Fewer genes, more noncoding RNA

Regulatory RNAs: role as scaffolds assembling protein complexes and their epigenetic deregulation

Recently, new data have been added to the interaction between non-coding RNAs (ncRNAs) and epigenetic machinery. Epigenetics includes enzymes involved in DNA methylation, histone modifications, and RNA modifications, and mechanisms underlying chromatin structure, repressive states, and active states operating in transcription. The main focus is on long ncRNAs (lncRNAs) acting as scaffolds to assemble protein complexes. This review does not cover RNA's role in sponging microRNAs, or decoy functions. Several lncRNAs were shown to regulate chromatin activation and repression by interacting with Polycomb repressive complexes and mixed-lineage leukemia (MLL) activating complexes. Various groups reported on enhancer of zeste homolog 2 (EZH2) interactions with regulatory RNAs. Knowledge of the function of these complexes opens the perspective to develop new therapeutics for cancer treatment. Lastly, the interplay between lncRNAs and epitranscriptomic modifications in cancers paves the way for new targets in cancer therapy. The approach to inhibit lncRNAs interaction with protein complexes and perspective to regulate epitrascriptomics-regulated RNAs may bring new compounds as therapeuticals in various types of cancer.

Association between Resistant Arterial Hypertension, Type 2 Diabetes, and Selected microRNAs

INTRODUCTION

In recent years, a very close relationship between miRNA and cardiovascular diseases has been found. RAH and T2DM are accompanied by a change in the microRNA expression spectrum.

OBJECTIVES

This study aimed to evaluate the clinical characteristics and expression of selected microRNAs in patients with idiopathic RAH and T2DM.

PATIENTS AND METHODS

A total of 115 patients with RAH were included in this study. Among them were 53 patients (46.09%) with T2DM. miRNA levels were determined using quantitative real-time polymerase chain reaction. The expression of the examined genes was calculated from the formula RQ = 2-ΔΔCT.

RESULTS

Analysis using the Mann-Whitney U test showed a statistically significant (p < 0.05) difference in the expression of MIR1-1 (p = 0.031) and MIR195 (p = 0.042) associated with the occurrence of T2DM in the subjects. The value of MIR1-1 gene expression was statistically significantly higher in patients with T2DM (median: 0.352; mean: 0.386; standard deviation: 0.923) compared to patients without T2DM (median: 0.147; mean: -0.02; standard deviation: 0.824). The value of MIR195 gene expression was statistically significantly higher in patients with T2DM (median: 0.389, mean: 0.442; standard deviation: 0.819) compared to patients without T2DM (median: -0.027; mean: 0.08; standard deviation: 0.942).

CONCLUSIONS

The values of MIR1-1 and MIR195 gene expression were statistically significantly higher in patients with RAH and T2DM compared to patients with RAH and without T2DM. Further studies are necessary to precisely clarify the roles of miRNAs in patients with RAH and T2DM. They should demonstrate the utility of these genetic markers in clinical practice.

A species-specific lncRNA modulates the reproductive ability of the asian tiger mosquito

Long non-coding RNA (lncRNA) research has emerged as an independent scientific field in recent years. Despite their association with critical cellular and metabolic processes in plenty of organisms, lncRNAs are still a largely unexplored area in mosquito research. We propose that they could serve as exceptional tools for pest management due to unique features they possess. These include low inter-species sequence conservation and high tissue specificity. In the present study, we investigated the role of ovary-specific lncRNAs in the reproductive ability of the Asian tiger mosquito, Aedes albopictus. Through the analysis of transcriptomic data, we identified several lncRNAs that were differentially expressed upon blood feeding; we called these genes Norma (NOn-coding RNA in Mosquito ovAries). We observed that silencing some of these Normas resulted in significant impact on mosquito fecundity and fertility. We further focused on Norma3 whose silencing resulted in 43% oviposition reduction, in smaller ovaries and 53% hatching reduction of the laid eggs, compared to anti-GFP controls. Moreover, a significant downregulation of 2 mucins withing a neighboring (∼100 Kb) mucin cluster was observed in smaller anti-Norma3 ovaries, indicating a potential mechanism of in-cis regulation between Norma3 and the mucins. Our work constitutes the first experimental proof-of-evidence connecting lncRNAs with mosquito reproduction and opens a novel path for pest management.

lncRNA-disease association prediction based on matrix decomposition of elastic network and collaborative filtering

In recent years, with the continuous development and innovation of high-throughput biotechnology, more and more evidence show that lncRNA plays an essential role in biological life activities and is related to the occurrence of various diseases. However, due to the high cost and time-consuming of traditional biological experiments, the number of associations between lncRNAs and diseases that rely on experiments to verify is minimal. Computer-aided study of lncRNA-disease association is an important method to study the development of the lncRNA-disease association. Using the existing data to establish a prediction model and predict the unknown lncRNA-disease association can make the biological experiment targeted and improve its accuracy of the biological experiment. Therefore, we need to find an accurate and efficient method to predict the relationship between lncRNA and diseases and help biologists complete the diagnosis and treatment of diseases. Most of the current lncRNA-disease association predictions do not consider the model instability caused by the actual data. Also, predictive models may produce data that overfit is not considered. This paper proposes a lncRNA-disease association prediction model (ENCFLDA) that combines an elastic network with matrix decomposition and collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association between unknown lncRNA and disease, updates the matrix by matrix decomposition combined with the elastic network, and then obtains the final prediction matrix by collaborative filtering. This method uses the existing lncRNA-miRNA association data and miRNA-disease association data to predict the association of unknown lncRNAs with diseases. First, since the known lncRNA-disease association matrix is very sparse, the cosine similarity and KNN are used to update the lncRNA-disease association matrix. The matrix is then updated by matrix decomposition combined with an elastic net algorithm, to increase the stability of the overall prediction model and eliminate data overfitting. The final prediction matrix is then obtained through collaborative filtering based on lncRNA.Through simulation experiments, the results show that the AUC value of ENCFLDA can reach 0.9148 under the framework of LOOCV, which is higher than the prediction result of the latest model.

Long non-coding RNA LINC01270 is an onco-promotor in lung adenocarcinoma by upregulating LARP1 via sponging miR-326

Accumulating evidence have proved the key role of long non-coding RNA in lung adenocarcinoma (LUAD) progression. Bioinformatics analysis is used to seek the differentially expressed lncRNA LINC01270 from TCGA database. The overexpression of LINC01270 was then verified in LUAD tumor tissues and cell lines by qRT-PCR. LINC01270 knockdown resulted in impaired cell proliferative and invasive ability via CCK-8 assay, EdU assay, colony formation assay, transwell assay, while aberrant upregulation of LINC01270 led to enhanced cell growth and invasion. Moreover, LINC01270 was found inhibiting miR-326 and thereby overexpressing the abundance of LARP1 to promote LUAD development via PI3K/AKT pathway. It was also proved that LINC01270 knockdown could suppress LUAD tumor growth in vivo. All of these findings demonstrate thatLINC01270 is a tumor promotor in LUAD via enhancing LARP1 expressed by sponging miR-326 to facilitate the development of LUAD. LINC01270 play a significant role in LUAD, which could serve as biomarkers for early diagnosis and a novel targeted remedy.

From circRNAs to fusion circRNAs in hematological malignancies

Circular RNAs (circRNAs) represent a type of endogenous noncoding RNA generated by back-splicing events. Unlike the majority of RNAs, circRNAs are covalently closed, without a 5' end or a 3' poly(A) tail. A few circRNAs can be associated with polysomes, suggesting a protein-coding potential. CircRNAs are not degraded by RNA exonucleases or ribonuclease R and are enriched in exosomes. Recent developments in experimental methods coupled with evolving bioinformatic approaches have accelerated functional investigation of circRNAs, which exhibit a stable structure, a long half-life, and tumor specificity and can be extracted from body fluids and used as potential biological markers for tumors. Moreover, circRNAs may regulate the occurrence and development of cancers and contribute to drug resistance through a variety of molecular mechanisms. Despite the identification of a growing number of circRNAs, their effects in hematological cancers remain largely unknown. Recent studies indicate that circRNAs could also originate from fusion genes (fusion circRNAs, f-circRNAs) next to chromosomal translocations, which are considered the primary cause of various cancers, notably hematological malignancies. This Review will focus on circRNAs and f-circRNAs in hematological cancers.

Gene regulation by antisense transcription: A focus on neurological and cancer diseases

Advances in high-throughput sequencing over the past decades have led to the identification of thousands of non-coding RNAs (ncRNAs), which play a major role in regulating gene expression. One emerging class of ncRNAs is the natural antisense transcripts (NATs), the RNA molecules transcribed from the opposite strand of a protein-coding gene locus. NATs are known to concordantly and discordantly regulate gene expression in both cis and trans manners at the transcriptional, post-transcriptional, translational, and epigenetic levels. Aberrant expression of NATs can therefore cause dysregulation in many biological pathways and has been observed in many genetic diseases. This review outlines the involvements and mechanisms of NATs in the pathogenesis of various diseases, with a special emphasis on neurodegenerative diseases and cancer. We also summarize recent findings on NAT knockdown and/or overexpression experiments and discuss the potential of NATs as promising targets for future gene therapies.

Engineering Crystal Packing in RNA Structures I: Past and Future Strategies for Engineering RNA Packing in Crystals

X-ray crystallography remains a powerful method to gain atomistic insights into the catalytic and regulatory functions of RNA molecules. However, the technique requires the preparation of diffraction-quality crystals. This is often a resource- and time-consuming venture because RNA crystallization is hindered by the conformational heterogeneity of RNA, as well as the limited opportunities for stereospecific intermolecular interactions between RNA molecules. The limited success at crystallization explains in part the smaller number of RNA-only structures in the Protein Data Bank. Several approaches have been developed to aid the formation of well-ordered RNA crystals. The majority of these are construct-engineering techniques that aim to introduce crystal contacts to favor the formation of well-diffracting crystals. A typical example is the insertion of tetraloop-tetraloop receptor pairs into non-essential RNA segments to promote intermolecular association. Other methods of promoting crystallization involve chaperones and crystallization-friendly molecules that increase RNA stability and improve crystal packing. In this review, we discuss the various techniques that have been successfully used to facilitate crystal packing of RNA molecules, recent advances in construct engineering, and directions for future research in this vital aspect of RNA crystallography.

Emerging role of non-coding RNA in health and disease

Human diseases have always been a significant turf of concern since the origin of mankind. It is cardinal to know the cause, treatment, and cure for every disease condition. With the advent and advancement in technology, the molecular arena at the microscopic level to study the mechanism, progression, and therapy is more rational and authentic pave than a macroscopic approach. Non-coding RNAs (ncRNAs) have now emerged as indispensable players in the diagnosis, development, and therapeutics of every abnormality concerning physiology, pathology, genetics, epigenetics, oncology, and developmental diseases. This is a comprehensive attempt to collate all the existing and proven strategies, techniques, mechanisms of genetic disorders including Silver Russell Syndrome, Fascio- scapula humeral muscular dystrophy, cardiovascular diseases (atherosclerosis, cardiac fibrosis, hypertension, etc.), neurodegenerative diseases (Spino-cerebral ataxia type 7, Spino-cerebral ataxia type 8, Spinal muscular atrophy, Opitz-Kaveggia syndrome, etc.) cancers (cervix, breast, lung cancer, etc.), and infectious diseases (viral) studied so far. This article encompasses discovery, biogenesis, classification, and evolutionary prospects of the existence of this junk RNA along with the integrated networks involving chromatin remodelling, dosage compensation, genome imprinting, splicing regulation, post-translational regulation and proteomics. In conclusion, all the major human diseases are discussed with a facilitated technology transfer, advancements, loopholes, and tentative future research prospects have also been proposed.

Long noncoding RNA ILF3-AS1 aggravates papillary thyroid carcinoma progression via regulating the miR-4306/PLAGL2 axis

BACKGROUND

It have been proven that long non-coding RNAs (lncRNAs) serve as regulators in carcinogenesis. Interleukin enhancer binding factor 3 antisense RNA 1 (ILF3-AS1) has been illuminated as a prognostic factor in some cancers. Nevertheless, its expression pattern and possible functions in papillary thyroid carcinoma (PTC) have not been studied.

METHODS

The expression of ILF3-AS1 was measured by RT-qPCR and ISH. Colony formation assay and EdU assay were used to probe cell proliferation. TUNEL assay was used for analysis of cell apoptosis. Immunofluorescence and western blot were conducted to evaluate the expression change of E-cadherin and N-cadherin. The RNA interaction was demonstrated by mechanism experiments, including pull down assay and dual luciferase reporter assay.

RESULTS

ILF3-AS1 expression was evidently upregulated in PTC cell lines. ILF3-AS1 knockdown restrained the proliferation, migration and invasion of PTC cells. Mechanical investigation revealed that miR-4306 could interact with ILF3-AS1. PLAGL2 was a downstream target of miR-4306. The effects of ILF3-AS1 knockdown on the cellular processes were abrogated by miR-4306 downregulation or pleiomorphic adenoma gene-like 2 (PLAGL2) overexpression.

CONCLUSION

ILF3-AS1 plays tumor-promoting role in PTC via targeting miR-4306/PLAGL2 axis.

A panel of 8-lncRNA predicts prognosis of breast cancer patients and migration of breast cancer cells

BACKGROUND

Breast cancer (BCa) is the most commonly diagnosed cancer and the leading cause of cancer death among females around the world. Recent studies have indicated that long non-coding RNAs (lncRNAs) can serve as an independent biomarker for diagnosis and prognosis in many types of cancer, including pancreatic adenocarcinoma, gastric cancer, liver cancer, and lung cancer. Previous studies have shown that many lncRNAs are associated with the occurrence and development of BCa. However, few studies have combined multiple lncRNAs to predict the prognosis of early-stage BCa patients.

METHODS

Systematic and comprehensive analysis of data from The Cancer Genome Atlas (TCGA) was conducted to identify lncRNA signatures with prognostic value in BCa. Additionally, the relative expression levels of the 8 lncRNA of several BCa cell lines were detected by quantitative real-time PCR (qPCR) and the results were substituted into a risk score formula. Finally, migration assays were used to verify the result from prognostic analysis according to the risk scores among cell lines with different risk scores.

RESULTS

Our study included 808 BCa patients with complete clinical data. A panel of 8 lncRNAs was identified using Wilcox tests as different between normal and tumor tissue of the BCa patients. This panel was used to analyze the survival of BCa patients. Patients with low risk scores had greater overall survival (OS) than those with high risk scores. Multivariate Cox regression analyses demonstrated that the lncRNA signature was an independent prognostic factor. Gene Set Enrichment Analysis (GSEA) suggested that the lncRNAs might be involved in several molecular signaling pathways implicated in BCa such as the DNA replication pathway, the cell cycle pathway, and the pentose phosphate pathway. Validation experiments in breast cancer cells to test cell migration by using wound-healing assays supported the results of the model.

CONCLUSION

Our study demonstrated that a panel of 8 lncRNAs has the potential to be used as an independent prognostic biomarker of BCa.

A Novel Approach for Potential Human LncRNA-Disease Association Prediction Based on Local Random Walk

In recent years, lncRNAs (long non-coding RNAs) have been proved to be closely related to many diseases that are seriously harmful to human health. Although researches on clarifying the relationships between lncRNAs and diseases are developing rapidly, associations between the lncRNAs and diseases are still remaining largely unknown. In this manuscript, a novel Local Random Walk based prediction model called LRWHLDA is proposed for inferring potential associations between human lncRNAs and diseases. In LRWHLDA, a new heterogeneous network is established first, which allows that LRWHLDA can be implemented in the case of lacking known lncRNA-disease associations. And then, an improved local random walk method is designed for prediction of novel lncRNA-disease associations, which can help LRWHLDA achieve high prediction accuracy but with low time complexity. Finally, in order to evaluate the prediction performance of LRWHLDA, different frameworks such as LOOCV, 2-folds CV, and 5-folds CV have been implemented, simulation results indicate that LRWHLDA can achieve reliable AUCs of 0.8037, 0.8354, and 0.8556 under the frameworks of 2-fold CV, 5-fold CV, and LOOCV, respectively. Hence, it is easy to know that LRWHLDA contains the potential to be a representative of emerging methods in the field of research on potential lncRNA-disease associations prediction.

Social Networking of Quasi-Species Consortia drive Virolution via Persistence

The emergence of cooperative quasi-species consortia (QS-C) thinking from the more accepted quasispecies equations of Manfred Eigen, provides a conceptual foundation from which concerted action of RNA agents can now be understood. As group membership becomes a basic criteria for the emergence of living systems, we also start to understand why the history and context of social RNA networks become crucial for survival and function. History and context of social RNA networks also lead to the emergence of a natural genetic code. Indeed, this QS-C thinking can also provide us with a transition point between the chemical world of RNA replicators and the living world of RNA agents that actively differentiate self from non-self and generate group identity with membership roles. Importantly the social force of a consortia to solve complex, multilevel problems also depend on using opposing and minority functions. The consortial action of social networks of RNA stem-loops subsequently lead to the evolution of cellular organisms representing a tree of life.

HIV-1 Natural Antisense Transcription and Its Role in Viral Persistence

Natural antisense transcripts (NATs) represent a class of RNA molecules that are transcribed from the opposite strand of a protein-coding gene, and that have the ability to regulate the expression of their cognate protein-coding gene via multiple mechanisms. NATs have been described in many prokaryotic and eukaryotic systems, as well as in the viruses that infect them. The human immunodeficiency virus (HIV-1) is no exception, and produces one or more NAT from a promoter within the 3’ long terminal repeat. HIV-1 antisense transcripts have been the focus of several studies spanning over 30 years. However, a complete appreciation of the role that these transcripts play in the virus lifecycle is still lacking. In this review, we cover the current knowledge about HIV-1 NATs, discuss some of the questions that are still open and identify possible areas of future research.

LncMachine: a machine learning algorithm for long noncoding RNA annotation in plants

Following the elucidation of the critical roles they play in numerous important biological processes, long noncoding RNAs (lncRNAs) have gained vast attention in recent years. Manual annotation of lncRNAs is restricted by known gene annotations and is prone to false prediction due to the incompleteness of available data. However, with the advent of high-throughput sequencing technologies, a magnitude of high-quality data has become available for annotation, especially for plant species such as wheat. Here, we compared prediction accuracies of several machine learning algorithms using a 10-fold cross-validation. This study includes a comprehensive feature selection step to refine irrelevant and repeated features. We present a crop-specific, alignment-free coding potential prediction tool, LncMachine, that performs at higher prediction accuracies than the currently available popular tools (CPC2, CPAT, and CNIT) when used with the Random Forest algorithm. Further, LncMachine with Random Forest performed well on human and mouse data, with an average accuracy of 92.67%. LncMachine only requires either a FASTA file or a TAB separated CSV file containing features as input files. LncMachine can deploy several user-provided algorithms in real time and therefore be effortlessly applied to a wide range of studies.

ICLRBBN: a tool for accurate prediction of potential lncRNA disease associations

Growing evidence has elucidated that long non-coding RNAs (lncRNAs) are involved in a variety of complex diseases in human bodies. In recent years, it has become a hot topic to develop effective computational models to identify potential lncRNA-disease associations. In this article, a novel method called ICLRBBN (Internal Confidence-Based Local Radial Basis Biological Network) is proposed to detect potential lncRNA-disease associations by adopting an internal confidence-based radial basis biological network. In ICLRBBN, a novel internal confidence-based collaborative filtering recommendation algorithm was designed first to mine hidden features between lncRNAs and diseases, which guarantees that ICLRBBN can be more effectively applied to predict new diseases. Then, a unique three-layer local radial basis function network consisting of diseases and lncRNAs was constructed, based on which the association probability between diseases and lncRNAs was calculated by combining different characteristics of lncRNAs with local information of diseases. Finally, we compared ICLRBBN with 6 state-of-the-art methods based on two different validation frameworks. Simulation results showed that area under the receiver operating characteristic curve (AUC) values achieved by ICLRBBN outperformed all competing methods. Furthermore, case studies illustrated that ICLRBBN has a promising future as a powerful tool in the practical application of lncRNA-disease association prediction. A web service for prediction of potential lncRNA-disease associations is available at http://leelab2997.cn/.

Regulatory Networks of LncRNA MALAT-1 in Cancer

Long noncoding (lnc)RNAs are a group of RNAs with a length greater than 200 nt that do not encode a protein but play an essential role in regulating the expression of target genes in normal biological contexts as well as pathologic processes including tumorigenesis. The lncRNA metastasis-associated lung adenocarcinoma transcript (MALAT)-1 has been widely studied in cancer. In this review, we describe the known functions of MALAT-1; its mechanisms of action; and associated signaling pathways and their clinical significance in different cancers. In most malignancies, including lung, colorectal, thyroid, and other cancers, MALAT-1 functions as an oncogene and is upregulated in tumors and tumor cell lines. MALAT-1 has a distinct mechanism of action in each cancer type and is thus at the center of large gene regulatory networks. Dysregulation of MALAT-1 affects cellular processes such as alternative splicing, epithelial–mesenchymal transition, apoptosis, and autophagy, which ultimately results in the abnormal cell proliferation, invasion, and migration that characterize cancers. In other malignancies, such as glioma and endometrial carcinoma, MALAT-1 functions as a tumor suppressor and thus forms additional regulatory networks. The current evidence indicates that MALAT-1 and its associated signaling pathways can serve as diagnostic or prognostic biomarker or therapeutic target in the treatment of many cancers.

Identification of expression signatures for non-small-cell lung carcinoma subtype classification

MOTIVATION

Non-small-cell lung carcinoma (NSCLC) mainly consists of two subtypes: lung squamous cell carcinoma (LUSC) and lung adenocarcinoma (LUAD). It has been reported that the genetic and epigenetic profiles vary strikingly between LUAD and LUSC in the process of tumorigenesis and development. Efficient and precise treatment can be made if subtypes can be identified correctly. Identification of discriminative expression signatures has been explored recently to aid the classification of NSCLC subtypes.

RESULTS

In this study, we designed a classification model integrating both mRNA and long non-coding RNA (lncRNA) expression data to effectively classify the subtypes of NSCLC. A gene selection algorithm, named WGRFE, was proposed to identify the most discriminative gene signatures within the recursive feature elimination (RFE) framework. GeneRank scores considering both expression level and correlation, together with the importance generated by classifiers were all taken into account to improve the selection performance. Moreover, a module-based initial filtering of the genes was performed to reduce the computation cost of RFE. We validated the proposed algorithm on The Cancer Genome Atlas (TCGA) dataset. The results demonstrate that the developed approach identified a small number of expression signatures for accurate subtype classification and particularly, we here for the first time show the potential role of LncRNA in building computational NSCLC subtype classification models.

AVAILABILITY AND IMPLEMENTATION

The R implementation for the proposed approach is available at https://github.com/RanSuLab/NSCLC-subtype-classification.

2lpiRNApred: a two-layered integrated algorithm for identifying piRNAs and their functions based on LFE-GM feature selection

Piwi-interacting RNAs (piRNAs) are indispensable in the transposon silencing, including in germ cell formation, germline stem cell maintenance, spermatogenesis, and oogenesis. piRNA pathways are amongst the major genome defence mechanisms, which maintain genome integrity. They also have important functions in tumorigenesis, as indicated by aberrantly expressed piRNAs being recently shown to play roles in the process of cancer development. A number of computational methods for this have recently been proposed, but they still have not yielded satisfactory predictive performance. Moreover, only one computational method that identifies whether piRNAs function in inducting target mRNA deadenylation been reported in the literature. In this study, we developed a two-layered integrated classifier algorithm, 2lpiRNApred. It identifies piRNAs in the first layer and determines whether they function in inducting target mRNA deadenylation in the second layer. A new feature selection algorithm, which was based on Luca fuzzy entropy and Gaussian membership function (LFE-GM), was proposed to reduce the dimensionality of the features. Five feature extraction strategies, namely, Kmer, General parallel correlation pseudo-dinucleotide composition, General series correlation pseudo-dinucleotide composition, Normalized Moreau-Broto autocorrelation, and Geary autocorrelation, and two types of classifier, Sparse Representation Classifier (SRC) and support vector machine with Mahalanobis distance-based radial basis function (SVMMDRBF), were used to construct a two-layered integrated classifier algorithm, 2lpiRNApred. The results indicate that 2lpiRNApred performs significantly better than six other existing prediction tools.

A novel target convergence set based random walk with restart for prediction of potential LncRNA-disease associations

BACKGROUND

In recent years, lncRNAs (long-non-coding RNAs) have been proved to be closely related to the occurrence and development of many serious diseases that are seriously harmful to human health. However, most of the lncRNA-disease associations have not been found yet due to high costs and time complexity of traditional bio-experiments. Hence, it is quite urgent and necessary to establish efficient and reasonable computational models to predict potential associations between lncRNAs and diseases.

RESULTS

In this manuscript, a novel prediction model called TCSRWRLD is proposed to predict potential lncRNA-disease associations based on improved random walk with restart. In TCSRWRLD, a heterogeneous lncRNA-disease network is constructed first by combining the integrated similarity of lncRNAs and the integrated similarity of diseases. And then, for each lncRNA/disease node in the newly constructed heterogeneous lncRNA-disease network, it will establish a node set called TCS (Target Convergence Set) consisting of top 100 disease/lncRNA nodes with minimum average network distances to these disease/lncRNA nodes having known associations with itself. Finally, an improved random walk with restart is implemented on the heterogeneous lncRNA-disease network to infer potential lncRNA-disease associations. The major contribution of this manuscript lies in the introduction of the concept of TCS, based on which, the velocity of convergence of TCSRWRLD can be quicken effectively, since the walker can stop its random walk while the walking probability vectors obtained by it at the nodes in TCS instead of all nodes in the whole network have reached stable state. And Simulation results show that TCSRWRLD can achieve a reliable AUC of 0.8712 in the Leave-One-Out Cross Validation (LOOCV), which outperforms previous state-of-the-art results apparently. Moreover, case studies of lung cancer and leukemia demonstrate the satisfactory prediction performance of TCSRWRLD as well.

CONCLUSIONS

Both comparative results and case studies have demonstrated that TCSRWRLD can achieve excellent performances in prediction of potential lncRNA-disease associations, which imply as well that TCSRWRLD may be a good addition to the research of bioinformatics in the future.

MicroRNAs and complex diseases: from experimental results to computational models

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

SKF-LDA: Similarity Kernel Fusion for Predicting lncRNA-Disease Association

Recently, prediction of lncRNA-disease associations has attracted more and more attentions. Various computational models have been proposed; however, there is still room to improve the prediction accuracy. In this paper, we propose a kernel fusion method with different types of similarities for the lncRNAs and diseases. The expression similarity and cosine similarity are used for lncRNAs, and the semantic similarity and cosine similarity are used for the diseases. To eliminate the noise effect, a neighbor constraint is enforced to refine all the similarity matrices before fusion. Experimental results show that the proposed similarity kernel fusion (SKF)-LDA method has the superiority performance in terms of AUC values and other measurements. In the schemes of LOOCV and 5-fold CV, AUC values of SKF-LDA achieve 0.9049 and 0.8743±0.0050 respectively. In addition, the conducted case studies of three diseases (hepatocellular carcinoma, lung cancer, and prostate cancer) show that SKF-LDA can predict related lncRNAs accurately.

Long noncoding RNA RP11-547D24.1 regulates proliferation and migration in papillary thyroid carcinoma: Identification and validation of a novel long noncoding RNA through integrated analysis of TCGA database

Long noncoding RNAs (lncRNAs) are known to be key regulators of numerous biological processes, and substantial evidence supports that abnormal lncRNA expression plays a significant role in tumorigenesis and tumor progression. However, the mechanism by which lncRNAs function in thyroid carcinoma are still unclear. To investigate the role of lncRNAs in the tumorigenesis of papillary thyroid carcinoma (PTC), we analyzed lncRNA data in The Cancer Genome Atlas RNA‐Seq database. A comparison of lncRNAs in cancerous thyroid tissues and normal tissues revealed hundreds of differentially expressed lncRNAs. Of 7589 lncRNAs identified in 561 thyroid cancer cases (503 cancerous tissues and 58 normal tissues), the expression levels of 144 were found to be aberrant (|log2 fold change| >2 and adjusted P < 0.05). The top 10 lncRNAs with the most significant differences were LINC01977, RP11‐363E7.4, RP3‐483K16.4, RP11‐547D24.1, RUNDC3A‐AS1, AC093609.1, CTD‐2008L17.2, HAGLROS, UNC5B‐AS1, and LINC01354. In addition, CTD‐2008L17.2, HAGLROS, AC093609.1, UNC5B‐AS1, and RUNDC3A‐AS1 were shown to play vital roles in determining the histological cancer type. Furthermore, RP11‐547D24.1 and UNC5B‐AS1 could distinguish patients with different stages of PTC. The lncRNA RP11‐547D24.1 was validated by loss‐of‐function assays, revealing that downregulation of this lncRNA regulates thyroid tumor cell proliferation and apoptosis, invasion, and migration. This study demonstrates the potential for using lncRNAs to interpret the pathogenesis and development of PTC.

NCPHLDA: a novel method for human lncRNA–disease association prediction based on network consistency projection

In recent years, an increasing number of biological experiments and clinical reports have shown that lncRNA is closely related to the development of various complex human diseases.

BPLLDA: Predicting lncRNA-Disease Associations Based on Simple Paths With Limited Lengths in a Heterogeneous Network

In recent years, it has been increasingly clear that long noncoding RNAs (lncRNAs) play critical roles in many biological processes associated with human diseases. Inferring potential lncRNA-disease associations is essential to reveal the secrets behind diseases, develop novel drugs, and optimize personalized treatments. However, biological experiments to validate lncRNA-disease associations are very time-consuming and costly. Thus, it is critical to develop effective computational models. In this study, we have proposed a method called BPLLDA to predict lncRNA-disease associations based on paths of fixed lengths in a heterogeneous lncRNA-disease association network. Specifically, BPLLDA first constructs a heterogeneous lncRNA-disease network by integrating the lncRNA-disease association network, the lncRNA functional similarity network, and the disease semantic similarity network. It then infers the probability of an lncRNA-disease association based on paths connecting them and their lengths in the network. Compared to existing methods, BPLLDA has a few advantages, including not demanding negative samples and the ability to predict associations related to novel lncRNAs or novel diseases. BPLLDA was applied to a canonical lncRNA-disease association database called LncRNADisease, together with two popular methods LRLSLDA and GrwLDA. The leave-one-out cross-validation areas under the receiver operating characteristic curve of BPLLDA are 0.87117, 0.82403, and 0.78528, respectively, for predicting overall associations, associations related to novel lncRNAs, and associations related to novel diseases, higher than those of the two compared methods. In addition, cervical cancer, glioma, and non-small-cell lung cancer were selected as case studies, for which the predicted top five lncRNA-disease associations were verified by recently published literature. In summary, BPLLDA exhibits good performances in predicting novel lncRNA-disease associations and associations related to novel lncRNAs and diseases. It may contribute to the understanding of lncRNA-associated diseases like certain cancers.

LncRNA PROX1-AS1 promotes proliferation, invasion, and migration in papillary thyroid carcinoma

Evidence has been provided that long noncoding RNAs (LncRNAs) play major roles in affecting essential physiological processes, and many of which seem to have functional roles in tumorigenesis and progression. However, the intrinsic molecular mechanism of LncRNAs acting on papillary thyroid carcinoma is not well understood. In the present study, we found that PROX1-AS1 levels were obviously increased in thyroid cancer cells compared with the normal thyroid epithelial cells. Knockdown of PROX1-AS1 gene expression by siRNA could inhibit cell proliferation. Subsequently, we also observed that silencing PROX1-AS1 might inhibit invasion and migration of thyroid cancer cell lines via modulating the expression of epithelial–mesenchymal transition related proteins. In conclusion, our study indicated that LncRNA PROX1-AS1 could promote papillary thyroid carcinoma development and might serve as a potential targeting marker for papillary thyroid carcinoma.

HOXA10‑AS: A novel oncogenic long non‑coding RNA in glioma

Glioma is the most common primary malignant tumor of the central nervous system. Emerging evidence has demonstrated that long non‑coding RNAs (lncRNAs) serve a major role of regulation in various types of human cancer, including glioma. However, the biological roles of thousands of lncRNAs remain unknown and require further identification. The present study investigated the functional role of lncRNA‑HOXA10‑AS in glioma. The present study examined the expression patterns of HOXA10‑AS in glioma and normal brain tissues, as well as glioma cell lines and normal human astrocytes (HA) via reverse transcription‑quantitative polymerase chain reaction. HOXA10‑AS knockdown cells were generated using lentiviral short hairpin RNA against HOXA10‑AS in A172 and U251 glioma cells. Cell growth was assessed by MTT assay, and a flow cytometer was used to investigate cell proliferation, cell cycle distribution and cell apoptosis. Western blot analysis was performed to analyze the expression levels of apoptosis‑related proteins. HOXA10‑AS was significantly upregulated in glioma tissues and cell lines, and increased HOXA10‑AS expression levels were associated with higher grades of glioma. Knockdown of HOXA10‑AS inhibited glioma cell proliferation and increased cell apoptosis rates compared with the control cells. HOXA10‑AS markedly regulated the expression of the homeobox A10 (HOXA10) gene. Similarly, HOXA10 expression was increased with higher grades of glioma, and silencing of HOXA10 by small interfering RNA suppressed glioma cell proliferation and induced cell apoptosis. The results of the present study demonstrated that HOXA10‑AS promoted cell growth and survival through activation of HOXA10 gene expression in glioma, which may potentially act as a novel biomarker and therapeutic target for clinical assay development.

Mechanisms and Measurement of Changes in Gene Expression

Research on gene expression (GE) provides insights into the physiology of a cell or group of cells at a given point in time. Studies of changes in GE can be used to identify patients at higher risk for various medical conditions, a higher symptom burden, and/or the adverse consequences associated with various treatments. The aims of this article are as follows: (1) to describe the different types of RNA transcripts, (2) to describe the processes involved in GE (i.e., RNA transcription, epigenetics, and posttranscriptional modifications), (3) to describe common sources of variation in GE, (4) to describe the most common methods used to measure GE, and (5) to discuss factors to consider when choosing tissue for a GE study. This article begins with an overview of the mechanisms involved in GE. Then, the factors that can influence the findings from GE experiments (e.g., tissue specificity, host age, host gender, and time of sample collection) are described and potential solutions are presented. This article concludes with a discussion of how the types of tissue used in GE studies can affect study findings. Given that the costs associated with the measurement of changes in GE are decreasing and the methods to analyze GE data are becoming easier to use, nurse scientists need to understand the basic principles that underlie any GE study.

A Bipartite Network and Resource Transfer-Based Approach to Infer lncRNA-Environmental Factor Associations

Phenotypes and diseases are often determined by the complex interactions between genetic factors and environmental factors (EFs). However, compared with protein-coding genes and microRNAs, there is a paucity of computational methods for understanding the associations between long non-coding RNAs (lncRNAs) and EFs. In this study, we focused on the associations between lncRNA and EFs. By using the common miRNA partners of any pair of lncRNA and EF, based on the competing endogenous RNA (ceRNA) hypothesis and the technique of resources transfer within the experimentally-supported lncRNA-miRNA and miRNA-EF association bipartite networks, we propose an algorithm for predicting new lncRNA-EF associations. Results show that, compared with another recently-proposed method, our approach is capable of predicting more credible lncRNA-EF associations. These results support the validity of our approach to predict biologically significant associations, which could lead to a better understanding of the molecular processes.

IRWRLDA: improved random walk with restart for lncRNA-disease association prediction

In recent years, accumulating evidences have shown that the dysregulations of lncRNAs are associated with a wide range of human diseases. It is necessary and feasible to analyze known lncRNA-disease associations, predict potential lncRNA-disease associations, and provide the most possible lncRNA-disease pairs for experimental validation. Considering the limitations of traditional Random Walk with Restart (RWR), the model of Improved Random Walk with Restart for LncRNA-Disease Association prediction (IRWRLDA) was developed to predict novel lncRNA-disease associations by integrating known lncRNA-disease associations, disease semantic similarity, and various lncRNA similarity measures. The novelty of IRWRLDA lies in the incorporation of lncRNA expression similarity and disease semantic similarity to set the initial probability vector of the RWR. Therefore, IRWRLDA could be applied to diseases without any known related lncRNAs. IRWRLDA significantly improved previous classical models with reliable AUCs of 0.7242 and 0.7872 in two known lncRNA-disease association datasets downloaded from the lncRNADisease database, respectively. Further case studies of colon cancer and leukemia were implemented for IRWRLDA and 60% of lncRNAs in the top 10 prediction lists have been confirmed by recent experimental reports.

Long non-coding PANDAR as a novel biomarker in human cancer: A systematic review

OBJECTIVES

Long non-coding RNAs (lncRNAs) are characterized as a group of RNAs that more than 200 nucleotides in length and have no protein-coding function. More and more evidences provided that lncRNAs serve as key molecules in the development of cancer. Deregulation of lncRNAs functions as either oncogenes or tumour suppressor genes in various diseases. Recently, increasing studies about PANDAR in cancer progression were reported. In our review, we will focus on the current research on the character of PANDAR include the clinical management, tumour progression and molecular mechanisms in human cancers.

MATERIALS AND METHODS

We summarize and analyze current studies concerning the biological functions and mechanisms of lncRNA PANDA in tumour development. The related studies were obtained through a systematic search of Pubmed.

RESULTS

PANDAR was a well-characterized oncogenic lncRNA and widely overexpressed in many tumours. PANDAR is upregulated in many types of cancer, including colorectal cancer, lung cancer, renal cell carcinoma, cholangiocarcinoma, osteosarcoma, thyroid cancer and other cancers. Upregulation of PANDAR was significantly associated with advanced tumour weights, TNM stage and overall survival. Furthermore, repressed of PANDAR would restrain proliferation, migration and invasion.

CONCLUSION

PANDAR may act as a powerful tumour biomarker for cancer diagnosis and treatment.

Noncoding RNAs in Cardiovascular Disease: Pathological Relevance and Emerging Role as Biomarkers and Therapeutics

Noncoding RNAs (ncRNA) include a diverse range of functional RNA species-microRNAs (miRNAs) and long noncoding RNAs (lncRNAs) being most studied in pathophysiology. Cardiovascular morbidity is associated with differential expression of myriad miRNAs; miR-21, miR-155, miR-126, miR-146a/b, miR-143/145, miR-223, and miR-221 are the top 9 most reported miRNAs in hypertension and atherosclerotic disease. A single miRNA may have hundreds of messenger RNA targets, which makes a full appreciation of the physiologic ramifications of such broad-ranging effects a challenge. miR-21 is the most prominent ncRNA associated with hypertension and atherosclerotic disease due to its role as a "mechano-miR", responding to arterial shear stresses. "Immuno-miRs", such as miR-155 and miR-223, affect cardiovascular disease (CVD) via regulation of hematopoietic cell differentiation, chemotaxis, and activation in response to many pro-atherogenic stimuli. "Myo-miRs", such as miR-1 and miR-133, affect cardiac muscle plasticity and remodeling in response to mechanical overload. This in-depth review analyzes observational and experimental reports of ncRNAs in CVD, including future applications of ncRNA-based strategies in diagnosis, prediction (e.g., survival and response to small molecule therapy), and biologic therapy.

RNA versatility, flexibility, and thermostability for practice in RNA nanotechnology and biomedical applications

In recent years, RNA has attracted widespread attention as a unique biomaterial with distinct biophysical properties for designing sophisticated architectures in the nanometer scale. RNA is much more versatile in structure and function with higher thermodynamic stability compared to its nucleic acid counterpart DNA. Larger RNA molecules can be viewed as a modular structure built from a combination of many 'Lego' building blocks connected via different linker sequences. By exploiting the diversity of RNA motifs and flexibility of structure, varieties of RNA architectures can be fabricated with precise control of shape, size, and stoichiometry. Many structural motifs have been discovered and characterized over the years and the crystal structures of many of these motifs are available for nanoparticle construction. For example, using the flexibility and versatility of RNA structure, RNA triangles, squares, pentagons, and hexagons can be constructed from phi29 pRNA three-way-junction (3WJ) building block. This review will focus on 2D RNA triangles, squares, and hexamers; 3D and 4D structures built from basic RNA building blocks; and their prospective applications in vivo as imaging or therapeutic agents via specific delivery and targeting. Methods for intracellular cloning and expression of RNA molecules and the in vivo assembly of RNA nanoparticles will also be reviewed. WIREs RNA 2018, 9:e1452. doi: 10.1002/wrna.1452 This article is categorized under: RNA Methods > RNA Nanotechnology RNA Structure and Dynamics > RNA Structure, Dynamics and Chemistry RNA in Disease and Development > RNA in Disease Regulatory RNAs/RNAi/Riboswitches > Regulatory RNAs.

BANCR: a novel oncogenic long non-coding RNA in human cancers

Long non-coding RNAs account for large proportion of non-coding transcripts in human genomes. Though they lack of open reading framework and cannot encode protein, they can control endogenous gene expression though regulating cell life activities. They serve as transcriptional modulator, posttranscriptional processor, chromatin remodeler and splicing regulator during the process of gene modification. Moreover, long non-coding RNAs were regarded as potential tumor markers for cancer diagnosis and prognosis. BANCR was identified as a cancer-promoting long non-coding RNA in melanoma tissues. Since then, increasing studies about BANCR in cancer progression were reported. BANCR was dysregulated in various cancers including melanoma, colorectal cancer, retinoblastoma, lung carcinoma and hepatocellular carcinoma, and increased BANCR expression cause poor prognosis and shorter survival rate of cancer patients. Furthermore, the functions and mechanisms of BANCR in cancer cells have been clarified. Here, we focus on the current research on the role of BANCR in the clinical management, progression and molecular mechanisms in human cancer.

Genome‑wide analysis and prediction of functional long noncoding RNAs in osteoblast differentiation under simulated microgravity

Long noncoding RNAs (lncRNAs) have been regarded as important regulators in numerous biological processes during cell development. However, the holistic lncRNA expression pattern and potential functions during osteoblast differentiation under simulated microgravity remain unknown. In the present study, a high throughput microarray assay was performed to detect lncRNA and mRNA expression profiles during MC3TC-E1 pre-osteoblast cell osteo-differentiation under simulated microgravity. The expression of 857 lncRNAs and 2,264 mRNAs was significantly altered when MC3T3-E1 cells were exposed to simulated microgravity. A relatively consistent distribution pattern on the chromosome and a co-expression network were observed between the differentially-expressed lncRNAs and mRNAs. Genomic context analysis further identified 132 differentially-expressed lncRNAs and nearby coding gene pairs. Subsequently, 3 lncRNAs were screened out for their possible function in osteoblast differentiation, based on their co-expression association and potential cis-acting regulatory pattern with the deregulated mRNAs. The present study aimed to provide a comprehensive understanding of and a foundation for future studies into lncRNA function in mechanical signal-mediated osteoblast differentiation.

Long noncoding RNAs: lincs between human health and disease

Long noncoding RNAs (lncRNAs) represent one of the largest classes of transcripts and are highly diverse in terms of characteristics and functions. Advances in high-throughput sequencing platforms have enabled the rapid discovery and identification of lncRNAs as key regulatory molecules involved in various cellular processes and their dysregulation in various human diseases. Here, we summarize the current knowledge of the functions and underlying mechanisms of lncRNA activity with a particular focus on cancer biology. We also discuss the potential of lncRNAs as diagnostic and therapeutic targets for clinical applications.

High density lipoprotein from coronary artery disease patients caused abnormal expression of long non-coding RNAs in vascular endothelial cells

Increased evidence has showed that normal high density lipoprotein (HDL) could convert to dysfunctional HDL in diseases states including coronary artery disease (CAD), which regulated vascular endothelial cell function differently. Long non-coding RNAs (lncRNAs) play an extensive role in various important biological processes including endothelial cell function. However, whether lncRNAs are involved in the regulation of HDL metabolism and HDL-induced changes of vascular endothelial function remains unclear. Cultured human umbilical vein endothelial cells (HUVECs) were treated with HDL from healthy subjects and patients with CAD and hypercholesterolemia for 24 h, then the cells were collected for lncRNA-Seq and the expressions of lncRNAs, genes and mRNAs were identified. The bioinformatic analysis was used to evaluate the relationship among lncRNAs, encoding genes and miRNAs. HDL from healthy subjects and patients with CAD and hypercholesterolemia leaded to different expressions of lncRNAs, genes and mRNAs, and further analysis suggested that the differentially expressed lncRNAs played an important role in the regulation of vascular endothelial function. Thus, HDL from patients with CAD and hypercholesterolemia could cause abnormal expression of lncRNAs in vascular endothelial cells to affect vascular function.

The Evolving Definition of the Term "Gene"

This paper presents a history of the changing meanings of the term "gene," over more than a century, and a discussion of why this word, so crucial to genetics, needs redefinition today. In this account, the first two phases of 20th century genetics are designated the "classical" and the "neoclassical" periods, and the current molecular-genetic era the "modern period." While the first two stages generated increasing clarity about the nature of the gene, the present period features complexity and confusion. Initially, the term "gene" was coined to denote an abstract "unit of inheritance," to which no specific material attributes were assigned. As the classical and neoclassical periods unfolded, the term became more concrete, first as a dimensionless point on a chromosome, then as a linear segment within a chromosome, and finally as a linear segment in the DNA molecule that encodes a polypeptide chain. This last definition, from the early 1960s, remains the one employed today, but developments since the 1970s have undermined its generality. Indeed, they raise questions about both the utility of the concept of a basic "unit of inheritance" and the long implicit belief that genes are autonomous agents. Here, we review findings that have made the classic molecular definition obsolete and propose a new one based on contemporary knowledge.

Advancement of the Emerging Field of RNA Nanotechnology

The field of RNA nanotechnology has advanced rapidly during the past decade. A variety of programmable RNA nanoparticles with defined shape, size, and stoichiometry have been developed for diverse applications in nanobiotechnology. The rising popularity of RNA nanoparticles is due to a number of factors: (1) removing the concern of RNA degradation in vitro and in vivo by introducing chemical modification into nucleotides without significant alteration of the RNA property in folding and self-assembly; (2) confirming the concept that RNA displays very high thermodynamic stability and is suitable for in vivo trafficking and other applications; (3) obtaining the knowledge to tune the immunogenic properties of synthetic RNA constructs for in vivo applications; (4) increased understanding of the 4D structure and intermolecular interaction of RNA molecules; (5) developing methods to control shape, size, and stoichiometry of RNA nanoparticles; (6) increasing knowledge of regulation and processing functions of RNA in cells; (7) decreasing cost of RNA production by biological and chemical synthesis; and (8) proving the concept that RNA is a safe and specific therapeutic modality for cancer and other diseases with little or no accumulation in vital organs. Other applications of RNA nanotechnology, such as adapting them to construct 2D, 3D, and 4D structures for use in tissue engineering, biosensing, resistive biomemory, and potential computer logic gate modules, have stimulated the interest of the scientific community. This review aims to outline the current state of the art of RNA nanoparticles as programmable smart complexes and offers perspectives on the promising avenues of research in this fast-growing field.

A novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction

Considering the various disadvantages of previous computational models, we proposed a novel computational model based on super-disease and miRNA for potential miRNA–disease association prediction (SDMMDA) to predict potential miRNA–disease associations by integrating known associations, disease semantic similarity, miRNA functional similarity, and Gaussian interaction profile kernel similarity for diseases and miRNAs.

New insights in melanoma biomarkers: long-noncoding RNAs

Melanoma is one of the leading cancers worldwide, distinguished for its malignancy and low survival rates. Although the poor outcome could improve with an early diagnosis and a good monitoring of the disease, current melanoma biomarkers display several limitations which make them useless. Interestingly, long-noncoding RNAs are secreted into the bloodstream inside exosomes by a wide range of malignant cells, and several of them have been validated as promising circulating molecular signatures of other tumors, but not melanoma. In this review we propose to explore the booming field of long-noncoding RNAs in order to find potential candidates to be tested as novel melanoma biomarkers, with the ultimate goal of improving melanoma detection, diagnosis and prognosis.

A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs

BACKGROUND

Predicting piwi-interacting RNA (piRNA) is an important topic in the small non-coding RNAs, which provides clues for understanding the generation mechanism of gamete. To the best of our knowledge, several machine learning approaches have been proposed for the piRNA prediction, but there is still room for improvements.

RESULTS

In this paper, we develop a genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs. We construct datasets for three species: Human, Mouse and Drosophila. For each species, we compile the balanced dataset and imbalanced dataset, and thus obtain six datasets to build and evaluate prediction models. In the computational experiments, the genetic algorithm-based weighted ensemble method achieves 10-fold cross validation AUC of 0.932, 0.937 and 0.995 on the balanced Human dataset, Mouse dataset and Drosophila dataset, respectively, and achieves AUC of 0.935, 0.939 and 0.996 on the imbalanced datasets of three species. Further, we use the prediction models trained on the Mouse dataset to identify piRNAs of other species, and the models demonstrate the good performances in the cross-species prediction.

CONCLUSIONS

Compared with other state-of-the-art methods, our method can lead to better performances. In conclusion, the proposed method is promising for the transposon-derived piRNA prediction. The source codes and datasets are available in https://github.com/zw9977129/piRNAPredictor .

Imagining roles for epigenetics in health promotion research

Discoveries from the Human Genome Project have invigorated discussions of epigenetic effects-modifiable chemical processes that influence DNA's ability to give instructions to turn gene expression on or off-on health outcomes. We suggest three domains in which new understandings of epigenetics could inform innovations in health promotion research: (1) increase the motivational potency of health communications (e.g., explaining individual differences in health outcomes to interrupt optimistic biases about health exposures); (2) illuminate new approaches to targeted and tailored health promotion interventions (e.g., relapse prevention targeted to epigenetic responses to intervention participation); and (3) inform more sensitive measures of intervention impact, (e.g., replace or augment self-reported adherence). We suggest a three-step process for using epigenetics in health promotion research that emphasizes integrating epigenetic mechanisms into conceptual model development that then informs selection of intervention approaches and outcomes. Lastly, we pose examples of relevant scientific questions worth exploring.

LncRNApred: Classification of Long Non-Coding RNAs and Protein-Coding Transcripts by the Ensemble Algorithm with a New Hybrid Feature

As a novel class of noncoding RNAs, long noncoding RNAs (lncRNAs) have been verified to be associated with various diseases. As large scale transcripts are generated every year, it is significant to accurately and quickly identify lncRNAs from thousands of assembled transcripts. To accurately discover new lncRNAs, we develop a classification tool of random forest (RF) named LncRNApred based on a new hybrid feature. This hybrid feature set includes three new proposed features, which are MaxORF, RMaxORF and SNR. LncRNApred is effective for classifying lncRNAs and protein coding transcripts accurately and quickly. Moreover,our RF model only requests the training using data on human coding and non-coding transcripts. Other species can also be predicted by using LncRNApred. The result shows that our method is more effective compared with the Coding Potential Calculate (CPC). The web server of LncRNApred is available for free at http://mm20132014.wicp.net:57203/LncRNApred/home.jsp.

Accurate Prediction of Transposon-Derived piRNAs by Integrating Various Sequential and Physicochemical Features

BACKGROUND

Piwi-interacting RNA (piRNA) is the largest class of small non-coding RNA molecules. The transposon-derived piRNA prediction can enrich the research contents of small ncRNAs as well as help to further understand generation mechanism of gamete.

METHODS

In this paper, we attempt to differentiate transposon-derived piRNAs from non-piRNAs based on their sequential and physicochemical features by using machine learning methods. We explore six sequence-derived features, i.e. spectrum profile, mismatch profile, subsequence profile, position-specific scoring matrix, pseudo dinucleotide composition and local structure-sequence triplet elements, and systematically evaluate their performances for transposon-derived piRNA prediction. Finally, we consider two approaches: direct combination and ensemble learning to integrate useful features and achieve high-accuracy prediction models.

RESULTS

We construct three datasets, covering three species: Human, Mouse and Drosophila, and evaluate the performances of prediction models by 10-fold cross validation. In the computational experiments, direct combination models achieve AUC of 0.917, 0.922 and 0.992 on Human, Mouse and Drosophila, respectively; ensemble learning models achieve AUC of 0.922, 0.926 and 0.994 on the three datasets.

CONCLUSIONS

Compared with other state-of-the-art methods, our methods can lead to better performances. In conclusion, the proposed methods are promising for the transposon-derived piRNA prediction. The source codes and datasets are available in S1 File.

MicroRNA: A new therapeutic strategy for cardiovascular diseases

Myocardial infarction, atherosclerosis, and hypertension are the most common heart-related diseases that affect both the heart and the blood vessels. Multiple independent risk factors have been shown to be responsible for cardiovascular diseases. The combination of a healthy diet, exercise, and smoking cessation keeps these risk factors in check and helps maintain homeostasis. The dynamic monolayer endothelial cell integrity and cell-cell communication are the fundamental mechanisms in maintaining homeostasis. Recently, it has been revealed that small noncoding RNAs (ncRNAs) play a critical role in regulation of genes involved in either posttranscriptional or pretranslational modifications. They also control diverse biological functions like development, differentiation, growth, and metabolism. Among ncRNAs, the short interfering RNAs (siRNAs), and microRNAs (miRNAs) have been extensively studied, but their specific functions remain largely unknown. In recent years, miRNAs are efficiently studied as one of the important candidates for involvement in most biological processes and have been implicated in many human diseases. Thus, the identification and the respective targets of miRNAs may provide novel molecular insight and new therapeutic strategies to treat diseases. This review summarizes the recent developments and insight on the role of miRNAs in cardiovascular disease prognosis, diagnostic and clinical applications.

KATZLDA: KATZ measure for the lncRNA-disease association prediction

Accumulating experimental studies have demonstrated important associations between alterations and dysregulations of lncRNAs and the development and progression of various complex human diseases. Developing effective computational models to integrate vast amount of heterogeneous biological data for the identification of potential disease-lncRNA associations has become a hot topic in the fields of human complex diseases and lncRNAs, which could benefit lncRNA biomarker detection for disease diagnosis, treatment, and prevention. Considering the limitations in previous computational methods, the model of KATZ measure for LncRNA-Disease Association prediction (KATZLDA) was developed to uncover potential lncRNA-disease associations by integrating known lncRNA-disease associations, lncRNA expression profiles, lncRNA functional similarity, disease semantic similarity, and Gaussian interaction profile kernel similarity. KATZLDA could work for diseases without known related lncRNAs and lncRNAs without known associated diseases. KATZLDA obtained reliable AUCs of 7175, 0.7886, 0.7719 in the local and global leave-one-out cross validation and 5-fold cross validation, respectively, significantly improving previous classical methods. Furthermore, case studies of colon, gastric, and renal cancer were implemented and 60% of top 10 predictions have been confirmed by recent biological experiments. It is anticipated that KATZLDA could be an important resource with potential values for biomedical researches.