Tissue-specific Co-expression of Long Non-coding and Coding RNAs Associated with Breast Cancer

The Role of Circulating MicroRNAs in the Prediction of Response to Neoadjuvant Chemotherapy in Locally Advanced Breast Cancer in the Indian Population

INTRODUCTION

MicroRNAs (miRNAs) are known to play an important role in cancer cell proliferation, susceptibility of cancer cells to chemotherapy, and patient survival. Identifying miRNAs that can predict response to chemotherapy in locally advanced breast cancer (LABC), the most common variant, can help to choose appropriate drug regimens to suit the epigenetic profile of individual patients.

OBJECTIVE

To investigate the expression of the differentially expressed miRNAs identified by next-generation sequencing from a pilot study involving cases and controls, in peripheral blood mononuclear cells (PBMC) of patients with LABC during the course of neoadjuvant chemotherapy (NAC) and determine their role in response to chemotherapy.

METHODS

This study included 30 newly diagnosed LABC patients. Peripheral blood from every participant was collected before the start of chemotherapy, at the end of the third cycle, and at the end of the seventh cycle of NAC. Based on the results of a pilot study in a similar population with suitable controls, four differentially expressed miRNAs namely miR-24-2, miR-192-5p, miR-3609, and miR-664b-3p were considered to be validated in this study. The expression of these four miRNAs was examined by qRT-PCR, and their association with response to chemotherapy was analyzed.

RESULT

 A significant change in the expression of miR-192-5p was found in responders (p = 0.001) over a period of seven cycles and the difference between the expression of miR-24-2 from baseline to the seventh cycle of NAC was higher in responders while compared to the non-responders (p < 0.05).

CONCLUSION

miR-192-5p and miR-24-2 were identified as predictive biomarkers for response to NAC in south Indian patients with LABC.

Inferring cell developmental stage-specific lncRNA regulation in the developing human neocortex with CDSlncR

Noncoding RNAs (ncRNAs) occupy ~98% of the transcriptome in human, and are usually not translated into proteins. Among ncRNAs, long non-coding RNAs (lncRNAs, >200 nucleotides) are important regulators to modulate gene expression, and are involved in many biological processes (e.g., cell development). To study lncRNA regulation, many computational approaches or tools have been proposed by using bulk transcriptomics data. Nevertheless, previous bulk data-driven methods are mostly limited to explore the lncRNA regulation regarding all of cells, instead of the lncRNA regulation specific to cell developmental stages. Fortunately, recent advance in single-cell sequencing data has provided a way to investigate cell developmental stage-specific lncRNA regulation. In this work, we present a novel computational method, CDSlncR (Cell Developmental Stage-specific lncRNA regulation), which combines putative lncRNA-target binding information with single-cell transcriptomics data to infer cell developmental stage-specific lncRNA regulation. For each cell developmental stage, CDSlncR constructs a cell developmental stage-specific lncRNA regulatory network in the cell developmental stage. To illustrate the effectiveness of CDSlncR, we apply CDSlncR into single-cell transcriptomics data of the developing human neocortex for exploring lncRNA regulation across different human neocortex developmental stages. Network analysis shows that the lncRNA regulation is unique in each developmental stage of human neocortex. As a case study, we also perform particular analysis on the cell developmental stage-specific lncRNA regulation related to 18 known lncRNA biomarkers in autism spectrum disorder. Finally, the comparison result indicates that CDSlncR is an effective method for predicting cell developmental stage-specific lncRNA targets. CDSlncR is available at https://github.com/linxi159/CDSlncR.

Long Non-Coding RNAs: Tools for Understanding and Targeting Cancer Pathways

The regulatory nature of long non-coding RNAs (lncRNAs) has been well established in various processes of cellular growth, development, and differentiation. Therefore, it is vital to examine their contribution to cancer development. There are ample examples of lncRNAs whose cellular levels are significantly associated with clinical outcomes. However, whether these non-coding molecules can work as either key drivers or barriers to cancer development remains unknown. The current review aims to discuss some well-characterised lncRNAs in the process of oncogenesis and extrapolate the extent of their decisive contribution to tumour development. We ask if these lncRNAs can independently initiate neoplastic lesions or they always need the modulation of well characterized oncogenes or tumour suppressors to exert their functional properties. Finally, we discuss the emerging genetic approaches and appropriate animal and humanised models that can significantly contribute to the functional dissection of lncRNAs in cancer development and progression.

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).

Differential Co-Expression Analyses Allow the Identification of Critical Signalling Pathways Altered during Tumour Transformation and Progression

Biological systems respond to perturbations through the rewiring of molecular interactions, organised in gene regulatory networks (GRNs). Among these, the increasingly high availability of transcriptomic data makes gene co-expression networks the most exploited ones. Differential co-expression networks are useful tools to identify changes in response to an external perturbation, such as mutations predisposing to cancer development, and leading to changes in the activity of gene expression regulators or signalling. They can help explain the robustness of cancer cells to perturbations and identify promising candidates for targeted therapy, moreover providing higher specificity with respect to standard co-expression methods. Here, we comprehensively review the literature about the methods developed to assess differential co-expression and their applications to cancer biology. Via the comparison of normal and diseased conditions and of different tumour stages, studies based on these methods led to the definition of pathways involved in gene network reorganisation upon oncogenes’ mutations and tumour progression, often converging on immune system signalling. A relevant implementation still lagging behind is the integration of different data types, which would greatly improve network interpretability. Most importantly, performance and predictivity evaluation of the large variety of mathematical models proposed would urgently require experimental validations and systematic comparisons. We believe that future work on differential gene co-expression networks, complemented with additional omics data and experimentally tested, will considerably improve our insights into the biology of tumours.

CORAZON: a web server for data normalization and unsupervised clustering based on expression profiles

Objective

Data normalization and clustering are mandatory steps in gene expression and downstream analyses, respectively. However, user-friendly implementations of these methodologies are available exclusively under expensive licensing agreements, or in stand-alone scripts developed, reflecting on a great obstacle for users with less computational skills.

Results

We developed an online tool called CORAZON (Correlations Analyses Zipper Online), which implements three unsupervised learning methods to cluster gene expression datasets in a friendly environment. It allows the usage of eight gene expression normalization/transformation methodologies and the attribute’s influence. The normalizations requiring the gene length only could be performed to RNA-seq, meanwhile the others can be used with microarray and/or NanoString data. Clustering methodologies performances were evaluated through five models with accuracies between 92 and 100%. We applied our tool to obtain functional insights of non-coding RNAs (ncRNAs) based on Gene Ontology enrichment of clusters in a dataset generated by the ENCODE project. The clusters where the majority of transcripts are coding genes were enriched in Cellular, Metabolic, Transports, and Systems Development categories. Meanwhile, the ncRNAs were enriched in the Detection of Stimulus, Sensory Perception, Immunological System, and Digestion categories. CORAZON source-code is freely available at https://gitlab.com/integrativebioinformatics/corazon and the web-server can be accessed at http://corazon.integrativebioinformatics.me.

Gene expression of Epithelial Membrane Protein 2 gene and β1-Integrin gene in patients with breast cancer

Background

Breast cancer is the most common invasive cancer and the leading cause of cancer death in women. The function of over a thousand genes is reported as affected by genetic modifications in breast cancer.

Objectives

To study the gene expression of Epithelial Membrane 2 (EMP2) and β1-Integrin genes in patients with breast cancer.

Subjects and methods

This study was carried out by cooperation between the Biochemistry Division Department of Chemistry, Faculty of Science and Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Menoufia University. This study included 120 subjects divided into 2 groups Group I: Included 60 women with breast cancer undergoing modified radical mastectomy. Tissue specimens were taken from the cancerous breast tissue and from the marginal healthy breast tissues. Group II: Included 60 age and sex-matched apparently healthy women served as a control group. All patients participants were subjected to full history taking, general clinical examination, abdominal ultrasound, CT-scan for abdomen, mammography, fine needle biopsy, histopathological examination, immunostaining of tissues, metastatic work up (chest x-ray and bone scan) and laboratory investigations including: Complete blood count (patients and controls), serum carbohydrate antigen 15–3 (patients and controls), detection of EMP2 and β1-Integrin genes expression in the tissue samples by formation of cDNA by reverse transcription PCR after RNA extraction and real-time PCR using SYBR Green technique.

Results

Compared to healthy tissues, the breast cancer tissues had significant higher EMP2 and β1-Integringene expression levels. Also, there was a significant increase in CA15-3 in patients group as compared with the control group. It was found that EMP2 and β1-Integrin expression in malignant tissue samples correlates with advanced and metastatic disease.

Conclusion

The gene expression of EMP2 and β1-Integrin are important markers for the severity of breast cancer and they are good indicators of its prognosis.

Inferring and analyzing module-specific lncRNA-mRNA causal regulatory networks in human cancer

It is known that noncoding RNAs (ncRNAs) cover ∼98% of the transcriptome, but do not encode proteins. Among ncRNAs, long noncoding RNAs (lncRNAs) are a large and diverse class of RNA molecules, and are thought to be a gold mine of potential oncogenes, anti-oncogenes and new biomarkers. Although only a minority of lncRNAs is functionally characterized, it is clear that they are important regulators to modulate gene expression and involve in many biological functions. To reveal the functions and regulatory mechanisms of lncRNAs, it is vital to understand how lncRNAs regulate their target genes for implementing specific biological functions. In this article, we review the computational methods for inferring lncRNA-mRNA interactions and the third-party databases of storing lncRNA-mRNA regulatory relationships. We have found that the existing methods are based on statistical correlations between the gene expression levels of lncRNAs and mRNAs, and may not reveal gene regulatory relationships which are causal relationships. Moreover, these methods do not consider the modularity of lncRNA-mRNA regulatory networks, and thus, the networks identified are not module-specific. To address the above two issues, we propose a novel method, MSLCRN, to infer and analyze module-specific lncRNA-mRNA causal regulatory networks. We have applied it into glioblastoma multiforme, lung squamous cell carcinoma, ovarian cancer and prostate cancer, respectively. The experimental results show that MSLCRN, as an expression-based method, could be a useful complementary method to study lncRNA regulations.

Dysregulated lncRNA-miRNA-mRNA Network Reveals Patient Survival-Associated Modules and RNA Binding Proteins in Invasive Breast Carcinoma

Breast cancer is the most common cancer in women, but few biomarkers are effective in clinic. Previous studies have shown the important roles of non-coding RNAs in diagnosis, prognosis, and therapy selection for breast cancer and have suggested the significance of integrating molecules at different levels to interpret the mechanism of breast cancer. Here, we collected transcriptome data including long non-coding RNA (lncRNA), microRNA (miRNA), and mRNA for ~1,200 samples, including 1079 invasive breast carcinoma samples and 104 normal samples, from The Cancer Genome Atlas (TCGA) project. We identified differentially expressed lncRNAs, miRNAs, and mRNAs that distinguished invasive carcinoma samples from normal samples. We further constructed an integrated dysregulated network consisting of differentially expressed lncRNAs, miRNAs, and mRNAs and found housekeeping and cancer-related functions. Moreover, 58 RNA binding proteins (RBPs) involved in biological processes that are essential to maintain cell survival were found in the dysregulated network, and 10 were correlated with overall survival. In addition, we identified two modules that stratify patients into high- and low-risk subgroups. The expression patterns of these two modules were significantly different in invasive carcinoma versus normal samples, and some molecules were high-confidence biomarkers of breast cancer. Together, these data demonstrated an important clinical application for improving outcome prediction for invasive breast cancers.

Transcriptome-wide Profiling of Cerebral Cavernous Malformations Patients Reveal Important Long noncoding RNA molecular signatures

Cerebral cavernous malformations (CCMs) are low-flow vascular malformations in the brain associated with recurrent hemorrhage and seizures. The current treatment of CCMs relies solely on surgical intervention. Henceforth, alternative non-invasive therapies are urgently needed to help prevent subsequent hemorrhagic episodes. Long non-coding RNAs (lncRNAs) belong to the class of non-coding RNAs and are known to regulate gene transcription and involved in chromatin remodeling via various mechanism. Despite accumulating evidence demonstrating the role of lncRNAs in cerebrovascular disorders, their identification in CCMs pathology remains unknown. The objective of the current study was to identify lncRNAs associated with CCMs pathogenesis using patient cohorts having 10 CCM patients and 4 controls from brain. Executing next generation sequencing, we performed whole transcriptome sequencing (RNA-seq) analysis and identified 1,967 lncRNAs and 4,928 protein coding genes (PCGs) to be differentially expressed in CCMs patients. Among these, we selected top 6 differentially expressed lncRNAs each having significant correlative expression with more than 100 differentially expressed PCGs. The differential expression status of the top lncRNAs, SMIM25 and LBX2-AS1 in CCMs was further confirmed by qRT-PCR analysis. Additionally, gene set enrichment analysis of correlated PCGs revealed critical pathways related to vascular signaling and important biological processes relevant to CCMs pathophysiology. Here, by transcriptome-wide approach we demonstrate that lncRNAs are prevalent in CCMs disease and are likely to play critical roles in regulating important signaling pathways involved in the disease progression. We believe, that detailed future investigations on this set of identified lncRNAs can provide useful insights into the biology and, ultimately, contribute in preventing this debilitating disease.

TOX3 is a favorable prognostic indicator and potential immunomodulatory factor in lung adenocarcinoma

Thymocyte selection-associated high mobility group box (TOX) genes represent a novel family of genes. Deregulated expression of TOXs has been reported in a variety of cancer types, including lung cancer. It has also been reported that TOXs are crucial regulators of the immune system. The present study systematically evaluated the prognostic values of TOX family members using a set of publicly accessible databases, including Oncomine, Kaplan-Meier plotter and cBioPortal. It was revealed that TOX expression profiles differed between lung cancer and normal tissues, and high expression of TOX mRNAs generally predicted improved survival outcomes. Notably, TOX3 expression was significantly increased in lung adenocarcinoma, compared with other pathological subtypes of lung cancer. Survival analysis demonstrated that elevated TOX3 expression was significantly associated with improved progression-free and overall survival in patients with lung adenocarcinoma. Furthermore, correlation analysis indicated that TOX3 expression was negatively correlated with the expression of programmed death-1 receptor (PD-1), PD-ligand 1 and Hepatitis A virus cellular receptor 2 in lung adenocarcinoma. These results indicated that TOX3 is a prognostic indicator and promising immunomodulatory factor in lung adenocarcinoma. Future studies investigating the role of TOX3 in lung cancer immunity are warranted.

Weighted Gene Co-Expression Analyses Point to Long Non-Coding RNA Hub Genes at Different Schistosoma mansoni Life-Cycle Stages

Long non-coding RNAs (lncRNAs) (>200 nt) are expressed at levels lower than those of the protein-coding mRNAs, and in all eukaryotic model species where they have been characterized, they are transcribed from thousands of different genomic loci. In humans, some four dozen lncRNAs have been studied in detail, and they have been shown to play important roles in transcriptional regulation, acting in conjunction with transcription factors and epigenetic marks to modulate the tissue-type specific programs of transcriptional gene activation and repression. In Schistosoma mansoni, around 10,000 lncRNAs have been identified in previous works. However, the limited number of RNA-sequencing (RNA-seq) libraries that had been previously assessed, together with the use of old and incomplete versions of the S. mansoni genome and protein-coding transcriptome annotations, have hampered the identification of all lncRNAs expressed in the parasite. Here we have used 633 publicly available S. mansoni RNA-seq libraries from whole worms at different stages (n = 121), from isolated tissues (n = 24), from cell-populations (n = 81), and from single-cells (n = 407). We have assembled a set of 16,583 lncRNA transcripts originated from 10,024 genes, of which 11,022 are novel S. mansoni lncRNA transcripts, whereas the remaining 5,561 transcripts comprise 120 lncRNAs that are identical to and 5,441 lncRNAs that have gene overlap with S. mansoni lncRNAs already reported in previous works. Most importantly, our more stringent assembly and filtering pipeline has identified and removed a set of 4,293 lncRNA transcripts from previous publications that were in fact derived from partially processed mRNAs with intron retention. We have used weighted gene co-expression network analyses and identified 15 different gene co-expression modules. Each parasite life-cycle stage has at least one highly correlated gene co-expression module, and each module is comprised of hundreds to thousands lncRNAs and mRNAs having correlated co-expression patterns at different stages. Inspection of the top most highly connected genes within the modules’ networks has shown that different lncRNAs are hub genes at different life-cycle stages, being among the most promising candidate lncRNAs to be further explored for functional characterization.

Genetic Variants of lncRNA MALAT1 Exert Diverse Impacts on the Risk and Clinicopathologic Characteristics of Patients with Hepatocellular Carcinoma

The long noncoding (lnc)RNA, metastasis-associated lung adenocarcinoma transcript 1 (MALAT1), plays a crucial role in the development of hepatocellular carcinoma (HCC). However, potential genetic variants (single nucleotide polymorphisms, SNPs) in MALAT1 that affect the susceptibility and progression of HCC have rarely been explored. Three tagging SNPs, viz., rs3200401 C > T, rs619586 A > G, and rs1194338 C > A, in MALAT1 were genotyped by a TaqMan allelic discrimination assay in 394 HCC patients and 1199 healthy controls. A stratified analysis showed that younger patients (<55 years) with the MALAT1 rs619586 G allele had a decreased risk of HCC under a codominant model (AOR = 0.289, 95% CI: 0.108–0.773, p = 0.013) and dominant model (AOR = 0.286, 95% CI: 0.107–0.765, p = 0.013). Female patients and patients with a smoking habit who carried the CA + AA genotype of rs1194338 had a lower risk of developing vascular invasion (p = 0.049) and a high Child–Pugh grade (B or C) (p = 0.036), respectively. Under the dominant model, smokers with the MALAT1 rs3200401 CT + TT genotype had a higher frequency of hepatitis B virus (HBV) infection (p = 0.034). Moreover, the aspartate aminotransferase was higher in patients with the rs3200401 CT + TT genotype. Furthermore, analyses of clinical datasets revealed that MALAT1 expression level was gradually unregulated during HCC development from normal liver, cirrhotic liver, dysplastic liver to HCC and correlated with poor survival rates in HCC patients, especially in the hepatitis virus-infected population.

Long intergenic non-protein coding RNA 511 promotes the progression of osteosarcoma cells through sponging microRNA 618 to upregulate the expression of maelstrom

Osteosarcoma is a tumor disease that commonly exists among young populations. Our research explored the role of the LINC00511/microRNA-618/MAEL axis in osteosarcoma.Expression profiles of long non-coding RNAs (lncRNAs) in osteosarcoma (OS) tissues were constructed, and LINC00511 expression levels were verified with qRT-PCR. LncRNA-miRNA and miRNA-mRNA interactions were predicted. Validation was performed using a dual-luciferase reporter assay. Protein expression levels of MAEL were evaluated by Western blot assays. The effects of LINC00511, miR-618 and MAEL on the proliferation, viability, and metastasis of OS cells were detected using colony formation, cell counting kit-8 (CCK-8) and transwell assays, respectively. The apoptosis rates of OS cells were investigated using flow cytometry. The tumor-suppressing effect of LINC00511 silencing was also analyzed using a xenograft model in nude mice.LINC00511 overexpression was observed in OS tissues and cell lines. Knockdown of LINC00511 in nude mice inhibited the tumorigenic ability of OS cells. Transfection-induced overexpression of LINC00511 and MAEL, as well as downregulation, highlighted the features of tumor cells, and LINC00511 overexpression reduced apoptosis in vitro.LINC00511 was confirmed to be beneficial for osteosarcoma development via sponging miR-618 and increasing MAEL expression and may thus be considered a potential target for osteosarcoma therapy.

MicroRNAs and Long Non-Coding RNAs and Their Hormone-Like Activities in Cancer

Hormones are messengers circulating in the body that interact with specific receptors on the cell membrane or inside the cells and regulate, at a distal site, the activities of specific target organs. The definition of hormone has evolved in the last years. Hormones are considered in the context of cell–cell communication and mechanisms of cellular signaling. The best-known mechanisms of this kind are chemical receptor-mediated events, the cell–cell direct interactions through synapses, and, more recently, the extracellular vesicle (EV) transfer between cells. Recently, it has been extensively demonstrated that EVs are used as a way of communication between cells and that they are transporters of specific messenger signals including non-coding RNAs (ncRNAs) such as microRNAs (miRNAs) and long non-coding RNAs (lncRNAs). Circulating ncRNAs in body fluids and extracellular fluid compartments may have endocrine hormone-like effects because they can act at a distance from secreting cells with widespread consequences within the recipient cells. Here, we discuss and report examples of the potential role of miRNAs and lncRNAs as mediator for intercellular communication with a hormone-like mechanism in cancer.

Emerging roles of long non-coding RNAs in cancer

Cancer is a physiological condition that has both the endogenous and exogenous influences on its progression. It originates from unusual cell growth, where the cells undergo massive genetic alterations, bypass the signaling machinery and compromise its genetic cohesion. Literature has well narrated the DNA damage studies including driver mutations that interfere with the treatment strategies. However, with evolving medical excellence, recent day studies are trying to unveil the contribution of RNAs in the progression of tumor malignancies. A number of non-coding RNAs have been identified as an active component in cancer genomics. This article aims to review the role of long non-coding RNAs in the spectra of cancers and its prognostic value as the biomarkers in molecular targeting with clinical utility and therapeutic beneficence.

Integrated analysis of long noncoding RNA and mRNA expression profiles reveals the potential role of long noncoding RNA in different bovine lactation stages

Long noncoding RNA (lncRNA) play a critical role in mammary development and breast cancer biology. Despite their important role in the mammary gland, little is known of the roles of lncRNA in bovine lactation, particularly regarding the molecular processes underlying it. To characterize the role of lncRNA in bovine lactation, 4 samples of Holstein cow mammary gland tissue at peak and late lactation stages were examined after biopsy. We then profiled the transcriptome of the mammary gland using RNA sequencing technology. Further, functional lncRNA-mRNA coexpression pairs were constructed to infer the function of lncRNA using a generalized linear model, followed by gene ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. More than 1,000 putative lncRNA were identified, 117 of which were differentially expressed between peak and late lactation stages. Bovine lncRNA were shorter, with fewer exon numbers, and expressed at significantly lower levels than protein-coding genes. Seventy-two differentially expressed (DE) lncRNA were coexpressed with 340 different protein-coding genes. The KEGG pathway analysis showed that target mRNA for DE lncRNA were mainly related to lipid and glucose metabolism, including the peroxisome proliferator-activated receptors and 5' adenosine monophosphate-activated protein kinase signaling pathways. Further bioinformatics and integrative analyses revealed that 12 DE lncRNA potentially played important roles in bovine lactation. Our findings provide a valuable resource for future bovine transcriptome studies, facilitate the understanding of bovine lactation biology, and offer functional information for cattle lactation.

Cross-species inference of long non-coding RNAs greatly expands the ruminant transcriptome

Background

mRNA-like long non-coding RNAs (lncRNAs) are a significant component of mammalian transcriptomes, although most are expressed only at low levels, with high tissue-specificity and/or at specific developmental stages. Thus, in many cases lncRNA detection by RNA-sequencing (RNA-seq) is compromised by stochastic sampling. To account for this and create a catalogue of ruminant lncRNAs, we compared de novo assembled lncRNAs derived from large RNA-seq datasets in transcriptional atlas projects for sheep and goats with previous lncRNAs assembled in cattle and human. We then combined the novel lncRNAs with the sheep transcriptional atlas to identify co-regulated sets of protein-coding and non-coding loci.

Results

Few lncRNAs could be reproducibly assembled from a single dataset, even with deep sequencing of the same tissues from multiple animals. Furthermore, there was little sequence overlap between lncRNAs that were assembled from pooled RNA-seq data. We combined positional conservation (synteny) with cross-species mapping of candidate lncRNAs to identify a consensus set of ruminant lncRNAs and then used the RNA-seq data to demonstrate detectable and reproducible expression in each species. In sheep, 20 to 30% of lncRNAs were located close to protein-coding genes with which they are strongly co-expressed, which is consistent with the evolutionary origin of some ncRNAs in enhancer sequences. Nevertheless, most of the lncRNAs are not co-expressed with neighbouring protein-coding genes.

Conclusions

Alongside substantially expanding the ruminant lncRNA repertoire, the outcomes of our analysis demonstrate that stochastic sampling can be partly overcome by combining RNA-seq datasets from related species. This has practical implications for the future discovery of lncRNAs in other species.

Electronic supplementary material

The online version of this article (10.1186/s12711-018-0391-0) contains supplementary material, which is available to authorized users.

Genome-Wide Analysis of Uveal Melanoma Metastasis-Associated LncRNAs and Their Functional Network

Uveal melanoma (UM) is the most common primary intraocular malignancy in adults. Up to 50% of primary UM cases will develop distant metastasis, but no effective therapies are currently available. The present study aimed to characterize the expression profile of the long noncoding RNAs (lncRNAs) and screen the potential metastasis-associated lncRNAs in UM. A genome-wide analysis of the transcriptome was performed on 11 primary UM tissues (6 metastasized and 5 nonmetastasized) through RNA sequencing. A total of 40,878 lncRNAs were detected in UM, 4,983 of which were novel candidates. We identified 329 differentially expressed lncRNAs (DELs) and 802 differentially expressed mRNAs (DEMs) by comparing the transcriptome profile between metastasized and nonmetastasized UM group. The DEL-DEM coexpression network revealed that the RP11-551L14.4, TCONS_00004101, and TCONS_00004845 DELs had the highest connectivity with the DEMs, coexpressed with 225, 28, and 10 DEMs, respectively, whereas the SPOCD1, PEA15, and SLC44A3 DEMs were most closely connected with the DELs, and were coexpressed with 89, 27, and 22 DELs, respectively. Moreover, 17 and 743 DEMs were targeted by the DELs through cis- or trans-action, respectively. These targeted DEMs were significantly enriched in D-Arginine and D-ornithine metabolism and glycerolipid metabolism of Kyoto Encyclopedia of Genes and Genomes pathways, and enriched in bradykinin receptor activity and haptoglobin binding of gene ontology biological processes. Quantitative real-time PCR confirmed the sequencing data. These findings have provided new insights into the molecular mechanism of UM metastasis and paved the way for further investigations regarding lncRNA in UM.

Profiling, clinicopathological correlation and functional validation of specific long non-coding RNAs for hepatocellular carcinoma

Background

Hepatocellular carcinoma (HCC) is one of the most prevalent and aggressive malignancies worldwide. Studies seeking to advance the overall understanding of lncRNA profiling in HCC remain rare.

Methods

The transcriptomic profiling of 12 HCC tissues and paired adjacent normal tissues was determined using high-throughput RNA sequencing. Fifty differentially expressed mRNAs (DEGs) and lncRNAs (DELs) were validated in 21 paired HCC tissues via quantitative real-time PCR. The correlation between the expression of DELs and various clinicopathological characteristics was analyzed using Student’s t-test or linear regression. Co-expression networks between DEGs and DELs were constructed through Pearson correlation co-efficient and enrichment analysis. Validation of DELs’ functions including proliferation and migration was performed via loss-of-function RNAi assays.

Results

In this study, we identified 439 DEGs and 214 DELs, respectively, in HCC. Furthermore, we revealed that multiple DELs, including NONHSAT003823, NONHSAT056213, NONHSAT015386 and especially NONHSAT122051, were remarkably correlated with tumor cell differentiation, portal vein tumor thrombosis, and serum or tissue alpha fetoprotein levels. In addition, the co-expression network analysis between DEGs and DELs showed that DELs were involved with metabolic, cell cycle, chemical carcinogenesis, and complement and coagulation cascade-related pathways. The silencing of the endogenous level of NONHSAT122051 or NONHSAT003826 could significantly attenuate the mobility of both SK-HEP-1 and SMMC-7721 HCC cells.

Conclusion

These findings not only add knowledge to the understanding of genome-wide transcriptional evaluation of HCC but also provide promising targets for the future diagnosis and treatment of HCC.

Gene regulation of mammalian long non-coding RNA

RNA polymerase II (Pol II) transcribes two classes of RNAs, protein-coding and non-protein-coding (ncRNA) genes. ncRNAs are also synthesized by RNA polymerases I and III (Pol I and III). In humans, the number of ncRNA genes exceeds more than twice that of protein-coding genes. However, the history of studying Pol II-synthesized ncRNA is relatively short. Since early 2000s, important biological and pathological functions of these ncRNA genes have begun to be discovered and intensively studied. And transcription mechanisms of long non-coding RNA (lncRNA) have been recently reported. Transcription of lncRNAs utilizes some transcription factors and mechanisms shared in that of protein-coding genes. In addition, tissue specificity in lncRNA gene expression has been shown. LncRNAs play essential roles in regulating the expression of neighboring or distal genes through different mechanisms. This leads to the implication of lncRNAs in a wide variety of biological pathways and pathological development. In this review, the newly discovered transcription mechanisms, characteristics, and functions of lncRNA are discussed.

Survey of High Throughput RNA-Seq Data Reveals Potential Roles for lncRNAs during Development and Stress Response in Bread Wheat

Long non-coding RNAs (lncRNAs) are a family of regulatory RNAs that play essential role in the various developmental processes and stress responses. Recent advances in sequencing technology and computational methods enabled identification and characterization of lncRNAs in certain plant species, but they are less known in Triticum aestivum (bread wheat). Herein, we analyzed 52 RNA seq data (>30 billion reads) and identified 44,698 lncRNAs in T. aestivum genome, which were characterized in comparison to the coding sequences (mRNAs). Similar to the mRNAs, lncRNAs were also derived from each sub-genome and chromosome, and showed tissue developmental stage specific and differential expression, as well. The modulated expression of lncRNAs during abiotic stresses like heat, drought, and salt indicated their putative role in stress response. The co-expression of lncRNAs with vital mRNAs including various transcription factors and enzymes involved in Abscisic acid (ABA) biosynthesis, and gene ontology mapping inferred their regulatory roles in numerous biological processes. A few lncRNAs were predicted as precursor (19 lncRNAs), while some as target mimics (1,047 lncRNAs) of known miRNAs involved in various regulatory functions. The results suggested numerous functions of lncRNAs in T. aestivum, and unfolded the opportunities for functional characterization of individual lncRNA in future studies.