lncRNAs功能注释和预测

随着测序技术的发展,在各种哺乳动物中发现越来越多的长非编码RNAs（long non-coding RNAs,lncRNAs）,但是大部分lncRNAs的功能却未知.鉴于lncRNAs在众多生物过程如免疫反应、发育和基因印迹中表现出对蛋白编码基因和其它非编码RNAs的重要调节作用,对lncRNAs的功能研究也成为生物学家和生物信息学家研究的热点. 其中,功能注释和预测是目前研究lncRNAs功能的主要方法之一.本文主要对lncRNAs功能注释和预测方法的研究进展作一综述,包括以下几个方面：基于共表达网络的方法、基于miRNAs的方法、基于蛋白质结合的方法、基于表观遗传修饰的方法以及基于ceRNA网络的方法. 为进一步研究lncRNAs的功能提供参考,同时为开发更加有效的注释或预测方法提供线索.     

Characterization of Differentially Expressed Genes Involved in Pathways Associated with Gastric Cancer

To explore the patterns of gene expression in gastric cancer, a total of 26 paired gastric cancer and noncancerous tissues from patients were enrolled for gene expression microarray analyses. Limma methods were applied to analyze the data, and genes were considered to be significantly differentially expressed if the False Discovery Rate (FDR) value was < 0.01, P-value was <0.01 and the fold change (FC) was >2. Subsequently, Gene Ontology (GO) categories were used to analyze the main functions of the differentially expressed genes. According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) database, we found pathways significantly associated with the differential genes. Gene-Act network and co-expression network were built respectively based on the relationships among the genes, proteins and compounds in the database. 2371 mRNAs and 350 lncRNAs considered as significantly differentially expressed genes were selected for the further analysis. The GO categories, pathway analyses and the Gene-Act network showed a consistent result that up-regulated genes were responsible for tumorigenesis, migration, angiogenesis and microenvironment formation, while down-regulated genes were involved in metabolism. These results of this study provide some novel findings on coding RNAs, lncRNAs, pathways and the co-expression network in gastric cancer which will be useful to guide further investigation and target therapy for this disease.     

Expression profile of long non-coding RNAs in cervical spondylotic myelopathy of rats by microarray and bioinformatics analysis

IntroductionIt has been reported that a wide range of long non-coding RNAs (lncRNAs) are implicated in numerous diseases such as tumor, cardiopathy and neurological disorders. Identifying the differentially expressed (DE) profile of lncRNAs in cervical spondylotic myelopathy (CSM) is essential to understand the mechanisms of CSM.MethodsMicroarray assay, quantitative real-time PCR (qRT-PCR) and bioinformatics analysis were employed to reveal the DE profile and potential functions of lncRNAs in CSM.ResultsMicroarray analysis displayed the DE profiles of lncRNAs and mRNAs in rats between the CSM group and the control (CON) group. Thereinto, 1266 DE lncRNAs (738 up-regulation and 528 down-regulation) and 847 mRNAs (487 up-regulation and 360 down-regulation) with >1.1 fold change (FC) were finally identified. Moreover, 17 lncRNAs (13 up-regulation and 4 down-regulation) and 18 mRNAs (13 up-regulation and 5 down-regulation) were found deregulated by >2 FC. Further bioinformatics analysis showed the most remarkable biological processes among up-regulated RNAs contain cellular response to interferon-beta, inflammatory response and innate immune response, which may involve in CSM. Besides, related DE mRNAs of 17 DE lncRNAs in the genome were related to signaling pathway about NOD-like receptor, TNF, and apoptosis. In addition, a co-expression network of lncRNA-mRNA was established for analyzing the biological roles of lncRNAs. Among these, we found a ceRNA network related to CSM. Finally, the expressions of the DE lncRNAs and ceRNA network confirmed by qRT-PCR were in agreement with microarray data.ConclusionsOur study revealed the DE profiles of lncRNAs and mRNAs for CSM. Those dysregulated RNAs may represent potential therapeutic targets of CSM for further study.     

GCEN: An Easy-to-Use Toolkit for Gene Co-Expression Network Analysis and lncRNAs Annotation

Gene co-expression network analysis has been widely used in gene function annotation, especially for long noncoding RNAs (lncRNAs). However, there is a lack of effective cross-platform analysis tools. For biologists to easily build a gene co-expression network and to predict gene function, we developed GCEN, a cross-platform command-line toolkit developed with C++. It is an efficient and easy-to-use solution that will allow everyone to perform gene co-expression network analysis without the requirement of sophisticated programming skills, especially in cases of RNA-Seq research and lncRNAs function annotation. Because of its modular design, GCEN can be easily integrated into other pipelines.     

LncRNAs Expression in Preeclampsia Placenta Reveals the Potential Role of LncRNAs Contributing to Preeclampsia Pathogenesis

Background

Long non-coding RNAs (lncRNAs) are an important class of pervasive genes involved in a variety of biological functions. They are aberrantly expressed in many types of diseases. In this study, we aimed to investigate the lncRNA profiles in preeclampsia. Preeclampsia has been observed in patients with molar pregnancy where a fetus is absent, which demonstrate that the placenta is sufficient to cause this condition. Thus, we analyzed the lncRNA profiles in preeclampsia placentas.

Methodology/Principal Findings

In this study, we described the lncRNA profiles in six preeclampsia placentas (T) and five normal pregnancy placentas (N) using microarray. With abundant and varied probes accounting for 33,045 LncRNAs in our microarray, 28,443 lncRNAs that were expressed at a specific level were detected. From the data, we found 738 lncRNAs that were differentially expressed (≥1.5-fold-change) among preeclampsia placentas compared with controls. Coding-non-coding gene co-expression networks (CNC network) were constructed based on the correlation analysis between the differentially expressed lncRNAs and mRNAs. According to the CNC network and GO analysis of differentially expressed lncRNAs/mRNAs, we selected three lncRNAs to analyze the relationship between lncRNAs and preeclampsia. LOC391533, LOC284100, and CEACAMP8 were evaluated using qPCR in 40 preeclampsia placentas and 40 controls. These results revealed that three lncRNAs were aberrantly expressed in preeclampsia placentas compared with controls.

Conclusions/Significance

Our study is the first study to determine the genome-wide lncRNAs expression patterns in preeclampsia placenta using microarray. These results revealed that clusters of lncRNAs were aberrantly expressed in preeclampsia placenta compared with controls, which indicated that lncRNAs differentially expressed in preeclampsia placenta might play a partial or key role in preeclampsia development. Misregulation of LOC391533, LOC284100, and CEACAMP8 might contribute to the mechanism underlying preeclampsia. Taken together, this study may provide potential targets for the future treatment of preeclampsia and novel insights into preeclampsia biology.     

Analysis of gene co-expression networks and function modules at different developmental stages of chicken breast muscle

The development of poultry muscle fibers after hatching is closely related to meat quality and production efficiency. It is necessary to identify functional modules (groups of functionally related genes) related to muscle development at different developmental stages, and to investigate their relationships based on the weighted gene co-expression network analysis (WGCNA) methods. Accordingly, we investigated the co-expression associations between genes related to chicken breast muscle at four different developmental stages (between 2 and 14 weeks of age), and systematically analyzed the network topology in Jinmao Hua chicken. As a result, 2341 differentially expressed genes were identified and subjected to co-expression analysis. Four modules were identified to be related to a particular growth stage for the development of breast muscle. A series of genes with the highest connectivity were identified in the pink (2 weeks), yellow (6 weeks), green (10 weeks) and black modules (14 weeks), respectively, and visualized by Cytoscape. These hub genes (FGF, MAPKAPK5, NRG1, SCD, ACSL1, PPAR etc.) were mainly enriched in 15 pathways, such as MAPK signaling pathway, NRG/ErbB signaling pathway, and insulin signaling pathway. They shared biological functions related to development of breast muscle and adipogenesis. This is the first study of gene network with different stages of muscle development in Jinmao Hua chicken to observe co-expression patterns. It may contribute to the underlied molecular mechanisms of chicken breast muscle development.     

A landscape of Long non-coding RNAs reveals the leading transcriptome alterations in murine aorta during aging

Considerable studies have given convincing evidence of a forefront position for vascular aging in preventing cardiovascular disease. Various functions of Long non-coding RNAs (lncRNAs) are becoming increasingly distinct in aging-related diseases. This study aims at a better insight into the expression profile and mechanisms of lncRNAs in vascular senescence. High-throughput sequencing was used to detect the differential expression (DE) of lncRNAs and mRNAs in the aorta of 96 W and 8 W-old mice, while 1423 lncRNAs and 80 mRNAs were differentially expressed. By performing GO and KEGG enrichment analysis, we found that DE lncRNAs were mainly involved in purine metabolism and cGMP-PKG signaling pathways. In addition, a co-expression functional network of DE lncRNAs and DE mRNAs was constructed, and ENSMUST00000218874 could interact with 41 DE mRNAs, suggesting that it may play an essential role in vascular senescence. This study reveals DE lncRNAs in naturally aging vascular, which may provide new ideas and targets for aging-related cardiovascular diseases.     

A novel autophagy-related lncRNA prognostic risk model for breast cancer

Long non-coding RNAs (lncRNAs) are well known as crucial regulators to breast cancer development and are implicated in controlling autophagy. LncRNAs are also emerging as valuable prognostic factors for breast cancer patients. It is critical to identify autophagy-related lncRNAs with prognostic value in breast cancer. In this study, we identified autophagy-related lncRNAs in breast cancer by constructing a co-expression network of autophagy-related mRNAs-lncRNAs from The Cancer Genome Atlas (TCGA). We evaluated the prognostic value of these autophagy-related lncRNAs by univariate and multivariate Cox proportional hazards analyses and eventually obtained a prognostic risk model consisting of 11 autophagy-related lncRNAs (U62317.4, LINC01016, LINC02166, C6orf99, LINC00992, BAIAP2-DT, AC245297.3, AC090912.1, Z68871.1, LINC00578 and LINC01871). The risk model was further validated as a novel independent prognostic factor for breast cancer patients based on the calculated risk score by Kaplan-Meier analysis, univariate and multivariate Cox regression analyses and time-dependent receiver operating characteristic (ROC) curve analysis. Moreover, based on the risk model, the low-risk and high-risk groups displayed different autophagy and oncogenic statues by principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) functional annotation. Taken together, these findings suggested that the risk model of the 11 autophagy-related lncRNAs has significant prognostic value for breast cancer and might be autophagy-related therapeutic targets in clinical practice.     

Comprehensive analysis of a ceRNA network reveals potential prognostic cytoplasmic lncRNAs involved in HCC progression

The aberrant expression of long noncoding RNAs (lncRNAs) has drawn increasing attention in the field of hepatocellular carcinoma (HCC) biology. In the present study, we obtained the expression profiles of lncRNAs, microRNAs (miRNAs), and messenger RNAs (mRNAs) in 371 HCC tissues and 50 normal tissues from The Cancer Genome Atlas (TCGA) and identified hepatocarcinogenesis-specific differentially expressed genes (DEGs, log fold change ≥ 2, FDR < 0.01), including 753 lncRNAs, 97 miRNAs, and 1,535 mRNAs. Because the specific functions of lncRNAs are closely related to their intracellular localizations and because the cytoplasm is the main location for competitive endogenous RNA (ceRNA) action, we analyzed not only the interactions among these DEGs but also the distributions of lncRNAs (cytoplasmic, nuclear or both). Then, an HCC-associated deregulated ceRNA network consisting of 37 lncRNAs, 10 miRNAs, and 26 mRNAs was constructed after excluding those lncRNAs located only in the nucleus. Survival analysis of this network demonstrated that 15 lncRNAs, 3 miRNAs, and 16 mRNAs were significantly correlated with the overall survival of HCC patients (p < 0.01). Through multivariate Cox regression and lasso analysis, a risk score system based on 13 lncRNAs was constructed, which showed good discrimination and predictive ability for HCC patient survival time. This ceRNA network-construction approach, based on lncRNA distribution, not only narrowed the scope of target lncRNAs but also provided specific candidate molecular biomarkers for evaluating the prognosis of HCC, which will help expand our understanding of the ceRNA mechanisms involved in the early development of HCC.     

Identification of messenger and long noncoding RNAs associated with gallbladder cancer via gene expression profile analysis

The present study aimed to investigate the long noncoding RNAs (lncRNAs) and messenger RNAs (mRNAs) involved in the progression of gallbladder cancer and explore the potential physiopathologic mechanisms of gallbladder cancer in terms of competing endogenous RNAs (ceRNAs). The original lncRNA and mRNA expression profile data (nine gallbladder cancer tissues samples and nine normal gallbladder samples) in GSE76633 was downloaded from the Gene Expression Omnibus database. Differentially expressed mRNAs and lncRNAs between gallbladder cancer tissue and normal control were selected and the pathways in which they are involved were analyzed using bioinformatics analyses. MicroRNAs (miRNAs) were also predicted based on the differentially expressed mRNAs. Finally, the co-expression relation between lncRNA and mRNA was analyzed and the ceRNA network was constructed by combining the lncRNA-miRNA, miRNA-mRNA, and lncRNA-mRNA pairs. Overall, 373 significantly differentially expressed mRNAs and 47 lncRNAs were identified between cancer and normal tissue samples. The upregulated genes were significantly enriched in the extracellular matrix (ECM)-receptor interaction pathway, while the downregulated genes were involved in the complement and coagulation cascades. Altogether, 128 co-expression relations between lncRNA and mRNA were obtained. In addition, 196 miRNA-mRNA regulatory relations and 145 miRNA-lncRNA relation pairs were predicted. Finally, the lncRNA-miRNA-gene ceRNA network was constructed by combining the three types of relation pairs, such as XLOC_011309-miR-548c-3p-SPOCK1 and XLOC_012588-miR-765-CEACAM6. mRNAs and lncRNAs may be involved in gallbladder cancer progression via ECM-receptor interaction pathways and the complement and coagulation cascades. Moreover, ceRNAs such as XLOC_011309-miR-548c-3p-SPOCK1 and XLOC_012588-miR-765-CEACAM6 can also be implicated in the pathogenesis of gallbladder cancer.