捻转血矛线虫丙硫咪唑耐药相关lncRNA的转录组学测序与分析

分析捻转血矛线虫敏感株和耐药株中长链非编码RNA (Long non-coding RNA,lncRNA)的表达谱,探讨lncRNA与捻转血矛线虫丙硫咪唑耐药机制的关联性,为捻转血矛线虫耐药机理提供依据。文中对捻转血矛线虫敏感株和耐药株进行cDNA测序文库构建,使用Illumina HiSeq 4000平台进行双端测序,筛选出差异的lncRNA,基于顺式调控(cis)和反式调控(trans)对差异显著的lncRNA进行靶基因预测,并对靶基因进行过Gene Ontology (GO)功能注释和KEGG Pathway富集分析,利用FPKM法估计lncRNA和mRNA的表达水平。结果表明,敏感株和耐药株候选lncRNA分别为6 377和6 356个,两个文库中筛选出168个差异显著的lncRNA,其中在敏感株中表达上调有92个,表达下调76个。筛选得到的差异显著lncRNA候选靶基因416个,这些基因共注释到641条GO terms和92条信号通路;其中富集到耐药性相关的通路有药物代谢-其他酶、药物代谢-细胞色素P450、细胞色素P450对异生素的代谢等。综上表明,部分lncRNA介导的靶基因与捻转血矛线虫丙硫咪唑耐药性相关,lncRNA在捻转血矛线虫耐药性中具有潜在的重要的调节作用。探究了对于敏感虫株和耐药虫株中lncRNA的表达谱,发现了敏感虫株和耐药虫株中差异表达的lncRNA,有助于找出捻转血矛线虫如何抵抗丙硫咪唑的发生机制,为探讨捻转血矛线虫丙硫咪唑耐药机制提供科学的依据。     

CTNNBIP1和ICMT基因共表达在膀胱尿路上皮癌中作用和功能分析

为了研究CTNNBIP1和ICMT基因共表达在膀胱尿路上皮癌中的作用及对患者预后的诊断价值,本研究选取癌症基因组图谱(TCGA)数据库中412例膀胱尿路上皮癌数据资料,利用cBioPortal数据库对膀胱尿路上皮癌CTNNBIP1基因、ICMT基因及其共表达基因作生存分析,将Pearson和Spearman相关系数均大于0.3的基因定义为中等程度以上共表达相关的基因。通过String数据库获取这些基因共表达的互作关系组。采用DAVID数据库、GO数据库、KEGG数据库分别对其进行信号通路和功能分析,生物学过程聚类分析,信号通路聚类分析。结果显示:TCGA数据库中的膀胱尿路上皮癌患者CTNNBIP1和ICMT存在共表达(Pearson相关系数=0.46和Spearman相关系数=0.47)。String数据库显示基因共表达有38组具有互作关系。KEGG数据库显示CTNNBIP1-ICMT基因共表达所富集的信号通路集中在细胞周期(p<0.05)。DAVID数据库分析其功能主要为调节细胞生长和有丝分裂、负性调控Wnt信号通路、蛋白激酶结合等。生存分析证实CTNNBIPI1和ICMT基因共表达与患者总生存率显著相关(p=0.002 72),CTNNBIPI1和ICMT共表达阳性患者的预后最差。由此得出结论:CTNNBIP1和ICMT在膀胱尿路上皮癌中存在共表达,对CTNNBIP1-ICMT基因共表达网络和生物信息学分析,可找到其在膀胱尿路上皮癌中的作用及信号通路,为深入研究膀胱尿路上皮癌的发病机制提供了理论基础,可提高膀胱尿路上皮癌患者的预后。     

缺氧相关长链非编码RNA作为肝癌预后预测标志物的潜在价值

肝细胞癌(hepatocellular carcinoma,简称肝癌)是一种常见的恶性肿瘤。缺氧是肝癌等实体肿瘤的一个重要特征,同时也是诱导肿瘤恶性进展的重要因素。然而,肝癌缺氧相关的长链非编码RNA(long non-coding RNA,lncRNA)的鉴定及其在临床生存预后等方面的价值仍未得到系统的研究。本研究旨在通过肝癌转录组的整合分析鉴定肝癌缺氧相关的lncRNA,并评估其在肝癌预后中的价值。基于癌症基因组图谱(The Cancer Genome Atlas,TCGA)计划的肝癌转录组数据的整合分析,初步鉴定到233个缺氧相关的候选lncRNA。进一步筛选具有预后价值的候选者,基于其中12个缺氧相关lncRNA(AC012676.1、PRR7-AS1、AC020915.2、AC008622.2、AC026401.3、MAPKAPK5-AS1、MYG1-AS1、AC015908.3、AC009275.1、MIR210HG、CYTOR和SNHG3)建立了肝癌预后风险模型。Cox比例风险回归分析显示,基于该模型计算的缺氧风险评分作为肝癌患者新的独立预后预测指标,优于传统的临床病理因...     

低氧mtDNA3010A/G基因型变异诱导的lncRNA-mRNA共表达网络变化

目的: 分析mtDNA3010A/G变异在急性缺氧条件下的长链非编码RNA(lncRNA)和信使RNA(mRNA)的共表达网络变化,探讨关键lncRNA和mRNA在低氧诱导的基因表达调控中的作用。方法: 筛选线粒体DNA(mtDNA)3010-5178-10400的基因型组合A-C-C和G-C-C,以骨肉瘤细胞经溴化乙锭处理后形成的无线粒体细胞(ρ⁰206细胞)为供体,构建mtDNA3010A和mtDNA3010G基因型融合细胞。经1%O₂处理24 h后,采用lncRNA-mRNA共表达芯片检测两种融合细胞的差异表达lncRNA和mRNA,荧光定量聚合酶链式法验证差异显著的mRNA,运用生物信息学方法构建lncRNA-mRNA共表达网络,预测差异lncRNA的靶基因,并对差异显著的mRNA和预测靶基因进行基因本体(GO)和京都基因与基因组大百科全书(KEGG)预测分析。结果: 经1%O₂处理24 h后,与mtDNA3010G融合细胞相比,mtDNA3010A融合细胞表达上调的lncRNA有688个,超过2倍的有21个,表达下调的lncRNA有1098个,超过2倍的有4个;表达上调的mRNA有1151个,超过2倍的有14个,表达下调的mRNA有539个,超过2倍的有3个。结论: mtDNA3010A/G基因型变异在缺氧条件下能够影响lncRNA-mRNA调控网络的变化,差异表达的lncRNA和mRNA可能在低氧诱导的基因表达调控网络中发挥重要作用,有望成为从线粒体角度调控低氧反应的靶点。     

lncRNA BDNF-AS在氧糖剥夺/复氧复糖条件下的表达、定位及互作蛋白质分析

脑缺血缺氧会引起长链非编码RNA (long non-coding RNA, lncRNA)表达变化。为了探究lncRNA BDNF-AS在氧糖剥夺/复氧复糖条件下的表达、定位及互作蛋白质,将SH-SY5Y细胞缺氧缺糖8 h、复氧复糖24 h,构建氧糖剥夺/复氧复糖细胞模型;采用CCK-8 (Cell Counting Kit-8)法检测细胞活力;采用qRT-PCR检测核质中lncRNA BDNF-AS表达水平;利用pull-down和质谱技术对lncRNA BDNF-AS互作蛋白质进行鉴定;利用基因本体论(Gene Ontology, GO)、京都基因和基因组数据库(Kyoto Encyclopedia of Genes and Genomes,KEGG)分析互作蛋白质的功能及参与的通路;利用STRING数据库分析蛋白质-蛋白质相互作用网络。结果显示:氧糖剥夺/复氧复糖条件下, lncRNA BDNF-AS表达量显著升高,且胞质表达量显著高于胞核;氧糖剥夺/复氧复糖组有120种蛋白质可能与lncRNA BDNF-AS存在潜在的相互作用。进一步的生物信息学分析表明,lncRNA-BDN...     

基于SNP共表达网络肝癌分子分型及预后分析

基于SNP突变数据与mRNA表达谱关联分析,构建一种肝癌分子分型方法并对比不同分型预后的差异,并对不同分型肝癌的发生发展机制进一步研究。首先通过TCGA数据库收集359例肝细胞癌患者的SNP突变数据和mRNA表达数据,采用Wilcoxon秩和检验,筛选突变后差异表达基因,并通过生物信息学工具String和Cytoscape 构建差异表达基因的蛋白互作网络,筛选连接度最高的10个Hub基因。利用Consensus Cluster Plus软件包,基于Hub基因mRNA表达水平构建NMF分子分型模型,再结合生存数据评估各分型患者的预后。最后利用加权基因共表达网络分析(WGCNA),识别与肝癌分子分型相关的模块,并针对关键模块的基因进行通路富集,从而对不同分型肝癌的基因表达谱进行比较。结果:NMF模型将肝癌分为高危、低危2个分型,其中CDKN2A和FOXO1基因对分型贡献度高。生存分析显示低危组患者的生存情况显著优于高危组,高危组富集多个与肿瘤细胞侵蚀、转移、复发过程相关的信号通路,低危组则与细胞周期和胰液分泌相关。本研究在无先验性信息的前提下,基于突变后显著差异表达的Hub基因表达水平构建的肝癌分子分型对肝癌患者预后评估具有一定的指导意义,其中CDKN2A和FOXO1突变是肝癌患者的不良预后因素,针对二者的靶向药研发,可能为肝癌患者提供新的治疗策略。     

结合自噬相关基因表达谱和临床因素的肺腺癌预后预测模型

对肺腺癌自噬相关基因进行生物信息学分析,结合多基因预后标志和临床参数构建能够预测肺腺癌患者预后的模型。首先,对TCGA肺腺癌数据中的938个自噬相关基因进行差异分析,获得了82个差异自噬相关基因,使用单因素Cox比例风险回归模型从差异自噬相关基因中筛选出候选基因,通过 lasso回归进一步筛选出预后相关基因,分别是ARNTL2、NAPSA、ATG9B、CAPN12、MAP1LC3C和KRT81。通过多因素Cox回归分析以构建风险评分模型,根据最优cutoff值将患者分为高低风险组,生存曲线显示高低风险组之间生存差异显著,ROC曲线显示风险评分的预测能力良好,并在内、外验证集中得到验证。同时对传统的临床因素进行单因素和多因素Cox回归分析,结果显示Stage、复发和风险评分能够独立预测预后,结合这三个独立的预后参数以构建列线图模型,使用一致性指数、校准曲线评估列线图的预测能力,结果显示预测结果与实际结果之间具有良好的一致性。通过与Stage和风险评分的比较发现,列线图的预测能力表现最佳。基于肺腺癌相关的自噬基因和临床参数构建了一个列线图模型来预测肺腺癌患者的预后生存,这可能为临床医生提供了一种可靠的预后评估工具。     

基于IRES序列的多基因共表达载体构建

目的:构建一个IRES序列介导的多基因共表达载体,实现两个目的基因和筛选标记基因共用一个启动子高效表达,提高多基因稳定共表达细胞株的筛选效率。方法:以实验室前期构建的载体pLV-MCS-Puro为骨架,设计并全基因合成双基因克隆表达元件,连接到骨架载体,构建多基因共表达载体pLV-2MCS-Puro,以DsRed2和EGFP荧光蛋白基因验证该载体用于多基因稳定共表达细胞株筛选的效率。结果:成功构建了pLV-2MCS-Puro载体以及DsRed2和EGFP共表达重组质粒pLV-DsRed2-EGFP-Puro。瞬时转染实验证明该载体能介导多基因共表达。抗性筛选获得了MDCK和HeLa两种细胞的多基因稳定共表达细胞池。细胞池涂片荧光显微镜观察和计数表明抗性细胞池DsRed2和EGFP双阳率接近100%。基因组和转录水平PCR及蛋白质免疫印迹实验表明,DsRed2和EGFP稳定整合到抗性细胞基因组,并且两种蛋白质表达水平较为一致。结论:成功构建了多基因共表达载体pLV-2MCS-Puro,实现了两个目的基因和抗性基因串联共表达,并且具有高效的多基因稳定共表达细胞株筛选效率。该载体在研究蛋白质相互作用及工程细胞构建等方面具有一定的应用前景。     

基于已知疾病基因构建共表达网络识别胃癌进展及预后相关非编码基因

本研究旨在总结整理已有胃癌研究的基础上,进一步挖掘出非编码基因在胃癌的进展及预后中起的关键作用。通过胃癌患者编码及非编码基因的表达数据,结合已知胃癌致病基因,进行编码基因与非编码基因的共表达计算,识别出由miRNA介导的并且与已知胃癌基因互作的lncRNA,挖掘出三者(mRNA-miRNA-lncRNA)相互作用的模块,进而对模块进行筛选,并对疾病相关的显著模块的基因进行生存分析。除已知的胃癌相关基因外,研究也使用了差异表达的胃癌基因,这些基因显著的富集在细胞增殖、细胞粘附、肌肉收缩、血管重塑、细胞分裂、染色体分离等生物过程,这些生物过程都与胃的基础功能及胃癌发生发展密切相关。分值最高的三元组模块内核心基因BGN在胃癌患者中显著高表达,而且和胃癌患者的预后显著相关;同时也发现该模块内的miRNA has-miRNA-153-5p和has-miRNA-5001-5p均为已证实的胃癌相关基因;模块内的mRNA和miRNA的表达异常可能是由于与他们显著相关的lncRNA的表达异常导致的。本研究为胃癌已知致病基因的表达异常研究找到了新突破口,潜在的胃癌相关的非编码基因的发现为胃癌预防与治疗提供了新的靶点,为未来的临床应用提供了依据。     

基于m5C相关基因构建三阴性乳腺癌预后预测模型及药物敏感性分析

为了探讨5-甲基胞嘧啶(5-methylcytosine,m5C)相关基因在三阴性乳腺癌(triple negative breast cancer,TNBC)患者治疗及预后中的潜在价值,构建了基于m5C相关基因的预后预测模型,用于评估TNBC患者的预后和生存状况。从基因表达总库(gene expression omnibus,GEO)数据库和癌症基因组图谱(the cancer genome atlas,TCGA)数据库中下载TNBC基因表达谱和相应的临床数据。通过Pearson分析确定了99个m5C相关基因,进一步采用单因素Cox分析鉴定出5个与预后有关的m5C相关基因(SLC6A14、BCL11A、UGT8、LMO4、PSAT1)并构建了风险评分(risk score)预测模型,根据风险评分中位值将患者划分为高风险组和低风险组。使用Kaplan-Meier(K-M)生存分析、受试者工作特征(receiver operating characteristic,ROC)曲线、多变量Cox回归分析、构建列线图和校准曲线评估了模型的预测效能。训练集和验证集的K-M生存曲线、受试者工作特征...     

Long non-coding RNAs correlate with genomic stability in prostate cancer: A clinical outcome and survival analysis

BackgroundLong non-coding RNAs (lncRNAs) participate in the regulation of genomic stability. Understanding their biological functions can help us identify the mechanisms of the occurrence and progression of cancers and can provide theoretical guidance and the basis for treatment.ResultsBased on the mutation hypothesis, we proposed a computational framework to identify genomic instability-related lncRNAs. Based on the differentially-expressed lncRNAs (DElncRNAs), we constructed a genomic instability-derived lncRNA signature (GILncSig) to calculate and stratify outcomes in patients with prostate cancer. It is an independent predictor of overall survival. The area under the curve = 0.805. This value may be more significant than the classic prognostic markers TP53 and Speckle-type POZ protein (SPOP) in terms of outcome prediction.ConclusionsIn summary, we conducted a computation approach and resource for mining genome instability-related lncRNAs. It may turn out to be highly significant for genomic instability and customized decision-making for patients with prostate cancer. It also may lead to effective methods and resources to study the molecular mechanism of genomic instability-related lncRNAs.     

An exosome-related lncRNA signature correlates with prognosis,immune microenvironment,and therapeutic responses in hepatocellular carcinoma

BackgroundExosomes act as essential modulators of cancer development and progression in hepatocellular carcinoma. However, little is known about the potential prognostic value and underlying molecular features of exosome-related long non-coding RNAs.MethodsGenes associated with exosome biogenesis, exosome secretion, and exosome biomarkers were collected. Exosome-related lncRNA modules were identified using PCA and WGCNA analysis. A prognostic model based on data from the TCGA, GEO, NODE, and ArrayExpress was developed and validated. A comprehensive analysis of the genomic landscape, functional annotation, immune profile, and therapeutic responses underlying the prognostic signature was performed on multi-omics data, and bioinformatics methods were also applied to predict potential drugs for patients with high risk scores. qRT-PCR was used to validate the differentially expressed lncRNAs in normal and cancer cell lines.ResultsTwenty-six hub lncRNAs were identified as highly correlated with exosomes and overall survival and were used for prognosis modeling. Three cohorts consistently showed higher scores in the high-risk group, with an AUC greater than 0.7 over time. These higher scores implied poorer overall survival, higher genomic instability, higher tumor purity, higher tumor stemness, pro-tumor pathway activation, lower anti-tumor immune cell and tertiary lymphoid structure infiltration, and poor responses to immune checkpoint blockade therapy and transarterial chemoembolization therapy.ConclusionThrough developing an exosome-related lncRNA predictor for HCC patients, we revealed the clinical relevance of exosome-related lncRNAs and their potential as prognostic biomarkers and therapeutic response predictors.     

Five key lncRNAs considered as prognostic targets for predicting pancreatic ductal adenocarcinoma

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and the 5‐year survival rate was only 7.7%. To improve prognosis, a screening biomarker for early diagnosis of pancreatic cancer is in urgent need. Long non‐coding RNA (lncRNA) expression profiles as potential cancer prognostic biomarkers play critical roles in development of tumorigenesis and metastasis of cancer. However, lncRNA signatures in predicting the survival of a patient with PDAC remain unknown. In the current study, we try to identify potential lncRNA biomarkers and their prognostic values in PDAC. LncRNAs expression profiles and corresponding clinical information for 182 cases with PDAC were acquired from The Cancer Genome Atlas (TCGA). A total of 14 470 lncRNA were identified in the cohort, and 175 PDAC patients had clinical variables. We obtained 108 differential expressed lncRNA via R packages. Univariate and multivariate Cox proportional hazards regression, lasso regression was performed to screen the potential prognostic lncRNA. Five lncRNAs have been recognized to significantly correlate with OS. We established a linear prognostic model of five lncRNA (C9orf139, MIR600HG, RP5‐965G21.4, RP11‐436K8.1, and CTC‐327F10.4) and divided patients into high‐ and low‐risk group according to the prognostic index. The five lncRNAs played independent prognostic biomarkers of OS of PDAC patients and the AUC of the ROC curve for the five lncRNAs signatures prediction 5‐year survival was 0.742. In addition, targeted genes of MIR600HG, C9orf139, and CTC‐327F10.4 were explored and functional enrichment was also conducted. These results suggested that this five‐lncRNAs signature could act as potential prognostic biomarkers in the prediction of PDAC patient's survival.     

The Use of Three Long Non-Coding RNAs as Potential Prognostic Indicators of Astrocytoma

Long noncoding RNAs (lncRNAs) are pervasively transcribed and play a key role in tumorigenesis. The aim of the study was to determine the lncRNA expression profile in astrocytomas and to assess its potential clinical value. We performed a three-step analysis to establish the lncRNA profile for astrocytoma: a) the lncRNA expression was examined on 3 astrocytomas as well as 3 NATs (normal adjacent tissues) using the lncRNA microarray; b) the top-hits were validated in 40 astrocytomas (WHO grade II-IV) by quantitative real time-PCR (qRT-PCR); c) the hits with significant differences were re-evaluated using qRT-PCR in 90 astrocytomas. Finally, 7 lncRNAs were found to have a significantly different expression profile in astrocytoma samples compared to the NAT samples. Unsupervised clustering analysis further revealed the potential of the 7-lncRNA profile to differentiate between tumors and NAT samples. The upregulation of ENST00000545440 and NR_002809 was associated with advanced clinical stages of astrocytoma. Using Kaplan-Meier survival analysis, we showed that the low expression of BC002811 or XLOC_010967, or the high expression of NR_002809 was significantly associated with poor patient survival. Moreover, Cox proportional hazard regression analysis revealed that this prognostic impact was independent of other clinicopathological factors. Our results indicate that the lncRNA profile may be a potential prognostic biomarker for the prediction of post-surgical outcomes.     

Development and validation of a five-lncRNA signature with prognostic value in colon cancer

Dysregulation of long noncoding RNAs (lncRNAs) has been found in a large number of human cancers, including colon cancer. Therefore, the implementation of potential lncRNAs biomarkers with prognostic prediction value are very much essential. GSE39582 data set was downloaded from database of Gene Expression Omnibus. Re-annotation analysis of lncRNA expression profiles was performed by NetAffx annotation files. Univariate and multivariate Cox proportional analyses helped select prognostic lncRNAs. Algorithm of random survival forest-variable hunting (RSF-VH) together with stepwise multivariate Cox proportional analysis were performed to establish lncRNA signature. The log-rank test was carried out to analyze and compare the Kaplan-Meier survival curves of patients’ overall survival (OS). Receiver operating characteristic (ROC) analysis was used for comparing the survival prediction regarding its specificity and sensitivity based on lncRNA risk score, followed by calculating the values of area under the curve (AUC). The single-sample GSEA (ssGSEA) analysis was used to describe biological functions associated with this signature. Finally, to determine the robustness of this model, we used the validation sets including GSE17536 and The Cancer Genome Atlas data set. After re-annotation analysis of lncRNAs, a total of 14 lncRNA probes were obtained by univariate and multivariate Cox proportional analysis. Then, the RSF-VH algorithm and stepwise multivariate Cox analysis helped to build a five-lncRNA prognostic signature for colon cancer. The patients in group with high risk showed an obviously shorter survival time compared with patients in group with low risk with AUC of 0.75. In addition, the five-lncRNA signature can be used to independently predict the survival of patients with colon cancer. The ssGSEA analysis revealed that pathways such as extracellular matrix-receptor interaction was activated with an increase in risk score. These findings determined the strong power of prognostic prediction value of this five-lncRNA signature for colon cancer.     

Identification of autophagy-related long non-coding RNA prognostic signature for breast cancer

Autophagy-related long non-coding RNAs (lncRNAs) disorders are related to the occurrence and development of breast cancer. The purpose of this study is to explore whether autophagy-related lncRNA can predict the prognosis of breast cancer patients. The autophagy-related lncRNAs prognostic signature was constructed by Least Absolute Shrinkage and Selection Operator (LASSO) Cox regression. We identified five autophagy-related lncRNAs (MAPT-AS1, LINC01871, AL122010.1, AC090912.1, AC061992.1) associated with prognostic value, and they were used to construct an autophagy-related lncRNA prognostic signature (ALPS) model. ALPS model offered an independent prognostic value (HR = 1.664, 1.381-2.006), where this risk score of the model was significantly related to the TNM stage, ER, PR and HER2 status in breast cancer patients. Nomogram could be utilized to predict survival for patients with breast cancer. Principal component analysis and Sankey Diagram results indicated that the distribution of five lncRNAs from the ALPS model tends to be low-risk. Gene set enrichment analysis showed that the high-risk group was enriched in autophagy and cancer-related pathways, and the low-risk group was enriched in regulatory immune-related pathways. These results indicated that the ALPS model composed of five autophagy-related lncRNAs could predict the prognosis of breast cancer patients.     

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.     

Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.     

Screening of significant biomarkers related with prognosis of liver cancer by lncRNA-associated ceRNAs analysis

This study aimed to identify significant biomarkers related to the prognosis of liver cancer using long noncoding RNA (lncRNA)-associated competing endogenous RNAs (ceRNAs) analysis. Differentially expressed mRNA and lncRNAs between liver cancer and paracancerous tissues were screened, and the functions of these mRNAs were predicted by gene ontology and pathway enrichment analyses. A ceRNA network consisting of differentially expressed mRNAs and lncRNAs was constructed. LncRNA FENDRR and lncRNA HAND2-AS1 were hub nodes in the ceRNA network. A risk score assessment model consisting of eight genes (PDE2A, ESR1, FBLN5, ALDH8A1, AKR1D1, EHHADH, ADRA1A, and GNE) associated with prognosis were developed. Multivariate Cox regression suggested that both pathologic_T and risk group could be regarded as independent prognostic factors. Furthermore, a nomogram model consisting of pathologic_T and risk group showed a good prediction ability for predicting the survival rate of liver cancer patients. The nomogram model consisting of pathologic_T and a risk score assessment model could be regarded as an independent factor for predicting prognosis of liver cancer.