目的应用生物信息学方法探讨鞘氨醇-1-磷酸转运体2(sphingosine-1-phosphate transporter 2,SPNS2)在乳腺癌中的表达情况,并分析SPNS2对乳腺癌预后、诊断或免疫浸润的影响。方法癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库检...目的应用生物信息学方法探讨鞘氨醇-1-磷酸转运体2(sphingosine-1-phosphate transporter 2,SPNS2)在乳腺癌中的表达情况,并分析SPNS2对乳腺癌预后、诊断或免疫浸润的影响。方法癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库检索乳腺癌组织和非乳腺癌样本中的SPNS2 mRNA差异表达数据,并分析SPNS2与乳腺癌之间的关系。用Cox单因素及多因素模型分析SPNS2 mRNA表达对乳腺癌预后的影响。使用Kaplan-Meier曲线评估SPNS2基因的表达与存活率之间的相关性,分析SPNS2对乳腺癌患者生存预后的影响。使用受试者工作特征曲线(receiver operating characteristic,ROC)分析SPNS2对乳腺癌的诊断效能。使用肿瘤免疫估算资源(Tumor Immune Estimation Resource,TIMER)数据库分析SPNS2表达与乳腺癌免疫微环境中不同类型免疫细胞的相关性。搜索相互作用基因检索的工具(Search Tool for the Retrieval of Interacting Genes,STRING)数据库分析乳腺癌中SPNS2与相关蛋白质之间的相互作用。分析京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)数据库中得到的差异基因富集的信号通路。提取正常乳腺上皮细胞和乳腺癌细胞系RNA,通过RT-qPCR实验比较SPNS2的表达水平。结果在TCGA乳腺癌数据库中,SPNS2在乳腺癌组织中表达水平显著低于癌旁组织(P<0.001),SPNS2 mRNA的表达与T、M分期、病理分期、PAM50分型、年龄和组织学类型等因素相关(P<0.05);Cox分析表明年龄>60岁、T4期、M1期等因素是乳腺癌发生预后不良的风险因素(P<0.01);Kaplan-Meier分析显示低表达SPNS2乳腺癌患者具有更长的疾病特异生存期(disease-specific survival,DSS)和无进展间隔期(progression-free interval,PFI)(P<0.05);ROC曲线提示SPNS2诊断具有较好的敏感性和特异性。TIMER数据库分析显示在Luminal型乳腺癌中,SPNS2与CD4+T细胞,巨噬细胞、中性粒细胞等免疫细胞呈正相关(P<0.05)。STRING和KEGG数据库分析表明SPNS2相关蛋白富集于细胞周期和PPAR信号传导等途径(P<0.05)。RT-qPCR实验结果显示与正常乳腺上皮细胞相比,SPNS2在乳腺癌细胞系中低表达(P<0.001)。结论SPNS2是一种潜在的诊断乳腺癌和评价预后的生物学标志物。展开更多
Solid pseudopapillary neoplasms (SPNs) are rare solid pancreatic tumors mainly affecting young women. Despite the high percentage of favorable prognosis, they are considered as low grade malignant neoplasms, and metas...Solid pseudopapillary neoplasms (SPNs) are rare solid pancreatic tumors mainly affecting young women. Despite the high percentage of favorable prognosis, they are considered as low grade malignant neoplasms, and metastases occur in 5%-15% of patients. Almost all SPNs (95%) have somatic activating mutations in the β-catenin gene [1].展开更多
Sum-product networks(SPNs)are an expressive deep probabilistic architecture with solid theoretical foundations,which allows tractable and exact inference.SPNs always act as black-box inference machine in many artifici...Sum-product networks(SPNs)are an expressive deep probabilistic architecture with solid theoretical foundations,which allows tractable and exact inference.SPNs always act as black-box inference machine in many artificial intelligence tasks.Due to their recursive definition,SPNs can also be naturally employed as hierarchical feature extractors.Recently,SPNs have been successfully employed as autoencoder framework in representation learning.However,SPNs autoencoder ignores the model structural duality and trains the models separately and independently.In this work,we propose a Dual-SPNs autoencoder which designs two SPNs autoencoders to compose as a dual form.This approach trains the models simultaneously,and explicitly exploits the structural duality between them to enhance the training process.Experimental results on several multilabel classification problems demonstrate that Dual-SPNs autoencoder is very competitive against with state-of-the-art autoencoder architectures.展开更多
文摘目的应用生物信息学方法探讨鞘氨醇-1-磷酸转运体2(sphingosine-1-phosphate transporter 2,SPNS2)在乳腺癌中的表达情况,并分析SPNS2对乳腺癌预后、诊断或免疫浸润的影响。方法癌症基因组图谱(The Cancer Genome Atlas,TCGA)数据库检索乳腺癌组织和非乳腺癌样本中的SPNS2 mRNA差异表达数据,并分析SPNS2与乳腺癌之间的关系。用Cox单因素及多因素模型分析SPNS2 mRNA表达对乳腺癌预后的影响。使用Kaplan-Meier曲线评估SPNS2基因的表达与存活率之间的相关性,分析SPNS2对乳腺癌患者生存预后的影响。使用受试者工作特征曲线(receiver operating characteristic,ROC)分析SPNS2对乳腺癌的诊断效能。使用肿瘤免疫估算资源(Tumor Immune Estimation Resource,TIMER)数据库分析SPNS2表达与乳腺癌免疫微环境中不同类型免疫细胞的相关性。搜索相互作用基因检索的工具(Search Tool for the Retrieval of Interacting Genes,STRING)数据库分析乳腺癌中SPNS2与相关蛋白质之间的相互作用。分析京都基因与基因组百科全书(Kyoto Encyclopedia of Genes and Genomes,KEGG)数据库中得到的差异基因富集的信号通路。提取正常乳腺上皮细胞和乳腺癌细胞系RNA,通过RT-qPCR实验比较SPNS2的表达水平。结果在TCGA乳腺癌数据库中,SPNS2在乳腺癌组织中表达水平显著低于癌旁组织(P<0.001),SPNS2 mRNA的表达与T、M分期、病理分期、PAM50分型、年龄和组织学类型等因素相关(P<0.05);Cox分析表明年龄>60岁、T4期、M1期等因素是乳腺癌发生预后不良的风险因素(P<0.01);Kaplan-Meier分析显示低表达SPNS2乳腺癌患者具有更长的疾病特异生存期(disease-specific survival,DSS)和无进展间隔期(progression-free interval,PFI)(P<0.05);ROC曲线提示SPNS2诊断具有较好的敏感性和特异性。TIMER数据库分析显示在Luminal型乳腺癌中,SPNS2与CD4+T细胞,巨噬细胞、中性粒细胞等免疫细胞呈正相关(P<0.05)。STRING和KEGG数据库分析表明SPNS2相关蛋白富集于细胞周期和PPAR信号传导等途径(P<0.05)。RT-qPCR实验结果显示与正常乳腺上皮细胞相比,SPNS2在乳腺癌细胞系中低表达(P<0.001)。结论SPNS2是一种潜在的诊断乳腺癌和评价预后的生物学标志物。
文摘Solid pseudopapillary neoplasms (SPNs) are rare solid pancreatic tumors mainly affecting young women. Despite the high percentage of favorable prognosis, they are considered as low grade malignant neoplasms, and metastases occur in 5%-15% of patients. Almost all SPNs (95%) have somatic activating mutations in the β-catenin gene [1].
基金the National Natural Science Foundation of China(No.61472161)the Science&Technology Development Project of Jilin Province(Nos.20180101334JC and 20160520099JH)。
文摘Sum-product networks(SPNs)are an expressive deep probabilistic architecture with solid theoretical foundations,which allows tractable and exact inference.SPNs always act as black-box inference machine in many artificial intelligence tasks.Due to their recursive definition,SPNs can also be naturally employed as hierarchical feature extractors.Recently,SPNs have been successfully employed as autoencoder framework in representation learning.However,SPNs autoencoder ignores the model structural duality and trains the models separately and independently.In this work,we propose a Dual-SPNs autoencoder which designs two SPNs autoencoders to compose as a dual form.This approach trains the models simultaneously,and explicitly exploits the structural duality between them to enhance the training process.Experimental results on several multilabel classification problems demonstrate that Dual-SPNs autoencoder is very competitive against with state-of-the-art autoencoder architectures.
