in future databases. This survey can be helpful as a comprehensive reference for choosing a suitable database by researchers and as a guideline for comparing the features and limitations of the database by developer or designer. Short abstract We summarized miRNAs-disease databases that researchers could search disease to identify critical miRNAs and developed that for clinical applications. This survey can help choose a suitable database for researchers.

Newly-Discovered Neural Features Expand the Pathobiological Knowledge of Blastic Plasmacytoid Dendritic Cell Neoplasm

Simple Summary

For the first time, neuronal features are described in blastic plasmacytoid dendritic cell neoplasm (BPDCN) by a complex array of molecular techniques, including microRNA and gene expression profiling, RNA and Chromatin immunoprecipitation sequencing, and immunohistochemistry. The discovery of unexpected neural features in BPDCN may change our vision of this disease, leading to the designing of a new BPDCN cell model and to re-thinking the relations occurring between BPDCN and nervous system. The observed findings contribute to explaining the extreme tumor aggressiveness and also to propose novel therapeutic targets. In view of this, the identification, in this work of new potential neural metastatic inducers might open the way to therapeutic approaches for BPDCN patients based on the use of anti-neurogenic agents.

Abstract

Blastic plasmacytoid dendritic cell neoplasm (BPDCN) is a rare and highly aggressive hematologic malignancy originating from plasmacytoid dendritic cells (pDCs). The microRNA expression profile of BPDCN was compared to that of normal pDCs and the impact of miRNA dysregulation on the BPDCN transcriptional program was assessed. MiRNA and gene expression profiling data were integrated to obtain the BPDCN miRNA-regulatory network. The biological process mainly dysregulated by this network was predicted to be neurogenesis, a phenomenon raising growing interest in solid tumors. Neurogenesis was explored in BPDCN by querying different molecular sources (RNA sequencing, Chromatin immunoprecipitation-sequencing, and immunohistochemistry). It was shown that BPDCN cells upregulated neural mitogen genes possibly critical for tumor dissemination, expressed neuronal progenitor markers involved in cell migration, exchanged acetylcholine neurotransmitter, and overexpressed multiple neural receptors that may stimulate tumor proliferation, migration and cross-talk with the nervous system. Most neural genes upregulated in BPDCN are currently investigated as therapeutic targets.

Aberrant expression of lncRNAs SNHG6, TRPM2-AS1, MIR4435-2HG, and hypomethylation of TRPM2-AS1 promoter in colorectal cancer

Accumulating evidence has indicated that deregulation of lncRNAs plays essential roles in colorectal cancer (CRC) carcinogenesis. The goal of this study was to analyze the expression of lncRNAs in colorectal cancer and their association with clinicopathological variables. Bioinformatics analysis of published CRC microarray data was performed to identify the important lncRNAs. The expression levels of candidate genes were assessed in the human colon cancer/normal cell lines, CRC, adenomatous colorectal polyps, and their marginal tissues by qRT-PCR. Moreover, the methylation status of the TRPM2-AS1 promoter was studied using qMSP assay. Furthermore, we investigated the molecular mechanisms of these lncRNAs in CRC progression using in silico analysis. Microarray analysis revealed that lncRNAs SNHG6, MIR4435-2HG, and TRPM2-AS1 were upregulated in CRC. These results were validated in colon cell lines. Moreover, qRT-PCR showed that the expression levels of SNHG6 and TRPM2-AS1 were upregulated in the colorectal tumor tissues compared with their paired tissues. Nonetheless, there was no significant increase in MIR4435-2HG expression in CRC samples. Furthermore, we observed a significant hypomethylation of TRPM2-AS1 promoter and its activation in CRC tissues. By in silico analysis, we found that the lncRNAs upregulation could promote proliferation and drug resistance of colorectal cancer cells via miRNAs sponging and modulation of their targets expression. In conclusion, based on our results upregulation of SNHG6 and TRPM2-AS1, and hypomethylation of TRPM2-AS1 promoter might be considered as potential diagnostic biomarkers for CRC initiation and development.

MvKFN-MDA: Multi-view Kernel Fusion Network for miRNA-disease association prediction

Predicting the associations between microRNAs (miRNAs) and diseases is of great significance for identifying miRNAs related to human diseases. Since it is time-consuming and costly to identify the association between miRNA and disease through biological experiments, computational methods are currently used as an effective supplement to identify the potential association between disease and miRNA. This paper presents a Multi-view Kernel Fusion Network (MvKFN) based prediction method (MvKFN-MDA) to address the problem of miRNA-disease associations prediction. A novel multiple kernel fusion framework Multi-view Kernel Fusion Network (MvKFN) is first proposed to effectively fuse different views similarity kernels constructed from different data sources in a highly nonlinear way. Using MvKFNs, both different base similarity kernels for miRNA, such as sequence, functional, semantic, Gaussian profile kernels and different base similarity kernels for diseases, such as semantic, Gaussian profile kernel are nonlinearly fused into two integrated similarity kernels, one for miRNA, another for disease. Then, miRNA and disease feature representations are extracted from the miRNA and disease integrated similarity kernels respectively. These features are then fed into a neural matrix completion framework which finally outputs the association prediction scores. The parameters of MvKFN-MDA are learned based on the known miRNA-disease association matrix in a supervised end-to-end way. We compare the proposed method with other state-of-the-art methods. The AUCs of our proposed method were superior to the existing methods in both 5-FCV and LOOCV on two open experimental datasets. Furthermore, 49, 48, and 47 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma, and kidney cancer, are verified respectively using experimental literature. Finally, 100% accuracy from the top 50 predicted miRNAs is achieved when breast cancer is used as a case study to evaluate the ability of MvKFN-MDA for predicting a new disease without any known related miRNAs.

An Integrative Analysis of The Micro-RNAs Contributing in Stemness, Metastasis and B-Raf Pathways in Malignant Melanoma and Melanoma Stem Cell

OBJECTIVE

Epithelial-mesenchymal transition (EMT) and the stemness potency in association with BRAF mutation are in dispensable to the progression of melanoma. Recently, microRNAs (miRNAs) have been introduced as the regulator of a multitude of oncogenic functions in most of tumors. Therefore identifying and interpreting the expression patterns of these miRNAs is essential. The present study sought to find common miRNAs regulating all three important pathways in melanoma development.

MATERIALS AND METHODS

In this experimental study, 18 miRNAs that importantly contribute to EMT and have a role in regulating self-renewal and the BRAF pathway were selected based on current literature and cross-analysis with available databases. Subsequently, their expression patterns were evaluated in 20 melanoma patients, normal tissues, serum from patients and control subjects, and melanospheres. Pattern discovery and integrative regulatory network analysis were used to find the most important miRNAs in melanoma progression.

RESULTS

Among 18 selected miRNAs, miR-205, -141, -203, -15b, and -9 were differentially expressed in tumor samples than normal tissues. Among them, miR-205, -15b, and -9 significantly expressed in serum samples and healthy donors. Attribute Weighting and decision trees (DT) analysis presented evidence that the combination of miR-205, -203, -9, and -15b can regulate self-renewal and EMT process, by affecting CDH1, CCND1, and VEGF expression.

CONCLUSION

We suggested here that miR-205, -15b, -203, -9 pattern as the key miRNAs linked to melanoma status, the pluripotency, proliferation, and motility of malignant cells. However, further investigations are required to find the mechanisms underlying the combinatory effects of the above mentioned miRNAs.

Long non-coding RNA AGAP2-AS1 is up regulated in colorectal cancer

Accumulating evidence has indicated that, aberrant lncRNA expression plays essential roles in the colorectal cancer (CRC) tumorigenesis. AGAP2-AS1 is upregulated in some cancers, however, its involvement in the CRC tumorigenesis in the population of North-West of Iran has remained unknown. In this study, we evaluated its deregulation in CRC microarray datasets, colon cell lines, CRC tumor, adenomatous colorectal polyps and their paired normal tissues. The results showed that AGAP2-AS1 is upregulated in CRC and might be considered as a potential biomarker for CRC development. Moreover, our results suggest AGAP2-AS1 promoted CRC progression by sponging the hsa-miR-15/16 family and upregulation of their targets.

Signposts for Aquatic Toxicity Evaluation in China: Text Mining using Event-Driven Taxonomy within and among Regions

Selection of toxicity endpoints affects outcomes of risk assessment. Scientific decisions based on more holistic evidence is preferable for designing bioassay batteries rather than subjective selections, particularly when systems are poorly understood. Here, we propose a novel event-driven taxonomy (EDT)-based text mining tool to prioritize stressors likely to elicit water quality deterioration. The tool integrated automated literature collection, natural language processing using adverse outcome pathway-based toxicological terminologies and machine learning to classify event drivers (EDs). From aquatic toxicity assessments within China over the past decade, we gathered over 14 000 sources of information. With a dictionary that included 1039 toxicological terms, 15 bioassay-related modes of actions were mapped, yet less than half of the bioassays could be elucidated by available adverse outcome pathways. To fill these mechanistic knowledge gaps, we developed a Naïve Bayesian ED-classifier to annotate apical responses. The classifier's 4-fold cross-validation reached 74% accuracy and labeled 85% bioassays as 26 EDs. Narcosis, estrogen receptor-, and aryl hydrogen receptor-mediators were the major EDs in aquatic systems across China, whereas individual regions had distinct ED fingerprints. The EDT-based tool provides a promising diagnostic strategy to inform region-specific bioassay design and selection for water quality assessments in a big data era.

Filtering of the Gene Signature as the Predictors of Cisplatin-Resistance in Ovarian Cancer

BACKGROUND

Gene expression profiling and prediction of drug responses based on the molecular signature indicate new molecular biomarkers which help to find the most effective drugs according to the tumor characteristics.

OBJECTIVES

In this study two independent datasets, GSE28646 and GSE15372 were subjected to meta-analysis based on Affymetrix microarrays.

MATERIAL AND METHODS

In-silico methods were used to determine differentially expressed genes (DEGs) in the previously reported sensitive and resistant A2780 cell lines to Cisplatin. Gene Fuzzy Scoring (GFS) and Principle Component Analysis (PCA) were then used to eliminate batch effects and reduce data dimension, respectively. Moreover, SVM method was performed to classify sensitive and resistant data samples. Furthermore, Wilcoxon Rank sum test was performed to determine DEGs. Following the selection of drug resistance markers, several networks including transcription factor-target regulatory network and miRNA-target network were constructed and Differential correlation analysis was performed on these networks.

RESULTS

The trained SVM successfully classified sensitive and resistant data samples. Moreover, Performing DiffCorr analysis on the sensitive and resistant samples resulted in detection of 27 and 25 significant (with correlation ≥|0.9|) pairs of genes that respectively correspond to newly constructed correlations and loss of correlations in the resistant samples.

CONCLUSIONS

Our results indicated the functional genes and networks in Cisplatin resistance of ovarian cancer cells and support the importance of differential expression studies in ovarian cancer chemotherapeutic agent responsiveness.

The Functional Characterization of Epigenetically Related lncRNAs Involved in Dysregulated CeRNA-CeRNA Networks Across Eight Cancer Types

Numerous studies have demonstrated that lncRNAs could compete with other RNAs to bind miRNAs, as competing endogenous RNAs (ceRNAs), to regulate each other. On the other hand, ceRNAs were found to be recurrently dysregulated in cancer status. However, limited studies considered the upstream epigenetic regulatory factors that disrupted the normal competing mechanism. In the present study, we constructed the lncRNA-associated dysregulated ceRNA networks across eight cancer types. lncRNAs in the individual dysregulated network and pan-cancer core dysregulated ceRNA subnetwork were found to play more important roles than mRNAs. Integrating lncRNA methylation profiles, we identified 49 epigenetically related (ER) lncRNAs involved in the dysregulated ceRNA networks, including 18 epigenetically activated (EA) lncRNAs, 18 epigenetically silenced (ES) lncRNAs, and 13 rewired ER lncRNAs across eight cancer types. Furthermore, we evaluated the epigenetic regulating patterns of these lncRNAs and screened nine pan-cancer ER lncRNAs (six EA and three ES lncRNAs). The nine lncRNAs were found to regulate the cancer hallmarks by competing with mRNAs. Moreover, we found that integrating the expression and methylation profiles of the nine lncRNAs could predict cancer incidence in eight cancer types robustly and the cancer outcome of several cancer types. These results provide an improved understanding of methylation regulation to ceRNA and offer novel potential molecular therapeutic targets for the diagnosis and prognosis across different cancer types.

Predicting miRNA-Disease Associations Based on Multi-View Variational Graph Auto-Encoder with Matrix Factorization

MicroRNAs (miRNAs) have been proved to play critical roles in diverse biological processes, including the human disease development process. Exploring the potential associations between miRNAs and diseases can help us better understand complex disease mechanisms. Given that traditional biological experiments are expensive and time-consuming, computational models can serve as efficient means to uncover potential miRNA-disease associations. This study presents a new computational model based on variational graph auto-encoder with matrix factorization (VGAMF) for miRNA-disease association prediction. More specifically, VGAMF first integrates four different types of information about miRNAs into an miRNA comprehensive similarity network and two types of information about diseases into a disease comprehensive similarity network, respectively. Then, VGAMF gets the non-linear representations of miRNAs and diseases, respectively, from those two comprehensive similarity networks with variational graph auto-encoders. Simultaneously, a non-negative matrix factorization is conducted on the miRNA-disease association matrix to get the linear representations of miRNAs and diseases. Finally, a fully connected neural network combines linear and non-linear representations of miRNAs and diseases to get the final predicted association score for all miRNA-disease pairs. In the 10-fold cross-validation experiments, VGAMF achieves an average AUC of 0.9280 on HMDD v2.0 and 0.9470 on HMDD v3.2, which outperforms other competing methods. Besides, the case studies on colon cancer and esophageal cancer further demonstrate the effectiveness of VGAMF in predicting novel miRNA-disease associations.

MicroRNA‑142‑3p suppresses cell proliferation, invasion and epithelial‑to‑mesenchymal transition via RAC1‑ERK1/2 signaling in colorectal cancer

Aberrant expression of microRNAs (miRNAs/miRs) is associated with the initiation and progression of colorectal cancer (CRC), but how they regulate colorectal tumorigenesis is still unknown. The present study was designed to investigate the expression profile of miRNAs in human CRC tissues, and to reveal the molecular mechanism of miRNA-142-3p in suppressing colon cancer cell proliferation. The expression of miRNA was examined using an Exiqon miRNA array. Bioinformatics was used to predict the target genes of differentially expressed miRNAs and to analyze their biological function in CRC. The effect of miR-142-3p in colon cancer cells was evaluated in vitro using cell proliferation, colony formation and Transwell assays. Dual-luciferase reporter gene assays were performed to investigate the association between miR-142-3p and Rac family small GTPase 1 (RAC1). The effect of miR-142-3p regulation on colon cancer proliferation was assessed through western blotting and quantitative polymerase chain reaction analysis. Compared with their expression in adjacent non-cancer mucosal tissues, 76 miRNAs were upregulated and 102 miRNAs were downregulated in CRC. One of the most significantly and differentially regulated miRNAs was miR-142-3p, which was downregulated in 81.0% (51/63) of primary CRC tissues. After transfection of miR-142-3p mimics into colon cancer cells, proliferation and colony formation were decreased, and migration and invasion were markedly suppressed. RAC1 was a possible target of miR-142-3p, which was confirmed by dual-luciferase reporter assay. Transfection of miR-142-3p mimics decreased the levels of RAC1 and suppressed epithelial-to-mesenchymal transition in colon cancer cells. The phosphorylation of extraceullar signal-regulated kinase (ERK) was decreased significantly by the inhibition of RAC1 or transfection of miR-142-3p mimics in colon cancer cells. In conclusion, aberrant miRNAs are implicated in CRC. Decreased expression of miR-142-3p may be associated with CRC tumorigenesis via Rac1-ERK signaling.

Comprehensive Analysis of Regulatory Network for LINC00472 in Clear Cell Renal Cell Carcinoma

Renal cell carcinoma (RCC) accounts for about 2% to 3% of adult malignancies, and clear cell renal cell carcinoma (ccRCC) is the most common and aggressive type of kidney cancer. It accounts for 75% of all kidney tumors. Although new targeted drugs continue to appear, they are still not suitable for all patients. Therefore, an in-depth study of the molecular mechanism of the development of ccRCC and exploration of new targets for the treatment of ccRCC will help to achieve precise treatment for ccRCC. With the development of molecular research, the study of long noncoding RNA (LncRNA) has given us a new understanding of tumors. Although LncRNA does not encode proteins, it directly interacts with proteins in various signaling pathways and affects cell functions. Therefore, it is of great significance to study the mechanism of LncRNA in ccRCC. The expression level of Linc00472 in ccRCC tissues is significantly lower than adjacent normal tissues, and its low expression is closely related to Furman's high grade. The low expression of Linc00472 is associated with poor prognosis in patients with ccRCC. The results of protein interaction and functional enrichment analysis indicate that genes upregulated in renal clear cell carcinoma may play a major role. Analysis of target gene prediction results showed that Linc00472 may be used as ceRNA in the miR-24-3p-HLA-DPB1 pathway, miR-24-3p-CXCL9 pathway, miR-221-3p-C3aR1-VEGFR2 pathway, miR-17-5p-HLA-DQA1/HLA-DQB1 pathway, and miR-17-5p-C3aR1/C5aR1-VEGFR2 pathway which play important functions. In addition, the regulatory relationship between miR-24-3p and TNFR2 (TNFRSF1B), CD36, and COL4A1 should also be noted. The value of Linc00472 in the diagnosis and treatment of ccRCC is worthy of further study.

Potential Non-coding RNAs from Microorganisms and their Therapeutic Use in the Treatment of Different Human Cancers

Cancer therapy describes the treatment of cancer, often with surgery, chemotherapy, and radiotherapy. Additionally, RNA interference (RNAi) is likely to be considered a new emerging, alternative therapeutic approach for silencing/targeting cancer-related genes. RNAi can exert antiproliferative and proapoptotic effects by targeting functional carcinogenic molecules or knocking down gene products of cancer-related genes. However, in contrast to conventional cancer therapies, RNAi based therapy seems to have fewer side effects. Transcription signal sequence and conserved sequence analysis-showed that microorganisms could be a potent source of non-coding RNAs. This review concluded that mapping of RNAi mechanism and RNAi based drug delivery approaches is expected to lead a better prospective of cancer therapy.

Bioinformatics Accelerates the Major Tetrad: A Real Boost for the Pharmaceutical Industry

With advanced technology and its development, bioinformatics is one of the avant-garde fields that has managed to make amazing progress in the pharmaceutical-medical field by modeling the infrastructural dimensions of healthcare and integrating computing tools in drug innovation, facilitating prevention, detection/more accurate diagnosis, and treatment of disorders, while saving time and money. By association, bioinformatics and pharmacovigilance promoted both sample analyzes and interpretation of drug side effects, also focusing on drug discovery and development (DDD), in which systems biology, a personalized approach, and drug repositioning were considered together with translational medicine. The role of bioinformatics has been highlighted in DDD, proteomics, genetics, modeling, miRNA discovery and assessment, and clinical genome sequencing. The authors have collated significant data from the most known online databases and publishers, also narrowing the diversified applications, in order to target four major areas (tetrad): DDD, anti-microbial research, genomic sequencing, and miRNA research and its significance in the management of current pandemic context. Our analysis aims to provide optimal data in the field by stratification of the information related to the published data in key sectors and to capture the attention of researchers interested in bioinformatics, a field that has succeeded in advancing the healthcare paradigm by introducing developing techniques and multiple database platforms, addressed in the manuscript.

An updated overview and classification of bioinformatics tools for MicroRNA analysis, which one to choose?

The term 'MicroRNA' (miRNA) refers to a class of small endogenous non-coding RNAs (ncRNAs) regenerated from hairpin transcripts. Recent studies reveal miRNAs' regulatory involvement in essential biological processes through translational repression or mRNA degradation. Recently, there is a growing body of literature focusing on the importance of miRNAs and their functions. In this respect, several databases have been developed to manage the dispersed data produced. Therefore, it is necessary to know the parameters and characteristics of each database to benefit their data. Besides, selecting the correct database is of great importance to scientists who do not have enough experience in this field. A comprehensive classification along with an explanation of the information contained in each database leads to facilitating access to these resources. In this regard, we have classified relevant databases into several categories, including miRNA sequencing and annotation, validated/predicted miRNA targets, disease-related miRNA, SNP in miRNA sequence or target site, miRNA-related pathways, or gene ontology, and mRNA-miRNA interactions. Hence, this review introduces available miRNA databases and presents a convenient overview to inform researchers of different backgrounds to find suitable miRNA-related bioinformatics web tools and relevant information rapidly.

TSCCA: A tensor sparse CCA method for detecting microRNA-gene patterns from multiple cancers

Existing studies have demonstrated that dysregulation of microRNAs (miRNAs or miRs) is involved in the initiation and progression of cancer. Many efforts have been devoted to identify microRNAs as potential biomarkers for cancer diagnosis, prognosis and therapeutic targets. With the rapid development of miRNA sequencing technology, a vast amount of miRNA expression data for multiple cancers has been collected. These invaluable data repositories provide new paradigms to explore the relationship between miRNAs and cancer. Thus, there is an urgent need to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data in a pan-cancer paradigm. In this study, we present a tensor sparse canonical correlation analysis (TSCCA) method for identifying cancer-related miRNA-gene modules across multiple cancers. TSCCA is able to overcome the drawbacks of existing solutions and capture both the cancer-shared and specific miRNA-gene co-expressed modules with better biological interpretations. We comprehensively evaluate the performance of TSCCA using a set of simulated data and matched miRNA/gene expression data across 33 cancer types from the TCGA database. We uncover several dysfunctional miRNA-gene modules with important biological functions and statistical significance. These modules can advance our understanding of miRNA regulatory mechanisms of cancer and provide insights into miRNA-based treatments for cancer.

MicroRNAs (miRNAs) are a class of small non-coding RNAs. Previous studies have revealed that miRNA-gene regulatory modules play key roles in the occurrence and development of cancer. However, little has been done to discover miRNA-gene regulatory modules from a pan-cancer view. Thus, it is urgently needed to develop new methods to explore the complex cancer-related miRNA-gene patterns by integrating multi-omics data of multi-cancers. To build the connections between miRNA-gene regulatory modules across different cancer types, we propose a tensor sparse canonical correlation analysis (TSCCA) method. Our specific contributions are two-fold: (1) We propose a sparse statistical learning model TSCCA and an efficient block-coordinate descent algorithm to solve it. (2) We apply TSCCA to a multi-omics data set of 33 cancer types from TCGA and identify some cancer-related miRNA-gene modules with important biological functions and statistical significance.

Circulating microRNA-762 upregulates colorectal cancer may be accompanied by Wnt-1/β-catenin signaling

Colorectal cancer (CRC) has become the third most common cause of cancer-related deaths. CRC occurs because of abnormal growth of cells that can invade other tissues and cause distant metastases. Researchers have suggested that aberrant microRNA (miRNA) expression is involved in the initiation and progression of cancers. However, the key miRNAs that regulate the growth and metastasis of CRC remain unclear. The circulating miRNAs from BALB/c mice with CRC CT26 cell implantation were assayed by microarray. Then, Mus musculus (house mouse) mmu-miR-762 mimic and inhibitor were transfected to CT26 cells for analysis of cell viability, invasion, and epithelial-mesenchymal transition (EMT), cell cycle, and regulatory molecule expression. Human subjects were included for comparison the circulating Homo sapiens (human) has-miR-762 levels in CRC patients and control donors, as well as the patients with and without distant metastasis. The result for miRNA levels in mice with CRC cell implantation indicated that plasma mmu-miR-762 was upregulated. Transfection of mmu-miR-762 mimic to CT26 cells increased cell viability, invasion, and EMT, whereas transfection of mmu-miR-762 inhibitor decreased the above abilities. Cells treated with high-concentration mmu-miR-762 inhibitor induced cell cycle arrest at G0/G1 phase. However, mmu-miR-762 did not cause apoptosis of cells. Western blot analysis showed that mmu-miR-762 mimic transfection upregulated the expression of Wnt-1 and β-catenin, as well as increased the nuclear translocation of β-catenin. Further analysis was performed to demonstrate the correlation of miR-762 with CRC, and blood samples were collected from CRC patients and control donors. The results showed that serum has-miR-762 levels in CRC patients were higher than in control donors. Among the CRC patients (n= 20), six patients with distant metastasis showed higher serum has-miR-762 levels than patients without distant metastasis. Conclusions, the present study suggests that circulating miR-762 might be a potential biomarker for upregulation of CRC cell growth and invasion, and may be accompanied by the Wnt/β-catenin signaling.

emiRIT: a text-mining-based resource for microRNA information

microRNAs (miRNAs) are essential gene regulators, and their dysregulation often leads to diseases. Easy access to miRNA information is crucial for interpreting generated experimental data, connecting facts across publications and developing new hypotheses built on previous knowledge. Here, we present extracting miRNA Information from Text (emiRIT), a text-miningbased resource, which presents miRNA information mined from the literature through a user-friendly interface. We collected 149 ,233 miRNA –PubMed ID pairs from Medline between January 1997 and May 2020. emiRIT currently contains ‘miRNA –gene regulation’ (69 ,152 relations), ‘miRNA disease (cancer)’ (12 ,300 relations), ‘miRNA –biological process and pathways’ (23, 390 relations) and circulatory ‘miRNAs in extracellular locations’ (3782 relations). Biological entities and their relation to miRNAs were extracted from Medline abstracts using publicly available and in-house developed text-mining tools, and the entities were normalized to facilitate querying and integration. We built a database and an interface to store and access the integrated data, respectively. We provide an up-to-date and user-friendly resource to facilitate access to comprehensive miRNA information from the literature on a large scale, enabling users to navigate through different roles of miRNA and examine them in a context specific to their information needs. To assess our resource’s information coverage, we have conducted two case studies focusing on the target and differential expression information of miRNAs in the context of cancer and a third case study to assess the usage of emiRIT in the curation of miRNA information.

Database URL: https://research.bioinformatics.udel.edu/emirit/

The regulation of microRNA in each of cancer stage from two different ethnicities as potential biomarker for breast cancer

miRNA has recently emerged as a potential biomarker for breast cancer. Even though many studies have identified ethnic variation affecting miRNA regulation, the effect of cancer stage within specific ethnicities on miRNA epigenetic remains unclear. The present study is designed to investigate miRNA regulation from two distinct ethnicities in specific cancer stages (non-Hispanic white and non-Hispanic black) using the TCGA dataset. Differentially expressed miRNAs were calculated by using the edgeR package. miRNAs with the highest or lowest log fold Change from each cancer stage were selected as a potential biomarker. miRNA-gene interaction was analyzed by using spearman correlation analysis, CLUEGO, and DIANA-mirpath. The association of biomarker candidates with diagnostic and prognostic performance was assessed using ROC and Kaplan-Meier survival analysis. miRNA-gene interaction analysis revealed the involvement of selected miRNAs in cancer progression. From eleven selected aberrant miRNAs, four of the miRNAs (hsa-mir-495, hsa-mir-592, hsa-mir-6501, and hsa-mir-937) are significantly detrimental to breast cancer diagnosis and prognosis. Hence, our result provides valuable information to explore miRNA's role in each cancer stage between non-Hispanic white and non-Hispanic black.

Expression analysis of circulating miR-146a and miR-155 as novel biomarkers related to effective immune responses in human cystic echinococcosis

Cystic echinococcosis, an important zoonotic disease, is caused by Echinococcus granulosus. MicroRNAs are a small group of single-stranded noncoding RNAs, which play an effective role in biological processes. This study aimed at comparing the expression levels of miR-146a and miR-155 in the plasma of patients with hydatidosis and healthy individuals. A group of 20 patients with hydatid cyst formed a study group and 20 healthy individuals with no known chronic diseases formed a control group. Plasma samples were collected from hydatidosis patients as well as sex- and age-matched healthy volunteers. After that, RNA extraction and cDNA synthesis were done and the expression levels of miR-146a and miR-155 were determined by quantitative real-time polymerase chain reaction (PCR) for both groups. The results indicated that the level of miR-146a increased in all patients with hydatidosis compared to the control group. Also, the level of miR-155 increased in all hydatidosis patients, but no correlation was observed in the level of miR-155 between the two groups. The results also revealed that miR-146a and miR-155 upregulation in the plasma leads to the development of novel biomarkers for echinococcosis. One of the reasons for the increase of miRNAs in hydatidosis may be their role in modulating the immune system. These miRNAs are likely to be considered as one of the most important biomarkers in determining the severity of hydatidosis.

miRcorrNet: machine learning-based integration of miRNA and mRNA expression profiles, combined with feature grouping and ranking

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, which performs machine learning-based integration to analyze miRNA and mRNA gene expression profiles. miRcorrNet groups mRNAs based on their correlation to miRNA expression levels and hence it generates groups of target genes associated with each miRNA. Then, these groups are subject to a rank function for classification. We have evaluated our tool using miRNA and mRNA expression profiling data downloaded from The Cancer Genome Atlas (TCGA), and performed comparative evaluation with existing tools. In our experiments we show that miRcorrNet performs as good as other tools in terms of accuracy (reaching more than 95% AUC value). Additionally, miRcorrNet includes ranking steps to separate two classes, namely case and control, which is not available in other tools. We have also evaluated the performance of miRcorrNet using a completely independent dataset. Moreover, we conducted a comprehensive literature search to explore the biological functions of the identified miRNAs. We have validated our significantly identified miRNA groups against known databases, which yielded about 90% accuracy. Our results suggest that miRcorrNet is able to accurately prioritize pan-cancer regulating high-confidence miRNAs. miRcorrNet tool and all other supplementary files are available at https://github.com/malikyousef/miRcorrNet.

Fusing Multiple Biological Networks to Effectively Predict miRNA-disease Associations

Background:

MicroRNAs (miRNAs) are a class of endogenous non-coding RNAs with about 22 nucleotides, and they play a significant role in a variety of complex biological processes. Many researches have shown that miRNAs are closely related to human diseases. Although the biological experiments are reliable in identifying miRNA-disease associations, they are timeconsuming and costly.

Objective:

Thus, computational methods are urgently needed to effectively predict miRNA-disease associations.

Methods:

In this paper, we proposed a novel method, BIRWMDA, based on a bi-random walk model to predict miRNA-disease associations. Specifically, in BIRWMDA, the similarity network fusion algorithm is used to combine the multiple similarity matrices to obtain a miRNA-miRNA similarity matrix and a disease-disease similarity matrix, then the miRNA-disease associations were predicted by the bi-random walk model.

Results:

To evaluate the performance of BIRWMDA, we ran the leave-one-out cross-validation and 5-fold cross-validation, and their corresponding AUCs were 0.9303 and 0.9223 ± 0.00067, respectively. To further demonstrate the effectiveness of the BIRWMDA, from the perspective of exploring disease-related miRNAs, we conducted three case studies of breast neoplasms, prostate neoplasms and gastric neoplasms, where 48, 50 and 50 out of the top 50 predicted miRNAs were confirmed by literature, respectively. From the perspective of exploring miRNA-related diseases, we conducted two case studies of hsa-mir-21 and hsa-mir-155, where 7 and 5 out of the top 10 predicted diseases were confirmed by literatures, respectively.

Conclusion:

The fusion of multiple biological networks could effectively predict miRNA-diseases associations. We expected BIRWMDA to serve as a biological tool for mining potential miRNAdisease associations.