目的:为探讨端粒维持相关基因在结肠腺癌中的表达特征,构建预后模型,并探究其与肿瘤免疫微环境的关系。方法:分析来自癌症基因组图谱(the cancer genome atlas,TCGA)的结肠腺癌转录组和临床数据,进行差异分析。采用Logrank检验、单因素...目的:为探讨端粒维持相关基因在结肠腺癌中的表达特征,构建预后模型,并探究其与肿瘤免疫微环境的关系。方法:分析来自癌症基因组图谱(the cancer genome atlas,TCGA)的结肠腺癌转录组和临床数据,进行差异分析。采用Logrank检验、单因素Cox回归分析筛选预后相关基因,LASSO回归分析构建基于端粒维持相关基因的预后预测模型,根据Cox回归分析确定独立预后危险因素构建列线图,并分析端粒维持相关基因、风险预测模型与免疫微环境的关系。结果:构建了14个基因的预后模型,低风险评分患者总生存时间显著长于高风险评分患者,ROC曲线显示该模型预测结肠腺癌患者1、3、5年生存曲线(area under curve,AUC)值分别为0.794、0.779和0.812,发现该模型预测结肠腺癌患者生存期的能效优于肿瘤分期及分级,高风险组和低风险组间在浸润水平上表现出显著差异。结论:基于端粒维持基因的结肠腺癌预后模型构建,能够准确预测患者生存期,并揭示端粒基因与免疫微环境的密切关系,为结肠腺癌个性化治疗提供新思路。展开更多
Sweet spots in the shale reservoirs of the Lower Silurian Longmaxi Formation in Weiyuan 201 Block of Sichuan Basin were predicted quantitatively using seismic data and fuzzy optimization method. First, based on seismi...Sweet spots in the shale reservoirs of the Lower Silurian Longmaxi Formation in Weiyuan 201 Block of Sichuan Basin were predicted quantitatively using seismic data and fuzzy optimization method. First, based on seismic and rock physics analysis, the rock physics characteristics of the reservoirs were determined, and elastic parameters sensitive to shale reservoirs with high gas content were selected. Second, data volumes with high precision of the elastic parameters were obtained from pre-stack simultaneous inversion. The horizontal distribution of key parameters for shale gas evaluation were calculated based on the results of rock physics analysis. Then, the fuzzy evaluation equation was established by fuzzy optimization method with test and logging data of horizontal wells with similar operation conditions. key parameters affecting the productivity of horizontal wells were sorted out and the weights of them in the sweet spots quantitative prediction were worked out by fuzzy optimization to set up a sweet spots evaluation system. Three classes of shale gas reservoirs which including two kinds of sweet spots were predicted with the above procedure, and the sweet spots have been predicted quantitatively by combining the above prediction results with the testing production. The testing results of 7 verification wells proved the reliability of the prediction results.展开更多
文摘目的:为探讨端粒维持相关基因在结肠腺癌中的表达特征,构建预后模型,并探究其与肿瘤免疫微环境的关系。方法:分析来自癌症基因组图谱(the cancer genome atlas,TCGA)的结肠腺癌转录组和临床数据,进行差异分析。采用Logrank检验、单因素Cox回归分析筛选预后相关基因,LASSO回归分析构建基于端粒维持相关基因的预后预测模型,根据Cox回归分析确定独立预后危险因素构建列线图,并分析端粒维持相关基因、风险预测模型与免疫微环境的关系。结果:构建了14个基因的预后模型,低风险评分患者总生存时间显著长于高风险评分患者,ROC曲线显示该模型预测结肠腺癌患者1、3、5年生存曲线(area under curve,AUC)值分别为0.794、0.779和0.812,发现该模型预测结肠腺癌患者生存期的能效优于肿瘤分期及分级,高风险组和低风险组间在浸润水平上表现出显著差异。结论:基于端粒维持基因的结肠腺癌预后模型构建,能够准确预测患者生存期,并揭示端粒基因与免疫微环境的密切关系,为结肠腺癌个性化治疗提供新思路。
文摘目的探讨玄参总苷的主要化学成分及其干预大鼠甲亢阴虚火旺证模型的作用机制。方法将SD大鼠随机分为对照组、模型组、玄参总苷给药组。腹腔注射3′,5-三碘代-L-甲状腺原氨酸(3,5,3'-triiodothyronine,T3)溶液[100μg·(100 g)^(-1)]建立阴虚火旺模型,进行环磷酸腺苷(cyclic adenosine monophosphate,cAMP)、环磷酸鸟苷(cyclic guanosine monophosphate,cGMP)水平等相关指标的检测,采用超高效液相色谱-四极杆-飞行时间串联质谱技术(ultra high performance liquid chromatography-quadrupole-time of flight mass spectrometry,UHPLC-Q-TOF-MS)对玄参总苷的化学成分和不同组别大鼠的血清内源性代谢物进行分析,并结合主成分分析(principal component analysis,PCA)和正交偏最小二乘判别分析(orthogonal partial least squares-discriminant analysis,OPLS-DA),筛选差异性代谢物并构建其代谢通路。结果从玄参总苷中共鉴定了31个化学成分,玄参总苷能显著降低阴虚大鼠cAMP、cAMP/cGMP水平,阴虚火旺模型大鼠血清中25个内源性代谢物发生显著变化,其中鞘脂、类固醇、脂质、亚油酸、花生四烯酸呈上升趋势,通路分析表明玄参总苷可能通过调节鞘脂类、甘油磷脂、花生四烯酸等代谢途径改善阴虚火旺证候。结论本研究运用血清代谢组学方法揭示了玄参总苷干预甲亢阴虚火旺证的作用机制,为玄参“滋阴降火”功效的物质基础和临床应用提供了理论依据。
基金Supported by the China National Science and Technology Major Project(2017ZX05035-02)
文摘Sweet spots in the shale reservoirs of the Lower Silurian Longmaxi Formation in Weiyuan 201 Block of Sichuan Basin were predicted quantitatively using seismic data and fuzzy optimization method. First, based on seismic and rock physics analysis, the rock physics characteristics of the reservoirs were determined, and elastic parameters sensitive to shale reservoirs with high gas content were selected. Second, data volumes with high precision of the elastic parameters were obtained from pre-stack simultaneous inversion. The horizontal distribution of key parameters for shale gas evaluation were calculated based on the results of rock physics analysis. Then, the fuzzy evaluation equation was established by fuzzy optimization method with test and logging data of horizontal wells with similar operation conditions. key parameters affecting the productivity of horizontal wells were sorted out and the weights of them in the sweet spots quantitative prediction were worked out by fuzzy optimization to set up a sweet spots evaluation system. Three classes of shale gas reservoirs which including two kinds of sweet spots were predicted with the above procedure, and the sweet spots have been predicted quantitatively by combining the above prediction results with the testing production. The testing results of 7 verification wells proved the reliability of the prediction results.
