Dual Convolutional Neural Networks With Attention Mechanisms Based Method for Predicting Disease-Related lncRNA Genes

A lot of studies indicated that aberrant expression of long non-coding RNA genes (lncRNAs) is closely related to human diseases. Identifying disease-related lncRNAs (disease lncRNAs) is critical for understanding the pathogenesis and etiology of diseases. Most of the previous methods focus on prioritizing the potential disease lncRNAs based on shallow learning methods. The methods fail to extract the deep and complex feature representations of lncRNA-disease associations. Furthermore, nearly all the methods ignore the discriminative contributions of the similarity, association, and interaction relationships among lncRNAs, disease, and miRNAs for the association prediction. A dual convolutional neural networks with attention mechanisms based method is presented for predicting the candidate disease lncRNAs, and it is referred to as CNNLDA. CNNLDA deeply integrates the multiple source data like the lncRNA similarities, the disease similarities, the lncRNA-disease associations, the lncRNA-miRNA interactions, and the miRNA-disease associations. The diverse biological premises about lncRNAs, miRNAs, and diseases are combined to construct the feature matrix from the biological perspectives. A novel framework based on the dual convolutional neural networks is developed to learn the global and attention representations of the lncRNA-disease associations. The left part of the framework exploits the various information contained by the feature matrix to learn the global representation of lncRNA-disease associations. The different connection relationships among the lncRNA, miRNA, and disease nodes and the different features of these nodes have the discriminative contributions for the association prediction. Hence we present the attention mechanisms from the relationship level and the feature level respectively, and the right part of the framework learns the attention representation of associations. The experimental results based on the cross validation indicate that CNNLDA yields superior performance than several state-of-the-art methods. Case studies on stomach cancer, lung cancer, and colon cancer further demonstrate CNNLDA's ability to discover the potential disease lncRNAs.

A Novel Method for Predicting Disease-Associated LncRNA-MiRNA Pairs Based on the Higher-Order Orthogonal Iteration

A lot of research studies have shown that many complex human diseases are associated not only with microRNAs (miRNAs) but also with long noncoding RNAs (lncRNAs). However, most of the current existing studies focus on the prediction of disease-related miRNAs or lncRNAs, and to our knowledge, until now, there are few literature studies reported to pay attention to the study of impact of miRNA-lncRNA pairs on diseases, although more and more studies have shown that both lncRNAs and miRNAs play important roles in cell proliferation and differentiation during the recent years. The identification of disease-related genes provides great insight into the underlying pathogenesis of diseases at a system level. In this study, a novel model called PADLMHOOI was proposed to predict potential associations between diseases and lncRNA-miRNA pairs based on the higher-order orthogonal iteration, and in order to evaluate its prediction performance, the global and local LOOCV were implemented, respectively, and simulation results demonstrated that PADLMHOOI could achieve reliable AUCs of 0.9545 and 0.8874 in global and local LOOCV separately. Moreover, case studies further demonstrated the effectiveness of PADLMHOOI to infer unknown disease-related lncRNA-miRNA pairs.

miR-192/215-5p act as tumor suppressors and link Crohn's disease and colorectal cancer by targeting common metabolic pathways: An integrated informatics analysis and experimental study

MicroRNAs have emerged as key regulators involved in a variety of biological processes. Previous studies have demonstrated that miR-192/215 participated in progression of Crohn's disease and colorectal cancer. However, their concrete relationships and regulation networks in diseases remain unclear. Here, we used bioinformatics methods to expound miR-192/215-5p macrocontrol regulatory networks shared by two diseases. For data mining and figure generation, several miRNA prediction tools, Human miRNA tissue atlas, FunRich, miRcancer, MalaCards, STRING, GEPIA, cBioPortal, GEO databases, Pathvisio, Graphpad Prism 6 software, etc . are extensively applied. miR-192/215-5p were specially distributed in colon tissues and enriched biological pathways were closely associated with human cancers. Emerging role of miR-192/215-5p and their common pathways in Crohn's disease and colorectal cancer was also analyzed. Based on results derived from multiple approaches, we identified the biological functions of miR-192/215-5p as a tumor suppressor and link Crohn's disease and colorectal cancer by targeting triglyceride synthesis and extracellular matrix remodeling pathways.

FCMDAP: using miRNA family and cluster information to improve the prediction accuracy of disease related miRNAs

Background

Biological experiments have confirmed the association between miRNAs and various diseases. However, such experiments are costly and time consuming. Computational methods help select potential disease-related miRNAs to improve the efficiency of biological experiments.

Methods

In this work, we develop a novel method using multiple types of data to calculate miRNA and disease similarity based on mutual information, and add miRNA family and cluster information to predict human disease-related miRNAs (FCMDAP). This method not only depends on known miRNA-diseases associations but also accurately measures miRNA and disease similarity and resolves the problem of overestimation. FCMDAP uses the k most similar neighbor recommendation algorithm to predict the association score between miRNA and disease. Information about miRNA cluster is also used to improve prediction accuracy.

Result

FCMDAP achieves an average AUC of 0.9165 based on leave-one-out cross validation. Results confirm the 100, 98 and 96% of the top 50 predicted miRNAs reported in case studies on colorectal, lung, and pancreatic neoplasms. FCMDAP also exhibits satisfactory performance in predicting diseases without any related miRNAs and miRNAs without any related diseases.

Conclusions

In this study, we present a computational method FCMDAP to improve the prediction accuracy of disease related miRNAs. FCMDAP could be an effective tool for further biological experiments.

Electronic supplementary material

The online version of this article (10.1186/s12918-019-0696-9) contains supplementary material, which is available to authorized users.

Navigating the disease landscape: knowledge representations for contextualizing molecular signatures

Large amounts of data emerging from experiments in molecular medicine are leading to the identification of molecular signatures associated with disease subtypes. The contextualization of these patterns is important for obtaining mechanistic insight into the aberrant processes associated with a disease, and this typically involves the integration of multiple heterogeneous types of data. In this review, we discuss knowledge representations that can be useful to explore the biological context of molecular signatures, in particular three main approaches, namely, pathway mapping approaches, molecular network centric approaches and approaches that represent biological statements as knowledge graphs. We discuss the utility of each of these paradigms, illustrate how they can be leveraged with selected practical examples and identify ongoing challenges for this field of research.

Long Noncoding RNA and Protein Interactions: From Experimental Results to Computational Models Based on Network Methods

Non-coding RNAs with a length of more than 200 nucleotides are long non-coding RNAs (lncRNAs), which have gained tremendous attention in recent decades. Many studies have confirmed that lncRNAs have important influence in post-transcriptional gene regulation; for example, lncRNAs affect the stability and translation of splicing factor proteins. The mutations and malfunctions of lncRNAs are closely related to human disorders. As lncRNAs interact with a variety of proteins, predicting the interaction between lncRNAs and proteins is a significant way to depth exploration functions and enrich annotations of lncRNAs. Experimental approaches for lncRNA–protein interactions are expensive and time-consuming. Computational approaches to predict lncRNA–protein interactions can be grouped into two broad categories. The first category is based on sequence, structural information and physicochemical property. The second category is based on network method through fusing heterogeneous data to construct lncRNA related heterogeneous network. The network-based methods can capture the implicit feature information in the topological structure of related biological heterogeneous networks containing lncRNAs, which is often ignored by sequence-based methods. In this paper, we summarize and discuss the materials, interaction score calculation algorithms, advantages and disadvantages of state-of-the-art algorithms of lncRNA–protein interaction prediction based on network methods to assist researchers in selecting a suitable method for acquiring more dependable results. All the related different network data are also collected and processed in convenience of users, and are available at https://github.com/HAN-Siyu/APINet/.

Global Genetics Research in Prostate Cancer: A Text Mining and Computational Network Theory Approach

Prostate cancer is the most common cancer type in men in Finland and second worldwide. In this paper, we analyze almost 150, 000 published papers about prostate cancer, authored by ten thousands of scientists worldwide, with an integrated text mining and computational network theory approach. We demonstrate how to integrate text mining with network analysis investigating research contributions of countries and collaborations within and between countries. Furthermore, we study the time evolution of individually and collectively studied genes. Finally, we investigate a collaboration network of Finland and compare studied genes with globally studied genes in prostate cancer genetics. Overall, our results provide a global overview of prostate cancer research in genetics. In addition, we present a specific discussion for Finland. Our results shed light on trends within the last 30 years and are useful for translational researchers within the full range from genetics to public health management and health policy.

MicroRNAs in Female Malignancies

MicroRNAs (miRNAs) are endogenous 22-nucleotide RNAs that can play a fundamental regulatory role in the gene expression of various organisms. Current research suggests that miRNAs can assume pivotal roles in carcinogenesis. In this article, through bioinformatics mining and computational analysis, we determine a single miRNA commonly involved in the development of breast, cervical, endometrial, ovarian, and vulvar cancer, whereas we underline the existence of 7 more miRNAs common in all examined malignancies with the exception of vulvar cancer. Furthermore, we identify their target genes and encoded biological functions. We also analyze common biological processes on which all of the identified miRNAs act and we suggest a potential mechanism of action. In addition, we analyze exclusive miRNAs among the examined malignancies and bioinformatically explore their functionality. Collectively, our data can be employed in in vitro assays as a stepping stone in the identification of a universal machinery that is derailed in female malignancies, whereas exclusive miRNAs may be employed as putative targets for future chemotherapeutic agents or cancer-specific biomarkers.

3D Mammary Epithelial Cell Models: A Goldmine of DCIS Biomarkers and Morphogenetic Mechanisms

Breast ductal carcinoma in situ (DCIS) has been typically recognized by pathologists on the basis of aberrant mammary duct morphology. Thus, there are increasing efforts to detect DCIS biomarkers and druggable targets. In this study we focused on the molecular mechanism involving Annexin A8 (ANXA8), a Ca²⁺ and phospholipid binding protein, which is regulated by all-trans Retinoic Acid (RA), and it is highly expressed in breast DCIS tissue samples relative to atypical ductal hyperplasia, and normal breast tissue. Using a panel of human mammary epithelial HME1 cell lines that share a common protein signature, and develop in vitro three dimensional (3D) “DCIS-like” amorphous structures, we identified by bioinformatics analysis protein-miRNA pairs, potentially involved in mammary morphogenetic mechanisms, including the ANXA8 mechanism. HME1 cells with genetic mutations hampering the physiological RA regulation of the RA receptor alpha (RARA) transcriptional function, but retain the RARA function controlling the PI3KCA-AKT signaling, develop 3D “DCIS-like” amorphous structures with upregulated ANXA8. Consistently, ectopic ANXA8 expression, by affecting the RARA transcriptional function, induced HME1 DCIS-like amorphous acini expressing phosphorylated AKT (P-AKT). Apparently, a RA-RARA-ANXA8 feedback loop fosters a vicious circle of aberrant morphogenesis. Interestingly, a few miRNAs regulated by RA are predicted to target ANXA8 mRNA. These miRNAs are candidate components of the RA-RARA-ANXA8 mechanism, and their deregulation might induce DCIS initiation.

MiRNA-195-5p Functions as a Tumor Suppressor and a Predictive of Poor Prognosis in Non-small Cell Lung Cancer by Directly Targeting CIAPIN1

Accumulating evidence suggests that microRNAs (miRNAs) has been proven to be a critical regulator in the tumor progression, of which miR-195-5p was reported to function as tumor suppressor in prostate cancer and oral squamous cell carcinoma. However, studies on the clinical significance and biological function of miR-195-5p in non-small cell lung cancer (NSCLC) were still unavailable. Here, we reported that the expression of miR-195-5p was decreased in NSCLC tissues and cell lines. Downregulation of miR-195-5p was significantly associated with TNM stage, tumor size and lymph node metastasis. The Kaplan-Meier survival analysis demonstrated that the survival time of NSCLC patients with high expression of miR-195-5p was longer than those with low expression during the 5-year follow up period (p = 0.0410). COX regression analysis indicated that miR-195-5p expression was an independent prognostic indicator for the survival of NSCLC patients (HR = 2.45, 95% CI: 1.53–4.63; p = 0.007). Results of functional analyses revealed that overexpression of miR-195-5p in A549 cells inhibited cell proliferation, induced cell cycle G0/G1 phase arrest and apoptosis using MTT and flow cytometry analysis. Furthermore, bioinformatics and luciferase reporter assays demonstrated that cytokine-induced apoptosis inhibitor 1 (CIAPIN1), an anti-apoptotic molecule was a direct target of miR-195-5p in NSCLC cells. Meta-analysis based on Oncomine database showed CIAPIN1 was significantly up-regulated in human lung cancer tissues. Consistently, knockdown of CIAPIN1 phenocopied the inhibitory effects of miR-195-5p overexpression in NSCLC cell function. These findings suggest that miR-195-5p could be used as a potential prognostic predictor and tumor suppressor in NSCLC.

Noncoding RNAs Databases: Current Status and Trends

One of the most important resources for researchers of noncoding RNAs is the information available in public databases spread over the internet. However, the effective exploration of this data can represent a daunting task, given the large amount of databases available and the variety of stored data. This chapter describes a classification of databases based on information source, type of RNA, source organisms, data formats, and the mechanisms for information retrieval, detailing the relevance of each of these classifications and its usability by researchers. This classification is used to update a 2012 review, indexing now more than 229 public databases. This review will include an assessment of the new trends for ncRNA research based on the information that is being offered by the databases. Additionally, we will expand the previous analysis focusing on the usability and application of these databases in pathogen and disease research. Finally, this chapter will analyze how currently available database schemas can help the development of new and improved web resources.

Statistical principle-based approach for recognizing and normalizing microRNAs described in scientific literature

The detection of MicroRNA (miRNA) mentions in scientific literature facilitates researchers with the ability to find relevant and appropriate literature based on queries formulated using miRNA information. Considering most published biological studies elaborated on signal transduction pathways or genetic regulatory information in the form of figure captions, the extraction of miRNA from both the main content and figure captions of a manuscript is useful in aggregate analysis and comparative analysis of the studies published. In this study, we present a statistical principle-based miRNA recognition and normalization method to identify miRNAs and link them to the identifiers in the Rfam database. As one of the core components in the text mining pipeline of the database miRTarBase, the proposed method combined the advantages of previous works relying on pattern, dictionary and supervised learning and provided an integrated solution for the problem of miRNA identification. Furthermore, the knowledge learned from the training data was organized in a human-interpretable manner to understand the reason why the system considers a span of text as a miRNA mention, and the represented knowledge can be further complemented by domain experts. We studied the ambiguity level of miRNA nomenclature to connect the miRNA mentions to the Rfam database and evaluated the performance of our approach on two datasets: the BioCreative VI Bio-ID corpus and the miRNA interaction corpus by extending the later corpus with additional Rfam normalization information. Our study highlights and also proposes a better understanding of the challenges associated with miRNA identification and normalization in scientific literature and the research gap that needs to be further explored in prospective studies.

Meta-Path Methods for Prioritizing Candidate Disease miRNAs

MicroRNAs (miRNAs) play critical roles in regulating gene expression at post-transcriptional levels. Numerous experimental studies indicate that alterations and dysregulations in miRNAs are associated with important complex diseases, especially cancers. Predicting potential miRNA-disease association is beneficial not only to explore the pathogenesis of diseases, but also to understand biological processes. In this work, we propose two methods that can effectively predict potential miRNA-disease associations using our reconstructed miRNA and disease similarity networks, which are based on the latest experimental data. We reconstruct a miRNA functional similarity network using the following biological information: the miRNA family information, miRNA cluster information, experimentally valid miRNA-target association and disease-miRNA information. We also reconstruct a disease similarity network using disease functional information and disease semantic information. We present Katz with specific weights and Katz with machine learning, on the comprehensive heterogeneous network. These methods, which achieve corresponding AUC values of 0.897 and 0.919, exhibit performance superior to the existing methods. Comprehensive data networks and reasonable considerations guarantee the high performance of our methods. Contrary to several methods, which cannot work in such situations, the proposed methods also predict associations for diseases without any known related miRNAs. A web service for the download and prediction of relationships between diseases and miRNAs is available at http://lab.malab.cn/soft/MDPredict/.

RumimiR: a detailed microRNA database focused on ruminant species

The ever-increasing use of next-generation sequencing technologies to explore the genome has generated large quantities of data in recent years. Numerous publications have described several thousand sequences of microRNAs, all species included. A new database (RumimiR) has been created from the literature to provide a detailed description of microRNAs for three ruminant species: cattle, goats and sheep. To date, 2887, 2733 and 5095 unique microRNAs from bovine, caprine and ovine species, respectively, are included. In addition to the most recent reference genomic position and sequence of each microRNA, this database contains details about the animals, tissue origins and experimental conditions mentioned in the publications. Identity to human or mouse microRNA is also indicated. The RumimiR database allows data filtering by selecting microRNAs on the basis of defined criteria such as animal status or tissue origin. For ruminant studies, RumimiR supplements the widely used miRBase database, by using complementary criteria to allow browsing and filtering, and integrates all newly described published sequences. The principal goal of this database is to provide easy access to all the ruminant microRNAs described in the literature.

An Integrated Approach for Identification of Functionally Similar MicroRNAs in Colorectal Cancer

Colorectal cancer (CRC) is one of the most prevalent cancers around the globe. However, the molecular reasons for pathogenesis of CRC are still poorly understood. Recently, the role of microRNAs or miRNAs in the initiation and progression of CRC has been studied. MicroRNAs are small, endogenous noncoding RNAs found in plants, animals, and some viruses, which function in RNA silencing and posttranscriptional regulation of gene expression. Their role in CRC development is studied and they are found to be potential biomarkers in diagnosis and treatment of CRC. Therefore, identification of functionally similar CRC related miRNAs may help in the development of a prognostic tool. In this regard, this paper presents a new algorithm, called μSim. It is an integrative approach for identification of functionally similar miRNAs associated with CRC. It integrates judiciously the information of miRNA expression data and miRNA-miRNA functionally synergistic network data. The functional similarity is calculated based on both miRNA expression data and miRNA-miRNA functionally synergistic network data. The effectiveness of the proposed method in comparison to other related methods is shown on four CRC miRNA data sets. The proposed method selected more significant miRNAs related to CRC as compared to other related methods.

Dual Convolutional Neural Network Based Method for Predicting Disease-Related miRNAs

Identification of disease-related microRNAs (disease miRNAs) is helpful for understanding and exploring the etiology and pathogenesis of diseases. Most of recent methods predict disease miRNAs by integrating the similarities and associations of miRNAs and diseases. However, these methods fail to learn the deep features of the miRNA similarities, the disease similarities, and the miRNA–disease associations. We propose a dual convolutional neural network-based method for predicting candidate disease miRNAs and refer to it as CNNDMP. CNNDMP not only exploits the similarities and associations of miRNAs and diseases, but also captures the topology structures of the miRNA and disease networks. An embedding layer is constructed by combining the biological premises about the miRNA–disease associations. A new framework based on the dual convolutional neural network is presented for extracting the deep feature representation of associations. The left part of the framework focuses on integrating the original similarities and associations of miRNAs and diseases. The novel miRNA and disease similarities which contain the topology structures are obtained by random walks on the miRNA and disease networks, and their deep features are learned by the right part of the framework. CNNDMP achieves the superior prediction performance than several state-of-the-art methods during the cross-validation process. Case studies on breast cancer, colorectal cancer and lung cancer further demonstrate CNNDMP’s powerful ability of discovering potential disease miRNAs.

BioGraph: a web application and a graph database for querying and analyzing bioinformatics resources

BACKGROUND

Several online databases provide a large amount of biomedical data of different biological entities. These resources are typically stored in systems implementing their own data model, user interface and query language. On the other hand, in many bioinformatics scenarios there is often the need to use more than one resource. The availability of a single bioinformatics platform that integrates many biological resources and services is, for those reasons a fundamental issue.

DESCRIPTION

Here, we present BioGraph, a web application that allows to query, visualize and analyze biological data belonging to several online available sources. BioGraph is built upon our previously developed graph database called BioGraphDB, that integrates and stores heterogeneous biological resources and make them available by means of a common structure and a unique query language. BioGraph implements state-of-the-art technologies and provides pre-compiled bioinformatics scenarios, as well as the possibility to perform custom queries and obtaining an interactive and dynamic visualization of results.

CONCLUSION

We present a case study about functional analysis of microRNA in breast cancer in order to demonstrate the functionalities of the system. BioGraph is freely available at http://biograph.pa.icar.cnr.it . Source files are available on GitHub at https://github.com/IcarPA-TBlab/BioGraph.

Advanced microRNA-based cancer diagnostics using amplified time-gated FRET

MicroRNAs (miRNAs) play an important role in cellular functions and in the development and progression of cancer. Precise quantification of endogenous miRNAs from different clinical patient and control samples combined with a one-to-one comparison to standard technologies is a challenging but necessary endeavor that is largely neglected by many emerging fluorescence technologies. Here, we present a simple, precise, sensitive, and specific ratiometric assay for absolute quantification of miRNAs. Isothermally amplified time-gated Förster resonance energy transfer (TG-FRET) between Tb donors and dye acceptors resulted in miRNA assays with single-nucleotide variant specificity and detection limits down to 4.2 ± 0.5 attomoles. Quantification of miR-21 from human tissues and plasma samples revealed the relevance for breast and ovarian cancer diagnostics. Analysis of miR-132 and miR-146a from acute monocytic leukemia cells (THP-1) demonstrated the broad applicability to different miRNAs and other types of clinical samples. Direct comparison to the gold standard RT-qPCR showed advantages of amplified TG-FRET concerning precision and specificity when quantifying low concentrations of miRNAs as required for diagnostic applications. Our results demonstrate that a careful implementation of rolling circle amplification and TG-FRET into one straightforward nucleic acid detection method can significantly advance the possibilities of miRNA-based cancer diagnostics and research.

Gold-containing compound BDG-I inhibits the growth of A549 lung cancer cells through the deregulation of miRNA expression

Gold complex bis(diethyldithiocarbamato-gold(I)) bis(diphenylphosphino) methane (BDG-I) is cytotoxic toward different cancer cell lines. We compared the cytotoxic effect of BDG-I with that of cisplatin in the A549 lung cancer cell line. Additionally, we investigated the molecular mechanism underlying the toxic effect of BDG-I toward the A549 cell line and the identification of cancer-related miRNAs likely to be involved in killing the lung cancer cells. Further, X-ray crystallographic data of the compound were acquired. Using microarray, global miRNA expression profiling in BDG-I-treated A549 cells revealed 64 upregulated and 86 downregulated miRNAs, which targeted 4689 and 2498 genes, respectively. Biological network connectivity of the miRNAs was significantly higher for the upregulated miRNAs than for the downregulated miRNAs. Two of the 10 most upregulated miRNAs (hsa-mir-20a-5p and hsa-mir-15b-5p) were associated with lung cancer. AmiGo2 server and Panther pathway analyses indicated significant enrichment in transcription regulation of miRNA target genes that promote intrinsic kinase-mediated signaling, TGF-β, and GnRH signaling pathways, as well as oxidative stress responses. BDG-I crystal structure X-ray diffraction studies revealed gold–gold intramolecular interaction [Au…Au = 3.1198 (3) Å] for a single independent molecule, reported to be responsible for its activity against cancer. Our present study sheds light on the development of novel gold complex with favorable anti-cancer therapeutic functionality.

CMTCN: a web tool for investigating cancer-specific microRNA and transcription factor co-regulatory networks

Transcription factors (TFs) and microRNAs (miRNAs) are well-characterized trans-acting essential players in gene expression regulation. Growing evidence indicates that TFs and miRNAs can work cooperatively, and their dysregulation has been associated with many diseases including cancer. A unified picture of regulatory interactions of these regulators and their joint target genes would shed light on cancer studies. Although online resources developed to support probing of TF-gene and miRNA-gene interactions are available, online applications for miRNA-TF co-regulatory analysis, especially with a focus on cancers, are lacking. In light of this, we developed a web tool, namely CMTCN (freely available at http://www.cbportal.org/CMTCN), which constructs miRNA-TF co-regulatory networks and conducts comprehensive analyses within the context of particular cancer types. With its user-friendly provision of topological and functional analyses, CMTCN promises to be a reliable and indispensable web tool for biomedical studies.

Expression of the miR-150 tumor suppressor is restored by and synergizes with rapamycin in a human leukemia T-cell line

miR-150 functions as a tumor suppressor in malignancies of the lymphocyte lineage and its expression is significantly reduced in these cells. However, the mechanism of miR-150 repression is unknown and so are pharmacological interventions that can reverse it. Here, we report that reduced expression of miR-150 in human Jurkat T-cell acute lymphoblastic leukemia (T-ALL) cells is mediated by constitutive mTOR signaling, a common characteristic of T-ALL cell lines and clinical isolates. Activating mTOR signaling in non-malignant T cells also resulted in a significant miR-150 down-regulation. Conversely, treatment with a pharmacological mTOR inhibitor, rapamycin, increased miR-150 expression in a dose-dependent manner in Jurkat cells, as well as in other leukemia cells. Interestingly, ectopic over-expression of miR-150 acted in a feed-forward loop and further sensitized Jurkat cells to a rapamycin-induced cell cycle arrest by targeting a large network of cell cycle genes. These findings suggest that miR-150 is normally expressed in quiescent T lymphocytes to reinforce an anti-proliferative state, and that mTOR signaling promotes cell proliferation in part by inhibiting miR-150 expression. Restoration of the miR-150-dependent anti-proliferative loop constitutes a novel mechanism underlying the efficacy of rapamycin in a T-ALL cell line. Further investigation of this mechanism in clinical isolates of T-ALL and other hematopoietic malignancies could help better guide development of targeted therapies.

MicroRNA‑495 targets Notch1 to prohibit cell proliferation and invasion in oral squamous cell carcinoma

MicroRNAs (miRNAs) are associated with the initiation and progression of oral squamous cell carcinoma (OSCC) by regulating a variety of cancer‑associated behaviors. Fully understanding the regulatory mechanism of miRNAs in the pathogenesis of OSCC may provide novel promising approaches for the identification of prognostic biomarkers and therapeutic targets for this particular malignancy. In the present study, reverse transcription‑quantitative polymerase chain reaction analysis was performed to detect miRNA (miR)‑495 expression in OSCC tissues and cell lines. The effects of miR‑495 on the proliferation and invasion of OSCC cells were determined using Cell Counting Kit‑8 and Matrigel invasion assays, respectively. The mechanisms underlying the action of miR‑495 in OSCC cells were also investigated. Results from the present study revealed that miR‑495 expression was downregulated in OSCC tissues and cell lines compare with in adjacent normal tissues and human oral keratinocytes, respectively. Exogenous expression of miR‑495 restricted cell proliferation and invasion of OSCC cells in vitro. Notch1 was identified as a direct functional target of miR‑495 in OSCC. Furthermore, Notch1 knockdown exhibited inhibitory effects, similar to those induced by miR‑495 overexpression in OSCC cells. Restoration of Notch1 expression rescued the suppressive effects of miR‑495 on OSCC cell proliferation and invasion. These findings suggested an important role for miR‑495 in the regulation of OSCC cell growth and metastasis, at least partly by directly targeting Notch1. In addition, the findings of the present study revealed the potential of miR‑495 as a novel therapeutic target for the treatment of patients with OSCC.

Identification of key differentially expressed MicroRNAs in cancer patients through pan-cancer analysis

microRNAs (miRNAs) functioning in gene silencing have been associated with cancer progression. However, common abnormal miRNA expression patterns and their potential roles in cancer have not yet been evaluated. To account for individual differences between patients, we retrieved miRNA sequencing data for 575 patients with both tumor and adjacent non-tumorous tissues from 14 cancer types from The Cancer Genome Atlas (TCGA). We then performed differential expression analysis using DESeq2 and edgeR. Results showed that cancer types can be grouped based on the distribution of miRNAs with different expression patterns between tumor and non-tumor samples. We found 81 significantly differentially expressed miRNAs (SDEmiRNAs) in a single cancer. We also found 21 key SDEmiRNAs (nine over-expressed and 12 under-expressed) associated with at least eight cancers each and enriched in more than 60% of patients per cancer, including four newly identified SDEmiRNAs (hsa-mir-4746, hsa-mir-3648, hsa-mir-3687, and hsa-mir-1269a). The downstream effects of these 21 SDEmiRNAs on cellular function were evaluated through enrichment and pathway analysis of 7186 protein-coding gene targets mined from literature reports of differential expression of miRNAs in cancer. This analysis enables identification of SDEmiRNA functional similarity in cell proliferation control across a wide range of cancers, and assembly of common regulatory networks over cancer-related pathways. These findings were validated by construction of a regulatory network in the PI3K pathway. This study provides evidence for the value of further analysis of SDEmiRNAs as potential biomarkers and therapeutic targets for cancer diagnosis and treatment.

Inferring disease-associated microRNAs in heterogeneous networks with node attributes

Identification of disease-associated microRNAs (disease miRNAs) is an essential step towards discovering causal miRNAs and understanding disease pathogenesis. Two sources of information can be exploited for predicting disease miRNAs: one includes the connections between miRNAs, between diseases, and between miRNAs and diseases, and the other has the attributes of miRNA nodes. The former contains information of miRNA similarities, disease similarities, and miRNA-disease associations. The latter includes the information of the families and clusters that miRNAs belong to. Similar diseases are usually associated with miRNAs that have similar functions and common attributes. However, most of the existing methods for disease miRNA prediction focus only on the connections of miRNAs and diseases. It remains challenging to adequately integrate the connections and miRNA node attributes to identify more reliable candidate disease miRNAs. We propose a non-negative matrix factorization based method, FamCluRank, for predicting disease miRNAs in heterogeneous networks with node attributes. One of the novelties of FamCluRank is to fully utilize these two oversighted characteristics of miRNAs and focuses particularly on a deep integration of miRNA families and cluster attributes. In particular, the integration was achieved by three different means. We first constructed a miRNA-disease heterogeneous network with node attributes where the miRNA nodes have their family and cluster attributes. Second, miRNAs sharing more common families and clusters are more likely to be associated with the diseases that are also related to these families and clusters. On the basis of the biological premise, we constructed a novel prediction model of FamCluRank to deeply integrate the family and cluster attributes of miRNAs. Third, two similar diseases tend to be associated with more common miRNA families and clusters, and vice versa. Hence FamCluRank's prediction model is constructed by concerning not only the possible associations between miRNAs and diseases but also the possible disease-family and disease-cluster associations. Comparison with the state-of-the-art methods showed FamCluRank's superior performance not only on the well-characterized diseases but also on the new ones. Case studies on colorectal neoplasms, pancreatic neoplasms, lung neoplasms, and 32 new diseases demonstrated its ability for discovering potential disease miRNAs. FamCluRank is a potent prioritization tool for screening the reliable candidates for subsequent studies concerning their involvement in the pathogenesis of diseases. The web service of FamCluRank, the candidate disease miRNAs for 329 diseases, and the dataset used to develop FamCluRank are available at http://www.famclurank.top.

Three-Dimensional FRET Multiplexing for DNA Quantification with Attomolar Detection Limits

Photoluminescence (PL) multiplexing usually relies on spectral or temporal separation. A combination into higher-order multiplexing for biosensing is extremely challenging because the PL intensity is required for target quantification at very low concentrations and the interplay of color, lifetime, and intensity must be carefully adapted. Here, we demonstrate time-gated Förster resonance energy transfer (TG-FRET) from a long-lifetime Tb complex to Cy3.5 and Cy5.5 dyes for spectrotemporal multiplexing of four different DNA targets in the same sample by single-color excitation and two-color detection. We used rolling circle amplification (RCA) for high specificity and sensitivity and for placing Tb donors and dye acceptors at controlled distances within the amplified DNA concatemers. This precise distance tuning led to target-specific PL decays of the FRET pairs and simple, separation-free, and higher-order multiplexed quantification of DNA. The RCA-FRET DNA assay could distinguish very homologous target sequences and provided limits of detection down to 40 zeptomoles (300 aM).

Inferences of individual drug responses across diverse cancer types using a novel competing endogenous RNA network

Differences in individual drug responses are an obstacle to progression in cancer treatment, and predicting responses would help to plan treatment. The accumulation of cancer molecular profiling and drug response data provides opportunities and challenges to identify novel molecular signatures and mechanisms of tumor responsiveness to drugs. This study evaluated drug responses with a competing endogenous RNA (ceRNA) system that depended on competition between diverse RNA species. We identified drug response‐related ceRNA (DRCEs) by combining the sequence and expression data of long noncoding RNA (lncRNA), microRNA (miRNA), and messenger RNA (mRNA), and the survival data of cancer patients treated with drugs. We constructed a patient–drug two‐layer integrated network and used a linear weighting method to predict individual drug responses. DRCEs were found to be significantly enriched in known cancer and drug‐associated data resources, involved in biological processes known to mediate drug responses, and correlated to drug activity in cancer cell lines. The dysregulation of DRCE expression influenced drug response‐associated functions and pathways, suggesting DRCEs as potential therapeutic targets affecting drug responses. A further case study in breast invasive carcinoma (BRCA) found that DRCE expression was consistent with the drug response pattern and the aberrant expression of the two NEAT1‐related DRCEs may lead to poor response to tamoxifen therapy for patients with TP53 mutations. In summary, this study provides a framework for ceRNA‐based evaluation of clinical drug responses across multiple cancer types. Understanding the underlying molecular mechanisms of drug responses will allow improved response to chemotherapy and outcomes of cancer treatment.

Identification and functional annotation of metabolism-associated lncRNAs and their related protein-coding genes in gastric cancer

BACKGROUND

Long noncoding RNAs (lncRNAs) play important roles in carcinogenesis. However, the roles of metabolism-associated lncRNAs in cancers are still unclear.

METHODS

A microarray of metabolism-associated lncRNAs was used to detect their expression patterns between gastric cancer and paired nontumorous tissues. Its results and gastric cancer differential gene expression data from public databases were used to screen the metabolic pathway-associated lncRNAs. A metabolic network with microRNAs (miRNAs), lncRNAs, and protein-coding genes was further constructed. Finally, the expression of TOPORS antisense RNA 1 (TOPORS-AS1), a screened highly expressed lncRNA and its associated protein-coding gene, NADH: ubiquinone oxidoreductase subunit B6 (NDUFB6), were verified by reverse transcription polymerase chain reaction.

RESULTS

A total of eight upregulated and one downregulated lncRNAs and 25 upregulated and 20 downregulated protein-coding genes were found to be involved in metabolism in gastric cancer. Within the lncRNAs-miRNAs-mRNAs metabolic network, 78 miRNA-target links, 546 positive coexpression relationships, and 191 protein-protein interactions were found. The expression of TOPORS-AS1 and its associated gene, NDUFB6 in gastric cancer tissues was significantly lower than that in adjacent nontumor tissues. Moreover, NDUFB6 expression was associated with the invasion and distal metastasis of gastric cancer.

CONCLUSIONS

The metabolism-associated lncRNAs play important roles in the occurrence of gastric cancer.

The tumor suppressor role of microRNA-338-3p in renal cell carcinoma

Human renal cell carcinoma (RCC) is the most common type of kidney malignancy in adults accounting for 2-3% of all adult malignancies. In China, RCC accounts for ~0.5% of all cancer-associated mortalities, ranking 16th among all cancer types. For early-stage RCC, surgery is the recommended treatment. Molecularly targeted therapy is the preferred first-line treatment for clear-cell RCC. However, more potential targets are required. MicroRNA-338-3p (miR-338-3p) functions as a tumor suppressor in various cancers, but has not been studied in RCC. Accordingly, the present study investigated the role of miR-338-3p of RCC. It was demonstrated that miR-338-3p was present at low levels in RCC tissues. Also, overexpression of miR-338-3p inhibited cell proliferation and promoted cell apoptosis, and downregulation of miR-338-3p promoted cell proliferation. The 3' untranslated region of AKT serine/threonine kinase 3 was targeted by miR-338-3p. In conclusion, the data of the present study revealed the inhibitory function of miR-338-3p in RCC and suggested that miR-338-3p is novel therapeutic target for RCC, but further investigation is needed.

A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network

Motivation

Increasing studies have demonstrated that many human complex diseases are associated with not only microRNAs, but also long-noncoding RNAs (lncRNAs). LncRNAs and microRNA play significant roles in various biological processes. Therefore, developing effective computational models for predicting novel associations between diseases and lncRNA-miRNA pairs (LMPairs) will be beneficial to not only the understanding of disease mechanisms at lncRNA-miRNA level and the detection of disease biomarkers for disease diagnosis, treatment, prognosis, and prevention, but also the understanding of interactions between diseases and LMPairs at disease level.

Results

It is well known that genes with similar functions are often associated with similar diseases. In this article, a novel model named PADLMP for predicting associations between diseases and LMPairs is proposed. In this model, a Disease-LncRNA-miRNA (DLM) tripartite network was designed firstly by integrating the lncRNA-disease association network and miRNA-disease association network; then we constructed the disease-LMPairs bipartite association network based on the DLM network and lncRNA-miRNA association network; finally, we predicted potential associations between diseases and LMPairs based on the newly constructed disease-LMPair network. Simulation results show that PADLMP can achieve AUCs of 0.9318, 0.9090 ± 0.0264, and 0.8950 ± 0.0027 in the LOOCV, 2-fold, and 5-fold cross validation framework, respectively, which demonstrate the reliable prediction performance of PADLMP.

MicroRNA and transcriptome analysis in periocular Sebaceous Gland Carcinoma

Sebaceous gland carcinoma (SGC) is a rare, but life-threatening condition with a predilection for the periocular region. Eyelid SGC can be broadly categorised into two subtypes, namely either nodular or pagetoid with the latter being more aggressive and requiring radical excision to save life. We have identified key altered microRNAs (miRNA) involved in SGC shared by both subtypes, hsa-miR-34a-5p and hsa-miR-16-5p. However, their gene targets BCL2 and MYC were differentially expressed with both overexpressed in pagetoid but unchanged in nodular suggesting different modes of action of these two miRNAs on BCL/MYC expression. Hsa-miR-150p is nodular-specifically overexpressed, and its target ZEB1 was significantly downregulated in nodular SGC suggesting a tumour suppressor role. Invasive pagetoid subtype demonstrated specific overexpression of hsa-miR-205 and downregulation of hsa-miR-199a. Correspondingly, miRNA gene targets, EZH2 (by hsa-miR-205) and CD44 (by hsa-miR-199a), were both overexpressed in pagetoid SGC. CD44 has been identified as a potential cancer stem cell marker in head and neck squamous cell carcinoma and its overexpression in pagetoid cells represents a novel treatment target. Aberrant miRNAs and their gene targets have been identified in both SGC subtypes, paving the way for better molecular understanding of these tumours and identifying new treatment targets.

Predicting microRNA-disease associations using label propagation based on linear neighborhood similarity

Interactions between microRNAs (miRNAs) and diseases can yield important information for uncovering novel prognostic markers. Since experimental determination of disease-miRNA associations is time-consuming and costly, attention has been given to designing efficient and robust computational techniques for identifying undiscovered interactions. In this study, we present a label propagation model with linear neighborhood similarity, called LPLNS, to predict unobserved miRNA-disease associations. Additionally, a preprocessing step is performed to derive new interaction likelihood profiles that will contribute to the prediction since new miRNAs and diseases lack known associations. Our results demonstrate that the LPLNS model based on the known disease-miRNA associations could achieve impressive performance with an AUC of 0.9034. Furthermore, we observed that the LPLNS model based on new interaction likelihood profiles could improve the performance to an AUC of 0.9127. This was better than other comparable methods. In addition, case studies also demonstrated our method's outstanding performance for inferring undiscovered interactions between miRNAs and diseases, especially for novel diseases.

Global Similarity Method Based on a Two-tier Random Walk for the Prediction of microRNA-Disease Association

microRNAs (miRNAs) mutation and maladjustment are related to the occurrence and development of human diseases. Studies on disease-associated miRNA have contributed to disease diagnosis and treatment. To address the problems, such as low prediction accuracy and failure to predict the relationship between new miRNAs and diseases and so on, we design a Laplacian score of graphs to calculate the global similarity of networks and propose a Global Similarity method based on a Two-tier Random Walk for the prediction of miRNA-disease association (GSTRW) to reveal the correlation between miRNAs and diseases. This method is a global approach that can simultaneously predict the correlation between all diseases and miRNAs in the absence of negative samples. Experimental results reveal that this method is better than existing approaches in terms of overall prediction accuracy and ability to predict orphan diseases and novel miRNAs. A case study on GSTRW for breast cancer and conlon cancer is also conducted, and the majority of miRNA-disease association can be verified by our experiment. This study indicates that this method is feasible and effective.

Regulatory network analysis of LINC00472, a long noncoding RNA downregulated by DNA hypermethylation in colorectal cancer

Colorectal cancer (CRC), one of the common malignant cancers in the world, is caused by accumulated alterations of genetic and epigenetic factors over a long period of time. Along with that protein-coding genes being identified as oncogenes or tumor suppressors in CRC, a number of lncRNAs have also been found to be associated with CRC. Considering the important regulatory role of lncRNAs, the first goal of this study was to identify CRC-associated lncRNAs from a public database. One such lncRNA, LINC00472, was verified to be downregulated in CRC cell lines and cancer tissues compared with adjacent tissues. In addition, the down-regulation of LINC00472 seemed to be caused by DNA hypermethylation at its promoter region. Furthermore, the expression of LINC00472 and DNA methylation of promoter were significantly correlated with clinicopathological features. And DNA hypermethylation of LINC00472 may serve as a better diagnostic biomarker than its expression for CRC. Finally, we predicted the functions of LINC00472 and constructed a regulatory network and found LINC00472 may be involved in cell cycle and cell proliferation processes. Our results may provide a clue to further research into the function and regulatory mechanism of LINC00472 in CRC.

SRMDAP: SimRank and Density-Based Clustering Recommender Model for miRNA-Disease Association Prediction

Aberrant expression of microRNAs (miRNAs) can be applied for the diagnosis, prognosis, and treatment of human diseases. Identifying the relationship between miRNA and human disease is important to further investigate the pathogenesis of human diseases. However, experimental identification of the associations between diseases and miRNAs is time-consuming and expensive. Computational methods are efficient approaches to determine the potential associations between diseases and miRNAs. This paper presents a new computational method based on the SimRank and density-based clustering recommender model for miRNA-disease associations prediction (SRMDAP). The AUC of 0.8838 based on leave-one-out cross-validation and case studies suggested the excellent performance of the SRMDAP in predicting miRNA-disease associations. SRMDAP could also predict diseases without any related miRNAs and miRNAs without any related diseases.

Universal, colorimetric microRNA detection strategy based on target-catalyzed toehold-mediated strand displacement reaction

In this work, we developed a novel, label-free, and enzyme-free strategy for the colorimetric detection of microRNA (miRNA), which relies on a target-catalyzed toehold-mediated strand displacement (TMSD) reaction. The system employs a detection probe that specifically binds to the target miRNA and sequentially releases a catalyst strand (CS) intended to trigger the subsequent TMSD reaction. Thus, the presence of target miRNA releases the CS that mediates the formation of an active G-quadruplex DNAzyme which is initially caged and inactivated by a blocker strand. In addition, a fuel strand that is supplemented for the recycling of the CS promotes another TMSD reaction, consequently generating a large number of active G-quadruplex DNAzymes. As a result, a distinct colorimetric signal is produced by the ABTS oxidation promoted by the peroxidase mimicking activity of the released G-quadruplex DNAzymes. Based on this novel strategy, we successfully detected miR-141, a promising biomarker for human prostate cancer, with high selectivity. The diagnostic capability of this system was also demonstrated by reliably determining target miR-141 in human serum, showing its great potential towards real clinical applications. Importantly, the proposed approach is composed of separate target recognition and signal transduction modules. Thus, it could be extended to analyze different target miRNAs by simply redesigning the detection probe while keeping the same signal transduction module as a universal signal amplification unit, which was successfully demonstrated by analyzing another target miRNA, let-7d.

Dual inhibition of PCDH9 expression by miR-215-5p up-regulation in gliomas

The clinical prognosis of malignant gliomas is poor and PCDH9 down-regulation is strongly associated with its poor prognosis. But the mechanism of PCDH9 down-regulation is unknown. Abnormal miRNAs profiles regulate tumor phenotypes through inhibiting their target genes and miRNAs could inhibit target genes more efficiently by binding to both the promoter and 3′UTR of target genes. In this study, to search the dual inhibitory miRNAs which suppress PCDH9 expression in gliomas, we performed an integrative analysis of databases including miRDB, TargetScan, microPIR and miRCancer. We identified three candidate miRNAs which were predicted to bind both the promoter and 3′UTR of PCDH9 and up-regulated in gliomas. Then, we validated miR-215-5p up-regulation and PCDH9 down-regulation in glioma samples and demonstrated that miR-215-5p could inhibit the mRNA and protein levels of PCDH9 in glioma cell lines by targeting its promoter and 3′ UTR at the same time. Moreover, miR-215-5p could increase glioma cell proliferation, clone formation, in-vitro migration and reduce apoptosis via inhibiting PCDH9 expression. Our study provides evidence for a novel dual inhibition of PCDH9 by miR-215-5p in gliomas and suggests that miR-215-5p might be a therapeutic target for the treatment of gliomas.

Association of differential miRNA expression with hepatic vs. peritoneal metastatic spread in colorectal cancer

BACKGROUND

Though peritoneal carcinomatosis reflects a late stage of colorectal cancer (CRC), only few patients present with synchronous or metachronous liver metastases alongside their peritoneal carcinomatosis. It is hypothesized that this phenomenon may be causally linked to molecular characteristics of the primary CRC. This study used miRNA profiling of primary CRC tissue either metastasized to the liver, to the peritoneum or not metastasized at all thus to identify miRNAs potentially associated with defining the site of metastatic spread in CRC.

METHODS

Tissue of the primary tumor stemming from CRC patients diagnosed for either liver metastasis (LM; n = 10) or peritoneal carcinomatosis (PER; n = 10) was analyzed in this study. Advanced CRC cases without metastasis (M0; n = 3) were also included thus to select on those miRNAs most potentially associated with determining metastatic spread in general. miRNA profiling of 754 different miRNAs was performed in each group. MiRNAs being either differentially expressed comparing PER and LM or even triple differentially expressed (PER vs. LM vs. M0) were identified. Differentially expressed miRNAs were further validated by in silico and functional analysis.

RESULTS

Comparative analysis identified 41 miRNAs to be differentially expressed comparing primary tumors metastasized to the liver as opposed to those spread to the peritoneum. A set of 31 miRNAs was significantly induced in primary tumors that spread to the peritoneum (PER), while the remaining 10 miRNAs were found to be repressed. Out of these 41 miRNAs a number of 25 miRNAs was triple-differentially expressed (i.e. differentially expressed comparing LM vs. PER vs. M0). The latter underwent in silico analysis. Finally, we demonstrated that miR-31 down-regulated c-MET in DLD-1 colon cancer cells.

CONCLUSIONS

This study demonstrates that CRC primary tumors spread to the peritoneum vs. metastasized to the liver display significantly different miRNA profiles. Larger patient cohorts will be needed to validate whether determination of e.g. miR-31 may aid to predict the course of disease and whether this may help to create individualized follow up or treatment protocols. To determine whether certain miRNAs may be involved in regulating the metastatic potential of CRC, functional studies will be essential.

miRCancerdb: a database for correlation analysis between microRNA and gene expression in cancer

Objectives

microRNAs regulate expression of target genes by specifically binding to their transcripts, subsequently leading to translational inhibition or mRNA degradation. Gene regulation by microRNAs has been implicated in a wide range of physiological and pathological conditions. Here, we leverage the use of public-access data and the available genomic annotations to pre-calculate the correlation of the expression of a large number of microRNAs with gene at the mRNA and protein level in the context of cancers.

Results

Expression data of miRNAs, mRNAs and proteins in cancer patients from The Cancer Genome Atlas along with TargetScan miRNAs-target annotations were used to calculate the expression correlations between miRNAs and features (mRNAs/proteins) in a number of cancer studies. We then packed the output of this analysis into a database and made it available through an interactive web application. The miRCancerdb is an easy-to-use database to investigate the microRNAs-dependent regulation of target genes involved in development of cancer.

The miR-17/92 cluster is involved in the molecular etiology of the SCLL syndrome driven by the BCR-FGFR1 chimeric kinase

MicroRNAs (miRNAs) have pathogenic roles in the development of a variety of leukemias. Here we identify miRNAs that have important roles in the development of B lymphomas resulting from the expression of the chimeric BCR-FGFR1 kinase. The miR-17/92 cluster was particularly implicated and forced expression resulted in increased cell proliferation, while inhibiting its function using miRNA sponges reduced cell growth and induced apoptosis. Cells treated with the potent BGJ389 FGFR1 inhibitor led to miR-17/92 downregulation, suggesting regulation by FGFR1. Transient luciferase reporter assays and qRT-PCR detection of endogenous miR-17/92 expression in stable transduced cell lines demonstrated that BCR-FGFR1 can regulate miR-17/92 expression. This positive association of miR-17/92 with BCR-FGFR1 was also confirmed in primary mouse SCLL tissues and primary human CLL samples. miR-17/92 promotes cell proliferation and survival by targeting CDKN1A and PTEN in B-lymphoma cell lines and primary tumors. An inverse correlation in expression levels was seen between miR-17/92 and both CDKN1A and PTEN in two cohorts of CLL patients. Finally, in vivo engraftment studies demonstrated that manipulation of miR-17/92 was sufficient to affect BCR-FGFR1-driven leukemogenesis. Overall, our results define miR-17/92 as a downstream effector of FGFR1 in BCR-FGFR1-driven B-cell lymphoblastic leukemia.

MNDR v2.0: an updated resource of ncRNA-disease associations in mammals

Accumulating evidence suggests that diverse non-coding RNAs (ncRNAs) are involved in the progression of a wide variety of diseases. In recent years, abundant ncRNA-disease associations have been found and predicted according to experiments and prediction algorithms. Diverse ncRNA-disease associations are scattered over many resources and mammals, whereas a global view of diverse ncRNA-disease associations is not available for any mammals. Hence, we have updated the MNDR v2.0 database (www.rna-society.org/mndr/) by integrating experimental and prediction associations from manual literature curation and other resources under one common framework. The new developments in MNDR v2.0 include (i) an over 220-fold increase in ncRNA-disease associations enhancement compared with the previous version (including lncRNA, miRNA, piRNA, snoRNA and more than 1400 diseases); (ii) integrating experimental and prediction evidence from 14 resources and prediction algorithms for each ncRNA-disease association; (iii) mapping disease names to the Disease Ontology and Medical Subject Headings (MeSH); (iv) providing a confidence score for each ncRNA-disease association and (v) an increase of species coverage to six mammals. Finally, MNDR v2.0 intends to provide the scientific community with a resource for efficient browsing and extraction of the associations between diverse ncRNAs and diseases, including >260 000 ncRNA-disease associations.

DEXTER: Disease-Expression Relation Extraction from Text

Gene expression levels affect biological processes and play a key role in many diseases. Characterizing expression profiles is useful for clinical research, and diagnostics and prognostics of diseases. There are currently several high-quality databases that capture gene expression information, obtained mostly from large-scale studies, such as microarray and next-generation sequencing technologies, in the context of disease. The scientific literature is another rich source of information on gene expression-disease relationships that not only have been captured from large-scale studies but have also been observed in thousands of small-scale studies. Expression information obtained from literature through manual curation can extend expression databases. While many of the existing databases include information from literature, they are limited by the time-consuming nature of manual curation and have difficulty keeping up with the explosion of publications in the biomedical field. In this work, we describe an automated text-mining tool, Disease-Expression Relation Extraction from Text (DEXTER) to extract information from literature on gene and microRNA expression in the context of disease. One of the motivations in developing DEXTER was to extend the BioXpress database, a cancer-focused gene expression database that includes data derived from large-scale experiments and manual curation of publications. The literature-based portion of BioXpress lags behind significantly compared to expression information obtained from large-scale studies and can benefit from our text-mined results. We have conducted two different evaluations to measure the accuracy of our text-mining tool and achieved average F-scores of 88.51 and 81.81% for the two evaluations, respectively. Also, to demonstrate the ability to extract rich expression information in different disease-related scenarios, we used DEXTER to extract information on differential expression information for 2024 genes in lung cancer, 115 glycosyltransferases in 62 cancers and 826 microRNA in 171 cancers. All extractions using DEXTER are integrated in the literature-based portion of BioXpress.Database URL: http://biotm.cis.udel.edu/DEXTER.

Prediction of microRNA–disease associations with a Kronecker kernel matrix dimension reduction model

A Kronecker kernel matrix dimension reduction model for predicting novel miRNA–disease associations.

Oral cancer databases: A comprehensive review

Cancer database is a systematic collection and analysis of information on various human cancers at genomic and molecular level that can be utilized to understand various steps in carcinogenesis and for therapeutic advancement in cancer field. Oral cancer is one of the leading causes of morbidity and mortality all over the world. The current research efforts in this field are aimed at cancer etiology and therapy. Advanced genomic technologies including microarrays, proteomics, transcrpitomics, and gene sequencing development have culminated in generation of extensive data and subjection of several genes and microRNAs that are distinctively expressed and this information is stored in the form of various databases. Extensive data from various resources have brought the need for collaboration and data sharing to make effective use of this new knowledge. The current review provides comprehensive information of various publicly accessible databases that contain information pertinent to oral squamous cell carcinoma (OSCC) and databases designed exclusively for OSCC. The databases discussed in this paper are Protein-Coding Gene Databases and microRNA Databases. This paper also describes gene overlap in various databases, which will help researchers to reduce redundancy and focus on only those genes, which are common to more than one databases. We hope such introduction will promote awareness and facilitate the usage of these resources in the cancer research community, and researchers can explore the molecular mechanisms involved in the development of cancer, which can help in subsequent crafting of therapeutic strategies.

Comparative assessment of differential network analysis methods

Differential network analysis (DiNA) denotes a recent class of network-based Bioinformatics algorithms which focus on the differences in network topologies between two states of a cell, such as healthy and disease, to identify key players in the discriminating biological processes. In contrast to conventional differential analysis, DiNA identifies changes in the interplay between molecules, rather than changes in single molecules. This ability is especially important in cases where effectors are changed, e.g. mutated, but their expression is not. A number of different DiNA approaches have been proposed, yet a comparative assessment of their performance in different settings is still lacking. In this paper, we evaluate 10 different DiNA algorithms regarding their ability to recover genetic key players from transcriptome data. We construct high-quality regulatory networks and enrich them with co-expression data from four different types of cancer. Next, we assess the results of applying DiNA algorithms on these data sets using a gold standard list (GSL). We find that local DiNA algorithms are generally superior to global algorithms, and that all DiNA algorithms outperform conventional differential expression analysis. We also assess the ability of DiNA methods to exploit additional knowledge in the underlying cellular networks. To this end, we enrich the cancer-type specific networks with known regulatory miRNAs and compare the algorithms performance in networks with and without miRNA. We find that including miRNAs consistently and considerably improves the performance of almost all tested algorithms. Our results underline the advantages of comprehensive cell models for the analysis of -omics data.