CLING: Candidate Cancer-Related lncRNA Prioritization via Integrating Multiple Biological Networks

Identification and characterization of lncRNAs in cancer with a view to their application in improving diagnosis and therapy remains a major challenge that requires new and innovative approaches. We have developed an integrative framework termed “CLING”, aimed to prioritize candidate cancer-related lncRNAs based on their associations with known cancer lncRNAs. CLING focuses on joint optimization and prioritization of all candidates for each cancer type by integrating lncRNA topological properties and multiple lncRNA-centric networks. Validation analyses revealed that CLING is more effective than prioritization based on a single lncRNA network. Reliable AUC (Area Under Curve) scores were obtained across 10 cancer types, ranging from 0.85 to 0.94. Several novel lncRNAs predicted in the top 10 candidates for various cancer types have been confirmed by recent biological experiments. Furthermore, using a case study on liver hepatocellular carcinoma as an example, CLING facilitated the successful identification of novel cancer lncRNAs overlooked by differential expression analyses (DEA). This time- and cost-effective computational model may provide a valuable complement to experimental studies and assist in future investigations on lncRNA involvement in the pathogenesis of cancers. We have developed a web-based server for users to rapidly implement CLING and visualize data, which is freely accessible at http://bio-bigdata.hrbmu.edu.cn/cling/. CLING has been successfully applied to predict a few potential lncRNAs from thousands of candidates for many cancer types.

Adaptive multi-source multi-view latent feature learning for inferring potential disease-associated miRNAs

Accumulating evidence has shown that microRNAs (miRNAs) play crucial roles in different biological processes, and their mutations and dysregulations have been proved to contribute to tumorigenesis. In silico identification of disease-associated miRNAs is a cost-effective strategy to discover those most promising biomarkers for disease diagnosis and treatment. The increasing available omics data sources provide unprecedented opportunities to decipher the underlying relationships between miRNAs and diseases by computational models. However, most existing methods are biased towards a single representation of miRNAs or diseases and are also not capable of discovering unobserved associations for new miRNAs or diseases without association information. In this study, we present a novel computational method with adaptive multi-source multi-view latent feature learning (M2LFL) to infer potential disease-associated miRNAs. First, we adopt multiple data sources to obtain similarity profiles and capture different latent features according to the geometric characteristic of miRNA and disease spaces. Then, the multi-modal latent features are projected to a common subspace to discover unobserved miRNA-disease associations in both miRNA and disease views, and an adaptive joint graph regularization term is developed to preserve the intrinsic manifold structures of multiple similarity profiles. Meanwhile, the Lp,q-norms are imposed into the projection matrices to ensure the sparsity and improve interpretability. The experimental results confirm the superior performance of our proposed method in screening reliable candidate disease miRNAs, which suggests that M2LFL could be an efficient tool to discover diagnostic biomarkers for guiding laborious clinical trials.

MicroRNA 362-3p Reduces hERG-related Current and Inhibits Breast Cancer Cells Proliferation

BACKGROUND/AIM

hERG potassium channels enhance tumor invasiveness and breast cancer proliferation. MicroRNA (miRNA) dysregulation during cancer controls gene regulation. The objective of this study was to identify miRNAs that regulate hERG expression in breast cancer.

MATERIALS AND METHODS

Putative miRNAs targeting hERG were identified by bioinformatic approaches and screened using a 3'UTR luciferase assay. Functional assessments of endogenous hERG regulation were made using whole-cell electrophysiology, proliferation assays, and cell-cycle analyses following miRNA, hERG siRNA, or control transfection.

RESULTS

miR-362-3p targeted hERG 3'UTR and was associated with higher survival rates in patients with breast cancer (HR=0.39, 95%CI=0.18-0.82). Enhanced miR-362-3p expression reduced hERG expression, peak current, and cell proliferation in cultured breast cancer cells (p<0.05).

CONCLUSION

miR-362-3p mediates the transcriptional regulation of hERG and is associated with survival in breast cancer. The potential for miR-362-3p to serve as a biomarker and inform therapeutic strategies warrants further investigation.

A graph regularized generalized matrix factorization model for predicting links in biomedical bipartite networks

Motivation

Predicting potential links in biomedical bipartite networks can provide useful insights into the diagnosis and treatment of complex diseases and the discovery of novel drug targets. Computational methods have been proposed recently to predict potential links for various biomedical bipartite networks. However, existing methods are usually rely on the coverage of known links, which may encounter difficulties when dealing with new nodes without any known link information.

Results

In this study, we propose a new link prediction method, named graph regularized generalized matrix factorization (GRGMF), to identify potential links in biomedical bipartite networks. First, we formulate a generalized matrix factorization model to exploit the latent patterns behind observed links. In particular, it can take into account the neighborhood information of each node when learning the latent representation for each node, and the neighborhood information of each node can be learned adaptively. Second, we introduce two graph regularization terms to draw support from affinity information of each node derived from external databases to enhance the learning of latent representations. We conduct extensive experiments on six real datasets. Experiment results show that GRGMF can achieve competitive performance on all these datasets, which demonstrate the effectiveness of GRGMF in prediction potential links in biomedical bipartite networks.

Availability and implementation

The package is available at https://github.com/happyalfred2016/GRGMF.

Contact

leouyang@szu.edu.cn

Supplementary information

Supplementary data are available at Bioinformatics online.

Comprehensive analysis of miRNA-gene regulatory network with clinical significance in human cancers

microRNAs (miRNAs), particularly the exosomal miRNAs have been widely used as biomarkers and promising therapeutic targets in cancer. However, a comprehensive analysis of miRNA-gene regulatory network with clinical significance remains scarce. The emergence of high-throughput multi-omics data over large, well-characterized patient cohorts provides an unprecedented opportunity to address this problem. Herein, we performed a clinic-centered analysis to identify cancer-associated miRNAs, miRNA-target axis. We first calculated the correlation among miRNA, mRNA and 75 unique clinico-pathological characteristics (CPCs) in 26 cancer types, and established an online resource (4CR). Interestingly, we found that the high expression of several DNA methylation-related enzymes was associated with adverse outcomes of cancer patients, and these genes were regulated by a cluster of miRNAs. Furthermore, by integrating exosomal miRNA and mRNA databases, we identified exosomal miRNA biomarkers for non-invasive cancer surveillance and therapy monitoring. Finally, we explored the role of CPC-related miRNAs for therapeutic effect prediction of drugs based on their shared targets. Our analysis pipeline illustrated the significance of clinic-centered analysis in miRNA-gene pair identification and provided helpful clues for future cancer studies.

Identification of aberrantly methylated differentially expressed genes targeted by differentially expressed miRNA in osteosarcoma

Background

Osteosarcoma (OS) is the most common primary bone tumors diagnosed in children and adolescents. Recent studies have shown a prognostic role of DNA methylation in various cancers, including OS. The aim of this study was to identify the aberrantly methylated genes that are prognostically relevant in OS.

Methods

The differentially expressed mRNAs, miRNAs and methylated genes (DEGs, DEMs and DMGs respectively) were screened from various GEO databases, and the potential target genes of the DEMs were predicted by the RNA22 program. The protein-protein interaction (PPI) networks were constructed using the STRING database and visualized by Cytoscape software. The functional enrichment and survival analyses of the screened genes was performed using the R software.

Results

Forty-seven downregulated hypermethylated genes and three upregulated hypomethylated genes were identified that were enriched in cell activation, migration and proliferation functions, and were involved in cancer-related pathways like JAK-STAT and PI3K-AKT. Eight downregulated hypermethylated tumor suppressor genes (TSGs) were identified among the screened genes based on the TSGene database. These hub genes are likely involved in OS genesis, progression and metastasis, and are potential prognostic biomarkers and therapeutic targets.

Conclusions

TSGs including PYCARD, STAT5A, CXCL12 and CXCL14 were aberrantly methylated in OS, and are potential prognostic biomarkers and therapeutic targets. Our findings provide new insights into the role of methylation in OS progression.

Function and regulation annotation of up-regulated long non-coding RNA LINC01234 in gastric cancer

Background

Accumulated evidences indicate that long non‐coding RNAs (lncRNAs) participate in many biological mechanisms. Moreover, it acts as an essential regulator in various human diseases such as gastric cancer (GC). Nevertheless, the comprehensive regulatory roles and clinical significance of most lncRNAs in GC are not fully understood.

Methods

In this research, our aim was to investigate the underlying mechanism of lncRNA LINC01234 in GC. Firstly, the usage of qRT‐PCR helped to establish expression pattern of LINC01234 in GC tissues. Following this, appropriate statistical tests were applied to analyze the relation between expression level and clinicopathological factors. Ultimately, potential functions and regulatory network of LINC01234 were concluded via GSEA and a series of bioinformatics tools or databases, respectively.

Results

Consequently, at the end of research we found LINC01234 is up‐regulated in GC tissues in comparison with adjacent normal tissues. Furthermore, its expression level is correlated with differentiation of patients with GC. It is also important to highlight bioinformatics analysis revealed that LINC01234 is involved in cancer‐associated pathways such as cell cycle and mismatch repair. Also, regulatory network of LINC01234 presented a probability in the involvement of tumorigenesis through regulating cancer‐associated genes.

Conclusion

Overall, our results suggested that LINC01234 may play a crucial role in GC.

Probing lncRNA-Protein Interactions: Data Repositories, Models, and Algorithms

Identifying lncRNA-protein interactions (LPIs) is vital to understanding various key biological processes. Wet experiments found a few LPIs, but experimental methods are costly and time-consuming. Therefore, computational methods are increasingly exploited to capture LPI candidates. We introduced relevant data repositories, focused on two types of LPI prediction models: network-based methods and machine learning-based methods. Machine learning-based methods contain matrix factorization-based techniques and ensemble learning-based techniques. To detect the performance of computational methods, we compared parts of LPI prediction models on Leave-One-Out cross-validation (LOOCV) and fivefold cross-validation. The results show that SFPEL-LPI obtained the best performance of AUC. Although computational models have efficiently unraveled some LPI candidates, there are many limitations involved. We discussed future directions to further boost LPI predictive performance.

piRDisease v1.0: a manually curated database for piRNA associated diseases

In recent years, researches focusing on PIWI-interacting RNAs (piRNAs) have increased rapidly. It has been revealed that piRNAs have strong association with a wide range of diseases; thus, it becomes very important to understand piRNAs’ role(s) in disease diagnosis, prognosis and assessment of treatment response. We searched more than 2500 articles using keywords, such as `PIWI-interacting RNAs’ and `piRNAs’, and further scrutinized the articles to collect piRNAs-disease association data. These data are highly complex and heterogeneous due to various types of piRNA idnetifiers (IDs) and different reference genome versions. We put considerable efforts into removing redundancy and anomalies and thus homogenized the data. Finally, we developed the piRDisease database, which incorporates experimentally supported data for piRNAs’ relationship with wide range of diseases. The piRDisease (piRDisease v1.0) is a novel, comprehensive and exclusive database resource, which provides 7939 manually curated associations of experimentally supported 4796 piRNAs involved in 28 diseases. piRDisease facilitates users by providing detailed information of the piRNA in respective disease, explored by experimental support, brief description, sequence and location information. Considering piRNAs’ role(s) in wide range of diseases, it is anticipated that huge amount of data would be produced in the near future. We thus offer a submitting page, on which users or researches can contribute in to update our piRDisease database.

Neural inductive matrix completion with graph convolutional networks for miRNA-disease association prediction

Motivation

Predicting the association between microRNAs (miRNAs) and diseases plays an import role in identifying human disease-related miRNAs. As identification of miRNA-disease associations via biological experiments is time-consuming and expensive, computational methods are currently used as effective complements to determine the potential associations between disease and miRNA.

Results

We present a novel method of neural inductive matrix completion with graph convolutional network (NIMCGCN) for predicting miRNA-disease association. NIMCGCN first uses graph convolutional networks to learn miRNA and disease latent feature representations from the miRNA and disease similarity networks. Then, learned features were input into a novel neural inductive matrix completion (NIMC) model to generate an association matrix completion. The parameters of NIMCGCN were learned based on the known miRNA-disease association data in a supervised end-to-end way. We compared the proposed method with other state-of-the-art methods. The area under the receiver operating characteristic curve results showed that our method is significantly superior to existing methods. Furthermore, 50, 47 and 48 of the top 50 predicted miRNAs for three high-risk human diseases, namely, colon cancer, lymphoma and kidney cancer, were verified using experimental literature. Finally, 100% prediction accuracy was achieved when breast cancer was used as a case study to evaluate the ability of NIMCGCN for predicting a new disease without any known related miRNAs.

Availability and implementation

https://github.com/ljatynu/NIMCGCN/

Supplementary information

Supplementary data are available at Bioinformatics online.

Bioinformatics Research Methodology of Non-coding RNAs in Cardiovascular Diseases

The transcriptional complexity generated by the human genomic output is within the core of cell and organ physiology, but also could be in the origin of pathologies. In cardiovascular diseases, the role of specific families of RNA transcripts belonging to the group of the non-coding RNAs started to be unveiled in the last two decades. The knowledge of the functional rules and roles of non-coding RNAs in the context of cardiovascular diseases is an important factor to derive new diagnostic methods, but also to design targeted therapeutic strategies. The characterization and analysis of ncRNA function requires a deep knowledge of the regulatory mechanism of these RNA species that often relies on intricated interaction networks. The use of specific bioinformatic tools to interrogate biological data and to derive functional implications is particularly relevant and needs to be extended to the general practice of translational researchers. This chapter briefly summarizes the bioinformatic tools and strategies that could be used for the characterization and functional analysis of non-coding RNAs, with special emphasis in their applications to the cardiovascular field.

MicroRNAs in ovarian cancer and recent advances in the development of microRNA-based biosensors

Ovarian cancer is the most aggressive of all gynaecological malignancies and is the leading cause of cancer-associated mortality worldwide.

LeukmiR: a database for miRNAs and their targets in acute lymphoblastic leukemia

Acute lymphoblastic leukemia (ALL) is one of the most common hematological malignancies in children. Recent studies suggest the involvement of multiple microRNAs in the tumorigenesis of various leukemias. However, until now, no comprehensive database exists for miRNAs and their cognate target genes involved specifically in ALL. Therefore, we developed 'LeukmiR' a dynamic database comprising in silico predicted microRNAs, and experimentally validated miRNAs along with the target genes they regulate in mouse and human. LeukmiR is a user-friendly platform with search strings for ALL-associated microRNAs, their sequences, description of target genes, their location on the chromosomes and the corresponding deregulated signaling pathways. For the user query, different search modules exist where either quick search can be carried out using any fuzzy term or by providing exact terms in specific modules. All entries for both human and mouse genomes can be retrieved through multiple options such as miRNA ID, their accession number, sequence, target genes, Ensemble-ID or Entrez-ID. User can also access miRNA: mRNA interaction networks in different signaling pathways, the genomic location of the targeted regions such as 3'UTR, 5'UTR and exons with their gene ontology and disease ontology information in both human and mouse systems. Herein, we also report 51 novel microRNAs which are not described earlier for ALL. Thus, LeukmiR database will be a valuable source of information for researchers to understand and investigate miRNAs and their targets with diagnostic and therapeutic potential in ALL. Database URL: http://tdb.ccmb.res.in/LeukmiR/.

Identifying miRNA synergism using multiple-intervention causal inference

BACKGROUND

Studying multiple microRNAs (miRNAs) synergism in gene regulation could help to understand the regulatory mechanisms of complicated human diseases caused by miRNAs. Several existing methods have been presented to infer miRNA synergism. Most of the current methods assume that miRNAs with shared targets at the sequence level are working synergistically. However, it is unclear if miRNAs with shared targets are working in concert to regulate the targets or they individually regulate the targets at different time points or different biological processes. A standard method to test the synergistic activities is to knock-down multiple miRNAs at the same time and measure the changes in the target genes. However, this approach may not be practical as we would have too many sets of miRNAs to test.

RESULTS

n this paper, we present a novel framework called miRsyn for inferring miRNA synergism by using a causal inference method that mimics the multiple-intervention experiments, e.g. knocking-down multiple miRNAs, with observational data. Our results show that several miRNA-miRNA pairs that have shared targets at the sequence level are not working synergistically at the expression level. Moreover, the identified miRNA synergistic network is small-world and biologically meaningful, and a number of miRNA synergistic modules are significantly enriched in breast cancer. Our further analyses also reveal that most of synergistic miRNA-miRNA pairs show the same expression patterns. The comparison results indicate that the proposed multiple-intervention causal inference method performs better than the single-intervention causal inference method in identifying miRNA synergistic network.

CONCLUSIONS

Taken together, the results imply that miRsyn is a promising framework for identifying miRNA synergism, and it could enhance the understanding of miRNA synergism in breast cancer.

MiRNA-disease interaction prediction based on kernel neighborhood similarity and multi-network bidirectional propagation

Background

Studies have shown that miRNAs are functionally associated with the development of many human diseases, but the roles of miRNAs in diseases and their underlying molecular mechanisms have not been fully understood. The research on miRNA-disease interaction has received more and more attention. Compared with the complexity and high cost of biological experiments, computational methods can rapidly and efficiently predict the potential miRNA-disease interaction and can be used as a beneficial supplement to experimental methods.

Results

In this paper, we proposed a novel computational model of kernel neighborhood similarity and multi-network bidirectional propagation (KNMBP) for miRNA-disease interaction prediction, especially for new miRNAs and new diseases. First, we integrated multiple data sources of diseases and miRNAs, respectively, to construct a novel disease semantic similarity network and miRNA functional similarity network. Secondly, based on the modified miRNA-disease interactions, we use the kernel neighborhood similarity algorithm to calculate the disease kernel neighborhood similarity and the miRNA kernel neighborhood similarity. Finally, we utilize bidirectional propagation algorithm to predict the miRNA-disease interaction scores based on the integrated disease similarity network and miRNA similarity network. As a result, the AUC value of 5-fold cross validation for all interactions by KNMBP is 0.93126 based on the commonly used dataset, and the AUC values for all interactions, for all miRNAs, for all disease is 0.93795、0.86363、0.86937 based on another dataset extracted by ourselves, which are higher than other state-of-the-art methods. In addition, our model has good parameter robustness. The case study further demonstrated the predictive performance of the model for novel miRNA-disease interactions.

Conclusions

Our KNMBP algorithm efficiently integrates multiple omics data from miRNAs and diseases to stably and efficiently predict potential miRNA-disease interactions. It is anticipated that KNMBP would be a useful tool in biomedical research.

Identifying Potential miRNAs-Disease Associations With Probability Matrix Factorization

In recent years, miRNAs have been verified to play an irreplaceable role in biological processes associated with human disease. Discovering potential disease-related miRNAs helps explain the underlying pathogenesis of the disease at the molecular level. Given the high cost and labor intensity of biological experiments, computational predictions will be an indispensable alternative. Therefore, we design a new model called probability matrix factorization (PMFMDA). Specifically, we first integrate miRNA and disease similarity. Next, the known association matrix and integrated similarity matrix are utilized to construct a probability matrix factorization algorithm to identify potentially relevant miRNAs for disease. We find that PMFMDA achieves reliable performance in the frameworks of global leave-one-out cross validation (LOOCV) and 5-fold cross validation (AUCs are 0.9237 and 0.9187, respectively) in the HMDD (V2.0) dataset, significantly outperforming a few state-of-the-art methods including CMFMDA, IMCMDA, NCPMDA, RLSMDA, and RWRMDA. In addition, case studies show that PMFMDA has good predictive performance for new associations, and the evidence can be identified by literature mining.

In silico identification of microRNAs as candidate colorectal cancer biomarkers

The involvement of microRNA in cancers plays a significant role in their pathogenesis. Specific expressions of these non-coding RNAs also serve as biomarkers for early colorectal cancer diagnosis, but their laboratory/molecular identification is challenging and expensive. The aim of this study was to identify potential microRNAs for colorectal cancer diagnosis using in silico approach. Sequence similarity search was employed to obtain the candidate microRNA from the datasets, and three target prediction software were employed to determine their target genes. To determine the involvement of these microRNAs in colorectal cancer, the microRNA gene list obtained was used alongside with colorectal cancer expressed genes from gbCRC and CoReCG databases for gene intersection analysis. The involvement of these genes in the cancer subtype was further strengthened with the DAVID database. KEGG and Gene Ontology were used for the pathway and functional analysis, while STRING was employed for the interactions of protein network and further visualized by Cytoscape. The cBioPortal database was used to prioritize the target genes; prognostic and expression analysis were finally performed on the candidate microRNAs and the prioritized targets. This study, therefore, identified five candidate microRNAs, two hub genes (CTNNB1 and epidermal growth factor receptor), and seven significant target genes associated with colorectal cancer. The molecular validation studies are ongoing to ascertain the biological fitness of these findings.

MicroRNA‑718 serves a tumor‑suppressive role in non‑small cell lung cancer by directly targeting CCNB1

MicroRNA‑718 (miR‑718) serves crucial roles in tumorigenesis and in the progression of a number of cancers. However, the expression profile, specific functions and mechanisms of action of miR‑718 in non‑small cell lung cancer (NSCLC) are still elusive. The aims of the present study were to quantify the expression of miR‑718, determine its biological roles and elucidate the molecular mechanisms responsible for its activities in NSCLC cells. Reverse transcription‑quantitative PCR was carried out to assess miR‑718 expression in NSCLC tissue samples and cell lines. The Cell Counting Kit‑8 assay, flow cytometry, cell migration and invasion assays, and a tumor xenograft experiment were performed to evaluate the effects of miR‑718 overexpression on the malignant biological behaviors of NSCLC cells. miR‑718 expression was demonstrated to be significantly decreased in NSCLC tissue samples and cell lines. This reduced expression was significantly associated with tumor, node, metastasis stage, tumor size, lymph node metastasis and poor overall survival among patients with NSCLC. Exogenous miR‑718 expression suppressed NSCLC cell proliferation, migration and invasion, and promoted apoptosis in vitro; whereas it hindered tumor growth in vivo. Experiments to elucidate the mechanisms involved revealed that miR‑718 functions by directly targeting cyclin B1 (CCNB1) mRNA. CCNB1 expression was found to be upregulated in NSCLC and inversely correlated with miR‑718 levels. CCNB1 depletion had effects similar to those of miR‑718 overexpression in NSCLC cells. Furthermore, restoration of CCNB1 expression attenuated the tumor‑suppressive effects of miR‑718 overexpression in NSCLC cells. These results indicated that miR‑718 suppressed NSCLC progression in vitro and in vivo by directly targeting CCNB1 mRNA, which may indicate a potential target for the diagnosis and treatment of this fatal disease.

MicroRNAs Contribute to Breast Cancer Invasiveness

Cancer statistics in 2018 highlight an 8.6 million incidence in female cancers, and 4.2 million cancer deaths globally. Moreover, breast cancer is the most frequent malignancy in females and twenty percent of these develop metastasis. This provides only a small chance for successful therapy, and identification of new molecular markers for the diagnosis and prognostic prediction of metastatic disease and development of innovative therapeutic molecules are therefore urgently required. Differentially expressed microRNAs (miRNAs) in cancers cause multiple changes in the expression of the tumorigenesis-promoting genes which have mostly been investigated in breast cancers. Herein, we summarize recent data on breast cancer-specific miRNA expression profiles and their participation in regulating invasive processes, in association with changes in cytoskeletal structure, cell-cell adhesion junctions, cancer cell-extracellular matrix interactions, tumor microenvironments, epithelial-to-mesenchymal transitions and cancer cell stem abilities. We then focused on the epigenetic regulation of individual miRNAs and their modified interactions with other regulatory genes, and reviewed the function of miRNA isoforms and exosome-mediated miRNA transfer in cancer invasiveness. Although research into miRNA’s function in cancer is still ongoing, results herein contribute to improved metastatic cancer management.

Exploring the Potential of MicroRNA Let-7c as a Therapeutic for Prostate Cancer

Prostate cancer (PCa) is one of the leading causes of mortality worldwide and often presents with aberrant microRNA (miRNA) expression. Identifying and understanding the unique expression profiles could aid in the detection and treatment of this disease. This review aims to identify miRNAs as potential therapeutic targets for PCa. Three bio-informatic searches were conducted to identify miRNAs that are reportedly implicated in the pathogenesis of PCa. Only hsa-Lethal-7 (let-7c), recognized for its role in PCa pathogenesis, was common to all three databases. Three further database searches were conducted to identify known targets of hsa-let-7c. Four targets were identified, HMGA2, c-Myc (MYC), TRAIL, and CASP3. An extensive review of the literature was undertaken to assess the role of hsa-let-7c in the progression of other malignancies and to evaluate its potential as a therapeutic target for PCa. The heterogeneous nature of cancer makes it logical to develop mechanisms by which the treatment of malignancies is tailored to an individual, harnessing specific knowledge of the underlying biology of the disease. Resetting cellular miRNA levels is an exciting prospect that will allow this ambition to be realized.

Integrated Network Analysis Reveals FOXM1 and MYBL2 as Key Regulators of Cell Proliferation in Non-small Cell Lung Cancer

Background: Loss of control on cell division is an important factor for the development of non-small cell lung cancer (NSCLC), however, its molecular mechanism and gene regulatory network are not clearly understood. This study utilized the systems bioinformatics approach to reveal the “driver-network” involve in tumorigenic processes in NSCLC.

Methods: A meta-analysis of gene expression data of NSCLC was integrated with protein-protein interaction (PPI) data to construct an NSCLC network. MCODE and iRegulone were used to identify the local clusters and its upstream transcription regulators involve in NSCLC. Pair-wise gene expression correlation was performed using GEPIA. The survival analysis was performed by the Kaplan-Meier plot.

Results: This study identified a local “driver-network” with highest MCODE score having 26 up-regulated genes involved in the process of cell proliferation in NSCLC. Interestingly, the “driver-network” is under the regulation of TFs FOXM1 and MYBL2 as well as miRNAs. Furthermore, the overexpression of member genes in “driver-network” and the TFs are associated with poor overall survival (OS) in NSCLC patients.

Conclusion: This study identified a local “driver-network” and its upstream regulators responsible for the cell proliferation in NSCLC, which could be promising biomarkers and therapeutic targets for NSCLC treatment.

miR2Diabetes: A Literature-Curated Database of microRNA Expression Patterns, in Diabetic Microvascular Complications

microRNAs (miRNAs) have been established as critical regulators of the pathogenesis of diabetes mellitus (DM), and diabetes microvascular complications (DMCs). However, manually curated databases for miRNAs, and DM (including DMCs) association studies, have yet to be established. Here, we constructed a user-friendly database, “miR2Diabetes,” equipped with a graphical web interface for simple browsing or searching manually curated annotations. The annotations in our database cover 14 DM and DMC phenotypes, involving 156 miRNAs, by browsing diverse sample origins (e.g., blood, kidney, liver, and other tissues). Additionally, we provide miRNA annotations for disease-model organisms (including rats and mice), of DM and DMCs, for the purpose of improving knowledge of the biological complexity of these pathologies. We assert that our database will be a comprehensive resource for miRNA biomarker studies, as well as for prioritizing miRNAs for functional validation, in DM and DMCs, with likely extension to other diseases.

miRNACancerMAP: an integrative web server inferring miRNA regulation network for cancer

Summary

MicroRNAs play critical roles in oncogenesis by targeting a few key regulators or a large cohort of genes impinging on downstream signaling pathways. Conversely, miRNA activity is also titrated by competitive endogenous RNA such as lncRNA with sponge effect. Web-based server, miRNACancerMap, aims to unravel lncRNA-miRNA-mRNA tripartite complexity to predict the function and clinical relevance of miRNA with network perspective. In conjunction with large-scale data and information integration, miRNACancerMap implements various algorithms and pipelines to construct dynamic miRNA-centered network with rigorous Systems Biology approaches and the state-of-the-art visualization tool. The capability of the server to generate testable hypotheses was exemplified with cases to identify hub miRNAs regulating most of the differentially-expressed genes involved in cancer stage transition, miRNA-TF pairs shared by pan-cancers and lncRNA sponges validated by multiple datasets. LncRNAs sharing the same miRNAs binding sites as mRNAs can sequester miRNAs and indirectly regulate the activity of the related mRNAs. We have re-annotated traditional microarray chips, and included these datasets in the server to enable validation of the predicted lncRNA-miRNA-mRNA regulations derived from TCGA RNA-seq data. Of note, our server enables identifying miRNAs associated with cancer signaling pathways, and related lncRNA sponges from pan-cancers with only a few mouse clicks.

Availability and implementation

http://cis.hku.hk/miRNACancerMAP.

Supplementary information

Supplementary data are available at Bioinformatics online.

MicroRNA-29a suppresses the invasion and migration of osteosarcoma cells by regulating the SOCS1/NF-κB signalling pathway through negatively targeting DNMT3B

The present study aimed to investigate the roles of the microRNA‑29a/DNA methyltransferase 3B/suppressor of cytokine signalling 1 (miR‑29a/DNMT3B/SOCS1) axis in the invasion and the migration of osteosarcoma (OS). The expression levels of miR‑29a, DNMT3B and SOCS1 were determined in tissue samples and OS cell lines by reverse transcription‑quantitative polymerase chain reaction (PCR). Apoptosis was measured using flow cytometry analysis. Transwell and wound healing assays were conducted to measure the invasion and migration abilities of OS cells, respectively. A dual‑luciferase reporter assay was also conducted to determine the interaction between DNMT3B and miR‑29a, while methylation‑specific PCR was used to detect the methylation of SOCS1. Western blotting was performed to determine the protein levels of DNMT3B and SOCS1, as well as the levels of proteins associated with epithelial‑mesenchymal transition (EMT), apoptosis and the nuclear factor (NF)‑κB signalling pathway. The results demonstrated that miR‑29a and SOCS1 were downregulated, and DNMT3B was upregulated in both OS tissues and cell lines. The expression of miR‑29a and SOCS1 was found to be associated with advanced clinical stage and distant metastasis. In addition, the dual‑luciferase reporter assay revealed that DNMT3B was a direct target of miR‑29a. Overexpression using miR‑29a mimics decreased DNMT3B expression and the methylation level of SOCS1, which resulted in the upregulation of SOCS1 in U2OS and MG‑63 cells, while miR‑29a inhibition led to the opposite results. Transfection with miR‑29a mimics also promoted the apoptosis, and inhibited the invasion, migration and EMT process of OS cells, while it markedly reduced the nuclear translocation of p65 and IκB‑α degradation. Treatment with 5‑aza‑2'‑deoxycytidine worked together with miR‑29a mimics to synergistically enhance the aforementioned effects. By contrast, the effects induced by miR‑29a were partly reversed upon co‑transfection with SOCS1 siRNA. In conclusion, miR‑29a promoted the apoptosis, and inhibited the invasion, migration and EMT process of OS cells via inhibition of the SOCS1/NF‑κB signalling pathway by directly targeting DNMT3B.

Trends in the development of miRNA bioinformatics tools

MicroRNAs (miRNAs) are small noncoding RNAs that regulate gene expression via recognition of cognate sequences and interference of transcriptional, translational or epigenetic processes. Bioinformatics tools developed for miRNA study include those for miRNA prediction and discovery, structure, analysis and target prediction. We manually curated 95 review papers and ∼1000 miRNA bioinformatics tools published since 2003. We classified and ranked them based on citation number or PageRank score, and then performed network analysis and text mining (TM) to study the miRNA tools development trends. Five key trends were observed: (1) miRNA identification and target prediction have been hot spots in the past decade; (2) manual curation and TM are the main methods for collecting miRNA knowledge from literature; (3) most early tools are well maintained and widely used; (4) classic machine learning methods retain their utility; however, novel ones have begun to emerge; (5) disease-associated miRNA tools are emerging. Our analysis yields significant insight into the past development and future directions of miRNA tools.

Survey of miRNA-miRNA cooperative regulation principles across cancer types

Cooperative regulation among multiple microRNAs (miRNAs) is a complex type of posttranscriptional regulation in human; however, the global view of the system-level regulatory principles across cancers is still unclear. Here, we investigated miRNA-miRNA cooperative regulatory landscape across 18 cancer types and summarized the regulatory principles of miRNAs. The miRNA-miRNA cooperative pan-cancer network exhibited a scale-free and modular architecture. Cancer types with similar tissue origins had high similarity in cooperative network structure and expression of cooperative miRNA pairs. In addition, cooperative miRNAs showed divergent properties, including higher expression, greater expression variation and a stronger regulatory strength towards targets and were likely to regulate cancer hallmark-related functions. We found a marked rewiring of miRNA-miRNA cooperation between various cancers and revealed conserved and rewired network miRNA hubs. We further identified the common hubs, cancer-specific hubs and other hubs, which tend to target known anticancer drug targets. Finally, miRNA cooperative modules were found to be associated with patient survival in several cancer types. Our study highlights the potential of pan-cancer miRNA-miRNA cooperative regulation as a novel paradigm that may aid in the discovery of tumorigenesis mechanisms and development of anticancer drugs.

Altered Fecal Small RNA Profiles in Colorectal Cancer Reflect Gut Microbiome Composition in Stool Samples

Dysbiotic configurations of the human gut microbiota have been linked to colorectal cancer (CRC). Human small noncoding RNAs are also implicated in CRC, and recent findings suggest that their release in the gut lumen contributes to shape the gut microbiota. Bacterial small RNAs (bsRNAs) may also play a role in carcinogenesis, but their role has been less extensively explored. Here, we performed small RNA and shotgun sequencing on 80 stool specimens from patients with CRC or with adenomas and from healthy subjects collected in a cross-sectional study to evaluate their combined use as a predictive tool for disease detection. We observed considerable overlap and a correlation between metagenomic and bsRNA quantitative taxonomic profiles obtained from the two approaches. We identified a combined predictive signature composed of 32 features from human and microbial small RNAs and DNA-based microbiome able to accurately classify CRC samples separately from healthy and adenoma samples (area under the curve [AUC] = 0.87). In the present study, we report evidence that host-microbiome dysbiosis in CRC can also be observed by examination of altered small RNA stool profiles. Integrated analyses of the microbiome and small RNAs in the human stool may provide insights for designing more-accurate tools for diagnostic purposes.IMPORTANCE The characteristics of microbial small RNA transcription are largely unknown, while it is of primary importance for a better identification of molecules with functional activities in the gut niche under both healthy and disease conditions. By performing combined analyses of metagenomic and small RNA sequencing (sRNA-Seq) data, we characterized both the human and microbial small RNA contents of stool samples from healthy individuals and from patients with colorectal carcinoma or adenoma. With the integrative analyses of metagenomic and sRNA-Seq data, we identified a human and microbial small RNA signature which can be used to improve diagnosis of the disease. Our analysis of human and gut microbiome small RNA expression is relevant to generation of the first hypotheses about the potential molecular interactions occurring in the gut of CRC patients, and it can be the basis for further mechanistic studies and clinical tests.

A network embedding-based multiple information integration method for the MiRNA-disease association prediction

BACKGROUND

MiRNAs play significant roles in many fundamental and important biological processes, and predicting potential miRNA-disease associations makes contributions to understanding the molecular mechanism of human diseases. Existing state-of-the-art methods make use of miRNA-target associations, miRNA-family associations, miRNA functional similarity, disease semantic similarity and known miRNA-disease associations, but the known miRNA-disease associations are not well exploited.

RESULTS

In this paper, a network embedding-based multiple information integration method (NEMII) is proposed for the miRNA-disease association prediction. First, known miRNA-disease associations are formulated as a bipartite network, and the network embedding method Structural Deep Network Embedding (SDNE) is adopted to learn embeddings of nodes in the bipartite network. Second, the embedding representations of miRNAs and diseases are combined with biological features about miRNAs and diseases (miRNA-family associations and disease semantic similarities) to represent miRNA-disease pairs. Third, the prediction models are constructed based on the miRNA-disease pairs by using the random forest. In computational experiments, NEMII achieves high-accuracy performances and outperforms other state-of-the-art methods: GRNMF, NTSMDA and PBMDA. The usefulness of NEMII is further validated by case studies. The studies demonstrate the great potential of network embedding method for the miRNA-disease association prediction, and SDNE outperforms other popular network embedding methods: DeepWalk, High-Order Proximity preserved Embedding (HOPE) and Laplacian Eigenmaps (LE).

CONCLUSION

We propose a new method, named NEMII, for predicting miRNA-disease associations, which has great potential to benefit the field of miRNA-disease association prediction.

Prediction of potential disease-associated microRNAs using structural perturbation method

Motivation

The identification of disease-related microRNAs (miRNAs) is an essential but challenging task in bioinformatics research. Similarity-based link prediction methods are often used to predict potential associations between miRNAs and diseases. In these methods, all unobserved associations are ranked by their similarity scores. Higher score indicates higher probability of existence. However, most previous studies mainly focus on designing advanced methods to improve the prediction accuracy while neglect to investigate the link predictability of the networks that present the miRNAs and diseases associations. In this work, we construct a bilayer network by integrating the miRNA-disease network, the miRNA similarity network and the disease similarity network. We use structural consistency as an indicator to estimate the link predictability of the related networks. On the basis of the indicator, a derivative algorithm, called structural perturbation method (SPM), is applied to predict potential associations between miRNAs and diseases.

Results

The link predictability of bilayer network is higher than that of miRNA-disease network, indicating that the prediction of potential miRNAs-diseases associations on bilayer network can achieve higher accuracy than based merely on the miRNA-disease network. A comparison between the SPM and other algorithms reveals the reliable performance of SPM which performed well in a 5-fold cross-validation. We test fifteen networks. The AUC values of SPM are higher than some well-known methods, indicating that SPM could serve as a useful computational method for improving the identification accuracy of miRNA‒disease associations. Moreover, in a case study on breast neoplasm, 80% of the top-20 predicted miRNAs have been manually confirmed by previous experimental studies.

Availability and implementation

https://github.com/lecea/SPM-code.git.

Supplementary information

Supplementary data are available at Bioinformatics online.

MicroRNAs and complex diseases: from experimental results to computational models

Plenty of microRNAs (miRNAs) were discovered at a rapid pace in plants, green algae, viruses and animals. As one of the most important components in the cell, miRNAs play a growing important role in various essential and important biological processes. For the recent few decades, amounts of experimental methods and computational models have been designed and implemented to identify novel miRNA-disease associations. In this review, the functions of miRNAs, miRNA-target interactions, miRNA-disease associations and some important publicly available miRNA-related databases were discussed in detail. Specially, considering the important fact that an increasing number of miRNA-disease associations have been experimentally confirmed, we selected five important miRNA-related human diseases and five crucial disease-related miRNAs and provided corresponding introductions. Identifying disease-related miRNAs has become an important goal of biomedical research, which will accelerate the understanding of disease pathogenesis at the molecular level and molecular tools design for disease diagnosis, treatment and prevention. Computational models have become an important means for novel miRNA-disease association identification, which could select the most promising miRNA-disease pairs for experimental validation and significantly reduce the time and cost of the biological experiments. Here, we reviewed 20 state-of-the-art computational models of predicting miRNA-disease associations from different perspectives. Finally, we summarized four important factors for the difficulties of predicting potential disease-related miRNAs, the framework of constructing powerful computational models to predict potential miRNA-disease associations including five feasible and important research schemas, and future directions for further development of computational models.

DIABLO: an integrative approach for identifying key molecular drivers from multi-omics assays

MOTIVATION

In the continuously expanding omics era, novel computational and statistical strategies are needed for data integration and identification of biomarkers and molecular signatures. We present Data Integration Analysis for Biomarker discovery using Latent cOmponents (DIABLO), a multi-omics integrative method that seeks for common information across different data types through the selection of a subset of molecular features, while discriminating between multiple phenotypic groups.

RESULTS

Using simulations and benchmark multi-omics studies, we show that DIABLO identifies features with superior biological relevance compared with existing unsupervised integrative methods, while achieving predictive performance comparable to state-of-the-art supervised approaches. DIABLO is versatile, allowing for modular-based analyses and cross-over study designs. In two case studies, DIABLO identified both known and novel multi-omics biomarkers consisting of mRNAs, miRNAs, CpGs, proteins and metabolites.

AVAILABILITY AND IMPLEMENTATION

DIABLO is implemented in the mixOmics R Bioconductor package with functions for parameters' choice and visualization to assist in the interpretation of the integrative analyses, along with tutorials on http://mixomics.org and in our Bioconductor vignette.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Computational Network Analysis Identifies Evolutionarily Conserved miRNA Gene Interactions Potentially Regulating Immune Response in Bovine Trypanosomosis

Bovine trypanosomosis is a devastating disease that causes huge economic loss to the global cattle industry on a yearly basis. Selection of accurate biomarkers are important in early disease diagnosis and treatment. Of late, micro-RNAs (miRNAs) are becoming the most useful biomarkers for both infectious and non-infectious diseases in humans, but this is not the case in animals. miRNAs are non-coding RNAs that regulate gene expression through binding to the 3'-, 5'-untranslated regions (UTR) or coding sequence (CDS) region of one or more target genes. The molecular identification of miRNAs that regulates the expression of immune genes responding to bovine trypanosomosis is poorly defined, as is the possibility that these miRNAs could serve as potential biomarkers for disease diagnosis and treatment currently unknown. To this end, we utilized in silico tools to elucidate conserved miRNAs regulating immune response genes during infection, in addition to cataloging significant genes. Based on the p value of 1.77E-32, we selected 25 significantly expressed immune genes. Using prediction analysis, we identified a total of 4,251 bovine miRNAs targeting these selected genes across the 3'UTR, 5'UTR and CDS regions. Thereafter, we identified candidate miRNAs based on the number of gene targets and their abundance at the three regions. In all, we found the top 13 miRNAs that are significantly conserved targeting 7 innate immune response genes, including bta-mir-2460, bta-mir-193a, bta-mir-2316, and bta-mir-2456. Our gene ontology analysis suggests that these miRNAs are involved in gene silencing, cellular protein modification process, RNA-induced silencing complex, regulation of humoral immune response mediated by circulating immunoglobulin and negative regulation of chronic inflammatory response, among others. In conclusion, this study identifies specific miRNAs that may be involved in the regulation of gene expression during bovine trypanosomosis. These miRNAs have the potential to be used as biomarkers in the animal and veterinary research community to facilitate the development of tools for early disease diagnosis/detection, drug targeting, and the rational design of drugs to facilitate disease treatment.

Up-regulation of miR-381 inhibits NAD+ salvage pathway and promotes apoptosis in breast cancer cells

Nicotinamide phosphoribosyltransferase (NAMPT), a rate-limiting enzyme involved in nicotinamide adenine dinucleotide (NAD) salvage pathway, is overexpressed in many human malignancies such as breast cancer. This enzyme plays a critical role in survival and growth of cancer cells. MicroRNAs (miRNAs) are among the most important regulators of gene expression, and serve as potential targets for diagnosis, prognosis, and therapy of breast cancer. Therefore, the aim of this study was to assess the effect of NAMPT inhibition by miR-381 on breast cancer cell survival. MCF-7 and MDA-MB-231 cancer cell lines were transfected with miR-381 mimic, inhibitor, and their corresponding negative controls (NCs). Subsequently, the level of NAMPT and NAD was assessed using real-time PCR, immuno-blotting, and enzymatic methods, respectively. In order to evaluate apoptosis, cells were labelled with Annexin V-FITC and propidium iodide and analyzed by flow cytometry. Bioinformatics analysis was performed to recognize whether NAMPT 3'-untranslated region (UTR) is a direct target of miR-381 and the results were authenticated by the luciferase reporter assay using a vector containing the 3'-UTR sequence of NAMPT. Our results revealed that the 3'-UTR of NAMPT was a direct target of miR-381 and its up-regulation decreased NAMPT gene and protein expression, leading to a notable reduction in intracellular NAD and subsequently cell survival and induction of apoptosis. It can be concluded that miR-381 has a vital role in tumor suppression by down-regulation of NAMPT, and it can be a promising candidate for breast cancer therapy.

Prediction of Disease-related microRNAs through Integrating Attributes of microRNA Nodes and Multiple Kinds of Connecting Edges

Identifying disease-associated microRNAs (disease miRNAs) contributes to the understanding of disease pathogenesis. Most previous computational biology studies focused on multiple kinds of connecting edges of miRNAs and diseases, including miRNA-miRNA similarities, disease-disease similarities, and miRNA-disease associations. Few methods exploited the node attribute information related to miRNA family and cluster. The previous methods do not completely consider the sparsity of node attributes. Additionally, it is challenging to deeply integrate the node attributes of miRNAs and the similarities and associations related to miRNAs and diseases. In the present study, we propose a novel method, known as MDAPred, based on nonnegative matrix factorization to predict candidate disease miRNAs. MDAPred integrates the node attributes of miRNAs and the related similarities and associations of miRNAs and diseases. Since a miRNA is typically subordinate to a family or a cluster, the node attributes of miRNAs are sparse. Similarly, the data for miRNA and disease similarities are sparse. Projecting the miRNA and disease similarities and miRNA node attributes into a common low-dimensional space contributes to estimating miRNA-disease associations. Simultaneously, the possibility that a miRNA is associated with a disease depends on the miRNA's neighbour information. Therefore, MDAPred deeply integrates projections of multiple kinds of connecting edges, projections of miRNAs node attributes, and neighbour information of miRNAs. The cross-validation results showed that MDAPred achieved superior performance compared to other state-of-the-art methods for predicting disease-miRNA associations. MDAPred can also retrieve more actual miRNA-disease associations at the top of prediction results, which is very important for biologists. Additionally, case studies of breast, lung, and pancreatic cancers further confirmed the ability of MDAPred to discover potential miRNA-disease associations.

A rectified factor network based biclustering method for detecting cancer-related coding genes and miRNAs, and their interactions

Detecting cancer-related genes and their interactions is a crucial task in cancer research. For this purpose, we proposed an efficient method, to detect coding genes, microRNAs (miRNAs), and their interactions related to a particular cancer or a cancer subtype using their expression data from the same set of samples. Firstly, biclusters specific to a particular type of cancer are detected based on rectified factor networks and ranked according to their associations with general cancers. Secondly, coding genes and miRNAs in each bicluster are prioritized by considering their differential expression and differential correlation values, protein-protein interaction data, and potential cancer markers. Finally, a rank fusion process is used to obtain the final comprehensive rank by combining multiple ranking results. We applied our proposed method on breast cancer datasets. Results show that our method outperforms other methods in detecting breast cancer-related coding genes and miRNAs. Furthermore, our method is very efficient in computing time, which can handle tens of thousands genes/miRNAs and hundreds of patients in hours on a desktop. This work may aid researchers in studying the genetic architecture of complex diseases, and improving the accuracy of diagnosis.

Noncoding RNAs in Extracellular Fluids as Cancer Biomarkers: The New Frontier of Liquid Biopsies

The last two decades of cancer research have been devoted in two directions: (1) understanding the mechanism of carcinogenesis for an effective treatment, and (2) improving cancer prevention and screening for early detection of the disease. This last aspect has been developed, especially for certain types of cancers, thanks also to the introduction of new concepts such as liquid biopsies and precision medicine. In this context, there is a growing interest in the application of alternative and noninvasive methodologies to search for cancer biomarkers. The new frontiers of the research lead to a search for RNA molecules circulating in body fluids. Searching for biomarkers in extracellular body fluids represents a better option for patients because they are easier to access, less painful, and potentially more economical. Moreover, the possibility for these types of samples to be taken repeatedly, allows a better monitoring of the disease progression or treatment efficacy for a better intervention and dynamic treatment of the patient, which is the fundamental basis of personalized medicine. RNA molecules, freely circulating in body fluids or packed in microvesicles, have all the characteristics of the ideal biomarkers owing to their high stability under storage and handling conditions and being able to be sampled several times for monitoring. Moreover, as demonstrated for many cancers, their plasma/serum levels mirror those in the primary tumor. There are a large variety of RNA species noncoding for proteins that could be used as cancer biomarkers in liquid biopsies. Among them, the most studied are microRNAs, but recently the attention of the researcher has been also directed towards Piwi-interacting RNAs, circular RNAs, and other small noncoding RNAs. Another class of RNA species, the long noncoding RNAs, is larger than microRNAs and represents a very versatile and promising group of molecules which, apart from their use as biomarkers, have also a possible therapeutic role. In this review, we will give an overview of the most common noncoding RNA species detectable in extracellular fluids and will provide an update concerning the situation of the research on these molecules as cancer biomarkers.

miR-22 Regulates Invasion, Gene Expression and Predicts Overall Survival in Patients with Clear Cell Renal Cell Carcinoma

Background:

Clear cell renal cell carcinoma (ccRCC) is molecularly diverse and distinct molecular subtypes show different clinical outcomes. MicroRNAs (miRNAs) are essential components of gene regulatory networks and play a crucial role in progression of many cancer types including ccRCC.

Objective:

Identify prognostic miRNAs and determine the role of miR-22 in ccRCC.

Methods:

Hierarchical clustering was done in R using gene expression profiles of over 450 ccRCC cases in The Cancer Genome Atlas (TCGA). Kaplan-Meier analysis was performed to identify prognostic miRNAs in the TCGA dataset. RNA-Seq was performed to identify miR-22 target genes in primary ccRCC cells and Matrigel invasion assay was performed to assess the effects of miR-22 overexpression on cell invasion.

Results:

Hierarchical clustering analysis using 2,621 prognostic genes previously identified by our group demonstrated that ccRCC patients with longer overall survival expressed lower levels of genes promoting proliferation or immune responses, while better maintaining gene expression associated with cortical differentiation and cell adhesion. Targets of 26 miRNAs were significantly enriched in the 2,621 prognostic genes and these miRNAs were prognostic by themselves. MiR-22 was associated with poor overall survival in the TCGA dataset. Overexpression of miR-22 promoted invasion of primary ccRCC cells in vitro and modulated transcriptional programs implicated in cancer progression including DNA repair, cell proliferation and invasion.

Conclusions:

Our results suggest that ccRCCs with differential clinical outcomes have distinct transcriptomes for which miRNAs could serve as master regulators. MiR-22, as a master regulator, promotes ccRCC progression at least in part by enhancing cell invasion.

miRTarBase update 2018: a resource for experimentally validated microRNA-target interactions

MicroRNAs (miRNAs) are small non-coding RNAs of ∼ 22 nucleotides that are involved in negative regulation of mRNA at the post-transcriptional level. Previously, we developed miRTarBase which provides information about experimentally validated miRNA-target interactions (MTIs). Here, we describe an updated database containing 422 517 curated MTIs from 4076 miRNAs and 23 054 target genes collected from over 8500 articles. The number of MTIs curated by strong evidence has increased ∼1.4-fold since the last update in 2016. In this updated version, target sites validated by reporter assay that are available in the literature can be downloaded. The target site sequence can extract new features for analysis via a machine learning approach which can help to evaluate the performance of miRNA-target prediction tools. Furthermore, different ways of browsing enhance user browsing specific MTIs. With these improvements, miRTarBase serves as more comprehensively annotated, experimentally validated miRNA-target interactions databases in the field of miRNA related research. miRTarBase is available at http://miRTarBase.mbc.nctu.edu.tw/.

Inferring the Disease-Associated miRNAs Based on Network Representation Learning and Convolutional Neural Networks

Identification of disease-associated miRNAs (disease miRNAs) are critical for understanding etiology and pathogenesis. Most previous methods focus on integrating similarities and associating information contained in heterogeneous miRNA-disease networks. However, these methods establish only shallow prediction models that fail to capture complex relationships among miRNA similarities, disease similarities, and miRNA-disease associations. We propose a prediction method on the basis of network representation learning and convolutional neural networks to predict disease miRNAs, called CNNMDA. CNNMDA deeply integrates the similarity information of miRNAs and diseases, miRNA-disease associations, and representations of miRNAs and diseases in low-dimensional feature space. The new framework based on deep learning was built to learn the original and global representation of a miRNA-disease pair. First, diverse biological premises about miRNAs and diseases were combined to construct the embedding layer in the left part of the framework, from a biological perspective. Second, the various connection edges in the miRNA-disease network, such as similarity and association connections, were dependent on each other. Therefore, it was necessary to learn the low-dimensional representations of the miRNA and disease nodes based on the entire network. The right part of the framework learnt the low-dimensional representation of each miRNA and disease node based on non-negative matrix factorization, and these representations were used to establish the corresponding embedding layer. Finally, the left and right embedding layers went through convolutional modules to deeply learn the complex and non-linear relationships among the similarities and associations between miRNAs and diseases. Experimental results based on cross validation indicated that CNNMDA yields superior performance compared to several state-of-the-art methods. Furthermore, case studies on lung, breast, and pancreatic neoplasms demonstrated the powerful ability of CNNMDA to discover potential disease miRNAs.

Prediction and interpretation of miRNA-disease associations based on miRNA target genes using canonical correlation analysis

BACKGROUND

It has been shown that the deregulation of miRNAs is associated with the development and progression of many human diseases. To reduce time and cost of biological experiments, a number of algorithms have been proposed for predicting miRNA-disease associations. However, the existing methods rarely investigated the cause-and-effect mechanism behind these associations, which hindered further biomedical follow-ups.

RESULTS

In this study, we presented a CCA-based model in which the possible molecular causes of miRNA-disease associations were comprehensively revealed by extracting correlated sets of genes and diseases based on the co-occurrence of miRNAs in target gene profiles and disease profiles. Our method directly suggested the underlying genes involved, which could be used for experimental tests and confirmation. The inference of associated diseases of a new miRNA was made by taking into account the weight vectors of the extracted sets. We extracted 60 pairs of correlated sets from 404 miRNAs with two profiles for 2796 target genes and 362 diseases. The extracted diseases could be considered as possible outcomes of miRNAs regulating the target genes which appeared in the same set, some of which were supported by independent source of information. Furthermore, we tested our method on the 404 miRNAs under the condition of 5-fold cross validations and received an AUC value of 0.84606. Finally, we extensively inferred miRNA-disease associations for 100 new miRNAs and some interesting prediction results were validated by established databases.

CONCLUSIONS

The encouraging results demonstrated that our method could provide a biologically relevant prediction and interpretation of associations between miRNAs and diseases, which were of great usefulness when guiding biological experiments for scientific research.

Predicting lncRNA-disease associations using network topological similarity based on deep mining heterogeneous networks

A kind of noncoding RNA with length more than 200 nucleotides named long noncoding RNA (lncRNA) has gained considerable attention in recent decades. Many studies have confirmed that human genome contains many thousands of lncRNAs. LncRNAs play significant roles in many important biological processes, including complex disease diagnosis, prognosis, prevention and treatment. For some important diseases such as cancer, lncRNAs have been novel candidate biomarkers. However, the role of lncRNAs in human diseases is still in its infancy, and only a small part of lncRNA-disease associations have been experimentally verified. Predicting lncRNA-disease association is an important way to understand the mechanism and function of lncRNA involved in diseases to enrich the annotations of lncRNA. Therefore, it is urgent to prioritize lncRNAs potentially associated with diseases. Biological system is a highly complex heterogenous network involved different molecules. Therefore, the algorithms based on network methods have been extensively applied in information fields which can provide a quantifiable characterization for the networks characterizing multifarious biological systems. A heterogeneous network topology possessing abundant interactions between biomedical entities is rarely utilized in similarity-based methods for predicting lncRNA-disease associations based on the array of varying features of lncRNAs and diseases. DeepWalk, encoding the relations of nodes in a continuous vector space, is an extension of language model and unsupervised learning from sequence-based word to network. In this article, we present a novel lncRNA-disease association prediction method based on DeepWalk, which enhances the existing association discovery methods through a topology-based similarity measure. We integrate the heterogeneous data to construct a Linked Tripartite Network which is a heterogeneous network containing three types od nodes which generated from bioinformatics linked datasets and use DeepWalk method to extract topological structure features of the nodes in the linked tripartite network for calculating similarities. Our proposed method can be separated into the following steps: Firstly, we integrate heterogeneous data to construct a Linked Tripartite Network: containing the topological interactions of known lncRNA-disease, lncRNA-microRNA and microRNA-disease. Secondly, the topological structure features of the nodes are extracted based on DeepWalk. Thirdly, similarity scores of disease-disease pairs and lncRNA-lncRNA pairs are computed based on the topology of this network. Finally, new lncRNA and disease associations are discovered by rule-based inference method with lncRNA-lncRNA similarities. Our proposed method shows superior predictive performance for prediction of lncRNA-disease associations based on topological similarity from heterogenous network. The AUC value is used to show the performance of our method. The similarity measurement using network topology based on DeepWalk provide a novel perspective which is different from the similarity derived from sequence or structure information. Availability: All the data and codes are freely availability at: https://github.com/Pengeace/lncRNA-disease-link.

MiR-216a-5p targets TCTN1 to inhibit cell proliferation and induce apoptosis in esophageal squamous cell carcinoma

Background

MiR-216a-5p has been reported to be associated with several tumors, including prostate cancer and melanoma. However, its expression level and potential role in esophageal squamous cell carcinoma (ESCC) remain uncertain.

Results

Here, we found that miR-216a-5p expression was significantly down-regulated in clinical ESCC tissues and cells. Functional assays were performed to evaluate the biological effects of miR-216a-5p on cell proliferation and cell apoptosis by CCK-8 assay and flow cytometry in ESCC cell lines, EC9706 and TE-9. The results showed that miR-216a-5p overexpression repressed cell proliferation and induced cell apoptosis. Through bioinformatics prediction and luciferase reporter assay, we revealed that miR-216a-5p could directly target tectonic family member 1 (TCTN1). Moreover, TCTN1 was obviously suppressed by miR-216a-5p overexpression. In addition, TCTN1 expression was significantly increased and inversely correlated with the levels of miR-216a-5p in ESCC tissues. More importantly, down-regulation of TCTN1 imitated, while restoration of TCTN reversed the effects of miR-216a-5p on cell proliferation and apoptosis. At the molecular level, we further found that TCTN1 overexpression reversed the effects of miR-216a-5p transfection on the expression of PCNA, Bcl-2 and Bad.

Conclusions

Our results demonstrate that miR-216a-5p might serve as a tumor suppressor in ESCC cells through negatively regulating TCTN1 expression, indicating the possibility that miR-216a-5p and TCTN1 might be attractive targets for ESCC therapeutic intervention.

Integrative Analysis of Genomic and Clinical Data Reveals Intrinsic Characteristics of Bladder Urothelial Carcinoma Progression

The progression of bladder cancer is generally a complex and dynamic process, involving a variety of biological factors. Here, we aimed to identify a set of survival-related genes that play an important role in the progression of bladder cancer and uncover their synergistic patterns. Based on the large-scale genomic profiling data and clinical information of 404 bladder cancer patients derived from The Cancer Genome Atlas (TCGA) database, we first discovered 1078 survival-related genes related to their survival states using univariate and multivariate Cox proportional hazardous regression. We then investigated the dynamic changes of the cooperative behaviors of these 1078 genes by analyzing their respective genomic features, including copy number variations, DNA methylations, somatic mutations, and microRNA regulatory networks. Our analyses showed that during the progression of bladder cancer, the biological disorder involving the identified survival-related genes can be reflected by multiple levels of abnormal gene regulation, ranging from genomic alteration to post-transcriptional dysregulation. In particular, the stage-specific co-expression networks of these genes undergo a series of structural variations. Our findings provide useful hints on understanding the underlying complex molecular mechanisms related to the evolution of bladder cancer and offer a new perspective on clinical diagnosis and treatment of bladder cancer.

Computational Resources for Prediction and Analysis of Functional miRNA and Their Targetome

microRNAs are evolutionarily conserved, endogenously produced, noncoding RNAs (ncRNAs) of approximately 19-24 nucleotides (nts) in length known to exhibit gene silencing of complementary target sequence. Their deregulated expression is reported in various disease conditions and thus has therapeutic implications. In the last decade, various computational resources are published in this field. In this chapter, we have reviewed bioinformatics resources, i.e., miRNA-centered databases, algorithms, and tools to predict miRNA targets. First section has enlisted more than 75 databases, which mainly covers information regarding miRNA registries, targets, disease associations, differential expression, interactions with other noncoding RNAs, and all-in-one resources. In the algorithms section, we have compiled about 140 algorithms from eight subcategories, viz. for the prediction of precursor (pre-) and mature miRNAs. These algorithms are developed on various sequence, structure, and thermodynamic based features incorporated into different machine learning techniques (MLTs). In addition, computational identification of miRNAs from high-throughput next generation sequencing (NGS) data and their variants, viz. isomiRs, differential expression, miR-SNPs, and functional annotation, are discussed. Prediction and analysis of miRNAs and their associated targets are also evaluated under miR-targets section providing knowledge regarding novel miRNA targets and complex host-pathogen interactions. In conclusion, we have provided comprehensive review of in silico resources published in miRNA research to help scientific community be updated and choose the appropriate tool according to their needs.

MicroRNA‑873 inhibits the progression of thyroid cancer by directly targeting ZEB1

Recent evidence has revealed that certain microRNAs (miRNAs) are dysregulated in thyroid cancer (TC), which has been associated with the pathogenesis and development of TC. Therefore, improved understanding of the detailed roles of miRNAs that are aberrantly expressed in TC may aid the development of valuable therapeutic methods for the management of patients with this malignancy. In this study, miRNA‑873‑5p (miR‑873) expression in TC tissues and cell lines was detected by reverse‑transcription quantitative polymerase chain reaction (RT‑qPCR). MTT and cell invasion assays were performed to investigate the effects of miR‑873 overexpression on TC cell proliferation and invasion in vitro. Bioinformatics analysis, luciferase reporter assay, RT‑qPCR and western blot analysis were employed to evaluate whether zinc finger E‑box‑binding homeobox 1 (ZEB1) is a direct target of miR‑873 in TC cells. The present study reported that miR‑873 was weakly expressed in human TC tissues and cell lines. Functionally, miR‑873 overexpression suppressed TC cell proliferation and invasion in vitro. Mechanistically, ZEB1 was predicted to be a putative target of miR‑873. In addition, miR‑873 was proposed to directly bind to the 3'‑untranslated region of ZEB1 and decrease its expression in TC cells at the mRNA and protein levels. Furthermore, ZEB1 expression was significantly upregulated in TC tissues and negatively correlated with miR‑873 expression. Furthermore, ZEB1 restoration partially reversed the suppressive effects of miR‑873 overexpression on TC cell proliferation and invasion. These results demonstrated that miR‑873 may serve tumour‑suppressive roles in the progression of TC, and its suppressive effects could be mediated by the inhibition of ZEB1. Therefore, miR‑873 may be a valuable therapeutic target in the management of patients with TC.