一个肝癌相关长链非编码RNA的克隆及序列分析

最近我们利用新一代测序(next generation sequencing,NGS)技术对肝细胞癌(hepatocellular carcinoma,HCC)患者活检标本及正常对照肝组织样品进行高通量RNA测序(RNA-sequencing,RNA-Seq),在肝癌样品中染色体11q13.1区域检测到几个相邻的RNA-Seq信号峰,而在正常对照组织中没有检测到,且该染色体区域目前尚无已知基因登录,提示这几个RNA-Seq峰可能代表一个或多个未知的新基因.以此为线索,证实这几个RNA-Seq峰来自同一个新基因,并克隆了该基因全长序列,在克隆该基因全长序列时,发现该基因编码的RNA存在多种剪接形式,最长的转录本为3 562 bp.将该基因编码的12条代表性RNA转录本序列递交到美国国立生物技术信息中心(National Center for Biotechnology Information,NCBI)的GenBank数据库中,GenBank ID号分别为KC136297~KC136308.该基因编码的RNA没有发现明显的开放阅读框(open reading fragment,ORF),提示该基因可能编码长链非编码RNA(long non-coding RNA,lncRNA).为了探讨该lncRNA基因可能的转录调控机制,我们用生物信息学方法预测了该lncRNA基因潜在启动子区域,发现在其转录起始位点上游-719~-469 bp处有一个潜在的启动子,其中包含7个Sp1、1个STAT5和1个EGR1转录因子结合位点.该lncRNA在肝细胞癌发生发展过程中的作用机制值得进一步深入研究.     

长链非编码RNA作为潜在药物靶点的研究进展

随着生命科学的不断发展,2012年DNA元件百科全书（ENCODE）项目进一步丰富了人类基因组功能元件的相关信息。该项目 发现人类基因组超过80%的序列会被转录,其中大部分转录本是非编码RNA（ncRNA）。目前,在这些非编码RNA中,小RNA的研究 相对深入,而长链非编码RNA（lncRNA）的研究相对较少。越来越多研究表明,很多lncRNA参与到人类重大疾病的发生、发展过程之 中,并且一些动物实验证实lncRNA可作为药物靶点。因此,从lncRNA角度筛选新的药物靶点也越来越受到研究者的关注。重点总结了 lncRNA的生物学功能及作为潜在药物靶点的研究进展。     

长链非编码RNA研究进展

基因组计划研究表明, 在组成人类基因组的30亿个碱基对中, 仅有1.5%的核酸序列用于蛋白质编码, 其余98.5%的基因组为非蛋白质编码序列。这些序列曾被认为是在进化过程中累积的“垃圾序列”而未予以关注, 但在随后启动的ENCODE研究计划中却发现, 75%的基因组序列能够被转录成RNA, 其中近74%的转录产物为非编码RNA(Non-coding RNA, ncRNA)。在非编码RNA中, 绝大多数转录本的长度大于200个碱基, 这些“长链非编码RNA(Long non-coding RNA, lncRNA)”能够在转录及转录后水平上调节蛋白编码基因的表达, 从而广泛地参与包括细胞分化、个体发育在内的重要生命过程, 其异常表达还与多种人类重大疾病的发生密切相关。文章综述了长链非编码RNA的发现、分类、表达、作用机制以及其在个体发育和人类疾病中的作用。     

转座子来源的植物长链非编码RNA

转座子是基因组的重要组分, 影响基因组的结构与稳定。长链非编码RNA (lncRNA)在转录及转录后水平调控多个生物学过程。转座子与lncRNA是物种进化的重要驱动力。含有转座子序列的lncRNA在自然界广泛存在。该文对植物lncRNA的发掘策略和功能研究进行概述, 围绕植物转座子来源lncRNA (TE-lncRNA)的分布和功能展开综述, 并对植物TE-lncRNA的调控机制、表观修饰及育种潜势等进行探讨与展望。     

长链非编码RNA研究进展

长链非编码RNA(long noncoding RNAs,lncRNAs)是一类长度超过200nt的非编码RNA分子,通过信号分子、诱饵分子、引导分子、支架分子等4种方式在转录水平和转录后水平调控基因的表达。lncRNAs的表达水平相对于蛋白编码基因较低,但它们在X染色体沉默、基因组印迹、染色体修饰、转录激活、转录干扰以及核内运输等方面具有重要的功能。相对于研究较多的非编码小RNA,lncRNAs的功能目前尚不完全清楚。该文从lncRNAs的起源、分类、分子机制、功能和进化等方面综述了lncRNAs的研究进展,为进一步探究lncRNAs的功能和作用机制提供依据。     

小麦长链非编码RNA的预测及功能分析

生物体有部分基因被转录成RNA,但是不编码相应蛋白质,称为长链非编码RNA(lncRNA)。它们参与基因的表观调控,这一过程对动物、植物的生长发育都有重要作用,但是,目前植物中发现和研究的lncRNA较少。为了研究lncRNA在植物中的功能,本研究建立了基于小麦全长cDNA的lncRNA识别程序。从6162条小麦全长cDNA中发现了231条lncRNAs,并从中鉴定出两个新miRNAs,这表明lncRNAs可以通过形成miRNAs前体基因形成其功能。此外,通过序列富集分析,我们从小麦lncRNAs中鉴定出三个保守的调控元件,结果显示小麦lncRNAs可能通过和其它蛋白质或DNA等分子作用,进而参与小麦生长、发育等过程的调控,这些结果对进一步研究植物体内的lncRNA的功能和作用机制具有重要意义。     

长链非编码RNA在心血管疾病发病中的研究进展

心血管疾病是一种威胁人类,特别是中老年人健康的常见疾病。每年死于心血管疾病的人数位居全球榜首。长链非编码RNA是长度大于200个核苷酸且不编码蛋白质的RNA,也是体内表达数量最多的RNA。越来越多的研究发现长链非编码RNA在心血管疾病的发生、发展中有重要的调节作用。本文将就长链非编码RNA和心血管疾病的最新研究进展进行综述。     

植物长链非编码RNA的研究进展

长链非编码RNA(long non-coding RNAs,lnc RNAs)是真核生物中一类长度大于200个核苷酸、无蛋白编码能力或编码能力极低的RNA转录本。lnc RNA种类和功能的多样性导致了对其进行研究的复杂性,特别是对植物中lnc RNA的认识相当有限。该文就近年来植物中已发现的lnc RNA的种类、相关转录酶、参与的生物学过程、发挥功能的分子机制以及其相关的研究策略等方面进行综述和展望,以期为深入认识植物lnc RNA提供借鉴。     

舌鳞状细胞癌相关长链非编码RNA的筛选与分析

[目的]获得舌鳞状细胞癌组织与正常舌组织中差异表达的长链非编码RNA,并进行功能注释和参与的生理过程进行初步分析。[方法]利用GEO数据库中GES13601的原始芯片数据,采用affymatrix软件套件对芯片数据进行重新均一化和标准化,并筛选差异表达的长链非编码RNA,查找其参与的生理过程。[结果]舌鳞状细胞癌组织与正常舌组织差异表达的基因个数为5 613个。其中在癌组织中上调的为2 951个,下调的共2 662个。从中筛选到长链非编码RNA 38个,上调19个,下调19个。其中的某些参与涉及肌肉、淋巴生长和癌细胞发送等生理过程,但是大部分的功能未知,尚需进一步研究。[结论]筛选到舌鳞状细胞癌相关的38个长链非编码RNA,为寻找早期舌鳞状细胞癌标志物提供了新的参考。     

长非编码RNA与蛋白质的相互作用

长非编码RNA(long non-coding RNA,lncRNA)是一类长度大于200个核苷酸但缺乏蛋白质编码潜力的调控型RNA。lncRNA在真核生物基因组中广泛转录,并且能够在多种层次以灵活的方式对基因表达进行调控。lncRNA能够与染色质修饰复合物及转录因子等蛋白质相互作用,这种相互作用提高了基因表达调控的灵活性和复杂性。本文将介绍部分具有代表性的lncRNA-蛋白质相互作用及其在基因表达调控中的作用。     

Screening and bioinformatics analysis of mRNA,long non-coding RNA and circular RNA expression profiles in mucoepidermoid carcinoma of salivary gland

Mucoepidermoid carcinoma (MEC) of salivary gland is a disease characterized by high rate of diatant metastasis, and associated with poor outcomes. However, the molecular mechanisms underlying the MEC remain poorly understand. Here, we simultaneously detected, for the first time, the expression profiles of mRNAs, lncRNAs, and circRNAs in four pairs of MEC and matched non-carcinoma tissues by microarrays. A total of 3612?mRNA, 3091 lncRNAs, and 284 circRNAs were altered during the pathogenesis of MEC. The functions of these differentially expressed RNAs were predicted by Gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. Co-expression networks of lncRNA-mRNA and circRNA-miRNA were conducted to uncovered the hidden ceRNA mechanisms. Moreover, NONHSAT154433.1 that associated with ADAM12 and hsa_circ_0012342 were further screened and confirmed using qRT-PCR analysis. In conclusion, this study provides a systematic perspective on the potential function of non-coding RNAs (ncRNAs) in the molecular mechanisms of MEC. Among these, NONHSAT154433.1 and hsa_circ_0012342 might be served as potential prognostic biomarkers and therapeutic target of MEC.     

Integrated analysis of a competing endogenous RNA network reveals key long noncoding RNAs as potential prognostic biomarkers for hepatocellular carcinoma

Growing evidence has revealed that long noncoding RNAs (lncRNAs) have an important impact on tumorigenesis and tumor progression via a mechanism involving competing endogenous RNAs (ceRNAs). However, their use in predicting the survival of a patient with hepatocellular carcinoma (HCC) remains unclear. The aim of this study was to develop a novel lncRNA expression–based risk score system to accurately predict the survival of patients with HCC. In our study, using expression profiles downloaded from The Cancer Genome Atlas database, the differentially expressed messenger RNAs (mRNAs), lncRNAs, and microRNAs (miRNAs) were explored in patients with HCC and normal liver tissues, and then a ceRNA network constructed. A risk score system was established between lncRNA expression of the ceRNA network and overall survival (OS) or recurrence-free survival (RFS); it was further analyzed for associations with the clinical features of patients with HCC. In HCC, 473 differentially expressed lncRNAs, 63 differentially expressed miRNAs, and 1417 differentially expressed mRNAs were detected. The ceRNA network comprised 41 lncRNA nodes, 12 miRNA nodes, 24 mRNA nodes, and 172 edges. The lncRNA expression–based risk score system for OS was constructed based on six lncRNAs (MYLK-AS1, AL359878.1, PART1, TSPEAR-AS1, C10orf91, and LINC00501), while the risk score system for RFS was based on four lncRNAs (WARS2-IT1, AL359878.1, AL357060.1, and PART1). Univariate and multivariate Cox analyses showed the risk score systems for OS or RFS were significant independent factors adjusted for clinical factors. Receiver operating characteristic curve analysis showed the area under the curve for the risk score system was 0.704 for OS, and 0.71 for RFS. Our result revealed a lncRNA expression–based risk score system for OS or RFS can effectively predict the survival of patients with HCC and aid in good clinical decision-making.     

Identification of a four-gene metabolic signature predicting overall survival for hepatocellular carcinoma

While hundreds of consistently altered metabolic genes had been identified in hepatocellular carcinoma (HCC), the prognostic role of them remains to be further elucidated. Messenger RNA expression profiles and clinicopathological data were downloaded from The Cancer Genome Atlas—Liver Hepatocellular Carcinoma and GSE14520 data set from the Gene Expression Omnibus database. Univariate Cox regression analysis and lasso Cox regression model established a novel four-gene metabolic signature (including acetyl-CoA acetyltransferase 1, glutamic-oxaloacetic transaminase 2, phosphatidylserine synthase 2, and uridine-cytidine kinase 2) for HCC prognosis prediction. Patients in the high-risk group shown significantly poorer survival than patients in the low-risk group. The signature was significantly correlated with other negative prognostic factors such as higher α-fetoprotein. The signature was found to be an independent prognostic factor for HCC survival. Nomogram including the signature shown some clinical net benefit for overall survival prediction. Furthermore, gene set enrichment analyses revealed several significantly enriched pathways, which might help explain the underlying mechanisms. Our study identified a novel robust four-gene metabolic signature for HCC prognosis prediction. The signature might reflect the dysregulated metabolic microenvironment and provided potential biomarkers for metabolic therapy and treatment response prediction in HCC.     

Analysis of competing endogenous RNA network identifies a poorly differentiated cancer-specific RNA signature for hepatocellular carcinoma

Plenty of evidence has suggested that long noncoding RNAs (lncRNAs) play a vital role in competing endogenous RNA (ceRNA) networks. Poorly differentiated hepatocellular carcinoma (PDHCC) is a malignant phenotype. This paper aimed to explore the effect and the underlying regulatory mechanism of lncRNAs on PDHCC as a kind of ceRNA. Additionally, prognosis prediction was assessed. A total of 943 messenger RNAs (mRNAs), 86 miRNAs, and 468 lncRNAs that were differentially expressed between 137 PDHCCs and 235 well-differentiated HCCs were identified. Thereafter, a ceRNA network related to the dysregulated lncRNAs was established according to bioinformatic analysis and included 29 lncRNAs, 9 miRNAs, and 96 mRNAs. RNA-related overall survival (OS) curves were determined using the Kaplan-Meier method. The lncRNA ARHGEF7-AS2 was markedly correlated with OS in HCC (P = .041). Moreover, Cox regression analysis revealed that patients with low ARHGEF7-AS2 expression were associated with notably shorter survival time (P = .038). In addition, the area under the curve values of the lncRNA signature for 1-, 3-, and 5-year survival were 0.806, 0.741, and 0.701, respectively. Furthermore, a lncRNA nomogram was established, and the C-index of the internal validation was 0.717. In vitro experiments were performed to demonstrate that silencing ARHGEF7-AS2 expression significantly promoted HCC cell proliferation and migration. Taken together, our findings shed more light on the ceRNA network related to lncRNAs in PDHCC, and ARHGEF7-AS2 may be used as an independent biomarker to predict the prognosis of HCC.     

A novel seven-lncRNA signature for prognosis prediction in hepatocellular carcinoma

Liver cancer is still one of the leading causes of cancer-related death worldwide. This study is dedicated to developing a multi–long noncoding RNA (lncRNA) model for risk stratification and prognosis prediction on patients with hepatocellular carcinoma (HCC). We first downloaded lncRNA expression profiles and corresponding clinical information of patients with liver cancer from The Cancer Genome Atlas database. Differentially expressed (DE) lncRNAs between HCC samples and normal samples were identified. In total, 308 patients with HCC were randomly divided into a training group (n = 154) and a testing group (n = 154). Univariate Cox regression and least absolute shrinkage and selection operator Cox regression analyses were performed to select the best survival-related candidates from these DE lncRNAs in the training set. Seven lncRNAs (AC009005.2, RP11-363N22.3, RP11-932O9.10, RP11-572O6.1, RP11-190C22.8, RP11-388C12.8, and ZFPM2-AS1) were finally identified and used to construct a seven-lncRNA signature. The signature could classify patients into high-risk and low-risk groups with significantly different overall survival. The area under the curve of receiver operating characteristic curve for the signature to predict 5-year survival reached more than 0.75. Besides, the prognostic value of the seven-lncRNA signature was independent of conventional clinical factors. The predictive performance of the signature was further validated in the testing set and the whole set. Functional enrichment analysis indicated that the seven prognostic lncRNAs may be involved in several essential biological processes and pathways. The current study demonstrated the potential clinical implications of the seven-lncRNA signature for survival prediction of patients with HCC.     

A novel multidimensional signature predicts prognosis in hepatocellular carcinoma patients

The abnormal expression of microRNAs (miRNAs) or protein-coding genes (PCGs) have been found to be associated with the prognosis of hepatocellular carcinoma (HCC) patients. Using bioinformatics analysis methods including Cox’s proportional hazards regression analysis, the random survival forest algorithm, Kaplan–Meier, and receiver operating characteristic (ROC) curve analysis, we mined the gene expression profiles of 469 HCC patients from The Cancer Genome Atlas (n = 379) and Gene Expression Omnibus (GSE14520; n = 90) public database. We selected a signature comprising one protein-coding gene (PCG; DNA polymerase μ) and three miRNAs (hsa-miR-149-5p, hsa-miR-424-5p, hsa-miR-579-5p) with highest accurate prediction (area under the ROC curve [AUC] = 0.72; n = 189) from the training data set. The signature stratified patients into high- and low-risk groups with significantly different survival (median 27.9 vs. 55.2 months, log-rank test, p < 0.001) in the training data set, and its risk stratification ability were validated in the test data set (median 47.4 vs. 84.4 months, log-rank test, p = 0.03) and an independent data set (median 31.0 vs. 46.0 months, log-rank test, p = 0.01). Multivariable Cox regression analysis showed that the signature was an independent prognostic factor. And the signature was proved to have a better survival prediction power than tumor–node–metastasis (TNM) stage (AUC _signature = 0.72/0.64/0.62 vs. AUC _TNM = 0.65/0.61/0.61; p < 0.05). Moreover, we validated the expression of these prognostic genes from the PCG-miRNA signature in Huh-7 cell by real-time polymerase chain reaction. In conclusion, we found a signature that can predict survival of HCC patients and serve as a prognostic marker for HCC.     

长非编码RNA编码微肽的研究进展

长非编码RNA(long non-coding RNA,lncRNA)是长度超过200 nt的非编码RNA,具有一个或多个短开放阅读框,可编码功能性微肽。这些功能性微肽在各种生物过程中扮演着重要角色,例如Ca2+转运、线粒体代谢、肌细胞融合和细胞衰老等过程。同时,这些生物过程又在机体稳态调控、疾病和癌症的发生与发展、胚胎发育等重要生理过程中起关键作用。因此,研究由lncRNA编码的微肽在生物体的潜在的调控机制,将有助于进一步解析生物体潜在调控过程,并为后续疾病的靶向治疗及动物生长性能的提高提供新的理论依据。本文综述了现阶段lncRNA编码微肽领域的最新研究进展,并对当前微肽在肌肉生理、炎症与免疫、人类常见癌症、胚胎发育等领域的研究进展进行描述与总结,最后简单阐述了lncRNA编码微肽现阶段面临的问题和存在的挑战,以期为后续微肽的深入研究提供科学参考及新思路。