Inferring potential microRNA-microRNA associations based on targeting propensity and connectivity in the context of protein interaction network

MHIF-MSEA: a novel model of miRNA set enrichment analysis based on multi-source heterogeneous information fusion

Introduction: MicroRNAs (miRNAs) are a class of non-coding RNA molecules that play a crucial role in the regulation of diverse biological processes across various organisms. Despite not encoding proteins, miRNAs have been found to have significant implications in the onset and progression of complex human diseases. Methods: Conventional methods for miRNA functional enrichment analysis have certain limitations, and we proposed a novel method called MiRNA Set Enrichment Analysis based on Multi-source Heterogeneous Information Fusion (MHIF-MSEA). Three miRNA similarity networks (miRSN-DA, miRSN-GOA, and miRSN-PPI) were constructed in MHIF-MSEA. These networks were built based on miRNA-disease association, gene ontology (GO) annotation of target genes, and protein-protein interaction of target genes, respectively. These miRNA similarity networks were fused into a single similarity network with the averaging method. This fused network served as the input for the random walk with restart algorithm, which expanded the original miRNA list. Finally, MHIF-MSEA performed enrichment analysis on the expanded list. Results and Discussion: To determine the optimal network fusion approach, three case studies were introduced: colon cancer, breast cancer, and hepatocellular carcinoma. The experimental results revealed that the miRNA-miRNA association network constructed using miRSN-DA and miRSN-GOA exhibited superior performance as the input network. Furthermore, the MHIF-MSEA model performed enrichment analysis on differentially expressed miRNAs in breast cancer and hepatocellular carcinoma. The achieved p-values were 2.17e(-75) and 1.50e(-77), and the hit rates improved by 39.01% and 44.68% compared to traditional enrichment analysis methods, respectively. These results confirm that the MHIF-MSEA method enhances the identification of enriched miRNA sets by leveraging multiple sources of heterogeneous information, leading to improved insights into the functional implications of miRNAs in complex diseases.

A microRNA-microRNA crosstalk network inferred from genome-wide single nucleotide polymorphism variants in natural populations of Arabidopsis thaliana

To adapt to variable natural conditions, plants have evolved several strategies to respond to different environmental stresses. MicroRNA (miRNA)-mediated gene regulation is one of such strategies. Variants, e.g., single nucleotide polymorphisms (SNPs) within the mature miRNAs or their target sites may cause the alteration of regulatory networks and serious phenotype changes. In this study, we proposed a novel approach to construct a miRNA-miRNA crosstalk network in Arabidopsis thaliana based on the notion that two cooperative miRNAs toward common targets are under a strong pressure to be inherited together across ecotypes. By performing a genome-wide scan of the SNPs within the mature miRNAs and their target sites, we defined a "regulation fate profile" to describe a miRNA-target regulation being static (kept) or dynamic (gained or lost) across 1,135 ecotypes compared with the reference genome of Col-0. The cooperative miRNA pairs were identified by estimating the similarity of their regulation fate profiles toward the common targets. The reliability of the cooperative miRNA pairs was supported by solid expressional correlation, high PPImiRFS scores, and similar stress responses. Different combinations of static and dynamic miRNA-target regulations account for the cooperative miRNA pairs acting on various biological characteristics of miRNA conservation, expression, homology, and stress response. Interestingly, the targets that are co-regulated dynamically by both cooperative miRNAs are more likely to be responsive to stress. Hence, stress-related genes probably bear selective pressures in a certain group of ecotypes, in which miRNA regulations on the stress genes reprogram. Finally, three case studies showed that reprogramming miRNA-miRNA crosstalk toward the targets in specific ecotypes was associated with these ecotypes' climatic variables and geographical locations. Our study highlights the potential of miRNA-miRNA crosstalk as a genetic basis underlying environmental adaptation in natural populations.

SRG-vote: Predicting miRNA-gene relationships via embedding and LSTM ensemble

AbstractTargeted therapy for one for a set of genes has made it possible to apply precision medicine for different patients due to the existence of tumor heterogeneity. However, how to regulate those genes are still problematic. One of the natural regulators of genes is microRNAs. Thus, a better understanding of the miRNA-gene interaction mechanism might contribute to future diagnosis, prevention, and cancer therapy. The interactions between microRNA and genes play an essential role in molecular genetics. The in-vivo experiments validating the relationships between them are time-consuming, money-costly, and labor-intensive. With the development of high-throughput technology, we dealt with tons of biological data. However, extracting features from tremendous raw data and making a mathematical model is still a challenging topic. Machine learning and deep learning algorithms have become powerful tools in dealing with biological data. Inspired by this, in this paper, we propose a model that combines features/embedding extraction methods, deep learning algorithms, and a voting system. We leverage doc2vec to generate sequential embedding from molecular sequences. The role2vec, GCN, and GMM for geometrical embedding were generated from the complex network from similarity and pair-wise datasets. For the deep learning algorithms, we leveraged LSTM and Bi-LSTM according to different embedding and features. Finally, we adopted a voting system to balance results from different data sources. The results have shown that our voting system could achieve a higher AUC than the existing benchmark. The case studies demonstrate that our model could reveal potential relationships between miRNAs and genes. The source code, features, and predictive results can be downloaded at https://github.com/Xshelton/SRG-vote.

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.

MISIM v2.0: a web server for inferring microRNA functional similarity based on microRNA-disease associations

MicroRNAs (miRNAs) are one class of important small non-coding RNA molecules and play critical roles in health and disease. Therefore, it is important and necessary to evaluate the functional relationship of miRNAs and then predict novel miRNA-disease associations. For this purpose, here we developed the updated web server MISIM (miRNA similarity) v2.0. Besides a 3-fold increase in data content compared with MISIM v1.0, MISIM v2.0 improved the original MISIM algorithm by implementing both positive and negative miRNA-disease associations. That is, the MISIM v2.0 scores could be positive or negative, whereas MISIM v1.0 only produced positive scores. Moreover, MISIM v2.0 achieved an algorithm for novel miRNA-disease prediction based on MISIM v2.0 scores. Finally, MISIM v2.0 provided network visualization and functional enrichment analysis for functionally paired miRNAs. The MISIM v2.0 web server is freely accessible at http://www.lirmed.com/misim/.

Computationally Modeling ncRNA-ncRNA Crosstalk

Our understanding of complex gene regulatory networks have been improved by the discovery of ncRNA-ncRNA crosstalk in normal and disease-specific physiological conditions. Previous studies have proposed numerous approaches for constructing ncRNA-ncRNA networks via ncRNA-mRNA regulation, functional information, or phenomics alone, or by combining heterogeneous data. Furthermore, it has been shown that ncRNA-ncRNA crosstalk can be rewired in different tissues or specific diseases. Therefore, it is necessary to integrate transcriptome data to construct context-specific ncRNA-ncRNA networks. In this chapter, we elucidated the commonly used ncRNA-ncRNA network modeling methods, and highlighted the need to integrate heterogeneous multi-mics data. Finally, we suggest future directions for studies of ncRNAs crosstalk. This comprehensive description and discussion elucidated in this chapter will provide constructive insights into ncRNA-ncRNA crosstalk.

miRNA-miRNA crosstalk: from genomics to phenomics

The discovery of microRNA (miRNA)-miRNA crosstalk has greatly improved our understanding of complex gene regulatory networks in normal and disease-specific physiological conditions. Numerous approaches have been proposed for modeling miRNA-miRNA networks based on genomic sequences, miRNA-mRNA regulation, functional information and phenomics alone, or by integrating heterogeneous data. In addition, it is expected that miRNA-miRNA crosstalk can be reprogrammed in different tissues or specific diseases. Thus, transcriptome data have also been integrated to construct context-specific miRNA-miRNA networks. In this review, we summarize the state-of-the-art miRNA-miRNA network modeling methods, which range from genomics to phenomics, where we focus on the need to integrate heterogeneous types of omics data. Finally, we suggest future directions for studies of crosstalk of noncoding RNAs. This comprehensive summarization and discussion elucidated in this work provide constructive insights into miRNA-miRNA crosstalk.

IntNetLncSim: an integrative network analysis method to infer human lncRNA functional similarity

Increasing evidence indicated that long non-coding RNAs (lncRNAs) were involved in various biological processes and complex diseases by communicating with mRNAs/miRNAs each other. Exploiting interactions between lncRNAs and mRNA/miRNAs to lncRNA functional similarity (LFS) is an effective method to explore function of lncRNAs and predict novel lncRNA-disease associations. In this article, we proposed an integrative framework, IntNetLncSim, to infer LFS by modeling the information flow in an integrated network that comprises both lncRNA-related transcriptional and post-transcriptional information. The performance of IntNetLncSim was evaluated by investigating the relationship of LFS with the similarity of lncRNA-related mRNA sets (LmRSets) and miRNA sets (LmiRSets). As a result, LFS by IntNetLncSim was significant positively correlated with the LmRSet (Pearson correlation γ2=0.8424) and LmiRSet (Pearson correlation γ2=0.2601). Particularly, the performance of IntNetLncSim is superior to several previous methods. In the case of applying the LFS to identify novel lncRNA-disease relationships, we achieved an area under the ROC curve (0.7300) in experimentally verified lncRNA-disease associations based on leave-one-out cross-validation. Furthermore, highly-ranked lncRNA-disease associations confirmed by literature mining demonstrated the excellent performance of IntNetLncSim. Finally, a web-accessible system was provided for querying LFS and potential lncRNA-disease relationships: http://www.bio-bigdata.com/IntNetLncSim.

A path-based measurement for human miRNA functional similarities using miRNA-disease associations

Compared with the sequence and expression similarity, miRNA functional similarity is so important for biology researches and many applications such as miRNA clustering, miRNA function prediction, miRNA synergism identification and disease miRNA prioritization. However, the existing methods always utilized the predicted miRNA target which has high false positive and false negative to calculate the miRNA functional similarity. Meanwhile, it is difficult to achieve high reliability of miRNA functional similarity with miRNA-disease associations. Therefore, it is increasingly needed to improve the measurement of miRNA functional similarity. In this study, we develop a novel path-based calculation method of miRNA functional similarity based on miRNA-disease associations, called MFSP. Compared with other methods, our method obtains higher average functional similarity of intra-family and intra-cluster selected groups. Meanwhile, the lower average functional similarity of inter-family and inter-cluster miRNA pair is obtained. In addition, the smaller p-value is achieved, while applying Wilcoxon rank-sum test and Kruskal-Wallis test to different miRNA groups. The relationship between miRNA functional similarity and other information sources is exhibited. Furthermore, the constructed miRNA functional network based on MFSP is a scale-free and small-world network. Moreover, the higher AUC for miRNA-disease prediction indicates the ability of MFSP uncovering miRNA functional similarity.

Annotating the Function of the Human Genome with Gene Ontology and Disease Ontology

Increasing evidences indicated that function annotation of human genome in molecular level and phenotype level is very important for systematic analysis of genes. In this study, we presented a framework named Gene2Function to annotate Gene Reference into Functions (GeneRIFs), in which each functional description of GeneRIFs could be annotated by a text mining tool Open Biomedical Annotator (OBA), and each Entrez gene could be mapped to Human Genome Organisation Gene Nomenclature Committee (HGNC) gene symbol. After annotating all the records about human genes of GeneRIFs, 288,869 associations between 13,148 mRNAs and 7,182 terms, 9,496 associations between 948 microRNAs and 533 terms, and 901 associations between 139 long noncoding RNAs (lncRNAs) and 297 terms were obtained as a comprehensive annotation resource of human genome. High consistency of term frequency of individual gene (Pearson correlation = 0.6401, p = 2.2e - 16) and gene frequency of individual term (Pearson correlation = 0.1298, p = 3.686e - 14) in GeneRIFs and GOA shows our annotation resource is very reliable.

Integration of Multiple Genomic and Phenotype Data to Infer Novel miRNA-Disease Associations

MicroRNAs (miRNAs) play an important role in the development and progression of human diseases. The identification of disease-associated miRNAs will be helpful for understanding the molecular mechanisms of diseases at the post-transcriptional level. Based on different types of genomic data sources, computational methods for miRNA-disease association prediction have been proposed. However, individual source of genomic data tends to be incomplete and noisy; therefore, the integration of various types of genomic data for inferring reliable miRNA-disease associations is urgently needed. In this study, we present a computational framework, CHNmiRD, for identifying miRNA-disease associations by integrating multiple genomic and phenotype data, including protein-protein interaction data, gene ontology data, experimentally verified miRNA-target relationships, disease phenotype information and known miRNA-disease connections. The performance of CHNmiRD was evaluated by experimentally verified miRNA-disease associations, which achieved an area under the ROC curve (AUC) of 0.834 for 5-fold cross-validation. In particular, CHNmiRD displayed excellent performance for diseases without any known related miRNAs. The results of case studies for three human diseases (glioblastoma, myocardial infarction and type 1 diabetes) showed that all of the top 10 ranked miRNAs having no known associations with these three diseases in existing miRNA-disease databases were directly or indirectly confirmed by our latest literature mining. All these results demonstrated the reliability and efficiency of CHNmiRD, and it is anticipated that CHNmiRD will serve as a powerful bioinformatics method for mining novel disease-related miRNAs and providing a new perspective into molecular mechanisms underlying human diseases at the post-transcriptional level. CHNmiRD is freely available at http://www.bio-bigdata.com/CHNmiRD.

Inferring plant microRNA functional similarity using a weighted protein-protein interaction network

BACKGROUND

MiRNAs play a critical role in the response of plants to abiotic and biotic stress. However, the functions of most plant miRNAs remain unknown. Inferring these functions from miRNA functional similarity would thus be useful. This study proposes a new method, called PPImiRFS, for inferring miRNA functional similarity.

RESULTS

The functional similarity of miRNAs was inferred from the functional similarity of their target gene sets. A protein-protein interaction network with semantic similarity weights of edges generated using Gene Ontology terms was constructed to infer the functional similarity between two target genes that belong to two different miRNAs, and the score for functional similarity was calculated using the weighted shortest path for the two target genes through the whole network. The experimental results showed that the proposed method was more effective and reliable than previous methods (miRFunSim and GOSemSim) applied to Arabidopsis thaliana. Additionally, miRNAs responding to the same type of stress had higher functional similarity than miRNAs responding to different types of stress.

CONCLUSIONS

For the first time, a protein-protein interaction network with semantic similarity weights generated using Gene Ontology terms was employed to calculate the functional similarity of plant miRNAs. A novel method based on calculating the weighted shortest path between two target genes was introduced.

Identifying breast cancer subtype related miRNAs from two constructed miRNAs interaction networks in silico method

BACKGROUND

It has been known that microRNAs (miRNAs) regulate the expression of multiple proteins and therefore are likely to emerge as more effective targets of selective therapeutic modalities for breast cancer. Although recent lines of evidence have approved that miRNAs are associated with the most common molecular breast cancer subtypes, the studies to breast cancer subtypes have not been well characterized.

OBJECTIVES

In this study, we propose a silico method to identify breast cancer subtype related miRNAs based on two constructed miRNAs interaction networks using miRNA-mRNA dual expression profiling data arising from the same samples.

METHODS

Firstly, we used a new mutual information estimation method to construct two miRNAs interaction networks based on miRNA-mRNA dual expression profiling data. Secondly, we compared and analyzed the topological properties of these two networks. Finally, miRNAs showing the outstanding topological properties in both of the two networks were identified. Results. Further functional analysis and literature evidence confirm that the identified potential breast cancer subtype related miRNAs are essential to unraveling their biological function.

CONCLUSIONS

This study provides a new silico method to predict candidate miRNAs of breast cancer subtype from a system biology level and can help exploit for functional studies of important breast cancer subtype related miRNAs.