构建铜死亡相关LncRNA风险模型预测肝细胞癌的预后及分子特征

铜死亡是一种新的程序性细胞死亡途径,由铜与脂酰化三羧酸循环蛋白直接结合而启动。调节肿瘤细胞中的铜死亡是一种新的治疗方法。然而,铜死亡相关长链非编码RNA(LncRNA)在肝细胞癌(HCC)中的潜在作用和临床意义尚不明确。本研究基于TCGA-LIHC数据集对19个铜死亡相关基因进行共表达分析,共鉴定出994个铜死亡相关LncRNA。采用LASSO回归和多因素Cox回归分析筛选出4个与铜死亡相关的预后LncRNA(TMCC1-AS1、AC009974.2、AL355574.1和DDX11-AS1)构建预后风险模型,并计算所有HCC患者样本的风险评分。按1:1的比例将肝癌患者分为高风险组和低风险组。Kaplan-Meier生存曲线分析显示,高风险组患者的总生存率(OS)明显低于低风险组。回归分析和ROC曲线证实了风险评分的预后价值。此外,本研究分析了风险评分与通路富集分析、免疫检查点基因、免疫细胞浸润、抗癌药物敏感性和体细胞基因突变之间的相关性。差异表达分析结果表明,TMCC1-AS1、AC009974.2、AL355574.1和DDX11-AS1在肿瘤组织中的表达均升高。最后,利用收集的8例行根治性手术肝癌患者的癌组织及癌旁肝组织进行实时荧光定量PCR(qRT-PCR)验证,以增加本模型的组织学证据。本研究构建了一个由4种铜死亡相关LncRNA组成的风险模型,该模型与患者的预后及免疫浸润环境明显相关,在预测患者免疫治疗效果及指导化疗药物选择方面具有一定的临床应用价值。     

DNA甲基化驱动的转录表达特征作为肝癌预后预测标志物的价值

肝细胞癌(hepatocellular carcinoma,简称肝癌)是最常见的恶性肿瘤之一。DNA甲基化的异常是恶性肿瘤的特征之一,并被发现在肝癌等肿瘤的发生发展中发挥重要作用。为了能为肝癌患者提供新的临床预后预测标志物,本研究首先采用整合组学分析策略在全基因组范围内鉴定与肝癌患者预后相关的DNA甲基化驱动的差异表达基因;然后,采用LASSO (least absolute shrinkage and selection operator)分析建立了10个最优基因组合的预后预测模型。Cox比例风险回归分析显示,在校正临床特征参数后,此预测模型高风险评分与患者不良预后显著相关,表明该模型具有潜在的独立预后价值。受试者工作特征(receiver operating characteristic,ROC)曲线分析显示该风险评分模型在预测患者短期和长期预后方面优于其他已被报道的肝癌预后预测模型。基因集富集分析(gene set enrichment analysis, GSEA)表明,高风险评分与细胞周期和DNA损伤修复通路相关。以上结果表明,本研究构建了一个基于10个DNA甲基化驱动基因的预后风险评分模型,该模型可作为肝癌患者的潜在预后生物标志物,有助于肝癌患者的生存预后评估和治疗策略的指导。     

DLX6-AS1通过竞争性结合miR-15上调Caspase-1诱导巨噬细胞焦亡

为探讨长链非编码RNA (long noncoding RNA, lncRNA) DLX6-AS1在巨噬细胞焦亡中的作用及相关机制,采用生物信息学分析、实时荧光定量PCR和双荧光素酶报告基因实验等,在THP-1巨噬细胞中分析并验证lncRNA DLX6-AS1下游微RNA (microRNA, mi RNA)及mi RNA的下游靶基因;通过免疫荧光染色等实验检测DLX6-AS1/miR-15/caspase-1轴对巨噬细胞焦亡的影响。结果显示, DLX6-AS1在焦亡巨噬细胞中表达上调,敲降DLX6-AS1可抑制巨噬细胞焦亡,但这种抑制作用被mi R-15抑制剂逆转; mi R-15通过调控其靶基因caspase-1表达,抑制巨噬细胞焦亡。本研究表明, DLX6-AS1通过竞争性结合mi R-15,解除mi R-15对其靶基因caspase-1的抑制作用,从而诱导巨噬细胞焦亡,这将丰富lncRNA调控巨噬细胞焦亡的理论基础。     

基于TCGA和GEO数据库的胃癌lncRNA筛选与ceRNA网络构建

胃癌（gastric cancer,GC）是我国最常见的恶性肿瘤之一,严重危害人类健康。胃癌发病机制复杂,缺乏特异性预后生物标志物。长链非编码RNA（long non?coding RNA,lncRNA）可作为竞争性内源RNA（competing endogenous RNA,ceRNA）,影响microRNA（miRNA）与mRNA的结合,从而影响胃癌的发生、发展。基于TCGA和GEO数据库的转录组数据,筛选GC中差异表达的lncRNAs,并构建基于6条lncRNAs（HAGLROS、TMEM92?AS1、LINC01745、HOXC?AS3、SEMA3B?AS1、FEZF1?AS1）的lncRNA?miRNA?mRNA网络。网络核心基因的KEGG/GO富集和蛋白质互作分析结果显示,lncRNA可能通过miRNA海绵吸附作用,调控胃癌的发生、发展与转移。AC011352.1、AC087636.1、AC093627.1、GAS1RR与胃癌患者的预后相关性具有统计学意义（P<0.05）,并可能成为胃癌患者潜在的预后生物标志物。     

死亡相关的LncRNAs在甲状腺癌中的预后价值及预后风险模型构建

目的:探讨铁死亡相关的长链非编码RNAs(LncRNAs)在甲状腺癌中的预后价值并构建预后风险模型。方法:从癌症基因组图谱(TCGA)数据库下载甲状腺癌的转录本数据和临床数据,铁死亡相关的基因数据集是从铁死亡数据库(http://www.zhounan.org/ferrdb/)下载的259个基因集。得到铁死亡相关LncRNAs,与患者临床信息合并后,通过单因素Cox回归分析和Kaplan-Meier生存分析两种方法得到与甲状腺癌预后相关的铁死亡LncRNAs,通过R的survival包构建COX模型以此来建立最佳预后风险模型并予以验证。结果:获得30个铁死亡相关的LncRNAs,多因素cox分析得到10个与甲状腺癌预后相关的铁死亡LncRNAs,包括AL136366.1、AL162231.2、CRNDE、AC004918.3、LINC02471、AC092279.1、AC046143.1、LINC02454、DOCK9-DT、AC008063.1。Kaplan-Meier生存分析表明高风险组预后较差。单因素和多因素Cox分析表明风险评分可以作为独立预后因子。KEGG通路富集分析发现,差异基因可能与嘧啶代谢、核苷酸切除修复、NOTCH_信号通路等通路有关。结论:通过生物信息学方法筛选出10个与甲状腺癌预后的铁死亡相关LncRNAs,并成功构建预后风险模型。     

lncRNA SGO1-AS1通过调节SIRT1去乙酰化酶活性抑制细胞凋亡

沉默信息调节因子1(silent information regulator 1, SIRT1)是NAD~+依赖的组蛋白去乙酰化酶,可以通过去乙酰化底物调节多种生物学功能。长链非编码RNA(long non-coding RNA, lncRNA)是一类新兴的基因表达调控子,可以影响多种生命进程。尽管如此,SIRT1与lncRNA之间的调控关系以及lncRNA在SIRT1介导的生物学功能中的作用还有待进一步阐明。因此,本研究旨在探讨SIRT1相关lncRNA SGO1-AS1在SIRT1介导的细胞凋亡中的作用及其分子机制。荧光定量PCR检测发现,过表达SIRT1可显著促进lncRNA SGO1-AS1的表达(P0.05),反之沉默SIRT1则抑制SGO1-AS1的表达(P0.001)。进一步利用Western印迹、胱天蛋白酶3/7活性检测和TUNEL实验发现,沉默SGO1-AS1可显著促进细胞凋亡(P0.05),但并不明显影响DNA损伤修复。此外,Western印迹结果显示,SGO1-AS1还可显著促进SIRT1蛋白的去乙酰化酶活性。综上所述,lncRNA SGO1-AS1可以抑制细胞凋亡,且长链非编码RNA SGO1-AS1有可能与SIRT1形成正反馈调节环路,从而调控细胞凋亡。尽管如此,SGO1-AS1调节细胞凋亡分子机制依然有待深入研究。     

lncRNA CBR3-AS1调节miR-145-5p/FSCN1轴对鼻咽癌细胞增殖、凋亡和侵袭的影响

该文旨在探究长链非编码RNA CBR3-AS1(lncRNA CBR3-AS1)靶向微小RNA-145-5p(miR-145-5p)/肌动蛋白束蛋白1(FSCN1)轴对鼻咽癌(NPC)细胞增殖、凋亡和侵袭的影响。qRTPCR法检测鼻咽癌组织中lncRNACBR3-AS1和miR-145-5p的表达水平。将鼻咽癌细胞CNE-1分为si-NC组、si-CBR3-AS1组、si-CBR3-AS1+anti-miR-NC组、si-CBR3-AS1+anti-miR-145-5p组、miR-NC组、miR-145-5pmimics组、miR-145-5pmimics+pcDNA组、miR-145-5pmimics+FSCN1组。双荧光素酶实验检测lncRNA CBR3-AS1和miR-145-5p及FSCN1和miR-145-5p的靶向关系;MTT法检测细胞增殖情况; Annexin V-FITC/PI法检测细胞凋亡情况; Transwell实验检测细胞侵袭能力; Western blot检测细胞周期负调控因子(P21)、B淋巴细胞瘤-2(Bcl-2)、Bcl-2相关X蛋白(Bax)、基质金属蛋...     

肺癌中LncRNA的表达及其与预后的相关性

[目的]筛选出在肺癌组织中特异性表达的长链非编码RNA(lncRNA),并探讨其在肺癌中的表达水平与患者预后的相关性。[方法]从癌症基因组图谱(the cancer genome atlas,TCGA)下载肺癌临床病理和基因表达谱信息。以|Fold Change|>2和校正后的P值(FDR)<0.05为标准筛选差异表达的lncRNA。对差异表达显著的FOXD3-AS1进行RNA结合蛋白(RBP)预测,并用关联矩阵法构建差异表达的lncRNA-RBP-mRNA共表达网络,继而对共表达mRNA进行Kyoto Encyclopedia of Genes and Genomes(KEGG)通路富集分析;通过Kaplan-Meier Plotter方法研究FOXD3-AS1与预后相关性。[结果]共筛选出1 362个在肺癌中差异表达的lncRNA,其中表达上调的1 184个,表达下调的178个。与FOXD3-AS1具有共表达关系的mRNA共有11个。KEGG分析这些共表达的mRNA主要富集在黏着连接、结肠直肠癌和松弛素信号通路,RBP预测发现FOXD3-AS1可以与人丝氨酸/精氨酸富有剪接因子SRSF1结合。FOXD3-AS1与肺癌患者生存曲线具有极显著相关性(P<0.01)。[结论]SRSF1居于lncRNA-RBP-mRNA共表达网络的核心位置,在肺癌的发生、发展中起到重要的调控作用,有望成为肺癌免疫治疗的分子靶标。差异表达的FOXD3-AS1可以很好地判断肺癌患者的预后,因此,可以作为潜在的肺癌检测分子标志物。     

lncRNA在白血病中对细胞增殖和凋亡的调控作用

长链非编码RNA(long non-coding RNA, lncRNA)参与基因表达的调控,在白血病的发生和发展过程中可通过多种机制调控细胞增殖、凋亡等过程,发挥促癌或抑癌作用。本文对lncRNA ZEB1-AS1、lncRNA NALT、lncRNA H19、lncRNA PLIN2、lncRNA MEG3、lncRNA CASC15、lincRNA-p21等lncRNA分别在白血病中的生物学功能及其调控细胞增殖和凋亡的机制进行了简要概述,旨在为白血病的诊疗和预后提供新策略。     

Long non-coding RNA SOS1-IT1 promotes endometrial cancer progression by regulating hypoxia signaling pathway

Endometrial cancer (EC) is one of the most common types of gynecological cancer. Hypoxia is an important clinical feature and regulates various tumor processes. However, the prognostic value of hypoxia-related lncRNA in EC remains to be further elucidated. Here, we aimed to characterize the molecular features of EC by the development of a classification system based on the expression profile of hypoxia-related lncRNA. Based on univariate Cox regression analysis, we identified 17 hypoxia-related lncRNAs significantly associated with overall survival. Next, the least absolute shrinkage and selection operator Cox regression model was utilized to construct a multigene signature in the TCGA EC cohort. The risk score was confirmed as an independent predictor for overall survival in multivariate Cox regression analysis and receiver operating characteristic (ROC) curve analysis. Besides, the survival time of EC patients in different risk group was significantly correlated to clinicopathologic factors, such as age, stage and grade. Furthermore, hypoxia-related lncRNA associated with the high-risk group were involved in various aspects of the malignant progression of EC via Gene Ontology, Kyoto Encyclopedia of Genes and Genomes pathway, and Gene Set Enrichment Analysis. Moreover, the risk score was closely correlated to immunotherapy response, microsatellite instability and tumor mutation burden. Finally, we select one hypoxia-related lncRNA SOS1-IT1 to validate its role in hypoxia and EC progression. Interestingly, we found SOS1-IT1 was overexpressed in tumor tissues, and closely correlated with clinicopathological parameters of EC. The expression level of SOS1-IT1 was significantly increased under hypoxia condition. Additionally, the important hypoxia regulatory factor HIF-1α can directly bind SOS1-IT1 promoter region, and affect its expression level. In summary, this study established a new EC classification based on the hypoxia-related lncRNA signature, thereby provide a novel sight to understand the potential mechanism of human EC development.Supplementary InformationThe online version contains supplementary material available at 10.1007/s12079-021-00651-1.     

A novel autophagy-related lncRNA survival model for lung adenocarcinoma

Long non-coding RNA (lncRNA) is an important regulatory factor in the development of lung adenocarcinoma, which is related to the control of autophagy. LncRNA can also be used as a biomarker of prognosis in patients with lung adenocarcinoma. Therefore, it is important to determine the prognostic value of autophagy-related lncRNA in lung adenocarcinoma. In this study, autophagy-related mRNAs-lncRNAs were screened from lung adenocarcinoma and a co-expression network of autophagy-related mRNAs-lncRNAs was constructed by using The Cancer Genome Atlas (TCGA). The univariate and multivariate Cox proportional hazard analyses were used to evaluate the prognostic value of the autophagy-related lncRNAs and finally obtained a survival model composed of 11 autophagy-related lncRNAs. Through Kaplan-Meier analysis, univariate and multivariate Cox regression analysis and time-dependent receiver operating characteristic (ROC) curve analysis, it was further verified that the survival model was a new independent prognostic factor for patients with lung adenocarcinoma. In addition, based on the survival model, gene set enrichment analysis (GSEA) was used to illustrate the function of genes in low-risk and high-risk groups. These 11 lncRNAs were GAS6-AS1, AC106047.1, AC010980.2, AL034397.3, NKILA, AL606489.1, HLA-DQB1-AS1, LINC01116, LINC01806, FAM83A-AS1 and AC090559.1. The hazard ratio (HR) of the risk score was 1.256 (1.196-1.320) (P < .001) in univariate Cox regression analysis and 1.215 (1.149-1.286) (P < .001) in multivariate Cox regression analysis. And the AUC value of the risk score was 0.809. The 11 autophagy-related lncRNA survival models had important predictive value for the prognosis of lung adenocarcinoma and may become clinical autophagy-related therapeutic targets.     

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 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.     

Identification of immune-related lncRNAs to improve the prognosis prediction for patients with papillary thyroid cancer

Objective: To identify immune-related long non-coding RNAs (lncRNAs) in papillary thyroid cancer (PTC).Methods: The Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) databases were used to obtain the gene expression profile. Immune-related lncRNAs were screened from the Molecular Signatures Database v4.0 (MsigDB). We performed a survival analysis of critical lncRNAs. Further, the function of prognostic lncRNAs was inferred using the Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) to clarify the possible mechanisms underlying their predictive ability. The assessment was performed in clinical samples and PTC cells.Results: We obtained 4 immune-related lncRNAs, 15 microRNAs (miRNAs), and 375 mRNAs as the key mediators in the pathophysiological processes of PTC from the GEO database. Further, Lasso regression analysis identified seven prognostic markers (LINC02550, SLC26A4-AS1, ACVR2B-AS1, {"type":"entrez-nucleotide","attrs":{"text":"AC005479.2","term_id":"4508155","term_text":"AC005479.2"}}AC005479.2, LINC02454, and AL136366.1), most of which were related to tumor development. The KEGG pathway enrichment analysis showed different, changed genes mainly enriched in the cancer-related pathways, PI3K-Akt signaling pathway, and focal adhesion. Only SLC26A4-AS1 had an intersection in the results of the two databases.Conclusion: LncRNA SLC26A4-AS1, which is the most associated with prognosis, may play an oncogenic role in the development of PTC.     

A novel hypoxia gene signature indicates prognosis and immune microenvironments characters in patients with hepatocellular carcinoma

Due to the lack of a suitable gene signature, it is difficult to assess the hypoxic exposure of HCC tissues. The clinical value of assessing hypoxia in HCC is short of tissue-level evidence. We tried to establish a robust and HCC-suitable hypoxia signature using microarray analysis and a robust rank aggregation algorithm. Based on the hypoxia signature, we obtained a hypoxia-associated HCC subtypes system using unsupervised hierarchical clustering and a hypoxia score system was provided using gene set variation analysis. A novel signature containing 21 stable hypoxia-related genes was constructed to effectively indicate the exposure of hypoxia in HCC tissues. The signature was validated by qRT-PCR and compared with other published hypoxia signatures in multiple large-size HCC cohorts. The subtype of HCC derived from this signature had different prognosis and other clinical characteristics. The hypoxia score obtained from the signature could be used to indicate clinical characteristics and predict prognoses of HCC patients. Moreover, we reveal a landscape of immune microenvironments in patients with different hypoxia score. In conclusion, we identified a novel HCC-suitable 21-gene hypoxia signature that could be used to estimate the hypoxia exposure in HCC tissues and indicated prognosis and a series of important clinical features in HCCs. It may enable the development of personalized counselling or treatment strategies for HCC patients with different levels of hypoxia exposure.