LncRBase: an enriched resource for lncRNA information

Long non-coding RNA (lncRNA) PVT1 in drug resistance of cancers: Focus on pathological mechanisms

According to estimates, cancer will be the leading cause of death globally in 2022, accounting for 9.6 million deaths. At present, the three main therapeutic modalities utilized to treat cancer are radiation therapy, chemotherapy, and surgery. However, during treatment, tumor cells resistant to chemotherapy may arise. Drug resistance remains a major oppose since it often leads to therapeutic failure. Furthermore, the term "acquired drug resistance" describes the situation where tumor cells already display drug resistance before undergoing chemotherapy. However, little is still known about the basic mechanisms underlying chemotherapy-induced drug resistance. The development of new technologies and bioinformatics has led to the discovery of additional genes associated with drug resistance. Long noncoding RNA plasmacytoma variant translocation 1 (PVT1) has been linked to an increased risk of cancer, according to a growing body of research. Apart from biological functions associated with cell division, development, pluripotency, and cell cycle, lncRNA PVT1 contributes significantly to the regulation of various aspects of genome function, such as transcription, splicing, and epigenetics. The article will address the mechanism by which lncRNA PVT1 influences drug resistance in cancer cells.

Tailored machine learning models for functional RNA detection in genome-wide screens

The in silico prediction of non-coding and protein-coding genetic loci has received considerable attention in comparative genomics aiming in particular at the identification of properties of nucleotide sequences that are informative of their biological role in the cell. We present here a software framework for the alignment-based training, evaluation and application of machine learning models with user-defined parameters. Instead of focusing on the one-size-fits-all approach of pervasive in silico annotation pipelines, we offer a framework for the structured generation and evaluation of models based on arbitrary features and input data, focusing on stable and explainable results. Furthermore, we showcase the usage of our software package in a full-genome screen of Drosophila melanogaster and evaluate our results against the well-known but much less flexible program RNAz.

Predicting latent lncRNA and cancer metastatic event associations via variational graph auto-encoder

Long non-coding RNA (lncRNA) are shown to be closely associated with cancer metastatic events (CME, e.g., cancer cell invasion, intravasation, extravasation, proliferation) that collaboratively accelerate malignant cancer spread and cause high mortality rate in patients. Clinical trials may accurately uncover the relationships between lncRNAs and CMEs; however, it is time-consuming and expensive. With the accumulation of data, there is an urgent need to find efficient ways to identify these relationships. Herein, a graph embedding representation-based predictor (VGEA-LCME) for exploring latent lncRNA-CME associations is introduced. In VGEA-LCME, a heterogeneous combined network is constructed by integrating similarity and linkage matrix that can maintain internal and external characteristics of networks, and a variational graph auto-encoder serves as a feature generator to represent arbitrary lncRNA and CME pair. The final robustness predicted result is obtained by ensemble classifier strategy via cross-validation. Experimental comparisons and literature verification show better remarkable performance of VGEA-LCME, although the similarities between CMEs are challenging to calculate. In addition, VGEA-LCME can further identify organ-specific CMEs. To the best of our knowledge, this is the first computational attempt to discover the potential relationships between lncRNAs and CMEs. It may provide support and new insight for guiding experimental research of metastatic cancers. The source code and data are available at https://github.com/zhuyuan-cug/VGAE-LCME.

Recent advances in predicting lncRNA-disease associations based on computational methods

Mutations in and dysregulation of long non-coding RNAs (lncRNAs) are closely associated with the development of various human complex diseases, but only a few lncRNAs have been experimentally confirmed to be associated with human diseases. Predicting new potential lncRNA-disease associations (LDAs) will help us to understand the pathogenesis of human diseases and to detect disease markers, as well as in disease diagnosis, prevention and treatment. Computational methods can effectively narrow down the screening scope of biological experiments, thereby reducing the duration and cost of such experiments. In this review, we outline recent advances in computational methods for predicting LDAs, focusing on LDA databases, lncRNA/disease similarity calculations, and advanced computational models. In addition, we analyze the limitations of various computational models and discuss future challenges and directions for development.

Myoparr-Associated and -Independent Multiple Roles of Heterogeneous Nuclear Ribonucleoprotein K during Skeletal Muscle Cell Differentiation

RNA-binding proteins (RBPs) regulate cell physiology via the formation of ribonucleic-protein complexes with coding and non-coding RNAs. RBPs have multiple functions in the same cells; however, the precise mechanism through which their pleiotropic functions are determined remains unknown. In this study, we revealed the multiple inhibitory functions of heterogeneous nuclear ribonucleoprotein K (hnRNPK) for myogenic differentiation. We first identified hnRNPK as a lncRNA Myoparr binding protein. Gain- and loss-of-function experiments showed that hnRNPK repressed the expression of myogenin at the transcriptional level. The hnRNPK-binding region of Myoparr was required to repress myogenin expression. Moreover, hnRNPK repressed the expression of a set of genes coding for aminoacyl-tRNA synthetases in a Myoparr-independent manner. Mechanistically, hnRNPK regulated the eIF2α/Atf4 pathway, one branch of the intrinsic pathways of the endoplasmic reticulum sensors, in differentiating myoblasts. Thus, our findings demonstrate that hnRNPK plays lncRNA-associated and -independent multiple roles during myogenic differentiation, indicating that the analysis of lncRNA-binding proteins will be useful for elucidating both the physiological functions of lncRNAs and the multiple functions of RBPs.

Non-Coding RNAs Associated With Radioresistance in Triple-Negative Breast Cancer

The resistance that Triple-Negative Breast Cancer (TNBC), the most aggressive breast cancer subtype, develops against radiotherapy is a complex phenomenon involving several regulators of cell metabolism and gene expression; understanding it is the only way to overcome it. We focused this review on the contribution of the two leading classes of regulatory non-coding RNAs, microRNAs (miRNAs) and long non-coding RNAs (lncRNAs), against ionizing radiation-based therapies. We found that these regulatory RNAs are mainly associated with DNA damage response, cell death, and cell cycle regulation, although they regulate other processes like cell signaling and metabolism. Several regulatory RNAs regulate multiple pathways simultaneously, such as miR-139-5p, the miR-15 family, and the lncRNA HOTAIR. On the other hand, proteins such as CHK1 and WEE1 are targeted by several regulatory RNAs simultaneously. Interestingly, the study of miRNA/lncRNA/mRNA regulation axes increases, opening new avenues for understanding radioresistance. Many of the miRNAs and lncRNAs that we reviewed here can be used as molecular markers or targeted by upcoming therapeutic options, undoubtedly contributing to a better prognosis for TNBC patients.

A comprehensive method protocol for annotation and integrated functional understanding of lncRNAs

Long non-coding RNAs (lncRNAs) are of fundamental biological importance; however, their functional role is often unclear or loosely defined as experimental characterization is challenging and bioinformatic methods are limited. We developed a novel integrated method protocol for the annotation and detailed functional characterization of lncRNAs within the genome. It combines annotation, normalization and gene expression with sequence-structure conservation, functional interactome and promoter analysis. Our protocol allows an analysis based on the tissue and biological context, and is powerful in functional characterization of experimental and clinical RNA-Seq datasets including existing lncRNAs. This is demonstrated on the uncharacterized lncRNA GATA6-AS1 in dilated cardiomyopathy.

Association predictions of genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, radiomics, drug, symptoms, environment factor, and disease networks: A comprehensive approach

Currently, the research of multi-omics, such as genomics, proteinomics, transcriptomics, microbiome, metabolomics, pathomics, and radiomics, are hot spots. The relationship between multi-omics data, drugs, and diseases has received extensive attention from researchers. At the same time, multi-omics can effectively predict the diagnosis, prognosis, and treatment of diseases. In essence, these research entities, such as genes, RNAs, proteins, microbes, metabolites, pathways as well as pathological and medical imaging data, can all be represented by the network at different levels. And some computer and biology scholars have tried to use computational methods to explore the potential relationships between biological entities. We summary a comprehensive research strategy, that is to build a multi-omics heterogeneous network, covering multimodal data, and use the current popular computational methods to make predictions. In this study, we first introduce the calculation method of the similarity of biological entities at the data level, second discuss multimodal data fusion and methods of feature extraction. Finally, the challenges and opportunities at this stage are summarized. Some scholars have used such a framework to calculate and predict. We also summarize them and discuss the challenges. We hope that our review could help scholars who are interested in the field of bioinformatics, biomedical image, and computer research.

Long Non-coding RNAs: Mechanisms, Experimental, and Computational Approaches in Identification, Characterization, and Their Biomarker Potential in Cancer

Long non-coding RNAs are diverse class of non-coding RNA molecules >200 base pairs of length having various functions like gene regulation, dosage compensation, epigenetic regulation. Dysregulation and genomic variations of several lncRNAs have been implicated in several diseases. Their tissue and developmental specific expression are contributing factors for them to be viable indicators of physiological states of the cells. Here we present an comprehensive review the molecular mechanisms and functions, state of the art experimental and computational pipelines and challenges involved in the identification and functional annotation of lncRNAs and their prospects as biomarkers. We also illustrate the application of co-expression networks on the TCGA-LIHC dataset for putative functional predictions of lncRNAs having a therapeutic potential in Hepatocellular carcinoma (HCC).

The role of lncRNA Meg3 in the proliferation of all-trans retinoic acid-treated mouse embryonic palate mesenchymal cells involves the Smad pathway

Mesenchymal cell proliferation is critical for the growth of the palate shelf. All-trans retinoic acid (atRA), as well as pathways associated with TGF-β/Smad signaling, play crucial roles in the proliferation of mouse embryonic palate mesenchymal (MEPM) cells. We have found that MEPM-cell proliferation was regulated by atRA and exogenous TGF-β3 could significantly antagonize the atRA-mediated suppression of MEPM cell proliferation, which is closely associated with the regulation of TGF-β/Smad signaling pathway. The long non-coding RNA (lncRNA) MEG3 has been reported to activate TGF-β/Smad signaling, thereby regulating cellular proliferation, differentiation, and related processes. Here, we found that Meg3 expression increased significantly in atRA-treated MEPM cells while TGF-β3 treatment markedly inhibited Meg3 expression and antagonized the effect of atRA on Meg3. Moreover, Smad2 was found to interact directly with Meg3, and atRA treatment significantly enriched Meg3 in Smad2-immunoprecipitated samples. After Meg3 deletion, the effects of atRA on the proliferation of MEPM cells and TGF-β3-dependent protein expression were lost. Hence, we speculate that Meg3 has a role in the RA-induced suppression of MEPM cell proliferation by targeting Smad2 and thereby mediating TGF-β/Smad signaling inhibition.

Lantern: an integrative repository of functional annotations for lncRNAs in the human genome

BACKGROUND

With advancements in omics technologies, the range of biological processes where long non-coding RNAs (lncRNAs) are involved, is expanding extensively, thereby generating the need to develop lncRNA annotation resources. Although, there are a plethora of resources for annotating genes, despite the extensive corpus of lncRNA literature, the available resources with lncRNA ontology annotations are rare.

RESULTS

We present a lncRNA annotation extractor and repository (Lantern), developed using PubMed's abstract retrieval engine and NCBO's recommender annotation system. Lantern's annotations were benchmarked against lncRNAdb's manually curated free text. Benchmarking analysis suggested that Lantern has a recall of 0.62 against lncRNAdb for 182 lncRNAs and precision of 0.8. Additionally, we also annotated lncRNAs with multiple omics annotations, including predicted cis-regulatory TFs, interactions with RBPs, tissue-specific expression profiles, protein co-expression networks, coding potential, sub-cellular localization, and SNPs for ~ 11,000 lncRNAs in the human genome, providing a one-stop dynamic visualization platform.

CONCLUSIONS

Lantern integrates a novel, accurate semi-automatic ontology annotation engine derived annotations combined with a variety of multi-omics annotations for lncRNAs, to provide a central web resource for dissecting the functional dynamics of long non-coding RNAs and to facilitate future hypothesis-driven experiments. The annotation pipeline and a web resource with current annotations for human lncRNAs are freely available on sysbio.lab.iupui.edu/lantern.

Long Non-Coding RNAs in Insects

Only a small subset of all the transcribed RNAs are used as a template for protein translation, whereas RNA molecules that are not translated play a very important role as regulatory non-coding RNAs (ncRNAs). Besides traditionally known RNAs (ribosomal and transfer RNAs), ncRNAs also include small non-coding RNAs (sncRNAs) and long non-coding RNAs (lncRNAs). The lncRNAs, which were initially thought to be junk, have gained a great deal attention because of their regulatory roles in diverse biological processes in animals and plants. Insects are the most abundant and diverse group of animals on this planet. Recent studies have demonstrated the role of lncRNAs in almost all aspects of insect development, reproduction, and genetic plasticity. In this review, we describe the function and molecular mechanisms of the mode of action of different insect lncRNAs discovered up to date.

LincSNP 3.0: an updated database for linking functional variants to human long non-coding RNAs, circular RNAs and their regulatory elements

We describe an updated comprehensive database, LincSNP 3.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), which aims to document and annotate disease or phenotype-associated variants in human long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs) or their regulatory elements. LincSNP 3.0 has updated with several novel features, including (i) more types of variants including single nucleotide polymorphisms (SNPs), linkage disequilibrium SNPs (LD SNPs), somatic mutations and RNA editing sites have been expanded; (ii) more regulatory elements including transcription factor binding sites (TFBSs), enhancers, DNase I hypersensitive sites (DHSs), topologically associated domains (TADs), footprintss, methylations and open chromatin regions have been added; (iii) the associations among circRNAs, regulatory elements and variants have been identified; (iv) more experimentally supported variant-lncRNA/circRNA-disease/phenotype associations have been manually collected; (v) the sources of lncRNAs, circRNAs, SNPs, somatic mutations and RNA editing sites have been updated. Moreover, four flexible online tools including Genome Browser, Variant Mapper, Circos Plotter and Functional Annotation have been developed to retrieve, visualize and analyze the data. Collectively, LincSNP 3.0 provides associations among functional variants, regulatory elements, lncRNAs and circRNAs in diseases. It will serve as an important and continually updated resource for investigating functions and mechanisms of lncRNAs and circRNAs in diseases.

LncRNA Meg3-mediated regulation of the Smad pathway in atRA-induced cleft palate

Palatal mesenchymal cell proliferation is essential to the process of palatogenesis, and the proliferation of mouse embryonic palate mesenchymal (MEPM) cells is impacted by both all-trans retinoic acid (atRA) and the TGF-β/Smad signaling pathway. The long non-coding RNA (lncRNA) MEG3 has been shown to activate TGF-β/Smad signaling and to thereby regulate cell proliferation, differentiation, and related processes. Herein, we found that atRA treatment (100 mg/kg) promoted Meg3 upregulation in MEPM cells, and that such upregulation was linked to the suppression of MEPM cell proliferation in the context of secondary palate fusion on gestational day (GD) 13 and 14. Moreover, the demethylation of specific CpG sites within the lncRNA Meg3 promoter was detected in atRA-treated MEPM cells, likely explaining the observed upregulation of this lncRNA. Smad signaling was also suppressed by atRA treatment in these cells, and RNA immunoprecipitation analyses revealed that Smad2 can directly interact with Meg3 in MEPM cells following atRA treatment. Therefore, we propose a model wherein Meg3 is involved in the suppression of MEPM cell proliferation, functioning at least in part via interacting with the Smad2 protein and thereby suppressing Smad signaling in the context of atRA-induced cleft palate.

LncRBase V.2: an updated resource for multispecies lncRNAs and ClinicLSNP hosting genetic variants in lncRNAs for cancer patients

The recent discovery of long non-coding RNA as a regulatory molecule in the cellular system has altered the concept of the functional aptitude of the genome. Since our publication of the first version of LncRBase in 2014, there has been an enormous increase in the number of annotated lncRNAs of multiple species other than Human and Mouse. LncRBase V.2 hosts information of 549,648 lncRNAs corresponding to six additional species besides Human and Mouse, viz. Rat, Fruitfly, Zebrafish, Chicken, Cow and C.elegans. It provides additional distinct features such as (i) Transcription Factor Binding Site (TFBS) in the lncRNA promoter region, (ii) sub-cellular localization pattern of lncRNAs (iii) lnc-pri-miRNAs (iv) Possible small open reading frames (sORFs) within lncRNA. (v) Manually curated information of interacting target molecules and disease association of lncRNA genes (vi) Distribution of lncRNAs across multiple tissues of all species. Moreover, we have hosted ClinicLSNP within LncRBase V.2. ClinicLSNP has a comprehensive catalogue of lncRNA variants present within breast, ovarian, and cervical cancer inferred from 561 RNA-Seq data corresponding to these cancers. Further, we have checked whether these lncRNA variants overlap with (i)Repeat elements,(ii)CGI, (iii)TFBS within lncRNA loci (iv)SNP localization in trait-associated Linkage Disequilibrium(LD) region, (v)predicted the potentially pathogenic variants and (vi)effect of SNP on lncRNA secondary structure. Overall, LncRBaseV.2 is a user-friendly database to survey, search and retrieve information about multi-species lncRNAs. Further, ClinicLSNP will serve as a useful resource for cancer specific lncRNA variants and their related information. The database is freely accessible and available at http://dibresources.jcbose.ac.in/zhumur/lncrbase2/.

The molecular structure of long non-coding RNAs: emerging patterns and functional implications

Long non-coding RNAs (lncRNAs) are recently-discovered transcripts that regulate vital cellular processes and are crucially connected to diseases. Despite their unprecedented molecular complexity, it is emerging that lncRNAs possess distinct structural motifs. Remarkably, the 3D shape and topology of full-length, native lncRNAs have been visualized for the first time in the last year. These studies reveal that lncRNA structures dictate lncRNA functions. Here, we review experimentally determined lncRNA structures and emphasize that lncRNA structural characterization requires synergistic integration of computational, biochemical and biophysical approaches. Based on these emerging paradigms, we discuss how to overcome the challenges posed by the complex molecular architecture of lncRNAs, with the goal of obtaining a detailed understanding of lncRNA functions and molecular mechanisms in the future.

lncRNAKB, a knowledgebase of tissue-specific functional annotation and trait association of long noncoding RNA

Long non-coding RNA Knowledgebase (lncRNAKB) is an integrated resource for exploring lncRNA biology in the context of tissue-specificity and disease association. A systematic integration of annotations from six independent databases resulted in 77,199 human lncRNA (224,286 transcripts). The user-friendly knowledgebase covers a comprehensive breadth and depth of lncRNA annotation. lncRNAKB is a compendium of expression patterns, derived from analysis of RNA-seq data in thousands of samples across 31 solid human normal tissues (GTEx). Thousands of co-expression modules identified via network analysis and pathway enrichment to delineate lncRNA function are also accessible. Millions of expression quantitative trait loci (cis-eQTL) computed using whole genome sequence genotype data (GTEx) can be downloaded at lncRNAKB that also includes tissue-specificity, phylogenetic conservation and coding potential scores. Tissue-specific lncRNA-trait associations encompassing 323 GWAS (UK Biobank) are also provided. LncRNAKB is accessible at http://www.lncrnakb.org/ , and the data are freely available through Open Science Framework ( https://doi.org/10.17605/OSF.IO/RU4D2 ).

The Interplay between Long Noncoding RNAs and Proteins of the Epigenetic Machinery in Ovarian Cancer

Comprehensive large-scale sequencing and bioinformatics analyses have uncovered a myriad of cancer-associated long noncoding RNAs (lncRNAs). Aberrant expression of lncRNAs is associated with epigenetic reprogramming during tumor development and progression, mainly due to their ability to interact with DNA, RNA, or proteins to regulate gene expression. LncRNAs participate in the control of gene expression patterns during development and cell differentiation and can be cell and cancer type specific. In this review, we described the potential of lncRNAs for clinical applications in ovarian cancer (OC). OC is a complex and heterogeneous disease characterized by relapse, chemoresistance, and high mortality rates. Despite advances in diagnosis and treatment, no significant improvements in long-term survival were observed in OC patients. A set of lncRNAs was associated with survival and response to therapy in this malignancy. We manually curated databases and used bioinformatics tools to identify lncRNAs implicated in the epigenetic regulation, along with examples of direct interactions between the lncRNAs and proteins of the epigenetic machinery in OC. The resources and mechanisms presented herein can improve the understanding of OC biology and provide the basis for further investigations regarding the selection of novel biomarkers and therapeutic targets.

Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

Long non-coding RNAs (lncRNAs) have been recognized as critical components of a broad genomic regulatory network and play pivotal roles in physiological and pathological processes. Identification of disease-associated lncRNAs is becoming increasingly crucial for fundamentally improving our understanding of molecular mechanisms of disease and developing novel biomarkers and therapeutic targets. Considering lower efficiency and higher time and labor cost of biological experiments, computer-aided inference of disease-associated RNAs has become a promising avenue for facilitating the study of lncRNA functions and provides complementary value for experimental studies. In this study, we first summarize data and knowledge resources publicly available for the study of lncRNA-disease associations. Then, we present an updated systematic overview of dozens of computational methods and models for inferring lncRNA-disease associations proposed in recent years. Finally, we explore the perspectives and challenges for further studies. Our study provides a guide for biologists and medical scientists to look for dedicated resources and more competent tools for accelerating the unraveling of disease-associated lncRNAs.

The Challenges and Opportunities of LncRNAs in Ovarian Cancer Research and Clinical Use

Ovarian cancer is one of the most lethal gynecological malignancies worldwide because it tends to be detected late, when the disease has already spread, and prognosis is poor. In this review we aim to highlight the importance of long non-coding RNAs (lncRNAs) in diagnosis, prognosis and treatment choice, to make progress towards increasingly personalized medicine in this malignancy. We review the effects of lncRNAs associated with ovarian cancer in the context of cancer hallmarks. We also discuss the molecular mechanisms by which lncRNAs become involved in cellular physiology; the onset, development and progression of ovarian cancer; and lncRNAs' regulatory mechanisms at the transcriptional, post-transcriptional and post-translational stages of gene expression. Finally, we compile a series of online resources useful for the study of lncRNAs, especially in the context of ovarian cancer. Future work required in the field is also discussed along with some concluding remarks.

Long noncoding RNAs: new insights in modulating mammalian spermatogenesis

Spermatogenesis is a complex differentiating developmental process in which undifferentiated spermatogonial germ cells differentiate into spermatocytes, spermatids, and finally, to mature spermatozoa. This multistage developmental process of spermatogenesis involves the expression of many male germ cell-specific long noncoding RNAs (lncRNAs) and highly regulated and specific gene expression. LncRNAs are a recently discovered large class of noncoding cellular transcripts that are still relatively unexplored. Only a few of them have post-meiotic; however, lncRNAs are involved in many cellular biological processes. The expression of lncRNAs is biologically relevant in the highly dynamic and complex program of spermatogenesis and has become a research focus in recent genome studies. This review considers the important roles and novel regulatory functions whereby lncRNAs modulate mammalian spermatogenesis.

Computational models for lncRNA function prediction and functional similarity calculation

From transcriptional noise to dark matter of biology, the rapidly changing view of long non-coding RNA (lncRNA) leads to deep understanding of human complex diseases induced by abnormal expression of lncRNAs. There is urgent need to discern potential functional roles of lncRNAs for further study of pathology, diagnosis, therapy, prognosis, prevention of human complex disease and disease biomarker detection at lncRNA level. Computational models are anticipated to be an effective way to combine current related databases for predicting most potential lncRNA functions and calculating lncRNA functional similarity on the large scale. In this review, we firstly illustrated the biological function of lncRNAs from five biological processes and briefly depicted the relationship between mutations or dysfunctions of lncRNAs and human complex diseases involving cancers, nervous system disorders and others. Then, 17 publicly available lncRNA function-related databases containing four types of functional information content were introduced. Based on these databases, dozens of developed computational models are emerging to help characterize the functional roles of lncRNAs. We therefore systematically described and classified both 16 lncRNA function prediction models and 9 lncRNA functional similarity calculation models into 8 types for highlighting their core algorithm and process. Finally, we concluded with discussions about the advantages and limitations of these computational models and future directions of lncRNA function prediction and functional similarity calculation. We believe that constructing systematic functional annotation systems is essential to strengthen the prediction accuracy of computational models, which will accelerate the identification process of novel lncRNA functions in the future.

SKmDB: an integrated database of next generation sequencing information in skeletal muscle

MOTIVATION

Skeletal muscles have indispensable functions and also possess prominent regenerative ability. The rapid emergence of Next Generation Sequencing (NGS) data in recent years offers us an unprecedented perspective to understand gene regulatory networks governing skeletal muscle development and regeneration. However, the data from public NGS database are often in raw data format or processed with different procedures, causing obstacles to make full use of them.

RESULTS

We provide SKmDB, an integrated database of NGS information in skeletal muscle. SKmDB not only includes all NGS datasets available in the human and mouse skeletal muscle tissues and cells, but also provide preliminary data analyses including gene/isoform expression levels, gene co-expression subnetworks, as well as assembly of putative lincRNAs, typical and super enhancers and transcription factor hotspots. Users can efficiently search, browse and visualize the information with the well-designed user interface and server side. SKmDB thus will offer wet lab biologists useful information to study gene regulatory mechanisms in the field of skeletal muscle development and regeneration.

AVAILABILITY AND IMPLEMENTATION

Freely available on the web at http://sunlab.cpy.cuhk.edu.hk/SKmDB.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Comprehensive Characterization of Somatic Mutations Impacting lncRNA Expression for Pan-Cancer

Somatic mutations have long been recognized as an important feature of cancer. However, analysis of somatic mutations, to date, has focused almost entirely on the protein coding regions of the genome. The potential roles of somatic mutations in human long noncoding RNAs (lncRNAs) are therefore largely unknown, particularly their functional significance across different cancer types. In this study, we characterized some lncRNAs whose expression was affected by somatic mutations (defined as MutLncs) and constructed global MutLnc landscapes across 17 cancer types by systematically integrating multiple levels of data. MutLncs were commonly downregulated and carried low mutation frequencies and non-silent mutations in most cancer types. Co-occurrence analysis in pan-cancer highlighted combined patterns of specific MutLncs, suggesting that a number of MutLncs influence diverse cancer types through combination effects. Several conserved and cancer-specific functions of MutLncs were determined. We further explored the somatic mutations affecting lncRNA expression via mixed and unmixed effects, which led to specific functions in pan-cancer. Survival analysis indicated that MutLncs and co-occurrence pairs can potentially serve as cancer biomarkers. Clarification of the specific roles of MutLncs in human cancers could be beneficial for understanding the molecular pathogenesis of different cancer types and developing the appropriate treatments.

A novel lncRNA-mediated trans-regulatory mechanism in the development of cleft palate in mouse

BACKGROUND

Increasing evidence indicates that long non-coding RNAs (lncRNAs) play crucial regulatory roles in epithelial-mesenchymal transition (EMT). However, the regulatory mechanisms during EMT of the medial edge epithelium (MEE) remain elusive. The aim of this work is to reveal a novel lncRNA-regulated dysfunction of EMT involved in the development of cleft palate (CP).

METHODS

C57BL/6 J mice at embryonic gestation day 14.5 (n = 6, 3 case samples vs. 3 control samples) were used to establish the CP model for lncRNA-mRNA co-expression profile analysis after high-throughput sequencing. Functional predictions for the differentially expressed lncRNA-mRNA co-expression with transcription factor (TF)-target gene relationship Gene Ontology/Kyoto Encyclopedia of Genes and Genomes pathway (GO/KEGG) analyses identified the regulatory "lncRNA-TF-target gene" trans model.

RESULTS

A total of 583 differentially expressed lncRNAs and 703 differentially expressed mRNAs were identified. The results of trans analysis revealed that some TFs (LEF1, SMAD4, and FOXD3) regulate lncRNAs and gene expression. Finally, we identified the NONMMUT034790.2-LEF1-SMAD7 co-expression trans-regulatory network that might be associated with CP.

CONCLUSIONS

Our results revealed that NONMMUT034790.2 might be a novel epigenetic biomarker in CP. The integration of lncRNA modulators into trans-regulatory networks will further enhance our understanding of lncRNA functions and regulatory mechanisms during palatal fusion in ATRA-induced mouse CP.

Workflow Development for the Functional Characterization of ncRNAs

During the last decade, ncRNAs have been investigated intensively and revealed their regulatory role in various biological processes. Worldwide research efforts have identified numerous ncRNAs and multiple RNA subtypes, which are attributed to diverse functionalities known to interact with different functional layers, from DNA and RNA to proteins. This makes the prediction of functions for newly identified ncRNAs challenging. Current bioinformatics and systems biology approaches show promising results to facilitate an identification of these diverse ncRNA functionalities. Here, we review (a) current experimental protocols, i.e., for Next Generation Sequencing, for a successful identification of ncRNAs; (b) sequencing data analysis workflows as well as available computational environments; and (c) state-of-the-art approaches to functionally characterize ncRNAs, e.g., by means of transcriptome-wide association studies, molecular network analyses, or artificial intelligence guided prediction. In addition, we present a strategy to cover the identification and functional characterization of unknown transcripts by using connective workflows.

Short and Long Noncoding RNAs Regulate the Epigenetic Status of Cells

SIGNIFICANCE

The concepts of junk DNA and transcriptional noise are long gone as the existence of noncoding RNAs (ncRNAs) has been tested extensively in recent years. Given that the epigenetic status of cells affects many biological processes, how ncRNAs mechanistically contribute to these processes is of great interest. Recent Advances: Recent studies show that various ncRNAs interact with epigenetic and/or transcription factors to modulate the epigenetic status of cells directly and/or indirectly. There exists growing interest in the field of cardiovascular research to understand the roles of ncRNAs. Due to the large number of ncRNAs in the mammalian genome, only a handful of ncRNAs have been functionally elucidated, which makes it difficult to understand how ncRNAs interact with protein-coding genes and their encoded proteins.

CRITICAL ISSUES

Although the canonical function of microRNAs (miRNAs) to inhibit the translation of protein-coding genes is well established, the number of functionally annotated long noncoding RNAs (lncRNAs) is still small, which is especially true in the heart.

FUTURE DIRECTIONS

Future studies must connect the epigenetic controls of various cellular phenomena by incorporating both miRNAs and lncRNAs. Antioxid. Redox Signal. 29, 832-845.

Developmental Dynamics of Long Noncoding RNA Expression during Sexual Fruiting Body Formation in Fusarium graminearum

Long noncoding RNA (lncRNA) plays important roles in sexual development in eukaryotes. In filamentous fungi, however, little is known about the expression and roles of lncRNAs during fruiting body formation. By profiling developmental transcriptomes during the life cycle of the plant-pathogenic fungus Fusarium graminearum, we identified 547 lncRNAs whose expression was highly dynamic, with about 40% peaking at the meiotic stage. Many lncRNAs were found to be antisense to mRNAs, forming 300 sense-antisense pairs. Although small RNAs were produced from these overlapping loci, antisense lncRNAs appeared not to be involved in gene silencing pathways. Genome-wide analysis of small RNA clusters identified many silenced loci at the meiotic stage. However, we found transcriptionally active small RNA clusters, many of which were associated with lncRNAs. Also, we observed that many antisense lncRNAs and their respective sense transcripts were induced in parallel as the fruiting bodies matured. The nonsense-mediated decay (NMD) pathway is known to determine the fates of lncRNAs as well as mRNAs. Thus, we analyzed mutants defective in NMD and identified a subset of lncRNAs that were induced during sexual development but suppressed by NMD during vegetative growth. These results highlight the developmental stage-specific nature and functional potential of lncRNA expression in shaping the fungal fruiting bodies and provide fundamental resources for studying sexual stage-induced lncRNAs.

Fusarium graminearum is the causal agent of the head blight on our major staple crops, wheat and corn. The fruiting body formation on the host plants is indispensable for the disease cycle and epidemics. Long noncoding RNA (lncRNA) molecules are emerging as key regulatory components for sexual development in animals and plants. To date, however, there is a paucity of information on the roles of lncRNAs in fungal fruiting body formation. Here we characterized hundreds of lncRNAs that exhibited developmental stage-specific expression patterns during fruiting body formation. Also, we discovered that many lncRNAs were induced in parallel with their overlapping transcripts on the opposite DNA strand during sexual development. Finally, we found a subset of lncRNAs that were regulated by an RNA surveillance system during vegetative growth. This research provides fundamental genomic resources that will spur further investigations on lncRNAs that may play important roles in shaping fungal fruiting bodies.

The emergence of long non-coding RNAs in hepatocellular carcinoma: an update

Hepatocellular carcinoma (HCC) accounting for roughly 90% of all primary liver neoplasms is the sixth most frequent neoplasm and the second prominent reason of tumor fatality worldwide. As regulators of diverse biological processes, long non-coding RNAs (lncRNAs) are involved in onset and development of neoplasms. With the continuous booming of well-featured lncRNAs in HCC from 2016 to now, we reviewed the newly-presented comprehension about the relationship between lncRNAs and HCC in this study. To be specific, we summarized the overview function and study tools of lncRNAs, elaborated the roles of lncRNAs in HCC, and sketched the molecule mechanisms of lncRNAs in HCC. In addition, the application of lncRNAs serving as biomarkers in early diagnosis and outcome prediction of HCC patients was highlighted.

Annotating Diseases Using Human Phenotype Ontology Improves Prediction of Disease-Associated Long Non-coding RNAs

Recently, many long non-coding RNAs (lncRNAs) have been identified and their biological function has been characterized; however, our understanding of their underlying molecular mechanisms related to disease is still limited. To overcome the limitation in experimentally identifying disease-lncRNA associations, computational methods have been proposed as a powerful tool to predict such associations. These methods are usually based on the similarities between diseases or lncRNAs since it was reported that similar diseases are associated with functionally similar lncRNAs. Therefore, prediction performance is highly dependent on how well the similarities can be captured. Previous studies have calculated the similarity between two diseases by mapping exactly each disease to a single Disease Ontology (DO) term, and then use a semantic similarity measure to calculate the similarity between them. However, the problem of this approach is that a disease can be described by more than one DO terms. Until now, there is no annotation database of DO terms for diseases except for genes. In contrast, Human Phenotype Ontology (HPO) is designed to fully annotate human disease phenotypes. Therefore, in this study, we constructed disease similarity networks/matrices using HPO instead of DO. Then, we used these networks/matrices as inputs of two representative machine learning-based and network-based ranking algorithms, that is, regularized least square and heterogeneous graph-based inference, respectively. The results showed that the prediction performance of the two algorithms on HPO-based is better than that on DO-based networks/matrices. In addition, our method can predict 11 novel cancer-associated lncRNAs, which are supported by literature evidence.

Functional enrichment analysis based on long noncoding RNA associations

Background

Differential gene expression analysis using RNA-seq data is a popular approach for discovering specific regulation mechanisms under certain environmental settings. Both gene ontology (GO) and KEGG pathway enrichment analysis are major processes for investigating gene groups that participate in common biological responses or possess related functions. However, traditional approaches based on differentially expressed genes only detect a few significant GO terms and pathways, which are frequently insufficient to explain all-inclusive gene regulation mechanisms.

Methods

Transcriptomes of survivin (birc5) gene knock-down experimental and wild-type control zebrafish embryos were sequenced and assembled, and a differential expression (DE) gene list was obtained for traditional functional enrichment analysis. In addition to including DE genes with significant fold-change levels, we considered additional associated genes near or overlapped with differentially expressed long noncoding RNAs (DE lncRNAs), which may directly or indirectly activate or inhibit target genes and play important roles in regulation networks. Both the original DE gene list and the additional DE lncRNA-associated genes were combined to perform a comprehensive overrepresentation analysis.

Results

In this study, a total of 638 DE genes and 616 DE lncRNA-associated genes (lncGenes) were leveraged simultaneously in searching for significant GO terms and KEGG pathways. Compared to the traditional approach of only using a differential expression gene list, the proposed method of employing DE lncRNA-associated genes identified several additional important GO terms and KEGG pathways. In GO enrichment analysis, 60% more GO terms were obtained, and several neuron development functional terms were retrieved as complete annotations. We also observed that additional important pathways such as the FoxO and MAPK signaling pathways were retrieved, which were shown in previous reports to play important roles in apoptosis and neuron development functions regulated by the survivin gene.

Conclusions

We demonstrated that incorporating genes near or overlapped with DE lncRNAs into the DE gene list outperformed the traditional enrichment analysis method for effective biological functional interpretations. These hidden interactions between lncRNAs and target genes could facilitate more comprehensive analyses.

T cells are influenced by a long non-coding RNA in the autoimmune associated PTPN2 locus

Non-coding SNPs in the protein tyrosine phosphatase non-receptor type 2 (PTPN2) locus have been linked with several autoimmune diseases, including rheumatoid arthritis, type I diabetes, and inflammatory bowel disease. However, the functional consequences of these SNPs are poorly characterized. Herein, we show in blood cells that SNPs in the PTPN2 locus are highly correlated with DNA methylation levels at four CpG sites downstream of PTPN2 and expression levels of the long non-coding RNA (lncRNA) LINC01882 downstream of these CpG sites. We observed that LINC01882 is mainly expressed in T cells and that anti-CD3/CD28 activated naïve CD4⁺ T cells downregulate the expression of LINC01882. RNA sequencing analysis of LINC01882 knockdown in Jurkat T cells, using a combination of antisense oligonucleotides and RNA interference, revealed the upregulation of the transcription factor ZEB1 and kinase MAP2K4, both involved in IL-2 regulation. Overall, our data suggests the involvement of LINC01882 in T cell activation and hints towards an auxiliary role of these non-coding SNPs in autoimmunity associated with the PTPN2 locus.

Identification of Transposable Elements Contributing to Tissue-Specific Expression of Long Non-Coding RNAs

It has been recently suggested that transposable elements (TEs) are re-used as functional elements of long non-coding RNAs (lncRNAs). This is supported by some examples such as the human endogenous retrovirus subfamily H (HERVH) elements contained within lncRNAs and expressed specifically in human embryonic stem cells (hESCs), as required to maintain hESC identity. There are at least two unanswered questions about all lncRNAs. How many TEs are re-used within lncRNAs? Are there any other TEs that affect tissue specificity of lncRNA expression? To answer these questions, we comprehensively identify TEs that are significantly related to tissue-specific expression levels of lncRNAs. We downloaded lncRNA expression data corresponding to normal human tissue from the Expression Atlas and transformed the data into tissue specificity estimates. Then, Fisher's exact tests were performed to verify whether the presence or absence of TE-derived sequences influences the tissue specificity of lncRNA expression. Many TE-tissue pairs associated with tissue-specific expression of lncRNAs were detected, indicating that multiple TE families can be re-used as functional domains or regulatory sequences of lncRNAs. In particular, we found that the antisense promoter region of L1PA2, a LINE-1 subfamily, appears to act as a promoter for lncRNAs with placenta-specific expression.

Long non-coding RNAs and complex diseases: from experimental results to computational models

LncRNAs have attracted lots of attentions from researchers worldwide in recent decades. With the rapid advances in both experimental technology and computational prediction algorithm, thousands of lncRNA have been identified in eukaryotic organisms ranging from nematodes to humans in the past few years. More and more research evidences have indicated that lncRNAs are involved in almost the whole life cycle of cells through different mechanisms and play important roles in many critical biological processes. Therefore, it is not surprising that the mutations and dysregulations of lncRNAs would contribute to the development of various human complex diseases. In this review, we first made a brief introduction about the functions of lncRNAs, five important lncRNA-related diseases, five critical disease-related lncRNAs and some important publicly available lncRNA-related databases about sequence, expression, function, etc. Nowadays, only a limited number of lncRNAs have been experimentally reported to be related to human diseases. Therefore, analyzing available lncRNA–disease associations and predicting potential human lncRNA–disease associations have become important tasks of bioinformatics, which would benefit human complex diseases mechanism understanding at lncRNA level, disease biomarker detection and disease diagnosis, treatment, prognosis and prevention. Furthermore, we introduced some state-of-the-art computational models, which could be effectively used to identify disease-related lncRNAs on a large scale and select the most promising disease-related lncRNAs for experimental validation. We also analyzed the limitations of these models and discussed the future directions of developing computational models for lncRNA research.

A comprehensive review of web-based non-coding RNA resources for cancer research

Non-coding RNAs include many kinds of RNAs that did not encode proteins. Recent evidences reveal that ncRNAs play critical roles in initiation and progression of cancers. But it is not easy for cancer biologists and medical doctors to easily know the potential roles of ncRNAs in cancer and retrieve the information of ncRNAs under their investigations. To make the available web-based resources more accessible and understandable, we made a comprehensive review for 49 web-based resources of three types of ncRNAs, i.e., microRNAs (miRNAs), long non-coding RNAs (lncRNAs) and circular RNAs (circRNAs). We also listed some preferred resources for 6 different types of analyses related to ncRNAs.

FARNA: knowledgebase of inferred functions of non-coding RNA transcripts

Non-coding RNA (ncRNA) genes play a major role in control of heterogeneous cellular behavior. Yet, their functions are largely uncharacterized. Current available databases lack in-depth information of ncRNA functions across spectrum of various cells/tissues. Here, we present FARNA, a knowledgebase of inferred functions of 10,289 human ncRNA transcripts (2,734 microRNA and 7,555 long ncRNA) in 119 tissues and 177 primary cells of human. Since transcription factors (TFs) and TF co-factors (TcoFs) are crucial components of regulatory machinery for activation of gene transcription, cellular processes and diseases in which TFs and TcoFs are involved suggest functions of the transcripts they regulate. In FARNA, functions of a transcript are inferred from TFs and TcoFs whose genes co-express with the transcript controlled by these TFs and TcoFs in a considered cell/tissue. Transcripts were annotated using statistically enriched GO terms, pathways and diseases across cells/tissues based on guilt-by-association principle. Expression profiles across cells/tissues based on Cap Analysis of Gene Expression (CAGE) are provided. FARNA, having the most comprehensive function annotation of considered ncRNAs across widest spectrum of human cells/tissues, has a potential to greatly contribute to our understanding of ncRNA roles and their regulatory mechanisms in human. FARNA can be accessed at: http://cbrc.kaust.edu.sa/farna

Long Noncoding RNAs as Biomarkers in Cancer

Long noncoding RNAs (lncRNAs) are a relatively well-characterized class of noncoding RNA (ncRNA) molecules, involved in the regulation of various cell processes, including transcription, intracellular trafficking, and chromosome remodeling. Their deregulation has been associated with the development and progression of various cancer types, the fact which makes them suitable as biomarkers for cancer diagnosis and prognosis. In recent years, detection of cancer-associated lncRNAs in body fluids of cancer patients has proven itself as an especially valuable method to effectively diagnose cancer. Cancer diagnosis and prognosis employing circulating lncRNAs are preferential when compared to classical biopsies of tumor tissues, especially due to their noninvasiveness, and have great potential for routine usage in clinical practice. Thus, this review focuses on summarizing the perspectives of lncRNAs as biomarkers in cancer, based on evaluating their expression profiles determined in body fluids of cancer patients.

Non-Coding RNAs in Lung Cancer: Contribution of Bioinformatics Analysis to the Development of Non-Invasive Diagnostic Tools

Lung cancer is currently the leading cause of cancer related mortality due to late diagnosis and limited treatment intervention. Non-coding RNAs are not translated into proteins and have emerged as fundamental regulators of gene expression. Recent studies reported that microRNAs and long non-coding RNAs are involved in lung cancer development and progression. Moreover, they appear as new promising non-invasive biomarkers for early lung cancer diagnosis. Here, we highlight their potential as biomarker in lung cancer and present how bioinformatics can contribute to the development of non-invasive diagnostic tools. For this, we discuss several bioinformatics algorithms and software tools for a comprehensive understanding and functional characterization of microRNAs and long non-coding RNAs.

BmncRNAdb: a comprehensive database of non-coding RNAs in the silkworm, Bombyx mori

BACKGROUND

Long non-coding RNAs (lncRNAs) may play critical roles in a wide range of developmental processes of higher organisms. Recently, lncRNAs have been widely identified across eukaryotes and many databases of lncRNAs have been developed for human, mouse, fruit fly, etc. However, there is rare information about them in the only completely domesticated insect, silkworm (Bombyx mori).

DESCRIPTION

In this study, we systematically scanned lncRNAs using the available silkworm RNA-seq data and public unigenes. Finally, we identified and collected 6281 lncRNAs in the silkworm. Besides, we also collected 1986 microRNAs (miRNAs) from previous studies. Then, we organized them into a comprehensive and web-based database, BmncRNAdb. This database offers a user-friendly interface for data browse and online analysis as well as the three online tools for users to predict the target genes of lncRNA or miRNA.

CONCLUSIONS

We have systematically identified and collected the silkworm lncRNAs and constructed a comprehensive database of the silkworm lncRNAs and miRNAs. This work gives a glimpse into lncRNAs of the silkworm and lays foundations for the ncRNAs study of the silkworm and other insects in the future. The BmncRNAdb is freely available at http://gene.cqu.edu.cn/BmncRNAdb/index.php .

LncRNA H19-mediated mouse cleft palate induced by all-trans retinoic acid

Long noncoding RNAs (lncRNAs) are the new class of transcripts and pervasively transcribed in the genome, which have been found to play important functional roles in many tissues and organs. LncRNAs can interact with target gene to exert their functions. However, the function and mechanism of lncRNA in cleft palate (CP) development remain elusive. Here, we investigated the role of lncRNA H19 and its target gene insulin-like growth factor 2 (IGF2) in CP of mice. All-trans retinoic acid (atRA) is a well-known teratogenic effecter of CP. After establishment of the CP mouse model using atRA in vivo, we found that the rate of CP in mice was 100%. The tail lengths of fetuses in atRA-treated mice were shorter than those of control mice from embryonic day (E)12 to E17. The expression of lncRNA H19 and IGF2 were embryo age-related differences between atRNA-treated and control mice. In addition, the the relationship between lncRNA H19 and IGF2 were negative correlation in the critical period of developmental palate. These findings suggest that lncRNA H19 mediate atRA-induced CP in mice.

Mapping Human Pluripotent-to-Cardiomyocyte Differentiation: Methylomes, Transcriptomes, and Exon DNA Methylation "Memories"

The directed differentiation of human cardiomyocytes (CMs) from pluripotent cells provides an invaluable model for understanding mechanisms of cell fate determination and offers considerable promise in cardiac regenerative medicine. Here, we utilize a human embryonic stem cell suspension bank, produced according to a good manufacturing practice, to generate CMs using a fully defined and small molecule-based differentiation strategy. Primitive and cardiac mesoderm purification was used to remove non-committing and multi-lineage populations and this significantly aided the identification of key transcription factors, lncRNAs, and essential signaling pathways that define cardiomyogenesis. Global methylation profiles reflect CM development and we report on CM exon DNA methylation "memories" persisting beyond transcription repression and marking the expression history of numerous developmentally regulated genes, especially transcription factors.

A novel long non-coding RNA in the rheumatoid arthritis risk locus TRAF1-C5 influences C5 mRNA levels

Long non-coding RNAs (lncRNAs) can regulate the transcript levels of genes in the same genomic region. These locally acting lncRNAs have been found deregulated in human disease and some have been shown to harbour quantitative trait loci (eQTLs) in autoimmune diseases. However, lncRNAs linked to the transcription of candidate risk genes in loci associated to rheumatoid arthritis (RA) have not yet been identified. The TRAF1 and C5 risk locus shows evidence of multiple eQTLs and transcription of intergenic non-coding sequences. Here, we identified a non-coding transcript (C5T1lncRNA) starting in the 3' untranslated region (UTR) of C5. RA-relevant cell types express C5T1lncRNA and RNA levels are further enhanced by specific immune stimuli. C5T1lncRNA is expressed predominantly in the nucleus and its expression correlates positively with C5 mRNA in various tissues (P=0.001) and in peripheral blood mononuclear cells (P=0.02) indicating transcriptional co-regulation. Knockdown results in a concurrent decrease in C5 mRNA levels but not of other neighbouring genes. Overall, our data show the identification of a novel lncRNA C5T1lncRNA that is fully located in the associated region and influences transcript levels of C5, a gene previously linked to RA pathogenesis.

Long non-coding RNA regulation of reproduction and development

Noncoding RNAs (ncRNAs) have long been known to play vital roles in eukaryotic gene regulation. Studies conducted over a decade ago revealed that maturation of spliced, polyadenylated coding mRNA occurs by reactions involving small nuclear RNAs and small nucleolar RNAs; mRNA translation depends on activities mediated by transfer RNAs and ribosomal RNAs, subject to negative regulation by micro RNAs; transcriptional competence of sex chromosomes and some imprinted genes is regulated in cis by ncRNAs that vary by species; and both small-interfering RNAs and piwi-interacting RNAs bound to Argonaute-family proteins regulate post-translational modifications on chromatin and local gene expression states. More recently, gene-regulating noncoding RNAs have been identified, such as long intergenic and long noncoding RNAs (collectively referred to as lncRNAs)--a class totaling more than 100,000 transcripts in humans, which include some of the previously mentioned RNAs that regulate dosage compensation and imprinted gene expression. Here, we provide an overview of lncRNA activities, and then review the role of lncRNAs in processes vital to reproduction, such as germ cell specification, sex determination and gonadogenesis, sex hormone responses, meiosis, gametogenesis, placentation, non-genetic inheritance, and pathologies affecting reproductive tissues. Results from many species are presented to illustrate the evolutionarily conserved processes lncRNAs are involved in.

Genetics of RA susceptibility, what comes next?

Genome-wide association studies (GWASs) have been used to great effect to identify genetic susceptibility loci for complex disease. A series of GWAS and meta-analyses have informed the discovery of over 100 loci for rheumatoid arthritis (RA). In common with findings in other autoimmune diseases the lead signals for the majority of these loci do not map to known gene sequences. In order to realise the benefit of investment in GWAS studies it is vital we determine how disease associated alleles function to influence disease processes. This is leading to rapid development in our knowledge as to the function of non-coding regions of the genome. Here we consider possible functional mechanisms for intergenic RA-associated variants which lie within lncRNA sequences.

Detection of long-chain non-encoding RNA differential expression in non-small cell lung cancer by microarray analysis and preliminary verification

Long‑chain non‑coding RNAs (lncRNAs) have been shown to be involved in the development and progression of non‑small cell lung cancer (NSCLC). However, the roles of lncRNAs in NSCLC are not well understood. In this study, a high‑throughput microarray was used to compare the lncRNA and mRNA expression profiles in NSCLC and normal tissue (NT) samples. Several candidate adenocarcinoma‑associated lncRNAs were verified by reverse transcription‑quantitative polymerase chain reaction (RT‑qPCR). Using abundant and varied probes, we were able to assess 30,586 lncRNAs and 26,109 coding transcripts in our microarray. It was observed that 1,242 lncRNAs and 1,102 mRNAs were differentially expressed (≥2‑fold change) in NSCLC compared with NT samples, indicating that numerous lncRNAs were significantly upregulated or downregulated in NSCLC. We also observed via RT‑qPCR that 10 lncRNAs were aberrantly expressed in NSCLC compared with histologically matched normal lung tissues. Among these, RP11‑385J1.2 and TUBA4B were the most aberrantly expressed lncRNAs, as estimated by RT‑qPCR in 90 pairs of NSCLC and NT samples. In conclusion, the present study detected the lncRNA expression patterns in NSCLC by microarray. The results revealed that a number of lncRNAs were differentially expressed in NSCLC tissues, suggesting that they may play a key role in tumor development.