Identification of miRNA-mRNA regulatory modules by exploring collective group relationships

Machine Learning Strategies in MicroRNA Research: Bridging Genome to Phenome

MicroRNAs (miRNAs) have emerged as a prominent layer of regulation of gene expression. This article offers the salient and current aspects of machine learning (ML) tools and approaches from genome to phenome in miRNA research. First, we underline that the complexity in the analysis of miRNA function ranges from their modes of biogenesis to the target diversity in diverse biological conditions. Therefore, it is imperative to first ascertain the miRNA coding potential of genomes and understand the regulatory mechanisms of their expression. This knowledge enables the efficient classification of miRNA precursors and the identification of their mature forms and respective target genes. Second, and because one miRNA can target multiple mRNAs and vice versa, another challenge is the assessment of the miRNA-mRNA target interaction network. Furthermore, long-noncoding RNA (lncRNA)and circular RNAs (circRNAs) also contribute to this complexity. ML has been used to tackle these challenges at the high-dimensional data level. The present expert review covers more than 100 tools adopting various ML approaches pertaining to, for example, (1) miRNA promoter prediction, (2) precursor classification, (3) mature miRNA prediction, (4) miRNA target prediction, (5) miRNA- lncRNA and miRNA-circRNA interactions, (6) miRNA-mRNA expression profiling, (7) miRNA regulatory module detection, (8) miRNA-disease association, and (9) miRNA essentiality prediction. Taken together, we unpack, critically examine, and highlight the cutting-edge synergy of ML approaches and miRNA research so as to develop a dynamic and microlevel understanding of human health and diseases.

miRModuleNet: Detecting miRNA-mRNA Regulatory Modules

Increasing evidence that microRNAs (miRNAs) play a key role in carcinogenesis has revealed the need for elucidating the mechanisms of miRNA regulation and the roles of miRNAs in gene-regulatory networks. A better understanding of the interactions between miRNAs and their mRNA targets will provide a better understanding of the complex biological processes that occur during carcinogenesis. Increased efforts to reveal these interactions have led to the development of a variety of tools to detect and understand these interactions. We have recently described a machine learning approach miRcorrNet, based on grouping and scoring (ranking) groups of genes, where each group is associated with a miRNA and the group members are genes with expression patterns that are correlated with this specific miRNA. The miRcorrNet tool requires two types of -omics data, miRNA and mRNA expression profiles, as an input file. In this study we describe miRModuleNet, which groups mRNA (genes) that are correlated with each miRNA to form a star shape, which we identify as a miRNA-mRNA regulatory module. A scoring procedure is then applied to each module to further assess their contribution in terms of classification. An important output of miRModuleNet is that it provides a hierarchical list of significant miRNA-mRNA regulatory modules. miRModuleNet was further validated on external datasets for their disease associations, and functional enrichment analysis was also performed. The application of miRModuleNet aids the identification of functional relationships between significant biomarkers and reveals essential pathways involved in cancer pathogenesis. The miRModuleNet tool and all other supplementary files are available at https://github.com/malikyousef/miRModuleNet/

A review on methods for predicting miRNA–mRNA regulatory modules

Identification of complex interactions between miRNAs and mRNAs in a regulatory network helps better understand the underlying biological processes. Previously, identification of these interactions was based on sequence-based predicted target binding information. With the advancement in high-throughput omics technologies, miRNA and mRNA expression for the same set of samples are available. This helps develop more efficient and flexible approaches that work by integrating miRNA and mRNA expression profiles with target binding information. Since these integrative approaches of miRNA–mRNA regulatory modules (MRMs) detection is sufficiently able to capture the minute biological details, 26 such algorithms/methods/tools for MRMs identification are comprehensively reviewed in this article. The study covers the significant features underlying every method. Therefore, the methods are classified into eight groups based on mathematical approaches to understand their working and suitability for one’s study. An algorithm could be selected based on the available information with the users and the biological question under investigation.

miR-149-3p Is a Potential Prognosis Biomarker and Correlated with Immune Infiltrates in Uterine Corpus Endometrial Carcinoma

BACKGROUND

Endocrine disruption is an important factor in the development of endometrial cancer. Expression of miR-149-3p is observed in some cancer types, while its role in uterine corpus endometrial carcinoma (UCEC) is unclear.

METHODS

The clinical and genomic data and prognostic information on UCEC were obtained for patients from the TCGA database. The Kruskal-Wallis test, Wilcoxon signed-rank test, and logistic regression were used to analyze the relationship between clinical characteristics and miR-149-3p expression. Kaplan-Meier survival curve analysis was used to study the influence of miR-149-3p expression and miR-149-3p target genes on the prognosis of UCEC patients. The TargetScan, PicTar, Gene Ontology (GO), and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to determine the involvement of miR-149-3p target genes in function. Immune infiltration analysis was used to analyze the functional involvement of miR-149-3p. QRT-PCR was used to validate the expression of miR-149-3p in UCEC cell lines.

RESULTS

High expression of miR-149-3p in UCEC was significantly associated with age (P < 0.001), histological type (P < 0.001), histological grade (P < 0.001), tumor invasion (P=0.014), and radiation therapy (P=0.011). High miR-149-3p expression predicted poorer overall survival (OS) (HR: 2.56; 95% CI: 1.64-4.00; P < 0.001), progression-free interval (PFI) (HR: 1.85; 95% CI: 1.29-2.65; P=0.001), and disease-specific survival (DSS) (HR: 2.33; 95% CI: 1.37-3.99; P=0.002). Low expressions of miR-149-3p target genes, including ADCYAP1R1, CGNL1, CHST3, CYGB, DNAH9, ESR1, HHIP, HIC1, HOXD11, IGF1, INMT, LSP1, MTMR10, NFIC, PLCE1, RARA, SNTN, SPRYD3, and ZBTB7A, were associated with poor OS in UCEC. MiR-149-3p may be involved in the occurrence and development of UCEC via pathways including PI3K-Akt signaling pathway, Ras signaling pathway, AGE-RAGE signaling pathway in diabetic complications, focal adhesion, and MAPK signaling pathway. miR-149-3p may inhibit the function of CD8 T cells, cytotoxic cells, eosinophils, iDC, mast cells, neutrophils, NK CD56bright cells, NK CD56dim cells, pDC, T cells, T helper cells, TFH, Th17 cells, and Treg. miR-149-3p was significantly upregulated in UCEC cell lines compared with endometriotic stromal cells.

CONCLUSION

High expression of miR-149-3p was significantly associated with poor survival in UCEC patients. It may be a promising biomarker of prognosis and response to immunotherapy for UCEC patients.

DriverGroup: a novel method for identifying driver gene groups

MOTIVATION

Identifying cancer driver genes is a key task in cancer informatics. Most existing methods are focused on individual cancer drivers which regulate biological processes leading to cancer. However, the effect of a single gene may not be sufficient to drive cancer progression. Here, we hypothesize that there are driver gene groups that work in concert to regulate cancer, and we develop a novel computational method to detect those driver gene groups.

RESULTS

We develop a novel method named DriverGroup to detect driver gene groups by using gene expression and gene interaction data. The proposed method has three stages: (i) constructing the gene network, (ii) discovering critical nodes of the constructed network and (iii) identifying driver gene groups based on the discovered critical nodes. Before evaluating the performance of DriverGroup in detecting cancer driver groups, we firstly assess its performance in detecting the influence of gene groups, a key step of DriverGroup. The application of DriverGroup to DREAM4 data demonstrates that it is more effective than other methods in detecting the regulation of gene groups. We then apply DriverGroup to the BRCA dataset to identify driver groups for breast cancer. The identified driver groups are promising as several group members are confirmed to be related to cancer in literature. We further use the predicted driver groups in survival analysis and the results show that the survival curves of patient subpopulations classified using the predicted driver groups are significantly differentiated, indicating the usefulness of DriverGroup.

AVAILABILITY AND IMPLEMENTATION

DriverGroup is available at https://github.com/pvvhoang/DriverGroup.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Omics Analysis of Educated Platelets in Cancer and Benign Disease of the Pancreas

Pancreatic ductal adenocarcinoma (PDAC) is traditionally associated with thrombocytosis/hypercoagulation and novel insights on platelet-PDAC "dangerous liaisons" are warranted. Here we performed an integrative omics study investigating the biological processes of mRNAs and expressed miRNAs, as well as proteins in PDAC blood platelets, using benign disease as a reference for inflammatory noise. Gene ontology mining revealed enrichment of RNA splicing, mRNA processing and translation initiation in miRNAs and proteins but depletion in RNA transcripts. Remarkably, correlation analyses revealed a negative regulation on SPARC transcription by isomiRs involved in cancer signaling, suggesting a specific "education" in PDAC platelets. Platelets of benign patients were enriched for non-templated additions of G nucleotides (#ntaG) miRNAs, while PDAC presented length variation on 3' (lv3p) as the most frequent modification on miRNAs. Additionally, we provided an actionable repertoire of PDAC and benign platelet-ome to be exploited for future studies. In conclusion, our data show that platelets change their biological repertoire in patients with PDAC, through dysregulation of miRNAs and splicing factors, supporting the presence of de novo protein machinery that can "educate" the platelet. These novel findings could be further exploited for innovative liquid biopsies platforms as well as possible therapeutic targets.

Circulating microRNAs and their role in the immune response in triple-negative breast cancer

Breast cancer (BC) is the most common type of cancer in women worldwide, and despite advances in treatments, its incidence and mortality are increasing. Therefore, it is necessary to develop new, non-invasive tests that provide more accurate diagnosis and prognosis in a timely manner. A promising approach is measuring the presence of biomarkers to detect tumors at various stages and determine their specific characteristics, thus allowing for more personalized treatment. MicroRNAs (miRNAs) serve a role in gene expression, primarily by interacting with messenger RNAs, and may be potential biomarkers for detecting cancer. They are detectable in tissues and blood, including plasma and/or serum, are stable and often tumor specific. Also, different miRNAs are associated with specific BC molecular subtypes. Triple-negative BC (TNBC) is a type of BC in which the primary targets for hormonal therapy are absent. It is an aggressive phenotype, which frequently metastasizes and is associated with an unfavorable prognosis. The present review focuses on circulating miRNAs in patients with TNBC, with an emphasis on their interaction with the immune response checkpoint genes PD-1, PD-L1 and CTLA4. Modulation and response of the immune system are of interest in cancer treatment due to the success of immunotherapy in the treatment of various neoplasms. Based on the findings of this literature review and the in silico analysis performed as part of this review, it is concluded that circulating hsa-miR-195 and hsa-miR-155 in TNBC interact with checkpoint genes involved in the immune response. Further analysis of the expression of these circulating miRNAs and their association with prognosis in patients with TNBC treated with immunotherapy should be assessed to evaluate their possible use as non-invasive predictive biomarkers. In addition, functional studies to analyze biologically relevant targets in the development and prognosis of TNBC, which could be therapeutic targets, are also recommended.

TRANSICIÓN EPITELIO-MESÉNQUIMA INDUCIDA POR VIRUS

La Transición Epitelio-Mesénquima (EMT) es un proceso de dediferenciación altamente conservado en vertebrados. Este ocurre en células epiteliales con la activación progresiva de la pérdida de la polaridad, la adquisición de motilidad individual y la capacidad invasiva a otros tejidos. La EMT es un proceso normal durante el desarrollo; no obstante, en condiciones patológicas está relacionada con la inducción de metástasis, lo cual representa una vía alterna al desarrollo de procesos oncogénicos tempranos. Aunque la EMT es activada principalmente por factores de crecimiento, también se puede desencadenar por infecciones de patógenos intracelulares mediante la activación de rutas moleculares inductoras de este proceso. Por lo tanto, una infección bacteriana o viral pueda generar predisposición al desarrollo de tumores. Nuestro interés está enfocado principalmente encaracterizar la relación virus-hospedero, y en el caso de los virus, varios ya se han descrito como inductores de la EMT. En este artículo de revisión se describenelfenómeno de la plasticidad celular y la ocurrencia detallada del proceso de EMT, los patógenos virales reportados como inductores, los mecanismos moleculares usados para ello y las vías de regulación mediante miRNAs. Por último, se discute cómo esta relación virus-hospedero puede explicar la patogénesis de la enfermedad causada por Dengue virus, favoreciendo la identificación de blancos moleculares para terapia, estrategia conocida como Antivirales dirigidos a blancos celulares o HTA (Host-targeting antivirals).

Suppression of miR-147b contributed to H37Rv-infected macrophage viability and migration in tuberculosis in vitro

BACKGROUND

Tuberculosis (TB) is a severe infectious disease. It was reported that microRNAs played important roles in tuberculosis. However, the role of miR-147b in the disease remained unveiling.

METHODS

Tuberculosis cell model was established using macrophage THP-1 cells infected with H37Rv strain. RT-qPCR was first for examination of miR-147b relative expression. Cell viabilities were then measured with MTT. Cell transfection was to interfere the relative expression of miR-147b or C11orf87 in infected cells. RT-qPCR was adopted to confirm the transfection efficiency. Luciferase assay verified the binding sites between miR-147b and C11orf87. Migration was examined by scratch and relative protein expression of EMT biomarkers and phosphorylation of Pi3K and AKT were assessed via Western blot.

RESULT

MiR-147b expression was higher and cell viability decreased in H32Rv-THP-1 cells. Cell viability was shown higher after miR-147b downregulation. Luciferase assay confirmed the binding. RT-qPCR found C11orf87 expression was lower in the H32Rv-THP-1 cells. MTT suggested that cell viability fell with the decrease of C11orf87 in infectious cells. Moreover, when H32Rv-THP-1 cells were co-transfected with miR-147b inhibitor and si-C11orf87, cell viability, migration and EMT and activation of Pi3K/AKT pathway was partially reversed compared with mere downregulation of miR-147b.

CONCLUSION

miR-147b might regulate macrophage proliferation and migration through targeting C11orf87 via Pi3K/AKT pathway in Tuberculosis in vitro, which calls for in-depth inter-cellular researches and animal researches to further support that miR-147b/C11orf87 axis might be a potential therapeutic target for the molecular treatment of Tuberculosis in the future.

MicroRNA heterogeneity in melanoma progression

The altered expression of miRNAs has been linked with neocarcinogenesis or the development of human malignancies including melanoma. Of significance, multiple clinical studies have documented that distinct sets of microRNAs (miRNAs) could be utilized as prognostic biomarkers for cancer development or predict the outcomes of treatment responses. To that end, an in-depth validation of such differentially expressed miRNAs is necessary in diverse settings of cancer patients in order to devise novel approaches to control tumor growth and/or enhance the efficacy of clinically-relevant therapeutic options. Moreover, considering the heterogeneity and sophisticated regulation of miRNAs, the precise delineation of their cellular targets could also be explored to design personalized medicine. Given the significance of miRNAs in regulating several key cellular processes of tumor cells including cell cycle progression and apoptosis, we review the findings of such miRNAs implicated in melanoma tumorigenesis. Understanding the novel mechanistic insights of such miRNAs will be useful for developing diagnostic or prognostic biomarkers or devising future therapeutic intervention for malignant melanoma.

microRNA-203 promotes proliferation, differentiation, and migration of osteoblasts by upregulation of Msh homeobox 2

Despite the improvements in fracture healing, about 10% of patients undergo abnormal healing. As a tumor suppressor, upregulation of microRNA (miR)-203 has been observed in osteogenic differentiation. Herein, we aimed to explore the functional role of miR-203 in osteoblasts as well as the underlying mechanisms. The expression of miR-203 in MC3T3-E1 cells that underwent osteogenic differentiation was determined by quantitative reverse transcription PCR (qRT-PCR). The effects of aberrantly expressed miR-203 on cell viability, migration, and expressions of proteins associated with proliferation, migration, and osteogenic differentiation were measured by using a Cell Counting Kit-8 assay, Transwell cell migration assay, and western blot/qRT-PCR, respectively. The possible downstream factor of miR-203 was subsequently studied. Finally, involvements of the mitogen-activated protein kinase (MAPK)/activator of transcription (STAT) pathways were assessed by western blot. We found that the miR-203 level was increased in osteogenic differentiation of MC3T3-E1 cells with increasing duration time (28th day, p < 0.001). After cell transfection, we interestingly found that miR-203 overexpression could increase cell viability (p < 0.05), promote proliferation, migration (p < 0.05), and osteogenic differentiation, and upregulate Msh homeobox 2 (Msx2) expression. Furthermore, Msx2 knockdown was proved to abrogate the effects of miR-203 overexpression on MC3T3-E1 cells. Finally, phosphorylated levels of key kinases in the MAPK/STAT pathways were increased by miR-203 overexpression via upregulating Msx2 expression. In conclusion, miR-203 overexpression promoted proliferation, migration, and osteogenic differentiation of MC3T3-E1 cells through upregulating Msx2 along with activation of the MAPK/STAT pathways.

Triazophos-induced toxicity in zebrafish: miRNA-217 inhibits nup43

Triazophos is a highly toxic organophosphorus pesticide, causing acute toxicity to brain tissue, and neurotoxicity and embryotoxicity to animals. Therefore, triazophos is considered as a public health problem due to its acute hazard index. MicroRNAs (miRNAs), a class of endogenous noncoding RNAs, can regulate the expression of target gene(s) by mediating mRNA cleavage or translational repression in organisms exposed to environmental chemicals. We found that nup43 is targeted by miR-217, which was significantly regulated in adult zebrafish (Danio rerio) exposed to triazophos (phenyl-1,2,4-triazolyl-3-(o,o-diethyl thionophosphate)). The expression of nup43 in both mRNA and protein levels was downregulated in a dose-dependent manner upon stimulation with triazophos. A dual luciferase reporter assay demonstrated that miR-217 interacted with the 3'-untranslated regions (3'-UTR) of nup43. The expression of nup43 in both mRNA and protein level was reduced in ZF4 cells when transfected with an miR-217 mimic, but increased when transfected with an miR-217 inhibitor. As a result, nup43 is targeted by miR-217 upon triazophos exposure. We suggest that miR-217 could be a potential toxicological biomarker for triazophos.

A six-microRNA signature predicts survival of patients with uterine corpus endometrial carcinoma

Uterine corpus endometrial carcinoma (UCEC) is one of the most common female gynecological malignant tumors that threaten women health seriously. MicroRNAs (miRNAs) has been proved to play critical roles in tumor pathogenesis and malignant progression. In this study, we aimed to explore a novel signature of microRNA expression for predicting the overall survival (OS) of patients with UCEC. The genome-wide miRNA expression profiles and relevant clinical characteristics of 348 patients with UCEC were downloaded from the Cancer Genome Atlas (TCGA) data portal and analyzed comprehensively. A total of 144 miRNAs were confirmed to be expressed differentially in tumor tissues. Among them, 6 miRNAs (hsa-mir-15a.MIMAT0000068, hsa-mir-142.MIMAT0000433, hsa-mir-142.MIMAT0000434, hsa-mir-3170.MIMAT0015045, hsa-mir-1976.MIMAT0009451, and hsa-mir-146a.MIMAT0000449) were validated to be significantly correlated with the OS of patients with UCEC. The risk indictor established by the 6-microRNA signature was proved be an independent prognostic factor (Hazard ratio = 0.391; 95% CI: 0.195-0.783; P = 0.008). In conclusion, we identified miRNAs that were correlated with the occurrence and progression of UCEC and established a 6-microRNA expression signature as a predictor for the OS of patients with UCEC.

Integrated micro/messenger RNA regulatory networks in essential thrombocytosis

Essential thrombocytosis (ET) is a chronic myeloproliferative disorder with an unregulated surplus of platelets. Complications of ET include stroke, heart attack, and formation of blood clots. Although platelet-enhancing mutations have been identified in ET cohorts, genetic networks causally implicated in thrombotic risk remain unestablished. In this study, we aim to identify novel ET-related miRNA-mRNA regulatory networks through comparisons of transcriptomes between healthy controls and ET patients. Four network discovery algorithms have been employed, including (a) Pearson correlation network, (b) sparse supervised canonical correlation analysis (sSCCA), (c) sparse partial correlation network analysis (SPACE), and, (d) (sparse) Bayesian network analysis-all through a combined data-driven and knowledge-based analysis. The result predicts a close relationship between an 8-miRNA set (miR-9, miR-490-5p, miR-490-3p, miR-182, miR-34a, miR-196b, miR-34b*, miR-181a-2*) and a 9-mRNA set (CAV2, LAPTM4B, TIMP1, PKIG, WASF1, MMP1, ERVH-4, NME4, HSD17B12). The majority of the identified variables have been linked to hematologic functions by a number of studies. Furthermore, it is observed that the selected mRNAs are highly relevant to ET disease, and provide an initial framework for dissecting both platelet-enhancing and functional consequences of dysregulated platelet production.

MIMPFC: Identifying miRNA-mRNA regulatory modules by combining phase-only correlation and improved rough-fuzzy clustering

MicroRNAs (miRNAs) play a key role in gene expression and regulation in various organisms. They control a wide range of biological processes and are involved in several types of cancers by causing mRNA degradation or translational inhibition. However, the functions of most miRNAs and their precise regulatory mechanisms remain elusive. With the accumulation of the expression data of miRNAs and mRNAs, many computational methods have been proposed to predict miRNA-mRNA regulatory relationship. However, most existing methods require the number of modules predefined that may be difficult to determine beforehand. Here, we propose a novel computational method to discover miRNA-mRNA regulatory modules by combining Phase-only correlation and improved rough-Fuzzy Clustering (MIMPFC). The proposed method is evaluated on three heterogeneous datasets, and the obtained results are further validated through relevant literatures, biological significance and functional enrichment analysis. The analysis results show that the identified modules are highly correlated with the biological conditions. A large part of the regulatory relationships found by MIMPFC has been confirmed in the experimentally verified databases. It demonstrates that the modules found by MIMPFC are biologically significant.

Modeling miRNA-mRNA interactions that cause phenotypic abnormality in breast cancer patients

Background

The dysregulation of microRNAs (miRNAs) alters expression level of pro-oncogenic or tumor suppressive mRNAs in breast cancer, and in the long run, causes multiple biological abnormalities. Identification of such interactions of miRNA-mRNA requires integrative analysis of miRNA-mRNA expression profile data. However, current approaches have limitations to consider the regulatory relationship between miRNAs and mRNAs and to implicate the relationship with phenotypic abnormality and cancer pathogenesis.

Methodology/Findings

We modeled causal relationships between genomic expression and clinical data using a Bayesian Network (BN), with the goal of discovering miRNA-mRNA interactions that are associated with cancer pathogenesis. The Multiple Beam Search (MBS) algorithm learned interactions from data and discovered that hsa-miR-21, hsa-miR-10b, hsa-miR-448, and hsa-miR-96 interact with oncogenes, such as, CCND2, ESR1, MET, NOTCH1, TGFBR2 and TGFB1 that promote tumor metastasis, invasion, and cell proliferation. We also calculated Bayesian network posterior probability (BNPP) for the models discovered by the MBS algorithm to validate true models with high likelihood.

Conclusion/Significance

The MBS algorithm successfully learned miRNA and mRNA expression profile data using a BN, and identified miRNA-mRNA interactions that probabilistically affect breast cancer pathogenesis. The MBS algorithm is a potentially useful tool for identifying interacting gene pairs implicated by the deregulation of expression.

DMirNet: Inferring direct microRNA-mRNA association networks

Background

MicroRNAs (miRNAs) play important regulatory roles in the wide range of biological processes by inducing target mRNA degradation or translational repression. Based on the correlation between expression profiles of a miRNA and its target mRNA, various computational methods have previously been proposed to identify miRNA-mRNA association networks by incorporating the matched miRNA and mRNA expression profiles. However, there remain three major issues to be resolved in the conventional computation approaches for inferring miRNA-mRNA association networks from expression profiles. 1) Inferred correlations from the observed expression profiles using conventional correlation-based methods include numerous erroneous links or over-estimated edge weight due to the transitive information flow among direct associations. 2) Due to the high-dimension-low-sample-size problem on the microarray dataset, it is difficult to obtain an accurate and reliable estimate of the empirical correlations between all pairs of expression profiles. 3) Because the previously proposed computational methods usually suffer from varying performance across different datasets, a more reliable model that guarantees optimal or suboptimal performance across different datasets is highly needed.

Results

In this paper, we present DMirNet, a new framework for identifying direct miRNA-mRNA association networks. To tackle the aforementioned issues, DMirNet incorporates 1) three direct correlation estimation methods (namely Corpcor, SPACE, Network deconvolution) to infer direct miRNA-mRNA association networks, 2) the bootstrapping method to fully utilize insufficient training expression profiles, and 3) a rank-based Ensemble aggregation to build a reliable and robust model across different datasets.

Our empirical experiments on three datasets demonstrate the combinatorial effects of necessary components in DMirNet. Additional performance comparison experiments show that DMirNet outperforms the state-of-the-art Ensemble-based model [1] which has shown the best performance across the same three datasets, with a factor of up to 1.29. Further, we identify 43 putative novel multi-cancer-related miRNA-mRNA association relationships from an inferred Top 1000 direct miRNA-mRNA association network.

Conclusions

We believe that DMirNet is a promising method to identify novel direct miRNA-mRNA relations and to elucidate the direct miRNA-mRNA association networks. Since DMirNet infers direct relationships from the observed data, DMirNet can contribute to reconstructing various direct regulatory pathways, including, but not limited to, the direct miRNA-mRNA association networks.

Electronic supplementary material

The online version of this article (doi:10.1186/s12918-016-0373-1) contains supplementary material, which is available to authorized users.

Discovering MicroRNA-Regulatory Modules in Multi-Dimensional Cancer Genomic Data: A Survey of Computational Methods

MicroRNAs (miRs) are small single-stranded noncoding RNA that function in RNA silencing and post-transcriptional regulation of gene expression. An increasing number of studies have shown that miRs play an important role in tumorigenesis, and understanding the regulatory mechanism of miRs in this gene regulatory network will help elucidate the complex biological processes at play during malignancy. Despite advances, determination of miR–target interactions (MTIs) and identification of functional modules composed of miRs and their specific targets remain a challenge. A large amount of data generated by high-throughput methods from various sources are available to investigate MTIs. The development of data-driven tools to harness these multi-dimensional data has resulted in significant progress over the past decade. In parallel, large-scale cancer genomic projects are allowing new insights into the commonalities and disparities of miR–target regulation across cancers. In the first half of this review, we explore methods for identification of pairwise MTIs, and in the second half, we explore computational tools for discovery of miR-regulatory modules in a cancer-specific and pan-cancer context. We highlight strengths and limitations of each of these tools as a practical guide for the computational biologists.

Characterization and identification of differentially expressed microRNAs during the process of the peribiliary fibrosis induced by Clonorchis sinensis

Clonorchis sinensis (C. sinensis) infection can lead to biliary fibrosis. MicroRNAs (miRNAs) play important roles in regulation of genes expression in the liver diseases. However, the differential expression of miRNAs that probably regulates the portal fibrogenesis caused by C. sinensis has not yet been investigated. Hepatic miRNAs expression profiles from C. sinensis-infected mice at different time-points were analyzed by miRNA microarray and validated by quantitative real-time PCR (qRT-PCR). 349 miRNAs were differentially expressed in the liver of the C. sinensis-infected mice at 2, 8 or 16weeks post infection (p.i.), compared with those at 0week p.i., and there were 143 down-regulated and 206 up-regulated miRNAs among them. These all dysregulated miRNAs were potentially involved in the pathological processes of clonorchiasis by regulation of cancer-related signaling pathway, TGF-β signaling pathway, MAPK signaling pathway, Toll-like receptor signaling pathway, PI3K /AKT signaling pathway, etc. 169 of these dysregulated miRNAs were predicted to be involved in the TGF/Smads signaling pathway which plays an important role in the biliary fibrosis caused by C. sinensis. Additionally, miRNA-32, miRNA-34a, miRNA-125b and miRNA-497 were negatively correlated with Smad7 expression, indicating these miRNAs may specifically down-regulate Smad7 expression and participate in regulation of biliary fibrosis caused by C. sinensis. The results of the present study for the first time demonstrated that miRNAs were differentially expressed in the liver of mice infected by C. sinensis, and these miRNAs may play important roles in regulation of peribiliary fibrosis caused by C. sinensis, which may provide possible therapeutic targets for clonorchiasis.