Analysis of Circulating miRNA Profile in Plasma Samples of Glioblastoma Patients

(1) Background: Glioblastoma multiforme (GBM) is among the most aggressive cancers with a poor prognosis. Treatment options are limited, clinicians lack efficient prognostic and predictive markers. Circulating miRNAs—besides being important regulators of cancer development—may have potential as diagnostic biomarkers of GBM. (2) Methods: In this study, profiling of 798 human miRNAs was performed on blood plasma samples from 6 healthy individuals and 6 patients with GBM, using a NanoString nCounter Analysis System. To validate our results, five miRNAs (hsa-miR-433-3p, hsa-miR-362-3p, hsa-miR-195-5p, hsa-miR-133a-3p, and hsa-miR-29a-3p) were randomly chosen for RT-qPCR detection. (3) Results: In all, 53 miRNAs were significantly differentially expressed in plasma samples of GBM patients when data were filtered for FC 1 and FDR 0.1. Target genes of the top 39 differentially expressed miRNAs were identified, and we carried out functional annotation and pathway enrichment analysis of target genes via GO and KEGG-based tools. General and cortex-specific protein–protein interaction networks were constructed from the target genes of top miRNAs to assess their functional connections. (4) Conclusions: We demonstrated that plasma microRNA profiles are promising diagnostic and prognostic molecular biomarkers that may find an actual application in the clinical practice of GBM, although more studies are needed to validate our results.

Simultaneous learning of individual microRNA-gene interactions and regulatory comodules

BACKGROUND

MicroRNAs (miRNAs) function in post-transcriptional regulation of gene expression by binding to target messenger RNAs (mRNAs). Because of the key part that miRNAs play, understanding the correct regulatory role of miRNAs in diverse patho-physiological conditions is of great interest. Although it is known that miRNAs act combinatorially to regulate genes, precise identification of miRNA-gene interactions and their specific functional roles in regulatory comodules remains a challenge. We developed THEIA, an effective method for simultaneously predicting miRNA-gene interactions and regulatory comodules, which group functionally related miRNAs and genes via non-negative matrix factorization (NMF).

RESULTS

We apply THEIA to RNA sequencing data from breast invasive carcinoma samples and demonstrate its effectiveness in discovering biologically significant regulatory comodules that are significantly enriched in spatial miRNA clusters, biological pathways, and various cancers.

CONCLUSIONS

THEIA is a theoretically rigorous optimization algorithm that simultaneously predicts the strength and direction (i.e., up-regulation or down-regulation) of the effect of modules of miRNAs on a gene. We posit that if THEIA is capable of recovering known clusters of genes and miRNA, then the clusters found by our method not previously identified by literature are also likely to have biological significance. We believe that these novel regulatory comodules found by our method will be a springboard for further research into the specific functional roles of these new functional ensembles of miRNAs and genes,especially those related to diseases like breast cancer.

SMALF: miRNA-disease associations prediction based on stacked autoencoder and XGBoost

BACKGROUND

Identifying miRNA and disease associations helps us understand disease mechanisms of action from the molecular level. However, it is usually blind, time-consuming, and small-scale based on biological experiments. Hence, developing computational methods to predict unknown miRNA and disease associations is becoming increasingly important.

RESULTS

In this work, we develop a computational framework called SMALF to predict unknown miRNA-disease associations. SMALF first utilizes a stacked autoencoder to learn miRNA latent feature and disease latent feature from the original miRNA-disease association matrix. Then, SMALF obtains the feature vector of representing miRNA-disease by integrating miRNA functional similarity, miRNA latent feature, disease semantic similarity, and disease latent feature. Finally, XGBoost is utilized to predict unknown miRNA-disease associations. We implement cross-validation experiments. Compared with other state-of-the-art methods, SAMLF achieved the best AUC value. We also construct three case studies, including hepatocellular carcinoma, colon cancer, and breast cancer. The results show that 10, 10, and 9 out of the top ten predicted miRNAs are verified in MNDR v3.0 or miRCancer, respectively.

CONCLUSION

The comprehensive experimental results demonstrate that SMALF is effective in identifying unknown miRNA-disease associations.

PMDFI: Predicting miRNA-Disease Associations Based on High-Order Feature Interaction

MicroRNAs (miRNAs) are non-coding RNA molecules that make a significant contribution to diverse biological processes, and their mutations and dysregulations are closely related to the occurrence, development, and treatment of human diseases. Therefore, identification of potential miRNA–disease associations contributes to elucidating the pathogenesis of tumorigenesis and seeking the effective treatment method for diseases. Due to the expensive cost of traditional biological experiments of determining associations between miRNAs and diseases, increasing numbers of effective computational models are being used to compensate for this limitation. In this study, we propose a novel computational method, named PMDFI, which is an ensemble learning method to predict potential miRNA–disease associations based on high-order feature interactions. We initially use a stacked autoencoder to extract meaningful high-order features from the original similarity matrix, and then perform feature interactive learning, and finally utilize an integrated model composed of multiple random forests and logistic regression to make comprehensive predictions. The experimental results illustrate that PMDFI achieves excellent performance in predicting potential miRNA–disease associations, with the average area under the ROC curve scores of 0.9404 and 0.9415 in 5-fold and 10-fold cross-validation, respectively.

A Fast Linear Neighborhood Similarity-Based Network Link Inference Method to Predict MicroRNA-Disease Associations

Increasing evidences revealed that microRNAs (miRNAs) play critical roles in important biological processes. The identification of disease-related miRNAs is critical to understand the molecular mechanisms of human diseases. Most existing computational methods require diverse features to predict miRNA-disease associations. However, diverse features are not available for all miRNAs or diseases. In addition, most methods can't predict links for miRNAs or diseases without association information. In this paper, we propose a fast linear neighborhood similarity-based network link inference method, named FLNSNLI, to predict miRNA-disease associations. First, known miRNA-disease associations are formulated as a bipartite network, and miRNAs (or diseases) are expressed as association profiles. Second, miRNA-miRNA similarity and disease-disease similarity are calculated by fast linear neighborhood similarity measure and association profiles. Third, the label propagation algorithm is respectively implemented on two sides to score candidate miRNA-disease associations. Finally, FLNSNLI adopts the weighted average strategy and makes predictions. Moreover, we develop a link complementing approach, and extend FLNSNLI to predict links for miRNAs (or diseases) without known associations. In computational experiments, FLNSNLI produces high-accuracy performances, and outperforms other state-of-the-art methods. More importantly, FLNSNLI requires less information but performs well. Case studies on three popular diseases show that FLNSNLI is useful for the microRNA-disease association prediction.

The Implications of ncRNAs in the Development of Human Diseases

The mammalian genome comprehends a small minority of genes that encode for proteins (barely 2% of the total genome in humans) and an immense majority of genes that are transcribed into RNA but not encoded for proteins (ncRNAs). These non-coding genes are intimately related to the expression regulation of protein-coding genes. The ncRNAs subtypes differ in their size, so there are long non-coding genes (lncRNAs) and other smaller ones, like microRNAs (miRNAs) and piwi-interacting RNAs (piRNAs). Due to their important role in the maintenance of cellular functioning, any deregulation of the expression profiles of these ncRNAs can dissemble in the development of different types of diseases. Among them, we can highlight some of high incidence in the population, such as cancer, neurodegenerative, or cardiovascular disorders. In addition, thanks to the enormous advances in the field of medical genomics, these same ncRNAs are starting to be used as possible drugs, approved by the FDA, as an effective treatment for diseases.

Prediction of miRNA-Disease Association Using Deep Collaborative Filtering

The existing studies have shown that miRNAs are related to human diseases by regulating gene expression. Identifying miRNA association with diseases will contribute to diagnosis, treatment, and prognosis of diseases. The experimental identification of miRNA-disease associations is time-consuming, tremendously expensive, and of high-failure rate. In recent years, many researchers predicted potential associations between miRNAs and diseases by computational approaches. In this paper, we proposed a novel method using deep collaborative filtering called DCFMDA to predict miRNA-disease potential associations. To improve prediction performance, we integrated neural network matrix factorization (NNMF) and multilayer perceptron (MLP) in a deep collaborative filtering framework. We utilized known miRNA-disease associations to capture miRNA-disease interaction features by NNMF and utilized miRNA similarity and disease similarity to extract miRNA feature vector and disease feature vector, respectively, by MLP. At last, we merged outputs of the NNMF and MLP to obtain the prediction matrix. The experimental results indicate that compared with other existing computational methods, our method can achieve the AUC of 0.9466 based on 10-fold cross-validation. In addition, case studies show that the DCFMDA can effectively predict candidate miRNAs for breast neoplasms, colon neoplasms, kidney neoplasms, leukemia, and lymphoma.

Transcriptomic analysis reveals tumor stage- or grade-dependent expression of miRNAs in serous ovarian cancer

Ovarian cancer (OC) is the most lethal gynecological malignancy and cellular mechanisms regulating OC progression are not completely understood. miRNAs are involved in many signaling pathways which are critical for the progression of malignant tumors, including OC. In the present study, we aim to identify miRNAs whose expression change in a tumor stage- and/or grade-dependent manner in serous OC. Computational analysis was performed in R using The Cancer Genome Atlas miRNA dataset. Kaplan-Meier plots were constructed to compare the survival of patients with low and high expressions of identified miRNAs. We found that 91 and 90 miRNAs out of 799 are differentially expressed in terms of tumor stage and grade, respectively. miR-152, miR-375 and miR-204 were top three hits in terms of tumor stage; and similarly, miR-125b, miR-768-5p and -3p in terms of tumor grade. Among top 15 miRNAs whose expression most significantly changed between tumor stages, 66.7% were upregulated in late stage. However, 53.3% of top 15 miRNAs identified in terms of tumor grade were upregulated in high grade. 11 miRNAs are differentially expressed in terms of both tumor stage and grade. Expression changes of some of the top miRNAs were found to be associated with shorter survival in serous OC. Text mining analysis showed that most of these miRNAs have not been previously studied in the context of OC. Mechanistic studies of these miRNAs in OC progression, differentiation and metastasis will be of high importance to develop novel strategies for the treatment of serous ovarian cancer.

Machine learning approaches for predicting biomolecule-disease associations

Biomolecules, such as microRNAs, circRNAs, lncRNAs and genes, are functionally interdependent in human cells, and all play critical roles in diverse fundamental and vital biological processes. The dysregulations of such biomolecules can cause diseases. Identifying the associations between biomolecules and diseases can uncover the mechanisms of complex diseases, which is conducive to their diagnosis, treatment, prognosis and prevention. Due to the time consumption and cost of biologically experimental methods, many computational association prediction methods have been proposed in the past few years. In this study, we provide a comprehensive review of machine learning-based approaches for predicting disease-biomolecule associations with multi-view data sources. Firstly, we introduce some databases and general strategies for integrating multi-view data sources in the prediction models. Then we discuss several feature representation methods for machine learning-based prediction models. Thirdly, we comprehensively review machine learning-based prediction approaches in three categories: basic machine learning methods, matrix completion-based methods and deep learning-based methods, while discussing their advantages and disadvantages. Finally, we provide some perspectives for further improving biomolecule-disease prediction methods.

A Cluster-Based Approach for Identifying Prognostic microRNA Signatures in Digestive System Cancers

Cancer remains the second leading cause of death all over the world. Aberrant expression of miRNA has shown diagnostic and prognostic value in many kinds of cancer. This study aims to provide a novel strategy to identify reliable miRNA signatures and develop improved cancer prognostic models from reported cancer-associated miRNAs. We proposed a new cluster-based approach to identify distinct cluster(s) of cancers and corresponding miRNAs. Further, with samples from TCGA and other independent studies, we identified prognostic markers and validated their prognostic value in prediction models. We also performed KEGG pathway analysis to investigate the functions of miRNAs associated with the cancer cluster of interest. A distinct cluster with 28 cancers and 146 associated miRNAs was identified. This cluster was enriched by digestive system cancers. Further, we screened out 8 prognostic miRNA signatures for STAD, 5 for READ, 18 for PAAD, 24 for LIHC, 12 for ESCA and 18 for COAD. These identified miRNA signatures demonstrated strong abilities in discriminating the overall survival time between high-risk group and low-risk group (p-value < 0.05) in both TCGA training and test datasets, as well as four independent Gene Expression Omnibus (GEO) validation datasets. We also demonstrated that these cluster-based miRNA signatures are superior to signatures identified in single cancers for prognosis. Our study identified significant miRNA signatures with improved prognosis accuracy in digestive system cancers. It also provides a novel method/strategy for cancer prognostic marker selection and offers valuable methodological directions to similar research topics.

CRMarker: A manually curated comprehensive resource of cancer RNA markers

Biomolecular markers have extremely important value for cancer research and treatment. However, as far as we know, there are still no searchable and predictable resources focusing on multiple classes of RNA molecular markers in cancers. Herein, we developed CRMarker, a manually curated comprehensive repository of cancer RNA markers. In the current release, CRMarker v1.1 consists of 5489 "known" cancer RNA markers based on 8756 valid publications in PubMed, including 2878 mRNAs (genes), 1314 miRNAs, 1097 lncRNAs and 200 circRNAs, and involving two functional molecules (diagnosis and prognosis), 21 organisms and 154 cancers. The search results provided by the database are comprehensive, including 11 items such as RNA molecule expression and risk level, type of tissue or sample, cancer subtype, reference type, etc. Moreover, CRMarker also provides more than 18,000 potential cancer RNA markers, which are predicted based on "guilt-by-association" analysis of the above-mentioned "known" RNA markers and three molecular interaction networks, and survival analysis of 18 gene expression data sets with survival data. CRMarker v1.1 has a friendly interface and is freely available online at http://crmarker.hnnu.edu.cn/. We aim to build a comprehensive platform that is convenient for cancer researchers and clinicians to inquire and retrieve.

Ultrasensitive isothermal method to detect microRNA based on target-induced chain amplification reaction

We herein describe an ultrasensitive isothermal method to detect microRNA (miRNA) by utilizing target-induced chain amplification reaction (CAR). The hairpin probe (HP) employed in this strategy is designed to be opened upon binding to target miRNA. The exponential amplification reaction (EXPAR) template (ET) then binds to the exposed stem of HP and DNA polymerase (DP) promotes the extension reactions for both HP and ET, consequently producing intermediate double-stranded DNA product (IP) and concomitantly recycling target miRNA to open another intact HP. The IPs would produce a large number of target-mimicking probes (TMPs) and trigger probes (TPs) through the continuously repeated nicking and extension reactions at the two separated nicking sites within the IP. TMP triggers another CAR cycle by binding to intact HP as target miRNA did while TP promotes conventional EXPAR by independently binding to free ET. As a consequence of these interconnected reaction systems, a large number of final double-stranded DNA products (FPs) are produced, which can be monitored by measuring the fluorescent signal produced from duplex-specific fluorescent dye. Based on this unique design principle, the target miRNA was successfully determined down to even a single copy with high selectivity against non-specific miRNAs. The practical applicability of this method was also verified by reliably detecting target miRNA included in the total RNA extracted from the human cancer cell.

miR-4319 inhibited retinoblastoma cells proliferation, migration, invasion and EMT progress via suppressing CD147 mediated MMPs expression

Tumor migration is the critical step that lead to the migration in retinoblastoma (RB), in which microRNAs (miRNAs) play important roles. This study aimed to investigate the role of microRNA-4319 (miR-4319) in the development of retinoblastoma by identifying its targets, as well as its underlying regulatory mechanisms. Our data shown that miR-4319 was downregulated in RB tissues and RB cell lines. Enhanced miR-4319 suppressed cell proliferation, migration, invasion and EMT progress, promoted cell apoptosis in SO-RB50 and RB-Y79 cells. Of note, extracellular matrix metalloproteinase inducer (EMMPRI/CD147) was identified as a direct target gene for miR-4319. MMPs were regulated by CD147 and participated in the miR-4319 regulatory network in SO-RB50 cells. In addition, overexpression of CD147 abrogated the inhibitory effect of miR-4319 on RB cells. In summary, miR-4319 overexpression suppressed cell proliferation, migration and invasion may through suppressing the CD147 mediated MMPs expression, suggesting that miR-4319 may serve as a potential diagnostic biomarker and treatment target for RB.

Identification of Potential Prognostic Competing Triplets in High-Grade Serous Ovarian Cancer

Increasing lncRNA-associated competing triplets were found to play important roles in cancers. With the accumulation of high-throughput sequencing data in public databases, the size of available tumor samples is becoming larger and larger, which introduces new challenges to identify competing triplets. Here, we developed a novel method, called LncMiM, to detect the lncRNA–miRNA–mRNA competing triplets in ovarian cancer with tumor samples from the TCGA database. In LncMiM, non-linear correlation analysis is used to cover the problem of weak correlations between miRNA–target pairs, which is mainly due to the difference in the magnitude of the expression level. In addition, besides the miRNA, the impact of lncRNA and mRNA on the interactions in triplets is also considered to improve the identification sensitivity of LncMiM without reducing its accuracy. By using LncMiM, a total of 847 lncRNA-associated competing triplets were found. All the competing triplets form a miRNA–lncRNA pair centered regulatory network, in which ZFAS1, SNHG29, GAS5, AC112491.1, and AC099850.4 are the top five lncRNAs with most connections. The results of biological process and KEGG pathway enrichment analysis indicates that the competing triplets are mainly associated with cell division, cell proliferation, cell cycle, oocyte meiosis, oxidative phosphorylation, ribosome, and p53 signaling pathway. Through survival analysis, 107 potential prognostic biomarkers are found in the competing triplets, including FGD5-AS1, HCP5, HMGN4, TACC3, and so on. LncMiM is available at https://github.com/xiaofengsong/LncMiM.

MNDR v3.0: mammal ncRNA-disease repository with increased coverage and annotation

Many studies have indicated that non-coding RNA (ncRNA) dysfunction is closely related to numerous diseases. Recently, accumulated ncRNA-disease associations have made related databases insufficient to meet the demands of biomedical research. The constant updating of ncRNA-disease resources has become essential. Here, we have updated the mammal ncRNA-disease repository (MNDR, http://www.rna-society.org/mndr/) to version 3.0, containing more than one million entries, four-fold increment in data compared to the previous version. Experimental and predicted circRNA-disease associations have been integrated, increasing the number of categories of ncRNAs to five, and the number of mammalian species to 11. Moreover, ncRNA-disease related drug annotations and associations, as well as ncRNA subcellular localizations and interactions, were added. In addition, three ncRNA-disease (miRNA/lncRNA/circRNA) prediction tools were provided, and the website was also optimized, making it more practical and user-friendly. In summary, MNDR v3.0 will be a valuable resource for the investigation of disease mechanisms and clinical treatment strategies.

MiRNA-Mediated Subpathway Identification and Network Module Analysis to Reveal Prognostic Markers in Human Pancreatic Cancer

Background

Pancreatic cancer (PC) remains one of the most lethal cancers. In contrast to the steady increase in survival for most cancers, the 5-year survival remains low for PC patients.

Methods

We describe a new pipeline that can be used to identify prognostic molecular biomarkers by identifying miRNA-mediated subpathways associated with PC. These modules were then further extracted from a comprehensive miRNA-gene network (CMGN). An exhaustive survival analysis was performed to estimate the prognostic value of these modules.

Results

We identified 105 miRNA-mediated subpathways associated with PC. Two subpathways within the MAPK signaling and cell cycle pathways were found to be highly related to PC. Of the miRNA-mRNA modules extracted from CMGN, six modules showed good prognostic performance in both independent validated datasets.

Conclusions

Our study provides novel insight into the mechanisms of PC. We inferred that six miRNA-mRNA modules could serve as potential prognostic molecular biomarkers in PC based on the pipeline we proposed.

A comprehensive survey on computational methods of non-coding RNA and disease association prediction

The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.

Identification of a Functional ceRNA Network to Explore Potential Biomarkers for Hepatocellular Carcinoma

PURPOSE

To establish a novel circRNA-miRNA-mRNA network associated with the poor prognosis of hepatocellular carcinoma (HCC).

MATERIALS AND METHODS

Quantitative real-time PCR was used to verify the differentially expressed circRNA. Moreover, the competing endogenous RNA networks were established using bioinformatics methods. Meanwhile, the prognostic value and potential mechanism of ceRNA network in hepatocellular carcinoma (HCC) were analyzed.

RESULTS

This work found that circ_0130911 was highly expressed in HCC tissues and early recurring HCC. Further, we effectively constructed a ceRNA network. The ceRNA network regulated by circ_0130911 might influence the prognosis of HCC by regulating cell cycle-related pathways.

CONCLUSION

The ceRNA network proposed here can be used as a novel biomarker for the prognosis of HCC, thereby providing new insights for the targeted therapy of HCC.

Tribbles-1 Expression and Its Function to Control Inflammatory Cytokines, Including Interleukin-8 Levels are Regulated by miRNAs in Macrophages and Prostate Cancer Cells

The pseudokinase TRIB1 controls cell function in a range of contexts, by regulating MAP kinase activation and mediating protein degradation via the COP1 ubiquitin ligase. TRIB1 regulates polarization of macrophages and dysregulated Trib1 expression in murine models has been shown to alter atherosclerosis burden and adipose homeostasis. Recently, TRIB1 has also been implicated in the pathogenesis of prostate cancer, where it is often overexpressed, even in the absence of genetic amplification. Well described TRIB1 effectors include MAP kinases and C/EBP transcription factors, both in immune cells and in carcinogenesis. However, the mechanisms that regulate TRIB1 itself remain elusive. Here, we show that the long and conserved 3’untranslated region (3’UTR) of TRIB1 is targeted by miRNAs in macrophage and prostate cancer models. By using a systematic in silico analysis, we identified multiple “high confidence” miRNAs potentially binding to the 3’UTR of TRIB1 and report that miR-101-3p and miR-132-3p are direct regulators of TRIB1 expression and function. Binding of miR-101-3p and miR-132-3p to the 3’UTR of TRIB1 mRNA leads to an increased transcription and secretion of interleukin-8. Our data demonstrate that modulation of TRIB1 by miRNAs alters the inflammatory profile of both human macrophages and prostate cancer cells.

Improving biomedical named entity recognition with syntactic information

Background

Biomedical named entity recognition (BioNER) is an important task for understanding biomedical texts, which can be challenging due to the lack of large-scale labeled training data and domain knowledge. To address the challenge, in addition to using powerful encoders (e.g., biLSTM and BioBERT), one possible method is to leverage extra knowledge that is easy to obtain. Previous studies have shown that auto-processed syntactic information can be a useful resource to improve model performance, but their approaches are limited to directly concatenating the embeddings of syntactic information to the input word embeddings. Therefore, such syntactic information is leveraged in an inflexible way, where inaccurate one may hurt model performance.

Results

In this paper, we propose BioKMNER, a BioNER model for biomedical texts with key-value memory networks (KVMN) to incorporate auto-processed syntactic information. We evaluate BioKMNER on six English biomedical datasets, where our method with KVMN outperforms the strong baseline method, namely, BioBERT, from the previous study on all datasets. Specifically, the F1 scores of our best performing model are 85.29% on BC2GM, 77.83% on JNLPBA, 94.22% on BC5CDR-chemical, 90.08% on NCBI-disease, 89.24% on LINNAEUS, and 76.33% on Species-800, where state-of-the-art performance is obtained on four of them (i.e., BC2GM, BC5CDR-chemical, NCBI-disease, and Species-800).

Conclusion

The experimental results on six English benchmark datasets demonstrate that auto-processed syntactic information can be a useful resource for BioNER and our method with KVMN can appropriately leverage such information to improve model performance.

Prediction of miRNA-disease associations based on Weighted [Formula: see text]-Nearest known neighbors and network consistency projection

MicroRNAs (miRNA) are a type of non-coding RNA molecules that are effective on the formation and the progression of many different diseases. Various researches have reported that miRNAs play a major role in the prevention, diagnosis, and treatment of complex human diseases. In recent years, researchers have made a tremendous effort to find the potential relationships between miRNAs and diseases. Since the experimental techniques used to find that new miRNA-disease relationships are time-consuming and expensive, many computational techniques have been developed. In this study, Weighted [Formula: see text]-Nearest Known Neighbors and Network Consistency Projection techniques were suggested to predict new miRNA-disease relationships using various types of knowledge such as known miRNA-disease relationships, functional similarity of miRNA, and disease semantic similarity. An average AUC of 0.9037 and 0.9168 were calculated in our method by 5-fold and leave-one-out cross validation, respectively. Case studies of breast, lung, and colon neoplasms were applied to prove the performance of our proposed technique, and the results confirmed the predictive reliability of this method. Therefore, reported experimental results have shown that our proposed method can be used as a reliable computational model to reveal potential relationships between miRNAs and diseases.

Spliceosome-Associated microRNAs Signify Breast Cancer Cells and Portray Potential Novel Nuclear Targets

MicroRNAs (miRNAs) act as negative regulators of gene expression in the cytoplasm. Previous studies have identified the presence of miRNAs in the nucleus. Here we study human breast cancer-derived cell-lines (MCF-7 and MDA-MB-231) and a non-tumorigenic cell-line (MCF-10A) and compare their miRNA sequences at the spliceosome fraction (SF). We report that the levels of miRNAs found in the spliceosome, their identity, and pre-miRNA segmental composition are cell-line specific. One such miRNA is miR-7704 whose genomic position overlaps HAGLR, a cancer-related lncRNA. We detected an inverse expression of miR-7704 and HAGLR in the tested cell lines. Specifically, inhibition of miR-7704 caused an increase in HAGLR expression. Furthermore, elevated levels of miR-7704 slightly altered the cell-cycle in MDA-MB-231. Altogether, we show that SF-miR-7704 acts as a tumor-suppressor gene with HAGLR being its nuclear target. The relative levels of miRNAs found in the spliceosome fractions (e.g., miR-100, miR-30a, and let-7 family) in non-tumorigenic relative to cancer-derived cell-lines was monitored. We found that the expression trend of the abundant miRNAs in SF was different from that reported in the literature and from the observation of large cohorts of breast cancer patients, suggesting that many SF-miRNAs act on targets that are different from the cytoplasmic ones. Altogether, we report on the potential of SF-miRNAs as an unexplored route for cancerous cell state.

The Lung Cancer Associated MicroRNAs and Single Nucleotides Polymorphisms: a Mendelian Randomization Analysis

Lung cancer is a major public health burden and among the highest incidence and mortality rates of the cancers. MicroRNAs (miRNAs) play an important role in the development of lung cancer. The aim of this study was to investigate whether there was a potential causal relation between miRNAs and non-small-cell lung cancer (NSCLC). 1,026 patients with NSCLC from The Cancer Genome Atlas (TCGA) were analyzed. NSCLC associated SNPs' allele scores were established, and candidate miRNAs were filtered from differential expression analysis. Mendelian randomization (MR) analysis was conducted for 5 candidate miRNA (hsa-miR-135b, hsa-miR-142, hsa-miR-182, hsa-miR-183 and hsa-miR-3607) and 76 candidate SNPs in lung adenocarcinoma (LUAD) group. According to the core assumptions of MR, there was no clear evidence of a causal relation between the 5 candidate miRNAs and LUAD. The reads per million miRNAs mapped (RPM) level of candidate miRNAs changed less than 3% per allele score. To our knowledge, this is the first study using the TCGA data set to investigate the causal relation between miRNAs and lung cancer using the MR approach, and also one of the first MR studies to use miRNA expression as an exposure factor, with the SNPs as instrumental variables.

Cutting the Brakes on Ras-Cytoplasmic GAPs as Targets of Inactivation in Cancer

Simple Summary

GTPase-Activating Proteins (RasGAPs) are a group of structurally related proteins with a fundamental role in controlling the activity of Ras in normal and cancer cells. In particular, loss of function of RasGAPs may contribute to aberrant Ras activation in cancer. Here we review the multiple molecular mechanisms and factors that are involved in downregulating RasGAPs expression and functions in cancer. Additionally, we discuss how extracellular stimuli from the tumor microenvironment can control RasGAPs expression and activity in cancer cells and stromal cells, indirectly affecting Ras activation, with implications for cancer development and progression.

Abstract

The Ras pathway is frequently deregulated in cancer, actively contributing to tumor development and progression. Oncogenic activation of the Ras pathway is commonly due to point mutation of one of the three Ras genes, which occurs in almost one third of human cancers. In the absence of Ras mutation, the pathway is frequently activated by alternative means, including the loss of function of Ras inhibitors. Among Ras inhibitors, the GTPase-Activating Proteins (RasGAPs) are major players, given their ability to modulate multiple cancer-related pathways. In fact, most RasGAPs also have a multi-domain structure that allows them to act as scaffold or adaptor proteins, affecting additional oncogenic cascades. In cancer cells, various mechanisms can cause the loss of function of Ras inhibitors; here, we review the available evidence of RasGAP inactivation in cancer, with a specific focus on the mechanisms. We also consider extracellular inputs that can affect RasGAP levels and functions, implicating that specific conditions in the tumor microenvironment can foster or counteract Ras signaling through negative or positive modulation of RasGAPs. A better understanding of these conditions might have relevant clinical repercussions, since treatments to restore or enhance the function of RasGAPs in cancer would help circumvent the intrinsic difficulty of directly targeting the Ras protein.

MCCMF: collaborative matrix factorization based on matrix completion for predicting miRNA-disease associations

Background

MicroRNAs (miRNAs) are non-coding RNAs with regulatory functions. Many studies have shown that miRNAs are closely associated with human diseases. Among the methods to explore the relationship between the miRNA and the disease, traditional methods are time-consuming and the accuracy needs to be improved. In view of the shortcoming of previous models, a method, collaborative matrix factorization based on matrix completion (MCCMF) is proposed to predict the unknown miRNA-disease associations.

Results

The complete matrix of the miRNA and the disease is obtained by matrix completion. Moreover, Gaussian Interaction Profile kernel is added to the miRNA functional similarity matrix and the disease semantic similarity matrix. Then the Weight K Nearest Known Neighbors method is used to pretreat the association matrix, so the model is close to the reality. Finally, collaborative matrix factorization method is applied to obtain the prediction results. Therefore, the MCCMF obtains a satisfactory result in the fivefold cross-validation, with an AUC of 0.9569 (0.0005).

Conclusions

The AUC value of MCCMF is higher than other advanced methods in the fivefold cross validation experiment. In order to comprehensively evaluate the performance of MCCMF, accuracy, precision, recall and f-measure are also added. The final experimental results demonstrate that MCCMF outperforms other methods in predicting miRNA-disease associations. In the end, the effectiveness and practicability of MCCMF are further verified by researching three specific diseases.

Regulatory Mechanisms of Epigenetic miRNA Relationships in Human Cancer and Potential as Therapeutic Targets

Simple Summary

By the virtue of targeting multiple genes, a microRNA (miRNA) can infer variable consequences on tumorigenesis by appearing as both a tumour suppressor and oncogene. miRNAs can regulate gene expression by modulating genome-wide epigenetic status of genes that are involved in various cancers. These miRNAs perform direct inhibition of key mediators of the epigenetic machinery, such as DNA methyltransferases (DNMTs) and histone deacetylases (HDACs) genes. Along with miRNAs gene expression, similar to other protein-coding genes, miRNAs are also controlled by epigenetic mechanisms. Overall, this reciprocal interaction between the miRNAs and the epigenetic architecture is significantly implicated in the aberrant expression of miRNAs detected in various human cancers. Comprehensive knowledge of the miRNA-epigenetic dynamics in cancer is essential for the discovery of novel anticancer therapeutics.

Abstract

Initiation and progression of cancer are under both genetic and epigenetic regulation. Epigenetic modifications including alterations in DNA methylation, RNA and histone modifications can lead to microRNA (miRNA) gene dysregulation and malignant cellular transformation and are hereditary and reversible. miRNAs are small non-coding RNAs which regulate the expression of specific target genes through degradation or inhibition of translation of the target mRNA. miRNAs can target epigenetic modifier enzymes involved in epigenetic modulation, establishing a trilateral regulatory “epi–miR–epi” feedback circuit. The intricate association between miRNAs and the epigenetic architecture is an important feature through which to monitor gene expression profiles in cancer. This review summarises the involvement of epigenetically regulated miRNAs and miRNA-mediated epigenetic modulations in various cancers. In addition, the application of bioinformatics tools to study these networks and the use of therapeutic miRNAs for the treatment of cancer are also reviewed. A comprehensive interpretation of these mechanisms and the interwoven bond between miRNAs and epigenetics is crucial for understanding how the human epigenome is maintained, how aberrant miRNA expression can contribute to tumorigenesis and how knowledge of these factors can be translated into diagnostic and therapeutic tool development.

A Global and Integrated Analysis of CINSARC-Associated Genetic Defects

The Complexity Index in Sarcomas (CINSARC) signature is a transcriptomic marker that identifies high-risk soft-tissue sarcomas and is associated with high metastatic potential. During the last decade, CINSARC has been successfully developed and validated and is currently being assessed in two prospective phase III clinical trials for stratification of therapy. Although the link between CINSARC expression and tumor aggressiveness is well established, questions remain about how CINSARC genes are regulated. In this study, we leveraged a The Cancer Genome Atlas multiomics study on sarcomas with complex genetics to appraise the association between CINSARC profile, genomic features, and two potential regulation mechanisms, DNA methylation and miRNA expression. CINSARC expression was associated with an increase of ploidy, intratumor heterogeneity, copy-number alteration, altered expression of 37 miRNAs, and a decrease of DNA methylation. These genetic changes are not independent, but rather act together to promote or repress CINSARC expression. These findings depict new insights into CINSARC regulation. SIGNIFICANCE: These findings demonstrate that CINSARC is associated with a variety of genomic aberrations that contribute to higher risk for metastasis and may serve as a prognostic factor in sarcomas and beyond.

Transcriptome and Gene Fusion Analysis of Synchronous Lesions Reveals lncMRPS31P5 as a Novel Transcript Involved in Colorectal Cancer

Fusion genes and epigenetic regulators (i.e., miRNAs and long non-coding RNAs) constitute essential pieces of the puzzle of the tumor genomic landscape, in particular in mechanisms behind the adenoma-to-carcinoma progression of colorectal cancer (CRC). In this work, we aimed to identify molecular signatures of the different steps of sporadic CRC development in eleven patients, of which synchronous samples of adenomas, tumors, and normal tissues were analyzed by RNA-Seq. At a functional level, tumors and adenomas were all characterized by increased activity of the cell cycle, cell development, cell growth, and biological proliferation functions. In contrast, organic survival and apoptosis-related functions were inhibited both in tumors and adenomas at different levels. At a molecular level, we found that three individuals shared a tumor-specific fusion named MRPS31-SUGT1, generated through an intra-chromosomal translocation on chromosome 13, whose sequence resulted in being 100% identical to the long non-coding RNA (lncRNA) MRPS31P5. Our analyses suggest that MRPS31P5 could take part to a competitive endogenous (ce)RNA network by acting as a miRNA sponge or/and as an interactor of other mRNAs, and thus it may be an important gene expression regulatory factor and could be used as a potential biomarker for the detection of early CRC events.

lncRNA-miRNA-mRNA interaction network for colorectal cancer; An in silico analysis

BACKGROUND

Colorectal cancer (CRC) is one of the most frequent and diagnosed diseases. Accumulating evidences showed that mRNAs and noncoding RNAs play important regulatory roles in tumorigenesis. Identification and determining the relationship between them can help diagnosis and treatment of cancer.

METHODS

Here we analyzed three microarray datasets; GSE110715, GSE32323 and GSE21510, to identify differentially expressed lncRNAs and mRNAs in CRC. The adjusted p-value ≤0.05 was considered statistically significant. Gene set enrichment analysis was carried out using DAVID tool. The miRCancer database was searched to obtain differentially expressed miRNAs in colorectal cancer, and the miRDB database was used to attain the targets of the obtained miRNAs. To predict the lncRNA-miRNA interactions we used DIANA-LncBase v2 and RegRNA 2.0. Finally the lncRNA-miRNA-mRNA-signaling pathway network was constructed using Cytoscape v3.1.

RESULTS

By analyzing the three datasets, a total of 21 mRNAs (15 up- and 6 down-regulated) and 24 lncRNAs (18 up- and 6 down-regulated) were identified as common differentially expressed genes between CRC tumor and marginal tissues. Nevertheless, the constructed lncRNA-miRNA-mRNA-signaling pathway network revealed a convergence on 6 lncRNAs (3 up- and 3 downregulated), 7 mRNAs (2 up- and 5 downregulated) and 6 miRNAs (3 up- and 3 downregulated). We found that dysregulation of lncRNAs such as PCBP1-AS1, UCA1 and SNHG16 could sequester several miRNAs such as hsa-miR-582-5p and hsa-miR-198 and promote the proliferation, invasion and drug resistance of colorectal cancer cells.

CONCLUSIONS

We introduced a set of lncRNAs, mRNAs and miRNAs differentially expressed in CRC which might be considered for further experimental research as potential biomarkers of CRC development.

Computational Methods and Software Tools for Functional Analysis of miRNA Data

miRNAs are important regulators of gene expression that play a key role in many biological processes. High-throughput techniques allow researchers to discover and characterize large sets of miRNAs, and enrichment analysis tools are becoming increasingly important in decoding which miRNAs are implicated in biological processes. Enrichment analysis of miRNA targets is the standard technique for functional analysis, but this approach carries limitations and bias; alternatives are currently being proposed, based on direct and curated annotations. In this review, we describe the two workflows of miRNAs enrichment analysis, based on target gene or miRNA annotations, highlighting statistical tests, software tools, up-to-date databases, and functional annotations resources in the study of metazoan miRNAs.

Discriminating miRNA Profiles between Endometrioid Well- and Poorly-Differentiated Tumours and Endometrioid and Serous Subtypes of Endometrial Cancers

The discrimination of different subtypes of endometrial carcinoma (EC) is frequently problematic when using the current histomorphological classification; therefore, new markers for this differentiation are needed. Here, we examined differences in miRNA expression between well- and poorly-differentiated (grades 1 and 3) endometrioid endometrial carcinoma (EEC) and between EEC and serous endometrial carcinoma (SEC). The expression of 84 tumour-suppressor miRNAs was analysed by real-time polymerase chain reactions in 62 EC and 20 non-neoplastic endometrial specimens. The potential functions of the differentially expressed miRNAs were determined by bioinformatics analyses. The expression of let-7c-5p, miR-125b-5p, miR-23b-3p, and miR-99a-5p in grade 3 EEC was decreased compared to grade 1 EEC. To discriminate between EEC and SEC, let-7g-5p, miR-195-5p, miR-34a-5p, and miR-497-5p expression was significantly downregulated in SEC. In bioinformatic analyses, miRNAs that could discriminate grade 1 from grade 3 mainly targeted genes involved in PI3K-AKT signaling, whereas miRNAs that could discriminate EEC from SEC targeted genes involved in several signaling pathways, but mainly MAPK signaling. Taken collectively, our results indicate that the activation of certain signaling pathways can be useful in the molecular characterization of EEC and SEC.

Variational graph auto-encoders for miRNA-disease association prediction

Cumulative experimental studies have demonstrated the critical roles of microRNAs (miRNAs) in the diverse fundamental and important biological processes, and in the development of numerous complex human diseases. Thus, exploring the relationships between miRNAs and diseases is helpful with understanding the mechanisms, the detection, diagnosis, and treatment of complex diseases. As the identification of miRNA-disease associations via traditional biological experiments is time-consuming and expensive, an effective computational prediction method is appealing. In this study, we present a deep learning framework with variational graph auto-encoder for miRNA-disease association prediction (VGAE-MDA). VGAE-MDA first gets the representations of miRNAs and diseases from the heterogeneous networks constructed by miRNA-miRNA similarity, disease-disease similarity, and known miRNA-disease associations. Then, VGAE-MDA constructs two sub-networks: miRNA-based network and disease-based network. Combining the representations based on the heterogeneous network, two variational graph auto-encoders (VGAE) are deployed for calculating the miRNA-disease association scores from two sub-networks, respectively. Lastly, VGAE-MDA obtains the final predicted association score for a miRNA-disease pair by integrating the scores from these two trained networks. Unlike the previous model, the VGAE-MDA can mitigate the effect of noises from random selection of negative samples. Besides, the use of graph convolutional neural (GCN) network can naturally incorporate the node features from the graph structure while the variational autoencoder (VAE) makes use of latent variables to predict associations from the perspective of data distribution. The experimental results show that VGAE-MDA outperforms the state-of-the-art approaches in miRNA-disease association prediction. Besides, the effectiveness of our model has been further demonstrated by case studies.

Principles and innovative technologies for decrypting noncoding RNAs: from discovery and functional prediction to clinical application

Noncoding RNAs (ncRNAs) are a large segment of the transcriptome that do not have apparent protein-coding roles, but they have been verified to play important roles in diverse biological processes, including disease pathogenesis. With the development of innovative technologies, an increasing number of novel ncRNAs have been uncovered; information about their prominent tissue-specific expression patterns, various interaction networks, and subcellular locations will undoubtedly enhance our understanding of their potential functions. Here, we summarized the principles and innovative methods for identifications of novel ncRNAs that have potential functional roles in cancer biology. Moreover, this review also provides alternative ncRNA databases based on high-throughput sequencing or experimental validation, and it briefly describes the current strategy for the clinical translation of cancer-associated ncRNAs to be used in diagnosis.

Involvement of Differentially Expressed microRNAs in the PEGylated Liposome Encapsulated 188Rhenium-Mediated Suppression of Orthotopic Hypopharyngeal Tumor

Hypopharyngeal cancer (HPC) accounts for the lowest survival rate among all types of head and neck cancers (HNSCC). However, the therapeutic approach for HPC still needs to be investigated. In this study, a theranostic ¹⁸⁸Re-liposome was prepared to treat orthotopic HPC tumors and analyze the deregulated microRNA expressive profiles. The therapeutic efficacy of ¹⁸⁸Re-liposome on HPC tumors was evaluated using bioluminescent imaging followed by next generation sequencing (NGS) analysis, in order to address the deregulated microRNAs and associated signaling pathways. The differentially expressed microRNAs were also confirmed using clinical HNSCC samples and clinical information from The Cancer Genome Atlas (TCGA) database. Repeated doses of ¹⁸⁸Re-liposome were administrated to tumor-bearing mice, and the tumor growth was apparently suppressed after treatment. For NGS analysis, 13 and 9 microRNAs were respectively up-regulated and down-regulated when the cutoffs of fold change were set to 5. Additionally, miR-206-3p and miR-142-5p represented the highest fold of up-regulation and down-regulation by ¹⁸⁸Re-liposome, respectively. According to Differentially Expressed MiRNAs in human Cancers (dbDEMC) analysis, most of ¹⁸⁸Re-liposome up-regulated microRNAs were categorized as tumor suppressors, while down-regulated microRNAs were oncogenic. The KEGG pathway analysis showed that cancer-related pathways and olfactory and taste transduction accounted for the top pathways affected by ¹⁸⁸Re-liposome. ¹⁸⁸Re-liposome down-regulated microRNAs, including miR-143, miR-6723, miR-944, and miR-136 were associated with lower survival rates at a high expressive level. ¹⁸⁸Re-liposome could suppress the HPC tumors in vivo, and the therapeutic efficacy was associated with the deregulation of microRNAs that could be considered as a prognostic factor.

microRNAs in oral cancer: Moving from bench to bed as next generation medicine

Oral cancer is the thirteenth most common cancer in the world, with India contributing to 33% of the global burden. Lack of specific non-invasive markers, non-improvement in patient survival and tumor recurrence remain a major clinical challenge in oral cancer. Epigenetic regulation in the form of microRNAs (miRs) that act as tumor suppressor miRs or oncomiRs has gained significant momentum with the advancement in the field, suggesting the potential for clinical application of miRs in oral cancer. The current review of literature identified miR-21, miR-27a(-3p), miR-31, miR-93, miR-134, miR-146, miR-155, miR-196a, miR-196b, miR-211, miR-218, miR-222, miR-372 and miR-373 to be up-regulated and let-7a, let-7b, let-7c, let-7d, let-7e, let-7f, let-7g, let-7i, miR-26a, miR-99a-5p, miR-137, miR-139-5p, miR-143-3p, miR-184 and miR-375 to be down-regulated in oral cancer. Mechanistic studies have uncovered several miRs that are deregulated at varying levels and in different stages of oral cancer progression, thus providing clinical utility in better diagnosis as well as usefulness in prognosis by identifying patients with poor prognosis or stratifying patients based on responsiveness to chemo- and radio-therapy. Lastly, exogenous modulation of miR expression using miRNA-based drugs in combination with first-line agents may be adopted as a new therapeutic modality to treat oral cancer. Knowledge of miRs and their involvement in key molecular processes, clinical association, responsiveness to therapy and clinical advancement may highlight additional avenues in order to improve patient morbidity and mortality. Furthermore, combinatorial approaches with miR-therapy may be efficacious in oral cancer.

The suppressive role of miR-362-3p in epithelial ovarian cancer

Ovarian cancer is a common cancer worldwide. Epithelial ovarian cancer (EOC) is the most common subtype of ovarian cancer. This study was designed to explore the function of miR-362-3p in EOC. QRT-PCR analysis was used to test miR-362-3p levels in EOC tissues and cell lines. Cell viability was tested via MTT assay. Transwell systems were applied to assay cell migration. The target gene of miR-362-3p was evaluated using dual luciferase reporter assays. The MyD88 protein in EOC cells was tested via western blot. Our data showed that miR-362-3p was expressed at low levels in EOC tissues and cells. miR-362-3p inhibited cell proliferation and migration, bound the 3′-untranslated region (UTR) of MyD88, and inhibited MyD88 expression. MyD88 was inversely correlated with miR-362-3p in EOC, and MyD88 overexpression partly reduced the anti-proliferative effect of miR-362-3p in EOC cells. In conclusion, our data showed that miR-362-3p has an anti-proliferative effect on EOC.

Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration

Background

Studies on tumor-secreted microRNAs point to a functional role of these in cellular communication and reprogramming of the tumor microenvironment. Uptake of tumor-secreted microRNAs by neighboring cells may result in the silencing of mRNA targets and, in turn, modulation of the transcriptome. Studying miRNAs externalized from tumors could improve cancer patient diagnosis and disease monitoring and help to pinpoint which miRNA-gene interactions are central for tumor properties such as invasiveness and metastasis.

Methods

Using a bioinformatics approach, we analyzed the profiles of secreted tumor and normal interstitial fluid (IF) microRNAs, from women with breast cancer (BC). We carried out differential abundance analysis (DAA), to obtain miRNAs, which were enriched or depleted in IFs, from patients with different clinical traits. Subsequently, miRNA family enrichment analysis was performed to assess whether any families were over-represented in the specific sets. We identified dysregulated genes in tumor tissues from the same cohort of patients and constructed weighted gene co-expression networks, to extract sets of co-expressed genes and co-abundant miRNAs. Lastly, we integrated miRNAs and mRNAs to obtain interaction networks and supported our findings using prediction tools and cancer gene databases.

Results

Network analysis showed co-expressed genes and miRNA regulators, associated with tumor lymphocyte infiltration. All of the genes were involved in immune system processes, and many had previously been associated with cancer immunity. A subset of these, BTLA, CXCL13, IL7R, LAMP3, and LTB, was linked to the presence of tertiary lymphoid structures and high endothelial venules within tumors. Co-abundant tumor interstitial fluid miRNAs within this network, including miR-146a and miR-494, were annotated as negative regulators of immune-stimulatory responses. One co-expression network encompassed differences between BC subtypes. Genes differentially co-expressed between luminal B and triple-negative breast cancer (TNBC) were connected with sphingolipid metabolism and predicted to be co-regulated by miR-23a. Co-expressed genes and TIF miRNAs associated with tumor grade were BTRC, CHST1, miR-10a/b, miR-107, miR-301a, and miR-454.

Conclusion

Integration of IF miRNAs and mRNAs unveiled networks associated with patient clinicopathological traits, and underlined molecular mechanisms, specific to BC sub-groups. Our results highlight the benefits of an integrative approach to biomarker discovery, placing secreted miRNAs within a biological context.

miRNA Clusters with Down-Regulated Expression in Human Colorectal Cancer and Their Regulation

Regulation of microRNA (miRNA) expression has been extensively studied with respect to colorectal cancer (CRC), since CRC is one of the leading causes of cancer mortality worldwide. Transcriptional control of miRNAs creating clusters can be, to some extent, estimated from cluster position on a chromosome. Levels of miRNAs are also controlled by miRNAs "sponging" by long non-coding RNAs (ncRNAs). Both types of miRNA regulation strongly influence their function. We focused on clusters of miRNAs found to be down-regulated in CRC, containing miR-1, let-7, miR-15, miR-16, miR-99, miR-100, miR-125, miR-133, miR-143, miR-145, miR-192, miR-194, miR-195, miR-206, miR-215, miR-302, miR-367 and miR-497 and analysed their genome position, regulation and functions. Only evidence provided with the use of CRC in vivo and/or in vitro models was taken into consideration. Comprehensive research revealed that down-regulated miRNA clusters in CRC are mostly located in a gene intron and, in a majority of cases, miRNA clusters possess cluster-specific transcriptional regulation. For all selected clusters, regulation mediated by long ncRNA was experimentally demonstrated in CRC, at least in one cluster member. Oncostatic functions were predominantly linked with the reviewed miRNAs, and their high expression was usually associated with better survival. These findings implicate the potential of down-regulated clusters in CRC to become promising multi-targets for therapeutic manipulation.

Single-Measurement Multiplexed Quantification of MicroRNAs from Human Tissue Using Catalytic Hairpin Assembly and Förster Resonance Energy Transfer

Absolute quantification of microRNAs (miRNAs) or other nucleic acid biomarkers is an important requirement for molecular and clinical biosensing. Emerging technologies with beneficial features concerning simplicity and multiplexing present an attractive route for advancing diagnostic tools toward rapid and low-cost bioanalysis. However, the actual translation into the clinic by miRNA quantification in human samples is often missing. Here, we show that implementing time-gated Förster resonance energy transfer (TG-FRET) into a catalytic hairpin assembly (CHA) can be used for the simultaneous quantification of two miRNAs with a single measurement from total RNA extracts of human tissues. A single terbium-dye FRET pair was conjugated at two specific distances within target-specific CHA hairpin probes, such that each miRNA resulted in distinct amplified photoluminescence (PL) decays that could be distinguished and quantified by TG PL intensity detection. Enzyme-free amplification in a separation-free assay format and the absence of autofluorescence background allowed for simple, specific, and sensitive detection of miR-21 and miR-20a with limits of detection down to 1.8 pM (250 amol). Reliable duplexed quantification of both miRNAs at low picomolar concentrations was confirmed by analyzing total RNA extracts from different colon and rectum tissues with single- and dual-target CHA-TG-FRET and reverse transcription-quantitative polymerase chain reaction (RT-qPCR) for comparison. These simple and multiplexed nucleic acid biomarker assays present a capable method for clinical diagnostics and biomolecular research.

Exploring the potential microRNA sponge interactions of breast cancer based on some known interactions

MicroRNA (miRNA) sponges' regulatory mechanisms play an important role in developing human cancer. Herein, we develop a new method to explore potential miRNA sponge interactions (EPMSIs) for breast cancer. Based on some known interactions, and a matching gene expression profile, EPMSIs explored other potential miRNA sponge interactions for breast cancer. Every interaction is inferred with a value representing interaction intensity. Then, we apply a clustering algorithm called BCPlaid to potential interactions. Ten modules are identified; nine of them are closely associated with biological enrichments. When we employ a classification algorithm to separate normal and tumor samples in each module, each module demonstrates powerful classification performance. Furthermore, EPMSI illustrates a new method to explore the miRNA sponge regulatory network for breast cancer by applying its superior performance.

Deep belief network-Based Matrix Factorization Model for MicroRNA-Disease Associations Prediction

MicroRNAs (miRNAs) are small single-stranded noncoding RNAs that have shown to play a critical role in regulating gene expression. In past decades, cumulative experimental studies have verified that miRNAs are implicated in many complex human diseases and might be potential biomarkers for various types of diseases. With the increase of miRNA-related data and the development of analysis methodologies, some computational methods have been developed for predicting miRNA-disease associations, which are more economical and time-saving than traditional biological experimental approaches. In this study, a novel computational model, deep belief network (DBN)-based matrix factorization (DBN-MF), is proposed for miRNA-disease association prediction. First, the raw interaction features of miRNAs and diseases were obtained from the miRNA-disease adjacent matrix. Second, 2 DBNs were used for unsupervised learning of the features of miRNAs and diseases, respectively, based on the raw interaction features. Finally, a classifier consisting of 2 DBNs and a cosine score function was trained with the initial weights of DBN from the last step. During the training, the miRNA-disease adjacent matrix was factorized into 2 feature matrices for the representation of miRNAs and diseases, and the final prediction label was obtained according to the feature matrices. The experimental results show that the proposed model outperforms the state-of-the-art approaches in miRNA-disease association prediction based on the 10-fold cross-validation. Besides, the effectiveness of our model was further demonstrated by case studies.

MicroRNA-939 Directly Targets HDGF to Inhibit the Aggressiveness of Prostate Cancer via Deactivation of the WNT/β-Catenin Pathway

Purpose

MicroRNA-939 (miR-939) has crucial roles in several types of human cancer. However, the expression profile and precise functions of miR-939 in prostate cancer (PCa) are still unclear. This study aimed to determine miR-939 expression in PCa and explore its roles in PCa tumorigenesis.

Methods

miR-939 expression was determined in PCa tissues and cell lines using reverse transcription–quantitative polymerase chain reaction. Cell Counting Kit-8, colony formation, and flow cytometric assays were used to determine the role of miR-939 in PCa cell proliferation and apoptosis in vitro, whereas a tumor xenograft model was generated to evaluate the effect of miR-939 on tumor growth in vivo. Transwell assays were performed to investigate whether miR-939 affects the migration and invasiveness of PCa cells.

Results

miR-939 was found to be downregulated in PCa tissues and cell lines, and this downregulation was significantly correlated with tumor stage and lymphatic metastasis. Patients with PCa exhibiting low miR-939 expression had shorter overall survival than those exhibiting high miR-939 expression. Exogenous miR-939 expression suppressed PCa cell proliferation, colony formation, migration, and invasion in vitro; enhanced apoptosis in vitro; and decreased tumor growth in vivo. Investigation of the underlying molecular mechanisms revealed hepatoma-derived growth factor (HDGF) as a direct target gene of miR-939 in PCa. HDGF was found to be significantly upregulated in PCa tissues, and its expression was inversely correlated with miR-939 expression. HDGF silencing and miR-939 upregulation showed similar effects in PCa. Restored HDGF expression counteracted the tumor-suppressive activity of miR-939 overexpression in PCa cells. Furthermore, ectopic miR-939 expression inhibited the WNT/β-catenin pathway activation in PCa both in vitro and in vivo by downregulating HDGF.

Conclusion

miR-939 functions as a tumor suppressor during PCa tumorigenesis by directly targeting HDGF and deactivating the WNT/β-catenin pathway, suggesting the miR-939/HDGF/WNT/β-catenin pathway as an effective target for PCa therapy.

Cross-type biomedical named entity recognition with deep multi-task learning

MOTIVATION

State-of-the-art biomedical named entity recognition (BioNER) systems often require handcrafted features specific to each entity type, such as genes, chemicals and diseases. Although recent studies explored using neural network models for BioNER to free experts from manual feature engineering, the performance remains limited by the available training data for each entity type.

RESULTS

We propose a multi-task learning framework for BioNER to collectively use the training data of different types of entities and improve the performance on each of them. In experiments on 15 benchmark BioNER datasets, our multi-task model achieves substantially better performance compared with state-of-the-art BioNER systems and baseline neural sequence labeling models. Further analysis shows that the large performance gains come from sharing character- and word-level information among relevant biomedical entities across differently labeled corpora.

AVAILABILITY AND IMPLEMENTATION

Our source code is available at https://github.com/yuzhimanhua/lm-lstm-crf.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Functional Landscape of Dysregulated MicroRNAs in Oral Squamous Cell Carcinoma: Clinical Implications

MicroRNA (miRNA) dysregulation is associated with the pathogenesis of oral squamous cell carcinoma (OSCC), and its elucidation could potentially provide information on patient outcome. A growing body of translational research on miRNA biology is focusing on precision oncology, aiming to decode the miRNA regulatory network in the development and progression of cancer. Tissue-specific expression and stable presence in all body fluids are unique features of miRNAs, which could be potentially exploited in the clinical setting. Recent understanding of miRNA properties has led them to be useful, attractive, and potential tools either as biomarkers (distinct miRNA expression signature) for diagnosis and prognostic outcomes or as targets for novel therapeutic entities, enabling personalized treatment for OSCC. In this review, we discuss recent research on different aspects of alterations in miRNA profiles along with their clinical significance and strive to identify probable potential miRNA biomarkers for diagnosis and prognosis of OSCC. We also discuss the current understanding and scope of development of miRNA-based therapeutics against OSCC.

The mechanistic, diagnostic and therapeutic novel nucleic acids for hepatocellular carcinoma emerging in past score years

Despite The Central Dogma states the destiny of gene as 'DNA makes RNA and RNA makes protein', the nucleic acids not only store and transmit genetic information but also, surprisingly, join in intracellular vital movement as a regulator of gene expression. Bioinformatics has contributed to knowledge for a series of emerging novel nucleic acids molecules. For typical cases, microRNA (miRNA), long noncoding RNA (lncRNA) and circular RNA (circRNA) exert crucial role in regulating vital biological processes, especially in malignant diseases. Due to extraordinarily heterogeneity among all malignancies, hepatocellular carcinoma (HCC) has emerged enormous limitation in diagnosis and therapy. Mechanistic, diagnostic and therapeutic nucleic acids for HCC emerging in past score years have been systematically reviewed. Particularly, we have organized recent advances on nucleic acids of HCC into three facets: (i) summarizing diverse nucleic acids and their modification (miRNA, lncRNA, circRNA, circulating tumor DNA and DNA methylation) acting as potential biomarkers in HCC diagnosis; (ii) concluding different patterns of three key noncoding RNAs (miRNA, lncRNA and circRNA) in gene regulation and (iii) outlining the progress of these novel nucleic acids for HCC diagnosis and therapy in clinical trials, and discuss their possibility for clinical applications. All in all, this review takes a detailed look at the advances of novel nucleic acids from potential of biomarkers and elaboration of mechanism to early clinical application in past 20 years.