目的基于美国癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库分析影响三阴性乳腺癌(triple-negative breast cancer,TNBC)预后的铁死亡相关长链非编码RNA(long non-coding RNA,lncRNA),并建立TNBC预后预测模型。方法从TCGA下载TNB...目的基于美国癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库分析影响三阴性乳腺癌(triple-negative breast cancer,TNBC)预后的铁死亡相关长链非编码RNA(long non-coding RNA,lncRNA),并建立TNBC预后预测模型。方法从TCGA下载TNBC样本的转录组数据及临床信息数据。用R语言limma包筛选乳腺癌差异表达基因,survival包筛选TNBC生存时间相关基因,并与搜集到的382个铁死亡基因取交集,获得与TNBC预后相关的铁死亡基因。采用Pearson法进行共表达分析以确定与铁死亡相关的lncRNA。采用Cox回归分析来确定生存相关的lncRNA和预后因素。LASSO回归分析构建TNBC预后回归模型,受试者工作特征(receiver operating characteristic,ROC)曲线分析该模型对预后的预测价值。结果单因素Cox回归分析显示,17个差异表达的lncRNA与TNBC患者生存时间相关(均P<0.05)。采用LASSO回归分析成功构建由6个铁死亡相关lncRNA(SGMS1-AS1、AC015908.2、LINC01014、AC083967.1、TTC39A-AS1和AL353708.3)构成的TNBC风险预测模型。计算每个样本的风险评分。Kaplan-Meier法分析显示,风险评分可以有效区分低风险患者和高风险患者(P<0.05)。该模型预测TNBC患者1、2和3年总生存率的ROC曲线下面积分别为0.808、0.840和0.807。结论基于TCGA数据库共筛选出17个铁死亡相关lncRNA与TNBC预后相关。基于SGMS1-AS1、AC015908.2、LINC01014、AC083967.1、TTC39A-AS1和AL353708.3这6个铁死亡相关lncRNA构建的模型可较好地预测TNBC患者预后,提示铁死亡相关lncRNA与患者预后具有一定的相关性。展开更多
OBJECTIVE:To identify the active ingredients and metabolites in rat bile after Guangtongxiao decoction(GTX)had been administered via the rectal route.METHODS:Drug-containing bile samples were collected via a catheter ...OBJECTIVE:To identify the active ingredients and metabolites in rat bile after Guangtongxiao decoction(GTX)had been administered via the rectal route.METHODS:Drug-containing bile samples were collected via a catheter in the bile duct and could be used 5 h after rectal administration.The main active components and their metabolites in rat bile following rectal administration of GTX were identified and analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry.RESULTS:Positive and negative modes were applied to analyze and identify the chemical ingredients in the bioactive fractions of GTX.Eight peaks were identified by comparison with the standard compounds:berberine hydrochloride,dehydrocorydaline,tetrahydropalmatine,corydaline,magnoflorine,magnolol,obacunone and albiflorin.Furthermore,60 metabolites were detected in rat bile based on mass-fragmentation behaviors,and 21 metabolites were reported for the first time.CONCLUSION:Our findings provide a solid basis for further pharmacologic and pharmacokinetic studies of GTX.展开更多
文摘目的基于美国癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库分析影响三阴性乳腺癌(triple-negative breast cancer,TNBC)预后的铁死亡相关长链非编码RNA(long non-coding RNA,lncRNA),并建立TNBC预后预测模型。方法从TCGA下载TNBC样本的转录组数据及临床信息数据。用R语言limma包筛选乳腺癌差异表达基因,survival包筛选TNBC生存时间相关基因,并与搜集到的382个铁死亡基因取交集,获得与TNBC预后相关的铁死亡基因。采用Pearson法进行共表达分析以确定与铁死亡相关的lncRNA。采用Cox回归分析来确定生存相关的lncRNA和预后因素。LASSO回归分析构建TNBC预后回归模型,受试者工作特征(receiver operating characteristic,ROC)曲线分析该模型对预后的预测价值。结果单因素Cox回归分析显示,17个差异表达的lncRNA与TNBC患者生存时间相关(均P<0.05)。采用LASSO回归分析成功构建由6个铁死亡相关lncRNA(SGMS1-AS1、AC015908.2、LINC01014、AC083967.1、TTC39A-AS1和AL353708.3)构成的TNBC风险预测模型。计算每个样本的风险评分。Kaplan-Meier法分析显示,风险评分可以有效区分低风险患者和高风险患者(P<0.05)。该模型预测TNBC患者1、2和3年总生存率的ROC曲线下面积分别为0.808、0.840和0.807。结论基于TCGA数据库共筛选出17个铁死亡相关lncRNA与TNBC预后相关。基于SGMS1-AS1、AC015908.2、LINC01014、AC083967.1、TTC39A-AS1和AL353708.3这6个铁死亡相关lncRNA构建的模型可较好地预测TNBC患者预后,提示铁死亡相关lncRNA与患者预后具有一定的相关性。
基金Supported by the Youth Program of National Natural Science Foundation of China(To Investigate the Spasmolysis of Intestinal Absorption Solution of Guang Tong Xiao Aqueous Extract&Effective Components for Anal Sphincter Based on Calcium Channels,No.81704077)。
文摘OBJECTIVE:To identify the active ingredients and metabolites in rat bile after Guangtongxiao decoction(GTX)had been administered via the rectal route.METHODS:Drug-containing bile samples were collected via a catheter in the bile duct and could be used 5 h after rectal administration.The main active components and their metabolites in rat bile following rectal administration of GTX were identified and analyzed using ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry.RESULTS:Positive and negative modes were applied to analyze and identify the chemical ingredients in the bioactive fractions of GTX.Eight peaks were identified by comparison with the standard compounds:berberine hydrochloride,dehydrocorydaline,tetrahydropalmatine,corydaline,magnoflorine,magnolol,obacunone and albiflorin.Furthermore,60 metabolites were detected in rat bile based on mass-fragmentation behaviors,and 21 metabolites were reported for the first time.CONCLUSION:Our findings provide a solid basis for further pharmacologic and pharmacokinetic studies of GTX.
