The landscape of long noncoding RNAs in the human transcriptome. Nat Genet. 2015;47:199-208. [PMID: 25599403 DOI: 10.1038/ng.3192]

Genome-Wide lncRNA Microarray Profiling Identifies Novel Circulating lncRNAs for Detection of Gastric Cancer

Long non-coding RNAs (lncRNAs) can serve as blood-based biomarkers for cancer detection. To identify novel lncRNA biomarkers for gastric cancer (GC), we conducted, for the first time, genome-wide lncRNA screening analysis in two sets of samples: five paired preoperative and postoperative day 14 plasma samples from GC patients, and tissue samples from tumor and adjacent normal tissues. Candidate tumor-related lncRNAs were then quantitated and evaluated in three independent phases comprising 321 participants. The expression levels of lncRNAs were also measured in GC cell lines and the corresponding culture medium. Biomarker panels, lncRNA-based Index I and carcinoembryonic antigen (CEA)-based Index II, were constructed using logistic regression, and their diagnostic performance compared. Fagan's nomogram was plotted to facilitate clinical application. As a result, we identified five novel plasma lncRNAs (TINCR, CCAT2, AOC4P, BANCR and LINC00857), which, when combined in the lncRNA-based Index I, outperformed the CEA-based Index II (P < 0.001) and could distinguish GC patients from healthy controls with an area under the receiver-operating curve (AUC) of 0.91 (95% confidence interval (CI): 0.88-0.95). The lncRNA-based index decreased significantly by postoperative day 14 (P = 0.016), indicating its ability to monitor tumor dynamics. High values of the lncRNA-based index were correlated with tumor size (P = 0.036), depth of invasion (P = 0.025), lymphatic metastasis (P = 0.012) and more advanced tumor stages (P = 0.003). The lncRNA-based index was also able to discriminate GC patients from precancerous individuals and patients with gastrointestinal stromal tumor with AUC values of 0.82 (95% CI: 0.71-0.92) and 0.80 (95% CI: 0.68-0.91), respectively. Taken together, our findings demonstrate that this panel of five plasma lncRNAs could serve as a set of novel diagnostic biomarkers for GC detection.

Integrated Analysis of Long Noncoding RNA and mRNA Expression Profile in Advanced Laryngeal Squamous Cell Carcinoma

Long non-coding RNA (lncRNA) plays an important role in tumorigenesis. However, the expression pattern and function of lncRNAs in laryngeal squamous cell carcinoma (LSCC) are still unclear. To investigate the aberrantly expressed lncRNAs and mRNAs in advanced LSCC, we screened lncRNA and mRNA expression profiles in 9 pairs of primary Stage IVA LSCC tissues and adjacent non-neoplastic tissues by lncRNA and mRNA integrated microarrays. Gene Ontology and pathway analysis were performed to find out the significant function and pathway of the differentially expressed mRNAs, gene-gene functional interaction network and ceRNA network were constructed to select core mRNAs, and lncRNA-mRNA expression correlation network was built to identify the interactions between lncRNA and mRNA. qRT-PCR was performed to further validate the expressions of selected lncRNAs and mRNAs in advanced LSCC. We found 1459 differentially expressed lncRNAs and 2381 differentially expressed mRNAs, including 846 up-regulated lncRNAs and 613 down-regulated lncRNAs, 1542 up-regulated mRNAs and 839 down-regulated mRNAs. The mRNAs ITGB1, HIF1A, and DDIT4 were selected as core mRNAs, which are mainly involved in biological processes, such as matrix organization, cell cycle, adhesion, and metabolic pathway. LncRNA-mRNA expression correlation network showed LncRNA NR_027340, MIR31HG were positively correlated with ITGB1, HIF1A respectively. LncRNA SOX2-OT was negatively correlated with DDIT4. qRT-PCR further validated the expression of these lncRNAs and mRNAs. The work provides convincing evidence that the identified lncRNAs and mRNAs are potential biomarkers in advanced LSCC for further future studies.

Long noncoding RNAs in the p53 network

The tumor-suppressor protein p53 is activated in response to numerous cellular stresses including DNA damage. p53 functions primarily as a sequence-specific transcription factor that controls the expression of hundreds of protein-coding genes and noncoding RNAs, including microRNAs (miRNAs) and long noncoding RNAs (lncRNAs). While the role of protein-coding genes and miRNAs in mediating the effects of p53 has been extensively studied, the physiological function and molecular mechanisms by which p53-regulated lncRNAs act is beginning to be understood. In this review, we discuss recent studies on lncRNAs that are directly or indirectly regulated by p53 and how they contribute to the biological outcomes of p53 activation. WIREs RNA 2017, 8:e1410. doi: 10.1002/wrna.1410 For further resources related to this article, please visit the WIREs website.

CRISPRi-based genome-scale identification of functional long noncoding RNA loci in human cells

The human genome produces thousands of long noncoding RNAs (lncRNAs)-transcripts >200 nucleotides long that do not encode proteins. Although critical roles in normal biology and disease have been revealed for a subset of lncRNAs, the function of the vast majority remains untested. We developed a CRISPR interference (CRISPRi) platform targeting 16,401 lncRNA loci in seven diverse cell lines, including six transformed cell lines and human induced pluripotent stem cells (iPSCs). Large-scale screening identified 499 lncRNA loci required for robust cellular growth, of which 89% showed growth-modifying function exclusively in one cell type. We further found that lncRNA knockdown can perturb complex transcriptional networks in a cell type-specific manner. These data underscore the functional importance and cell type specificity of many lncRNAs.

Biogenesis and Transcriptional Regulation of Long Noncoding RNAs in the Human Immune System

The central dogma of molecular biology states that DNA makes RNA makes protein. Discoveries over the last quarter of a century found that the process of DNA transcription into RNA gives rise to a diverse array of functional RNA species, including genes that code for protein and noncoding RNAs. For decades, the focus has been on understanding how protein-coding genes are regulated to influence protein expression. However, with the completion of the Human Genome Project and follow-up ENCODE data, it is now appreciated that only 2-3% of the genome codes for protein-coding gene exons and that the bulk of the transcribed genome, apart from ribosomal RNAs, is at the level of noncoding RNA genes. In this article, we focus on the biogenesis and regulation of a distinct class of noncoding RNA molecules termed long, noncoding RNAs in the context of the immune system.

Expression of lncRNAs in Low-Grade Gliomas and Glioblastoma Multiforme: An In Silico Analysis

BACKGROUND

Each year, over 16,000 patients die from malignant brain cancer in the US. Long noncoding RNAs (lncRNAs) have recently been shown to play critical roles in regulating neurogenesis and brain tumor progression. To better understand the role of lncRNAs in brain cancer, we performed a global analysis to identify and characterize all annotated and novel lncRNAs in both grade II and III gliomas as well as grade IV glioblastomas (glioblastoma multiforme [GBM]).

METHODS AND FINDINGS

We determined the expression of all lncRNAs in over 650 brain cancer and 70 normal brain tissue RNA sequencing datasets from The Cancer Genome Atlas (TCGA) and other publicly available datasets. We identified 611 induced and 677 repressed lncRNAs in glial tumors relative to normal brains. Hundreds of lncRNAs were specifically expressed in each of the three lower grade glioma (LGG) subtypes (IDH1/2 wt, IDH1/2 mut, and IDH1/2 mut 1p19q codeletion) and the four subtypes of GBMs (classical, mesenchymal, neural, and proneural). Overlap between the subtype-specific lncRNAs in GBMs and LGGs demonstrated similarities between mesenchymal GBMs and IDH1/2 wt LGGs, with 2-fold higher overlap than would be expected by random chance. Using a multivariate Cox regression survival model, we identified 584 and 282 lncRNAs that were associated with a poor and good prognosis, respectively, in GBM patients. We developed a survival algorithm for LGGs based on the expression of 64 lncRNAs that was associated with patient prognosis in a test set (hazard ratio [HR] = 2.168, 95% CI = 1.765-2.807, p < 0.001) and validation set (HR = 1.921, 95% CI = 1.333-2.767, p < 0.001) of patients from TCGA. The main limitations of this study are that further work is needed to investigate the clinical relevance of our findings, and that validation in an independent dataset is needed to determine the robustness of our survival algorithm.

CONCLUSIONS

This work identifies a panel of lncRNAs that appear to be prognostic in gliomas and provides a critical resource for future studies examining the role of lncRNAs in brain cancers.

A computational analysis of the genetic and transcript diversity at the kallikrein locus

The kallikrein related peptidase gene family (KLKs) comprises 15 genes located between 19q13.3-13.4. KLKs have chymotrypsin and/or trypsin like activity, but the tissue/organ expression profile of each KLK varies considerably. Thus, the role of KLKs in human biology is also very diverse, and the deregulation of their function results in a wide-range of diseases. Here, we have cataloged the transcript (variants and fusions) and genetic (single nucleotide polymorphisms, small insertions/deletions, copy number variations (CNVs), and short tandem repeats) diversity at the KLK locus, providing a data set for researchers to explore the mechanisms through which KLK function may be deregulated. We reveal that the KLK locus hosts 85 fusion transcripts, and 80 variant transcripts. Interestingly, some fusion transcripts comprise up to 6 KLK genes. Our analysis of genetic variations of 2504 individuals from the 1000 Genome Project indicated that the KLK locus is rich in genetic diversity, with some fusion transcripts harboring over 1000 single nucleotide variations. We also found evidence from the literature linking 2387 KLK genetic variants with many types of diseases. Finally, genotyping data from the 131 KLK genetic variants in the NCI-60 cancer cell lines is provided as a resource for the cancer and KLK field.

Cellular, physiological and pathological aspects of the long non-coding RNA NEAT1

BACKGROUND

The majority of mammalian genomes have been found to be transcribed into non-coding RNAs. One category of non-coding RNAs is classified as long non-coding RNAs (lncRNAs) based on their transcript sizes larger than 200 nucleotides. Growing evidence has shown that lncRNAs are not junk transcripts and play regulatory roles in multiple aspects of biological processes. Dysregulation of lncRNA expression has also been linked to diseases, in particular cancer. Therefore, studies of lncRNAs have attracted significant interest in the field of medical research. Nuclear enriched abundant transcript 1 (NEAT1), a nuclear lncRNA, has recently emerged as a key regulator involved in various cellular processes, physiological responses, developmental processes, and disease development and progression.

OBJECTIVE

This review will summarize and discuss the most recent findings with regard to the roles of NEAT1 in the function of the nuclear paraspeckle, cellular pathways, and physiological responses and processes. Particularly, the most recently reported studies regarding the pathological roles of deregulated NEAT1 in cancer are highlighted in this review.

METHODS

We performed a systematic literature search using the Pubmed search engine. Studies published over the last 8 years (between January 2009 and August 2016) were the sources of literature review. The following keywords were used: "Nuclear enriched abundant transcript 1", "NEAT1", and "paraspeckles".

RESULTS

The Pubmed search identified 34 articles related to the topic of the review. Among the identified literature, thirteen articles report findings related to cellular functions of NEAT1 and eight articles are the investigations of physiological functions of NEAT1. The remaining thirteen articles are studies of the roles of NEAT1 in cancers.

CONCLUSION

Recent advances in NEAT1 studies reveal the multifunctional roles of NEAT1 in various biological processes, which are beyond its role in nuclear paraspeckles. Recent studies also indicate that dysregulation of NEAT1 function contributes to the development and progression of various cancers. More investigations will be needed to address the detailed mechanisms regarding how NEAT1 executes its cellular and physiological functions and how NEAT1 dysregulation results in tumorigenesis, and to explore the potential of NEAT1 as a target in cancer diagnosis, prognosis and therapy.

Long intergenic non-coding RNA expression signature in human breast cancer

Breast cancer is a complex disease, characterized by gene deregulation. There is less systematic investigation of the capacity of long intergenic non-coding RNAs (lincRNAs) as biomarkers associated with breast cancer pathogenesis or several clinicopathological variables including receptor status and patient survival. We designed a two-stage study, including 1,000 breast tumor RNA-seq data from The Cancer Genome Atlas (TCGA) as the discovery stage, and RNA-seq data of matched tumor and adjacent normal tissue from 50 breast cancer patients as well as 23 normal breast tissue from healthy women as the replication stage. We identified 83 lincRNAs showing the significant expression changes in breast tumors with a false discovery rate (FDR) < 1% in the discovery dataset. Thirty-seven out of the 83 were validated in the replication dataset. Integrative genomic analyses suggested that the aberrant expression of these 37 lincRNAs was probably related with the expression alteration of several transcription factors (TFs). We observed a differential co-expression pattern between lincRNAs and their neighboring genes. We found that the expression levels of one lincRNA (RP5-1198O20 with Ensembl ID ENSG00000230615) were associated with breast cancer survival with P < 0.05. Our study identifies a set of aberrantly expressed lincRNAs in breast cancer.

TACO produces robust multisample transcriptome assemblies from RNA-seq

Accurate transcript structure and abundance inference from RNA-Seq data is foundational for molecular discovery. Here we present TACO, a computational method to reconstruct a consensus transcriptome from multiple RNA-Seq datasets. TACO employs novel change-point detection to demarcate transcript start and end sites, leading to dramatically improved reconstruction accuracy compared to other tools in its class. The tool is available at http://tacorna.github.io and can be readily incorporated into RNA-Seq analysis workflows.

Integrative classification of human coding and noncoding genes through RNA metabolism profiles

Pervasive transcription of the human genome results in a heterogeneous mix of coding RNAs and long noncoding RNAs (lncRNAs). Only a small fraction of lncRNAs have demonstrated regulatory functions, thus making functional lncRNAs difficult to distinguish from nonfunctional transcriptional byproducts. This difficulty has resulted in numerous competing human lncRNA classifications that are complicated by a steady increase in the number of annotated lncRNAs. To address these challenges, we quantitatively examined transcription, splicing, degradation, localization and translation for coding and noncoding human genes. We observed that annotated lncRNAs had lower synthesis and higher degradation rates than mRNAs and discovered mechanistic differences explaining slower lncRNA splicing. We grouped genes into classes with similar RNA metabolism profiles, containing both mRNAs and lncRNAs to varying extents. These classes exhibited distinct RNA metabolism, different evolutionary patterns and differential sensitivity to cellular RNA-regulatory pathways. Our classification provides an alternative to genomic context-driven annotations of lncRNAs.

Towards Precision Medicine in the Clinic: From Biomarker Discovery to Novel Therapeutics

Precision medicine continues to be the benchmark to which we strive in cancer research. Seeking out actionable aberrations that can be selectively targeted by drug compounds promises to optimize treatment efficacy and minimize toxicity. Utilizing these different targeted agents in combination or in sequence may further delay resistance to treatments and prolong antitumor responses. Remarkable progress in the field of immunotherapy adds another layer of complexity to the management of cancer patients. Corresponding advances in companion biomarker development, novel methods of serial tumor assessments, and innovative trial designs act synergistically to further precision medicine. Ongoing hurdles such as clonal evolution, intra- and intertumor heterogeneity, and varied mechanisms of drug resistance continue to be challenges to overcome. Large-scale data-sharing and collaborative networks using next-generation sequencing (NGS) platforms promise to take us further into the cancer 'ome' than ever before, with the goal of achieving successful precision medicine.

An imprinted long noncoding RNA located between genes Meg8 and Meg9 in the cattle Dlk1-Dio3 domain

The Dlk1-Dio3 imprinted domain is located on the cattle chromosome 21 and contains three paternally expressed protein-coding genes and a number of maternally expressed short or long noncoding RNA genes. We have previously obtained two maternally expressed long noncoding RNA genes, Meg8 and Meg9, from the cattle. In this study, we identified a novel noncoding RNA located between Meg8 and Meg9 known as LINC24061 according to the GENCODE annotated bibliography. Two alternatively spliced transcripts (LINC24061-v1 and LINC24061-v2) were obtained using RT-PCR and RACE, and the expression pattern of LINC24061-v1 and LINC24061-v2 was shown to be tissue-specific. The LINC24061-v1 splice variant was expressed in only three types of tissues: heart, kidney and muscle; in contrast, LINC24061-v2 was expressed in all eight tissues examined, including heart, liver, spleen, lung, kidney, skeletal muscle, subcutaneous fat, and brain of adult cattle. The allele-specific expression of LINC24061 was identified based on a single nucleotide polymorphism (SNP) in exon 2 of LINC24061. The results showed that LINC24061 exhibited monoallelic expression in all the examined cattle tissues.

MEG3 Long Noncoding RNA Contributes to the Epigenetic Regulation of Epithelial-Mesenchymal Transition in Lung Cancer Cell Lines

Histone methylation is implicated in a number of biological and pathological processes, including cancer development. In this study, we investigated the molecular mechanism for the recruitment of Polycomb repressive complex-2 (PRC2) and its accessory component, JARID2, to chromatin, which regulates methylation of lysine 27 of histone H3 (H3K27), during epithelial-mesenchymal transition (EMT) of cancer cells. The expression of MEG3 long noncoding RNA (lncRNA), which could interact with JARID2, was clearly increased during transforming growth factor-β (TGF-β)-induced EMT of human lung cancer cell lines. Knockdown of MEG3 inhibited TGF-β-mediated changes in cell morphology and cell motility characteristic of EMT and counteracted TGF-β-dependent changes in the expression of EMT-related genes such as CDH1, ZEB family, and the microRNA-200 family. Overexpression of MEG3 influenced the expression of these genes and enhanced the effects of TGF-β in their expressions. Chromatin immunoprecipitation (ChIP) revealed that MEG3 regulated the recruitment of JARID2 and EZH2 and histone H3 methylation on the regulatory regions of CDH1 and microRNA-200 family genes for transcriptional repression. RNA immunoprecipitation and chromatin isolation by RNA purification assays indicated that MEG3 could associate with JARID2 and the regulatory regions of target genes to recruit the complex. This study demonstrated a crucial role of MEG3 lncRNA in the epigenetic regulation of the EMT process in lung cancer cells.

AnnoLnc: a web server for systematically annotating novel human lncRNAs

BACKGROUND

Long noncoding RNAs (lncRNAs) have been shown to play essential roles in almost every important biological process through multiple mechanisms. Although the repertoire of human lncRNAs has rapidly expanded, their biological function and regulation remain largely elusive, calling for a systematic and integrative annotation tool.

RESULTS

Here we present AnnoLnc ( http://annolnc.cbi.pku.edu.cn ), a one-stop portal for systematically annotating novel human lncRNAs. Based on more than 700 data sources and various tool chains, AnnoLnc enables a systematic annotation covering genomic location, secondary structure, expression patterns, transcriptional regulation, miRNA interaction, protein interaction, genetic association and evolution. An intuitive web interface is available for interactive analysis through both desktops and mobile devices, and programmers can further integrate AnnoLnc into their pipeline through standard JSON-based Web Service APIs.

CONCLUSIONS

To the best of our knowledge, AnnoLnc is the only web server to provide on-the-fly and systematic annotation for newly identified human lncRNAs. Compared with similar tools, the annotation generated by AnnoLnc covers a much wider spectrum with intuitive visualization. Case studies demonstrate the power of AnnoLnc in not only rediscovering known functions of human lncRNAs but also inspiring novel hypotheses.

A human haploid gene trap collection to study lncRNAs with unusual RNA biology

Many thousand long non-coding (lnc) RNAs are mapped in the human genome. Time consuming studies using reverse genetic approaches by post-transcriptional knock-down or genetic modification of the locus demonstrated diverse biological functions for a few of these transcripts. The Human Gene Trap Mutant Collection in haploid KBM7 cells is a ready-to-use tool for studying protein-coding gene function. As lncRNAs show remarkable differences in RNA biology compared to protein-coding genes, it is unclear if this gene trap collection is useful for functional analysis of lncRNAs. Here we use the uncharacterized LOC100288798 lncRNA as a model to answer this question. Using public RNA-seq data we show that LOC100288798 is ubiquitously expressed, but inefficiently spliced. The minor spliced LOC100288798 isoforms are exported to the cytoplasm, whereas the major unspliced isoform is nuclear localized. This shows that LOC100288798 RNA biology differs markedly from typical mRNAs. De novo assembly from RNA-seq data suggests that LOC100288798 extends 289kb beyond its annotated 3' end and overlaps the downstream SLC38A4 gene. Three cell lines with independent gene trap insertions in LOC100288798 were available from the KBM7 gene trap collection. RT-qPCR and RNA-seq confirmed successful lncRNA truncation and its extended length. Expression analysis from RNA-seq data shows significant deregulation of 41 protein-coding genes upon LOC100288798 truncation. Our data shows that gene trap collections in human haploid cell lines are useful tools to study lncRNAs, and identifies the previously uncharacterized LOC100288798 as a potential gene regulator.

A lncRNA Perspective into (Re)Building the Heart

Our conception of the human genome, long focused on the 2% that codes for proteins, has profoundly changed since its first draft assembly in 2001. Since then, an unanticipatedly expansive functionality and convolution has been attributed to the majority of the genome that is transcribed in a cell-type/context-specific manner into transcripts with no apparent protein coding ability. While the majority of these transcripts, currently annotated as long non-coding RNAs (lncRNAs), are functionally uncharacterized, their prominent role in embryonic development and tissue homeostasis, especially in the context of the heart, is emerging. In this review, we summarize and discuss the latest advances in understanding the relevance of lncRNAs in (re)building the heart.

lncRNAs as Novel Indicators of Patients' Prognosis in Stage I Epithelial Ovarian Cancer: A Retrospective and Multicentric Study

Purpose: Stage I epithelial ovarian cancer (EOC) represents about 10% of all EOCs and is characterized by good prognosis with fewer than 20% of patients relapsing. As it occurs less frequently than advanced-stage EOC, its molecular features have not been thoroughly investigated. We have demonstrated that in stage I EOC miR-200c-3p can predict patients' outcome. In the present study, we analyzed the expression of long non-coding RNAs (lncRNA) to enable potential definition of a non-coding transcriptional signature with prognostic relevance for stage I EOC.Experimental Design: 202 snap-frozen stage I EOC tumor biopsies, 47 of which relapsed, were gathered together from three independent tumor tissue collections and subdivided into a training set (n = 73) and a validation set (n = 129). Median follow up was 9 years. LncRNAs' expression profiles were correlated in univariate and multivariate analysis with overall survival (OS) and progression-free survival (PFS).Results: The expression of lnc-SERTAD2-3, lnc-SOX4-1, lnc-HRCT1-1, and PVT1 was associated in univariate and multivariate analyses with relapse and poor outcome in both training and validation sets (P < 0.001). Using the expression profiles of PVT1, lnc-SERTAD2-3, and miR-200c-3p simultaneously, it was possible to stratify patients into high and low risk. The OS for high- and low-risk individuals are 36 and 123 months, respectively (OR, 15.55; 95% confidence interval, 3.81-63.36).Conclusions: We have identified a non-coding transcriptional signature predictor of survival and biomarker of relapse for stage I EOC. Clin Cancer Res; 23(9); 2356-66. ©2016 AACR.

Polycomb-Mediated Disruption of an Androgen Receptor Feedback Loop Drives Castration-Resistant Prostate Cancer

The lethal phenotype of castration-resistant prostate cancer (CRPC) is generally caused by augmented signaling from the androgen receptor (AR). Here, we report that the AR-repressed gene CCN3/NOV inhibits AR signaling and acts in a negative feedback loop to block AR function. Mechanistically, a cytoplasmic form of CCN3 interacted with the AR N-terminal domain to sequester AR in the cytoplasm of prostate cancer cells, thereby reducing AR transcriptional activity and inhibiting cell growth. However, constitutive repression of CCN3 by the Polycomb group protein EZH2 disrupted this negative feedback loop in both CRPC and enzalutamide-resistant prostate cancer cells. Notably, restoring CCN3 was sufficient to effectively reduce CPRC cell proliferation in vitro and to abolish xenograft tumor growth in vivo Taken together, our findings establish CCN3 as a pivotal regulator of AR signaling and prostate cancer progression and suggest a functional intersection between Polycomb and AR signaling in CRPC. Cancer Res; 77(2); 412-22. ©2016 AACR.

The emerging role of long non-coding RNAs in cutaneous melanoma

Malignant melanoma is a highly aggressive form of skin cancer, the incidence of which is rising rapidly. Although MAPK-targeting therapies and immune checkpoint blockade are emerging as attractive therapeutic approaches, their utility is limited to only a subset of patients who often acquire resistance. A better understanding of the aetiologies and genetic underpinnings of melanoma is therefore critical for the development of adjuvant or alternative therapeutic strategies aimed at increasing the proportion of responders and improving treatment efficacy. A key step in identifying novel therapeutic targets may be the shift in focus from the protein-coding components to the non-coding portion of the genome. The latter, representing about 98% of the genome, serves as a template for the transcription of many thousands of long non-coding RNAs (lncRNAs). Intriguingly, lncRNA loci are frequently mutated or altered in a variety of cancers, including melanoma, and there is growing evidence that lncRNAs can function as cancer-causing oncogenes or tumour suppressors. In this review, we summarize recent data highlighting the importance of lncRNAs in the biology of melanoma and their potential utility as biomarkers and therapeutic targets.

Network analysis of psoriasis reveals biological pathways and roles for coding and long non-coding RNAs

Background

Psoriasis is an immune-mediated, inflammatory disorder of the skin characterized by chronic inflammation and hyperproliferation of the epidermis. Differential expression analysis of microarray or RNA-seq data have shown that thousands of coding and non-coding genes are differentially expressed between psoriatic and healthy control skin. However, differential expression analysis may fail to detect perturbations in gene coexpression networks. Sensitive detection of such networks may provide additional insight into important disease-associated pathways. In this study, we applied weighted gene coexpression network analysis (WGCNA) on RNA-seq data from psoriasis patients and healthy controls.

Results

RNA-seq was performed on skin samples from 18 psoriasis patients (pre-treatment and post-treatment with the TNF-α inhibitor adalimumab) and 16 healthy controls, generating an average of 52.3 million 100-bp paired-end reads per sample. Using WGCNA, we identified 3 network modules that were significantly correlated with psoriasis and 6 network modules significantly correlated with biologic treatment, with only 16 % of the psoriasis-associated and 5 % of the treatment-associated coexpressed genes being identified by differential expression analysis. In a majority of these correlated modules, more than 50 % of coexpressed genes were long non-coding RNAs (lncRNA). Enrichment analysis of these correlated modules revealed that short-chain fatty acid metabolism and olfactory signaling are amongst the top pathways enriched for in modules associated with psoriasis, while regulation of leukocyte mediated cytotoxicity and regulation of cell killing are amongst the top pathways enriched for in modules associated with biologic treatment. A putative autoantigen, LL37, was coexpressed in the module most correlated with psoriasis.

Conclusions

This study has identified several networks of coding and non-coding genes associated with psoriasis and biologic drug treatment, including networks enriched for short-chain fatty acid metabolism and olfactory receptor activity, pathways that were not previously identified through differential expression analysis and may be dysregulated in psoriatic skin. As these networks are comprised mostly of non-coding genes, it is likely that non-coding genes play critical roles in the regulation of pathways involved in the pathogenesis of psoriasis.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-016-3188-y) contains supplementary material, which is available to authorized users.

Pan-cancer transcriptomic analysis associates long non-coding RNAs with key mutational driver events

Thousands of long non-coding RNAs (lncRNAs) lie interspersed with coding genes across the genome, and a small subset has been implicated as downstream effectors in oncogenic pathways. Here we make use of transcriptome and exome sequencing data from thousands of tumours across 19 cancer types, to identify lncRNAs that are induced or repressed in relation to somatic mutations in key oncogenic driver genes. Our screen confirms known coding and non-coding effectors and also associates many new lncRNAs to relevant pathways. The associations are often highly reproducible across cancer types, and while many lncRNAs are co-expressed with their protein-coding hosts or neighbours, some are intergenic and independent. We highlight lncRNAs with possible functions downstream of the tumour suppressor TP53 and the master antioxidant transcription factor NFE2L2. Our study provides a comprehensive overview of lncRNA transcriptional alterations in relation to key driver mutational events in human cancers.

Long non-coding RNAs are implicated in multiple aspects of tumourigenesis. Here, the authors generate a landscape of these macromolecules in a wide array of cancer types and examine which RNAs are transcriptionally altered in relation to somatic driver mutations in established coding cancer genes.

Subpathway-LNCE: Identify dysfunctional subpathways competitively regulated by lncRNAs through integrating lncRNA-mRNA expression profile and pathway topologies

Recently, studies have reported that long noncoding RNAs (lncRNAs) can act as modulators of mRNAs through competitively binding to microRNAs (miRNAs) and have relevance to tumorigenesis as well as other diseases. Identify lncRNA competitively regulated subpathway not only can gain insight into the initiation and progression of disease, but also help for understanding the functional roles of lncRNAs in the disease context. Here, we present an effective method, Subpathway-LNCE, which was specifically designed to identify lncRNAs competitively regulated functions and the functional roles of these competitive regulation lncRNAs have not be well characterized in diseases. Moreover, the method integrated lncRNA-mRNA expression profile and pathway topologies. Using prostate cancer datasets and LUAD data sets, we confirmed the effectiveness of our method in identifying disease associated dysfunctional subpathway that regulated by lncRNAs. By analyzing kidney renal clear cell carcinoma related lncRNA competitively regulated subpathway network, we show that Subpathway-LNCE can help uncover disease key lncRNAs. Furthermore, we demonstrated that our method is reproducible and robust. Subpathway-LNCE provide a flexible tool to identify lncRNA competitively regulated signal subpathways underlying certain condition, and help to expound the functional roles of lncRNAs in various status. Subpathway-LNCE has been developed as an R package freely available at https://cran.rstudio.com/web/packages/SubpathwayLNCE/.

The state of play in higher eukaryote gene annotation

A genome sequence is worthless if it cannot be deciphered; therefore, efforts to describe - or 'annotate' - genes began as soon as DNA sequences became available. Whereas early work focused on individual protein-coding genes, the modern genomic ocean is a complex maelstrom of alternative splicing, non-coding transcription and pseudogenes. Scientists - from clinicians to evolutionary biologists - need to navigate these waters, and this has led to the design of high-throughput, computationally driven annotation projects. The catalogues that are being produced are key resources for genome exploration, especially as they become integrated with expression, epigenomic and variation data sets. Their creation, however, remains challenging.

LincSNP 2.0: an updated database for linking disease-associated SNPs to human long non-coding RNAs and their TFBSs

We describe LincSNP 2.0 (http://bioinfo.hrbmu.edu.cn/LincSNP), an updated database that is used specifically to store and annotate disease-associated single nucleotide polymorphisms (SNPs) in human long non-coding RNAs (lncRNAs) and their transcription factor binding sites (TFBSs). In LincSNP 2.0, we have updated the database with more data and several new features, including (i) expanding disease-associated SNPs in human lncRNAs; (ii) identifying disease-associated SNPs in lncRNA TFBSs; (iii) updating LD-SNPs from the 1000 Genomes Project; and (iv) collecting more experimentally supported SNP-lncRNA-disease associations. Furthermore, we developed three flexible online tools to retrieve and analyze the data. Linc-Mart is a convenient way for users to customize their own data. Linc-Browse is a tool for all data visualization. Linc-Score predicts the associations between lncRNA and disease. In addition, we provided users a newly designed, user-friendly interface to search and download all the data in LincSNP 2.0 and we also provided an interface to submit novel data into the database. LincSNP 2.0 is a continually updated database and will serve as an important resource for investigating the functions and mechanisms of lncRNAs in human diseases.

A lincRNA connected to cell mortality and epigenetically-silenced in most common human cancers

Immortality is an essential characteristic of human carcinoma cells. We recently developed an efficient, reproducible method that immortalizes human mammary epithelial cells (HMEC) in the absence of gross genomic changes by targeting 2 critical senescence barriers. Consistent transcriptomic changes associated with immortality were identified using microarray analysis of isogenic normal finite pre-stasis, abnormal finite post-stasis, and immortal HMECs from 4 individuals. A total of 277 genes consistently changed in cells that transitioned from post-stasis to immortal. Gene ontology analysis of affected genes revealed biological processes significantly altered in the immortalization process. These immortalization-associated changes showed striking similarity to the gene expression changes seen in The Cancer Genome Atlas (TCGA) clinical breast cancer data. The most dramatic change in gene expression seen during the immortalization step was the downregulation of an unnamed, incompletely annotated transcript that we called MORT, for mortality, since its expression was closely associated with the mortal, finite lifespan phenotype. We show here that MORT (ZNF667-AS1) is expressed in all normal finite lifespan human cells examined to date and is lost in immortalized HMEC. MORT gene silencing at the mortal/immortal boundary was due to DNA hypermethylation of its CpG island promoter. This epigenetic silencing is also seen in human breast cancer cell lines and in a majority of human breast tumor tissues. The functional importance of DNA hypermethylation in MORT gene silencing is supported by the ability of 5-aza-2'-deoxycytidine to reactivate MORT expression. Analysis of TCGA data revealed deregulation of MORT expression due to DNA hypermethylation in 15 out of the 17 most common human cancers. The epigenetic silencing of MORT in a large majority of the common human cancers suggests a potential fundamental role in cellular immortalization during human carcinogenesis.

Integrative Transcriptome Analyses of Metabolic Responses in Mice Define Pivotal LncRNA Metabolic Regulators

To systemically identify long noncoding RNAs (lncRNAs) regulating energy metabolism, we performed transcriptome analyses to simultaneously profile mRNAs and lncRNAs in key metabolic organs in mice under pathophysiologically representative metabolic conditions. Of 4,759 regulated lncRNAs, function-oriented filters yield 359 tissue-specifically regulated and metabolically sensitive lncRNAs that are predicted by lncRNA-mRNA correlation analyses to function in diverse aspects of energy metabolism. Specific regulations of liver metabolically sensitive lncRNAs (lncLMS) by nutrients, metabolic hormones, and key transcription factors were further defined in primary hepatocytes. Combining genome-wide screens, bioinformatics function predictions, and cell-based analyses, we developed an integrative roadmap to identify lncRNA metabolic regulators. An lncLMS was experimentally confirmed in mice to suppress lipogenesis by forming a negative feedback loop in the SREBP1c pathway. Taken together, this study supports that a class of lncRNAs function as important metabolic regulators and establishes a framework for systemically investigating the role of lncRNAs in physiological homeostasis.

Genetic Polymorphisms in the Long Noncoding RNA MIR2052HG Offer a Pharmacogenomic Basis for the Response of Breast Cancer Patients to Aromatase Inhibitor Therapy

Genetic risks in breast cancer remain only partly understood. Here, we report the results of a genome-wide association study of germline DNA from 4,658 women, including 252 women experiencing a breast cancer recurrence, who were entered on the MA.27 adjuvant trial comparing the aromatase inhibitors (AI) anastrozole and exemestane. Single-nucleotide polymorphisms (SNP) of top significance were identified in the gene encoding MIR2052HG, a long noncoding RNA of unknown function. Heterozygous or homozygous individuals for variant alleles exhibited a ∼40% or ∼63% decrease, respectively, in the hazard of breast cancer recurrence relative to homozygous wild-type individuals. Functional genomic studies in lymphoblastoid cell lines and ERα-positive breast cancer cell lines showed that expression from MIR2052HG and the ESR1 gene encoding estrogen receptor-α (ERα) was induced by estrogen and AI in a SNP-dependent manner. Variant SNP genotypes exhibited increased ERα binding to estrogen response elements, relative to wild-type genotypes, a pattern that was reversed by AI treatment. Further, variant SNPs were associated with lower expression of MIR2052HG and ERα. RNAi-mediated silencing of MIR2052HG in breast cancer cell lines decreased ERα expression, cell proliferation, and anchorage-independent colony formation. Mechanistic investigations revealed that MIR2052HG sustained ERα levels both by promoting AKT/FOXO3-mediated ESR1 transcription and by limiting ubiquitin-mediated, proteasome-dependent degradation of ERα. Taken together, our results define MIR2052HS as a functionally polymorphic gene that affects risks of breast cancer recurrence in women treated with AI. More broadly, our results offer a pharmacogenomic basis to understand differences in the response of breast cancer patients to AI therapy. Cancer Res; 76(23); 7012-23. ©2016 AACR.

Quantitative assessment of polymorphisms in H19 lncRNA and cancer risk: a meta-analysis of 13,392 cases and 18,893 controls

H19 refers to a long non-coding RNA (lncRNA) that functions as an oncogenic molecule in different cancer cells. Genetic variants of H19 may affect the activity of certain regulatory factors, which subsequently regulate the aberrant expression of H19. This feedback loop might be one of the underlying mechanisms influencing tumour susceptibility and prognosis. Although there have been several recent studies that examined possible links between polymorphisms in H19 and cancer risk, the results have been inconclusive. Thus, we performed a meta-analysis to estimate the associations between H19 polymorphisms (rs2107425, rs2839698 and rs217727) and cancer risk. Ten studies comprising 13,392 cases and 18,893 controls were included in the study. Overall, the variant T allele of rs2107425 correlated with a significantly decreased risk of developing cancer (dominant model: OR = 0.86; 95% CI = 0.76-0.98). In addition, a marginally significant association between the rs2839698 and cancer risk was observed (dominant model: OR = 1.09; 95% CI = 0.99-1.20). After stratification for ethnicity, it became apparent that Asians with the variant A allele of rs2839698 exhibited a significantly higher risk of developing cancer (dominant model: OR = 1.11; 95% CI = 1.01-1.23). Interestingly, the rs2839698 variant was also significant associated with an increased risk of cancers of the digestive system (dominant model: OR = 1.23; 95% CI = 1.08-1.41). These findings provided evidence that H19 rs2107425 may modify general cancer susceptibility, while rs2839698 may modify cancer susceptibility based on ethnicity and type. Further experimental studies to evaluate the limits of this hypothesis are warranted, and future functional studies are required to clarify the possible mechanisms.

Computational identification of putative lincRNAs in mouse embryonic stem cell

As the regulatory factors, lncRNAs play critical roles in embryonic stem cells. And lincRNAs are most widely studied lncRNAs, however, there might still might exist a large member of uncovered lncRNAs. In this study, we constructed the de novo assembly of transcriptome to detect 6,701 putative long intergenic non-coding transcripts (lincRNAs) expressed in mouse embryonic stem cells (ESCs), which might be incomplete with the lack coverage of 5' ends assessed by CAGE peaks. Comparing the TSS proximal regions between the known lincRNAs and their closet protein coding transcripts, our results revealed that the lincRNA TSS proximal regions are associated with the characteristic genomic and epigenetic features. Subsequently, 1,293 lincRNAs were corrected at their 5' ends using the putative lincRNA TSS regions predicted by the TSS proximal region prediction model based on genomic and epigenetic features. Finally, 43 putative lincRNAs were annotated by Gene Ontology terms. In conclusion, this work provides a novel catalog of mouse ESCs-expressed lincRNAs with the relatively complete transcript length, which might be useful for the investigation of transcriptional and post-transcriptional regulation of lincRNA in mouse ESCs and even mammalian development.

lncScore: alignment-free identification of long noncoding RNA from assembled novel transcripts

RNA-Seq based transcriptome assembly has been widely used to identify novel lncRNAs. However, the best-performing transcript reconstruction methods merely identified 21% of full-length protein-coding transcripts from H. sapiens. Those partial-length protein-coding transcripts are more likely to be classified as lncRNAs due to their incomplete CDS, leading to higher false positive rate for lncRNA identification. Furthermore, potential sequencing or assembly error that gain or abolish stop codons also complicates ORF-based prediction of lncRNAs. Therefore, it remains a challenge to identify lncRNAs from the assembled transcripts, particularly the partial-length ones. Here, we present a novel alignment-free tool, lncScore, which uses a logistic regression model with 11 carefully selected features. Compared to other state-of-the-art alignment-free tools (e.g. CPAT, CNCI, and PLEK), lncScore outperforms them on accurately distinguishing lncRNAs from mRNAs, especially partial-length mRNAs in the human and mouse datasets. In addition, lncScore also performed well on transcripts from five other species (Zebrafish, Fly, C. elegans, Rat, and Sheep). To speed up the prediction, multithreading is implemented within lncScore, and it only took 2 minute to classify 64,756 transcripts and 54 seconds to train a new model with 21,000 transcripts with 12 threads, which is much faster than other tools. lncScore is available at https://github.com/WGLab/lncScore.

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma

Background

Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.

Methods

Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.

Results

In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10^-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.

Conclusions

Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

miR-1298 Inhibits Mutant KRAS-Driven Tumor Growth by Repressing FAK and LAMB3

Global miRNA functional screens can offer a strategy to identify synthetic lethal interactions in cancer cells that might be exploited therapeutically. In this study, we applied this strategy to identify novel gene interactions in KRAS-mutant cancer cells. In this manner, we discovered miR-1298, a novel miRNA that inhibited the growth of KRAS-driven cells both in vitro and in vivo Using miR-TRAP affinity purification technology, we identified the tyrosine kinase FAK and the laminin subunit LAMB3 as functional targets of miR-1298. Silencing of FAK or LAMB3 recapitulated the synthetic lethal effects of miR-1298 expression in KRAS-driven cancer cells, whereas coexpression of both proteins was critical to rescue miR-1298-induced cell death. Expression of LAMB3 but not FAK was upregulated by mutant KRAS. In clinical specimens, elevated LAMB3 expression correlated with poorer survival in lung cancer patients with an oncogenic KRAS gene signature, suggesting a novel candidate biomarker in this disease setting. Our results define a novel regulatory pathway in KRAS-driven cancers, which offers a potential therapeutic target for their eradication. Cancer Res; 76(19); 5777-87. ©2016 AACR.

Non-coding RNAs in Development and Disease: Background, Mechanisms, and Therapeutic Approaches

Advances in RNA-sequencing techniques have led to the discovery of thousands of non-coding transcripts with unknown function. There are several types of non-coding linear RNAs such as microRNAs (miRNA) and long non-coding RNAs (lncRNA), as well as circular RNAs (circRNA) consisting of a closed continuous loop. This review guides the reader through important aspects of non-coding RNA biology. This includes their biogenesis, mode of actions, physiological function, as well as their role in the disease context (such as in cancer or the cardiovascular system). We specifically focus on non-coding RNAs as potential therapeutic targets and diagnostic biomarkers.

Consensus Analysis of Whole Transcriptome Profiles from Two Breast Cancer Patient Cohorts Reveals Long Non-Coding RNAs Associated with Intrinsic Subtype and the Tumour Microenvironment

Long non-coding RNAs (lncRNAs) are emerging as crucial regulators of cellular processes and diseases such as cancer; however, their functions remain poorly characterised. Several studies have demonstrated that lncRNAs are typically disease and tumour subtype specific, particularly in breast cancer where lncRNA expression alone is sufficient to discriminate samples based on hormone status and molecular intrinsic subtype. However, little attempt has been made to assess the reproducibility of lncRNA signatures across more than one dataset. In this work, we derive consensus lncRNA signatures indicative of breast cancer subtype based on two clinical RNA-Seq datasets: the Utah Breast Cancer Study and The Cancer Genome Atlas, through integration of differential expression and hypothesis-free clustering analyses. The most consistent signature is associated with breast cancers of the basal-like subtype, leading us to generate a putative set of six lncRNA basal-like breast cancer markers, at least two of which may have a role in cis-regulation of known poor prognosis markers. Through in silico functional characterization of individual signatures and integration of expression data from pre-clinical cancer models, we discover that discordance between signatures derived from different clinical cohorts can arise from the strong influence of non-cancerous cells in tumour samples. As a consequence, we identify nine lncRNAs putatively associated with breast cancer associated fibroblasts, or the immune response. Overall, our study establishes the confounding effects of tumour purity on lncRNA signature derivation, and generates several novel hypotheses on the role of lncRNAs in basal-like breast cancers and the tumour microenvironment.

Mammary Tumor-Associated RNAs Impact Tumor Cell Proliferation, Invasion, and Migration

Long non-coding RNAs (lncRNAs) represent the largest and most diverse class of non-coding RNAs, comprising almost 16,000 currently annotated transcripts in human and 10,000 in mouse. Here, we investigated the role of lncRNAs in mammary tumors by performing RNA-seq on tumor sections and organoids derived from MMTV-PyMT and MMTV-Neu-NDL mice. We identified several hundred lncRNAs that were overexpressed compared to normal mammary epithelium. Among these potentially oncogenic lncRNAs we prioritized a subset as Mammary Tumor Associated RNAs (MaTARs) and determined their human counterparts, hMaTARs. To functionally validate the role of MaTARs, we performed antisense knockdown and observed reduced cell proliferation, invasion, and/or organoid branching in a cancer-specific context. Assessing the expression of hMaTARs in human breast tumors revealed that 19 hMaTARs are significantly upregulated and many of these correlate with breast cancer subtype and/or hormone receptor status, indicating potential clinical relevance.

The lncRNA landscape of breast cancer reveals a role for DSCAM-AS1 in breast cancer progression

Molecular classification of cancers into subtypes has resulted in an advance in our understanding of tumour biology and treatment response across multiple tumour types. However, to date, cancer profiling has largely focused on protein-coding genes, which comprise <1% of the genome. Here we leverage a compendium of 58,648 long noncoding RNAs (lncRNAs) to subtype 947 breast cancer samples. We show that lncRNA-based profiling categorizes breast tumours by their known molecular subtypes in breast cancer. We identify a cohort of breast cancer-associated and oestrogen-regulated lncRNAs, and investigate the role of the top prioritized oestrogen receptor (ER)-regulated lncRNA, DSCAM-AS1. We demonstrate that DSCAM-AS1 mediates tumour progression and tamoxifen resistance and identify hnRNPL as an interacting protein involved in the mechanism of DSCAM-AS1 action. By highlighting the role of DSCAM-AS1 in breast cancer biology and treatment resistance, this study provides insight into the potential clinical implications of lncRNAs in breast cancer.

LncRNAs have been associated with cancer. Here, the authors carry out a systematic review of lncRNAs in breast cancer and show that DSCAM-AS1 is highly expressed in oestrogen receptor positive tumours and enhances cancer through an interaction with hnRNPL; and is also associated with tamoxifen resistance.

LncRNA ontology: inferring lncRNA functions based on chromatin states and expression patterns

Accumulating evidences suggest that long non-coding RNAs (lncRNAs) perform important functions. Genome-wide chromatin-states area rich source of information about cellular state, yielding insights beyond what is typically obtained by transcriptome profiling. We propose an integrative method for genome-wide functional predictions of lncRNAs by combining chromatin states data with gene expression patterns. We first validated the method using protein-coding genes with known function annotations. Our validation results indicated that our integrative method performs better than co-expression analysis, and is accurate across different conditions. Next, by applying the integrative model genome-wide, we predicted the probable functions for more than 97% of human lncRNAs. The putative functions inferred by our method match with previously annotated by the targets of lncRNAs. Moreover, the linkage from the cellular processes influenced by cancer-associated lncRNAs to the cancer hallmarks provided a “lncRNA point-of-view” on tumor biology. Our approach provides a functional annotation of the lncRNAs, which we developed into a web-based application, LncRNA Ontology, to provide visualization, analysis, and downloading of lncRNA putative functions.

Analysis of long non-coding RNA expression profiles in pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) remains one of the most aggressive and lethal malignancies. Long non-coding RNAs (lncRNAs) are a novel class of non-protein-coding transcripts that have been implicated in cancer biogenesis and prognosis. By repurposing microarray probes, we herein analysed the lncRNA expression profiles in two public PDAC microarray datasets and identified 34 dysregulated lncRNAs in PDAC. In addition, the expression of 6 selected lncRNAs was confirmed in Ren Ji cohort and pancreatic cell lines, and their association with 80 PDAC patients' clinicopathological features and prognosis was investigated. Results indicated that AFAP1-AS1, UCA1 and ENSG00000218510 might be involved in PDAC progression and significantly associated with overall survival of PDAC. UCA1 and ENSG00000218510 expression status may serve as independent prognostic biomarkers for overall survival of PDAC. Gene set enrichment analysis (GSEA) analysis suggested that high AFAP1-AS1, UCA1 and low ENSG00000218510 expression were correlated with several tumorigenesis related pathways. Functional experiments demonstrated that AFAP1-AS1 and UCA1 were required for efficient invasion and/or proliferation promotion in PDAC cell lines, while ENSG00000218510 acted the opposite. Our findings provide novel information on lncRNAs expression profiles which might be beneficial to the precise diagnosis, subcategorization and ultimately, the individualized therapy of PDAC.

Hypothalamic transcriptomes of 99 mouse strains reveal trans eQTL hotspots, splicing QTLs and novel non-coding genes

Previous studies had shown that the integration of genome wide expression profiles, in metabolic tissues, with genetic and phenotypic variance, provided valuable insight into the underlying molecular mechanisms. We used RNA-Seq to characterize hypothalamic transcriptome in 99 inbred strains of mice from the Hybrid Mouse Diversity Panel (HMDP), a reference resource population for cardiovascular and metabolic traits. We report numerous novel transcripts supported by proteomic analyses, as well as novel non coding RNAs. High resolution genetic mapping of transcript levels in HMDP, reveals both local and trans expression Quantitative Trait Loci (eQTLs) demonstrating 2 trans eQTL 'hotspots' associated with expression of hundreds of genes. We also report thousands of alternative splicing events regulated by genetic variants. Finally, comparison with about 150 metabolic and cardiovascular traits revealed many highly significant associations. Our data provide a rich resource for understanding the many physiologic functions mediated by the hypothalamus and their genetic regulation.

DOI:http://dx.doi.org/10.7554/eLife.15614.001

Metabolism is a term that describes all the chemical reactions that are involved in keeping a living organism alive. Diseases related to metabolism – such as obesity, heart disease and diabetes – are a major health problem in the Western world. The causes of these diseases are complex and include both environmental factors, such as diet and exercise, and genetics. Indeed, many genetic variants that contribute to obesity have been uncovered in both humans and mice. However, it is only dimly understood how these genetic variants affect the underlying networks of interacting genes that cause metabolic disorders.

Measuring gene activity or expression, and tracing how genetic instructions are carried from DNA into RNA and proteins, can reliably identify groups of genes that correlate with metabolic traits in specific organs. This strategy was successfully used in previous studies to reveal new information about abnormalities linked to obesity in specific tissues such as the liver and fat tissues. It was also shown that this approach might suggest new molecules that could be targeted to treat metabolic disorders.

A brain region called the hypothalamus is key to the control of metabolism, including feeding behavior and obesity. Hasin-Brumshtein et al. set out to explore gene expression in the hypothalamus of 99 different strains of mice, in the hope that the data will help identify new connections between gene expression and metabolism.

This approach showed that thousands of new and known genes are expressed in the mouse hypothalamus, some of which coded for proteins, and some of which did not. Hasin-Brumshtein et al. uncovered two genetic variants that controlled the expression of hundreds of other genes. Further analysis then revealed thousands of genetic variants that regulated the expression of, and type of RNA (so-called "spliceforms") produced from neighboring genes. Also, the expression of many individual genes showed significant similarities with about 150 metabolic measurements that had been evaluated previously in the mice.

This new dataset is a unique resource that can be coupled with different approaches to test existing ideas and develop new ones about the role of particular genes or genetic mechanisms in obesity. Future studies will likely focus on new genes that show strong associations with attributes that are relevant to metabolic disorders, such as insulin levels, weight and fat mass.

DOI:http://dx.doi.org/10.7554/eLife.15614.002

Long noncoding RNAs are novel potential prognostic biomarkers for esophageal squamous cell carcinoma: an overview

Esophageal squamous cell carcinoma (ESCC) still has a poor prognosis. The prognostic biomarkers of ESCC are not yet well established. Long noncoding RNAs (lncRNAs) have recently been intensively investigated in various cancers including ESCC, and are found to be closely correlated to ESCC. Dysregulated expression of lncRNAs was widely observed in ESCC tumor tissue and was closely related to the tumorigenesis and progression of ESCC. More and more studies have found that lncRNAs were significantly correlated with the prognosis and diagnosis of patients with ESCC. Therefore, all those accumulating evidence indicated that lncRNAs could serve as a prognostic biomarker of ESCC. In this, we summarized the relation between lncRNAs and ESCC as well as the potential biomarker role of lncRNAs in ESCC, especially the prognostic value of lncRNAs. Our current review highlighted the need of further studies to explore the biomarker functions as well as therapeutic values of lncRNAs in ESCC.

HIPSTR and thousands of lncRNAs are heterogeneously expressed in human embryos, primordial germ cells and stable cell lines

Eukaryotic genomes are transcribed into numerous regulatory long non-coding RNAs (lncRNAs). Compared to mRNAs, lncRNAs display higher developmental stage-, tissue-, and cell-subtype-specificity of expression, and are generally less abundant in a population of cells. Despite the progress in single-cell-focused research, the origins of low population-level expression of lncRNAs in homogeneous populations of cells are poorly understood. Here, we identify HIPSTR (Heterogeneously expressed from the Intronic Plus Strand of the TFAP2A-locus RNA), a novel lncRNA gene in the developmentally regulated TFAP2A locus. HIPSTR has evolutionarily conserved expression patterns, its promoter is most active in undifferentiated cells, and depletion of HIPSTR in HEK293 and in pluripotent H1_BP cells predominantly affects the genes involved in early organismal development and cell differentiation. Most importantly, we find that HIPSTR is specifically induced and heterogeneously expressed in the 8-cell-stage human embryos during the major wave of embryonic genome activation. We systematically explore the phenomenon of cell-to-cell variation of gene expression and link it to low population-level expression of lncRNAs, showing that, similar to HIPSTR, the expression of thousands of lncRNAs is more highly heterogeneous than the expression of mRNAs in the individual, otherwise indistinguishable cells of totipotent human embryos, primordial germ cells, and stable cell lines.

COME: a robust coding potential calculation tool for lncRNA identification and characterization based on multiple features

Recent genomic studies suggest that novel long non-coding RNAs (lncRNAs) are specifically expressed and far outnumber annotated lncRNA sequences. To identify and characterize novel lncRNAs in RNA sequencing data from new samples, we have developed COME, a coding potential calculation tool based on multiple features. It integrates multiple sequence-derived and experiment-based features using a decompose-compose method, which makes it more accurate and robust than other well-known tools. We also showed that COME was able to substantially improve the consistency of predication results from other coding potential calculators. Moreover, COME annotates and characterizes each predicted lncRNA transcript with multiple lines of supporting evidence, which are not provided by other tools. Remarkably, we found that one subgroup of lncRNAs classified by such supporting features (i.e. conserved local RNA secondary structure) was highly enriched in a well-validated database (lncRNAdb). We further found that the conserved structural domains on lncRNAs had better chance than other RNA regions to interact with RNA binding proteins, based on the recent eCLIP-seq data in human, indicating their potential regulatory roles. Overall, we present COME as an accurate, robust and multiple-feature supported method for the identification and characterization of novel lncRNAs. The software implementation is available at https://github.com/lulab/COME.