Objective: Despite evidence that estrogens and insulin are involved in the development and progression of many cancers, their synergistic role in endometrial carcinoma(EC) has not been analyzed yet.Methods: Here, we i...Objective: Despite evidence that estrogens and insulin are involved in the development and progression of many cancers, their synergistic role in endometrial carcinoma(EC) has not been analyzed yet.Methods: Here, we investigated how estrogens act synergistically with insulin to promote EC progression. Cell growth in vitro and in vivo, effects of estradiol and insulin on apoptosis and cell cycle distribution, and expression and activation of estrogen receptor(ER), insulin receptor(InsR), and key proteins in the PI3K and MAPK pathways were examined after combined stimulation with estradiol and insulin.Results: Compared to EC cells treated with estradiol or insulin alone, those treated with both estradiol and insulin exhibited stronger stimulation. Estradiol significantly induced phosphorylation of InsR-β and IRS-1, whereas insulin significantly induced phosphorylation of ER-α. In addition, treatment with both insulin and estradiol together significantly increased the expression and phosphorylation of Akt, MAPK, and ERK. Notably, InsR-β inhibition had a limited effect on estradiol-dependent proliferation,cell cycle, and apoptosis, whereas ER-α inhibition had a limited insulin-dependent effect, in EC cell lines. Insulin and estradiol individually and synergistically promoted EC xenograft growth in mice.Conclusions: Estrogen and insulin play synergistic roles in EC carcinogenesis and progression by activating InsR-β and ER-α,promoting a crosstalk between them, and thereby resulting in the activation of downstream PI3K/Akt and MAPK/ERK signaling pathways.展开更多
目的建立基于MRI术前子宫内膜癌(endometrial cancer,EC)的分子亚型的生境影像组学预测模型。方法回顾性收集2家医学中心经病理证实的EC患者,分别纳入训练组(n=270)和测试组(n=70)。所有患者均进行了术前MRI及病理组织学和分子亚型诊断...目的建立基于MRI术前子宫内膜癌(endometrial cancer,EC)的分子亚型的生境影像组学预测模型。方法回顾性收集2家医学中心经病理证实的EC患者,分别纳入训练组(n=270)和测试组(n=70)。所有患者均进行了术前MRI及病理组织学和分子亚型诊断。首先根据扩散加权成像(diffusion-weighted imaging,DWI)和对比增强(contrast enhancement,CE)图像对肿瘤进行生境亚区域分区,随后从T1加权成像(T1-weighted imaging,T1WI)、T2加权成像(T2-weighted imaging,T2WI)、DWI和CE图像的不同亚区域提取生境影像组学特征。应用3种机器学习分类器,包括逻辑回归、支持向量机和随机森林,分别建立预测p53异常型EC的模型并进行效能验证,表现出最佳综合预测性能的模型被选为生境影像组学模型。采用相同程序,建立基于T1WI、T2WI、DWI和CE共4个序列的全区域影像组学模型及临床模型。采用受试者工作特性曲线评估模型的效能,使用DeLong检验比较模型的差异。使用决策曲线分析评价模型应用的临床收益。结果经特征选择后保留8个生境影像组学特征建立生境影像组学模型、10个全区域影像组学特征建立影像组学模型和3个临床特征建立临床模型。生境影像组学模型曲线下面积(area under the curve,AUC)最高,分别为0.855(0.788~0.922,训练集)和0.769(0.631~0.907,验证集)。DeLong检验显示训练集的生境影像组学模型效能优于全区域影像组学模型(P=0.001),但测试集差异不显著(P=0.543);两组生境影像组学模型效能均优于临床模型(P=0.007,训练集;P=0.038,验证集)。DCA曲线显示该模型在阈值概率0.2~0.8之间均可对临床诊断提供收益。结论基于MRI的生境影像组学模型可以较准确地预测p53异常型的EC,效能优于全区域影像组学和临床模型,有助于术前EC的无创性分子亚型分型。展开更多
基金supported by grants from the National Natural Science Foundation of China (Grant No. 30772316 and 81572568)
文摘Objective: Despite evidence that estrogens and insulin are involved in the development and progression of many cancers, their synergistic role in endometrial carcinoma(EC) has not been analyzed yet.Methods: Here, we investigated how estrogens act synergistically with insulin to promote EC progression. Cell growth in vitro and in vivo, effects of estradiol and insulin on apoptosis and cell cycle distribution, and expression and activation of estrogen receptor(ER), insulin receptor(InsR), and key proteins in the PI3K and MAPK pathways were examined after combined stimulation with estradiol and insulin.Results: Compared to EC cells treated with estradiol or insulin alone, those treated with both estradiol and insulin exhibited stronger stimulation. Estradiol significantly induced phosphorylation of InsR-β and IRS-1, whereas insulin significantly induced phosphorylation of ER-α. In addition, treatment with both insulin and estradiol together significantly increased the expression and phosphorylation of Akt, MAPK, and ERK. Notably, InsR-β inhibition had a limited effect on estradiol-dependent proliferation,cell cycle, and apoptosis, whereas ER-α inhibition had a limited insulin-dependent effect, in EC cell lines. Insulin and estradiol individually and synergistically promoted EC xenograft growth in mice.Conclusions: Estrogen and insulin play synergistic roles in EC carcinogenesis and progression by activating InsR-β and ER-α,promoting a crosstalk between them, and thereby resulting in the activation of downstream PI3K/Akt and MAPK/ERK signaling pathways.
文摘目的建立基于MRI术前子宫内膜癌(endometrial cancer,EC)的分子亚型的生境影像组学预测模型。方法回顾性收集2家医学中心经病理证实的EC患者,分别纳入训练组(n=270)和测试组(n=70)。所有患者均进行了术前MRI及病理组织学和分子亚型诊断。首先根据扩散加权成像(diffusion-weighted imaging,DWI)和对比增强(contrast enhancement,CE)图像对肿瘤进行生境亚区域分区,随后从T1加权成像(T1-weighted imaging,T1WI)、T2加权成像(T2-weighted imaging,T2WI)、DWI和CE图像的不同亚区域提取生境影像组学特征。应用3种机器学习分类器,包括逻辑回归、支持向量机和随机森林,分别建立预测p53异常型EC的模型并进行效能验证,表现出最佳综合预测性能的模型被选为生境影像组学模型。采用相同程序,建立基于T1WI、T2WI、DWI和CE共4个序列的全区域影像组学模型及临床模型。采用受试者工作特性曲线评估模型的效能,使用DeLong检验比较模型的差异。使用决策曲线分析评价模型应用的临床收益。结果经特征选择后保留8个生境影像组学特征建立生境影像组学模型、10个全区域影像组学特征建立影像组学模型和3个临床特征建立临床模型。生境影像组学模型曲线下面积(area under the curve,AUC)最高,分别为0.855(0.788~0.922,训练集)和0.769(0.631~0.907,验证集)。DeLong检验显示训练集的生境影像组学模型效能优于全区域影像组学模型(P=0.001),但测试集差异不显著(P=0.543);两组生境影像组学模型效能均优于临床模型(P=0.007,训练集;P=0.038,验证集)。DCA曲线显示该模型在阈值概率0.2~0.8之间均可对临床诊断提供收益。结论基于MRI的生境影像组学模型可以较准确地预测p53异常型的EC,效能优于全区域影像组学和临床模型,有助于术前EC的无创性分子亚型分型。
文摘目的·分析子宫内膜癌组织和正常内膜组织中长链非编码RNA(long noncoding RNA,lncRNA)的差异表达谱。方法·分别抽提21例子宫内膜癌组织和5例正常内膜组织的RNA,利用转录组测序技术分析并筛选2组差异表达lncRNAs。对获得的差异表达lncRNAs进行GO(Gene Ontology)功能分析和KEGG(Kyoto Encyclopedia of Genes and Genomes)通路分析,并结合TCGA数据库对差异表达lncRNAs进行交集分析。结果·共筛选获得3060个差异表达lncRNAs,其中上调表达的有2046个,下调的有1014个。GO功能分析和KEGG通路分析结果显示,这些lncRNAs与细胞黏附、免疫反应、炎症反应、细胞增殖相关,并且主要富集在PI3KAkt信号通路、细胞黏附、细胞因子受体等通路上。交集分析显示,57个lncRNAs在测序结果和TCGA数据库中同时上调或下调。结论·lncRNAs在子宫内膜癌和正常内膜组织中的表达有显著差异,提示其可能在子宫内膜癌的发生及发展中起到了重要作用。
