BACKGROUND Recent evidence shows that long non-coding RNAs(lncRNAs) are closely related to hepatogenesis and a few aggressive features of hepatocellular carcinoma(HCC). Increasing studies demonstrate that lncRNAs are ...BACKGROUND Recent evidence shows that long non-coding RNAs(lncRNAs) are closely related to hepatogenesis and a few aggressive features of hepatocellular carcinoma(HCC). Increasing studies demonstrate that lncRNAs are potential prognostic factors for HCC. Moreover, several studies reported the combination of lncRNAs for predicting the overall survival(OS) of HCC, but the results varied. Thus,more effort including more accurate statistical approaches is needed for exploring the prognostic value of lncRNAs in HCC.AIM To develop a robust lncRNA signature associated with HCC recurrence to improve prognosis prediction of HCC.METHODS Univariate COX regression analysis was performed to screen the lncRNAs significantly associated with recurrence-free survival(RFS) of HCC in GSE76427 for the least absolute shrinkage and selection operator(LASSO) modelling. The established lncRNA signature was validated and developed in The Cancer Genome Atlas(TCGA) series using Kaplan-Meier curves. The expression values of the identified lncRNAs were compared between the tumor and non-tumor tissues. Pathway enrichment of these lncRNAs was conducted based on the significantly co-expressed genes. A prognostic nomogram combining the lncRNA signature and clinical characteristics was constructed.RESULTS The lncRNA signature consisted of six lncRNAs: MSC-AS1, POLR2 J4, EIF3 J-AS1,SERHL, RMST, and PVT1. This risk model was significantly associated with the RFS of HCC in the TCGA cohort with a hazard ratio(HR) being 1.807(95%CI[confidence interval]: 1.329-2.457) and log-rank P-value being less than 0.001. The best candidates of the six-lncRNA signature were younger male patients with HBV infection in relatively early tumor-stage and better physical condition but with higher preoperative alpha-fetoprotein. All the lncRNAs were significantly upregulated in tumor samples compared to non-tumor samples(P < 0.05). The most significantly enriched pathways of the lncRNAs were TGF-β signaling pathway, cellular apoptosis-associated pathways, etc. The nomogram showed great utility of the lncRNA signature in HCC recurrence risk stratification.CONCLUSION We have constructed a six-lncRNA signature for prognosis prediction of HCC.This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.展开更多
Non-invasive prenatal gene diagnosis has been developed rapidly in the recent years, and numerous medical researchers are focusing on it. Such techniques could not only achieve prenatal diagnosis accurately, but also ...Non-invasive prenatal gene diagnosis has been developed rapidly in the recent years, and numerous medical researchers are focusing on it. Such techniques could not only achieve prenatal diagnosis accurately, but also prevent tangential illness in fetuses and thus, reduce the incidence of diseases. Moreover, it is non-invasive prenatal gene diagnosis that prevents potential threaten and danger to both mothers and fetuses. Therefore, it is welcomed by clinical gynecologist and obstetrian, researchers of medical genetics, and especially, pregnancies. This review article touches briefly on the advanced development of using cell-free DNA, RNA in maternal plasma and urine for non-invasive prenatal gene diagnosis.展开更多
目的通过生物信息学分析发现羊水游离RNA中能够反映胎儿发育异常的组织特性基因。方法从Human Protein Atlas数据库下载基因在正常组织表达数据,从Gene Expression Omnibus数据库下载唐氏综合征及爱德华综合征胎儿羊水游离RNA芯片检测...目的通过生物信息学分析发现羊水游离RNA中能够反映胎儿发育异常的组织特性基因。方法从Human Protein Atlas数据库下载基因在正常组织表达数据,从Gene Expression Omnibus数据库下载唐氏综合征及爱德华综合征胎儿羊水游离RNA芯片检测数据。利用R语言统计分析正常组织表达数据中的组织特异性基因。利用limma程序包分析唐氏综合征及爱德华综合征的差异表达基因。取两者交集获取组织特异性差异表达基因。结果与正常胎儿比较,唐氏综合征胎儿有717个差异基因,爱德华综合征胎儿有1038个差异基因,71个基因为共同差异基因。DOK7、ARHGEF39、FAM111B、CCHCR1、R3HDML、WNK3、FIBCD1、SMIM10L2B及SMIM10L2A 9个基因为组织特异性差异表达基因,这些基因参与大脑、甲状腺、睾丸、消化道等多个组织的发育过程。结论差异表达基因分析和组织特异性基因相结合的方法是筛选羊水游离RNA中胎儿发育异常标志物的可行方法。展开更多
RNA二级结构预测是计算分子生物学中的一个重要领域.本文介绍了RNA二级结构的预测方法,包括该问题的数学模型、主要算法思想以及每种算法对应的软件.在tRNA和RNase P RNA数据库中随机选取了几组样例对目前主要的7种软件进行测试,同时对...RNA二级结构预测是计算分子生物学中的一个重要领域.本文介绍了RNA二级结构的预测方法,包括该问题的数学模型、主要算法思想以及每种算法对应的软件.在tRNA和RNase P RNA数据库中随机选取了几组样例对目前主要的7种软件进行测试,同时对每种软件的优缺点进行了详细比较.实验证明,当存在同源序列时,Pfold的效果优于其它软件.最后,在总结分析现有算法的基础上探讨了该领域进一步的研究方向.展开更多
目的分析孕中期羊水游离RNA(AfcfRNA)转录组,筛选神经发育共表达关键基因。方法利用基因共表达网络分析,建立AfcfRNA转录组的基因共表达网络模块。筛选各共表达模块中的神经特异性基因,建立神经系统特异性共表达模块。利用基因间相互作...目的分析孕中期羊水游离RNA(AfcfRNA)转录组,筛选神经发育共表达关键基因。方法利用基因共表达网络分析,建立AfcfRNA转录组的基因共表达网络模块。筛选各共表达模块中的神经特异性基因,建立神经系统特异性共表达模块。利用基因间相互作用筛选神经组织特异性共表达模块中的关键基因。结果通过加权基因共表达网络分析共建立27个以颜色命名的共表达模块,在Human Protein Atlas数据库中筛选到832个神经组织特异性基因。在蓝色、棕色、蓝绿色以及黄色模块中富集到前脑发育、神经突触组装和功能、神经递质释放过程、轴突发生以及学习和记忆过程相关的GO术语。通过基因间相互作用以及基因在孕中期的平均表达量分析,共发现蓝色模块(SLC18A3、TACR3、SYT2)、棕色模块(SSTR5、STX1A、SNAP25、GHSR、SSTR4、GABBR2)、蓝绿色模块(DRD2、SLC32A1、GNG3、OPN4、PENK)以及黄色模块(RAB3A、HCRT、GRM5)中的17个关键基因。结论该研究获得了神经系统发育密切相关的并且具有共表达关系的关键基因,可作为潜在的产前诊断中监测神经系统发育的标志物。展开更多
基金Supported by The National Natural Science Foundation of China,No.81773128 and No.81871998the Natural Science Basic Research Plan in Shaanxi Province of China,No.2017JM8039+1 种基金China Postdoctoral Science Foundation,No.2018m641000Research Fund for Young Star of Science and Technology in Shaanxi Province,No.2018KJXX-022
文摘BACKGROUND Recent evidence shows that long non-coding RNAs(lncRNAs) are closely related to hepatogenesis and a few aggressive features of hepatocellular carcinoma(HCC). Increasing studies demonstrate that lncRNAs are potential prognostic factors for HCC. Moreover, several studies reported the combination of lncRNAs for predicting the overall survival(OS) of HCC, but the results varied. Thus,more effort including more accurate statistical approaches is needed for exploring the prognostic value of lncRNAs in HCC.AIM To develop a robust lncRNA signature associated with HCC recurrence to improve prognosis prediction of HCC.METHODS Univariate COX regression analysis was performed to screen the lncRNAs significantly associated with recurrence-free survival(RFS) of HCC in GSE76427 for the least absolute shrinkage and selection operator(LASSO) modelling. The established lncRNA signature was validated and developed in The Cancer Genome Atlas(TCGA) series using Kaplan-Meier curves. The expression values of the identified lncRNAs were compared between the tumor and non-tumor tissues. Pathway enrichment of these lncRNAs was conducted based on the significantly co-expressed genes. A prognostic nomogram combining the lncRNA signature and clinical characteristics was constructed.RESULTS The lncRNA signature consisted of six lncRNAs: MSC-AS1, POLR2 J4, EIF3 J-AS1,SERHL, RMST, and PVT1. This risk model was significantly associated with the RFS of HCC in the TCGA cohort with a hazard ratio(HR) being 1.807(95%CI[confidence interval]: 1.329-2.457) and log-rank P-value being less than 0.001. The best candidates of the six-lncRNA signature were younger male patients with HBV infection in relatively early tumor-stage and better physical condition but with higher preoperative alpha-fetoprotein. All the lncRNAs were significantly upregulated in tumor samples compared to non-tumor samples(P < 0.05). The most significantly enriched pathways of the lncRNAs were TGF-β signaling pathway, cellular apoptosis-associated pathways, etc. The nomogram showed great utility of the lncRNA signature in HCC recurrence risk stratification.CONCLUSION We have constructed a six-lncRNA signature for prognosis prediction of HCC.This risk model provides new clinical evidence for the accurate diagnosis and targeted treatment of HCC.
文摘Non-invasive prenatal gene diagnosis has been developed rapidly in the recent years, and numerous medical researchers are focusing on it. Such techniques could not only achieve prenatal diagnosis accurately, but also prevent tangential illness in fetuses and thus, reduce the incidence of diseases. Moreover, it is non-invasive prenatal gene diagnosis that prevents potential threaten and danger to both mothers and fetuses. Therefore, it is welcomed by clinical gynecologist and obstetrian, researchers of medical genetics, and especially, pregnancies. This review article touches briefly on the advanced development of using cell-free DNA, RNA in maternal plasma and urine for non-invasive prenatal gene diagnosis.
文摘RNA二级结构预测是计算分子生物学中的一个重要领域.本文介绍了RNA二级结构的预测方法,包括该问题的数学模型、主要算法思想以及每种算法对应的软件.在tRNA和RNase P RNA数据库中随机选取了几组样例对目前主要的7种软件进行测试,同时对每种软件的优缺点进行了详细比较.实验证明,当存在同源序列时,Pfold的效果优于其它软件.最后,在总结分析现有算法的基础上探讨了该领域进一步的研究方向.
文摘目的分析孕中期羊水游离RNA(AfcfRNA)转录组,筛选神经发育共表达关键基因。方法利用基因共表达网络分析,建立AfcfRNA转录组的基因共表达网络模块。筛选各共表达模块中的神经特异性基因,建立神经系统特异性共表达模块。利用基因间相互作用筛选神经组织特异性共表达模块中的关键基因。结果通过加权基因共表达网络分析共建立27个以颜色命名的共表达模块,在Human Protein Atlas数据库中筛选到832个神经组织特异性基因。在蓝色、棕色、蓝绿色以及黄色模块中富集到前脑发育、神经突触组装和功能、神经递质释放过程、轴突发生以及学习和记忆过程相关的GO术语。通过基因间相互作用以及基因在孕中期的平均表达量分析,共发现蓝色模块(SLC18A3、TACR3、SYT2)、棕色模块(SSTR5、STX1A、SNAP25、GHSR、SSTR4、GABBR2)、蓝绿色模块(DRD2、SLC32A1、GNG3、OPN4、PENK)以及黄色模块(RAB3A、HCRT、GRM5)中的17个关键基因。结论该研究获得了神经系统发育密切相关的并且具有共表达关系的关键基因,可作为潜在的产前诊断中监测神经系统发育的标志物。
