目的观察甲基化抑制剂5-氮杂-2′-脱氧胞苷(5-aza-2′-deoxycytidine,5-Aza-CdR)对肺癌SPC-A1细胞增殖、细胞划痕和凋亡的影响,探讨抑癌基因分泌型卷曲相关蛋白1(secreted frizzled related protein 1,SFRP1)和O6-甲基鸟嘌呤-DNA甲基转...目的观察甲基化抑制剂5-氮杂-2′-脱氧胞苷(5-aza-2′-deoxycytidine,5-Aza-CdR)对肺癌SPC-A1细胞增殖、细胞划痕和凋亡的影响,探讨抑癌基因分泌型卷曲相关蛋白1(secreted frizzled related protein 1,SFRP1)和O6-甲基鸟嘌呤-DNA甲基转移酶(O6-methylguanine-DNA-methyltransferase,MGMT)基因启动子区DNA甲基化mRNA和蛋白在其中的表达和意义。方法CCK-8法检测不同浓度的5-Aza-CdR对人肺癌SPC-A1细胞增殖影响,划痕实验测定5-Aza-CdR对SPC-A1细胞迁移能力的影响,Hoechst 33258染色检测(0、3、10、30μmol·L^(-1))5-Aza-CdR处理肺癌SPC-A1细胞24h后细胞凋亡情况,RT-PCR、Western blot法检测SPC-A1细胞中SFRP1和MGMT的mRNA、蛋白表达。结果5-Aza-CdR可以浓度梯度的抑制肺癌SPC-A1细胞增殖,IC 50为21.2μmol·L^(-1);5-Aza-CdR(3、10、30μmol·L^(-1))作用48 h后,肺癌SPC-A1细胞划痕愈合分别为对照组的(92.4±2.6)%、(83.6±4.2)%、(76.7±4.5)%;5-Aza-CdR处理肺癌SPC-A1细胞后,出现典型的细胞凋亡形态学改变;不同浓度5-Aza-CdR(3、10、30μmol·L^(-1))处理SPC-A1细胞24 h后,SFRP1、MGMT mRNA和蛋白表达增加(P<0.05)。结论5-Aza-CdR可抑制肺癌SPC-A1细胞增殖,抑制肺癌细胞划痕修复和促进凋亡,可能与升高MGMT和SFRPl的甲基化表达有关。展开更多
This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preser...This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA.展开更多
基金Sponsored by the National Science Foundation of China( Grant No. 61201370,61100103)the Independent Innovation Foundation of Shandong University( Grant No. 2012DX07)
文摘This paper presents two novel algorithms for feature extraction-Subpattern Complete Two Dimensional Linear Discriminant Principal Component Analysis (SpC2DLDPCA) and Subpattern Complete Two Dimensional Locality Preserving Principal Component Analysis (SpC2DLPPCA). The modified SpC2DLDPCA and SpC2DLPPCA algorithm over their non-subpattern version and Subpattern Complete Two Dimensional Principal Component Analysis (SpC2DPCA) methods benefit greatly in the following four points: (1) SpC2DLDPCA and SpC2DLPPCA can avoid the failure that the larger dimension matrix may bring about more consuming time on computing their eigenvalues and eigenvectors. (2) SpC2DLDPCA and SpC2DLPPCA can extract local information to implement recognition. (3)The idea of subblock is introduced into Two Dimensional Principal Component Analysis (2DPCA) and Two Dimensional Linear Discriminant Analysis (2DLDA). SpC2DLDPCA combines a discriminant analysis and a compression technique with low energy loss. (4) The idea is also introduced into 2DPCA and Two Dimensional Locality Preserving projections (2DLPP), so SpC2DLPPCA can preserve local neighbor graph structure and compact feature expressions. Finally, the experiments on the CASIA(B) gait database show that SpC2DLDPCA and SpC2DLPPCA have higher recognition accuracies than their non-subpattern versions and SpC2DPCA.
