目的 观察下调Ras同源类似物E (RhoE)表达对人乳腺癌细胞231生物学行为的影响。 方法 蛋白质印迹技术检测小干扰RNA（siRNA）转染前后RhoE在乳腺癌细胞231中的表达；RhoE siRNA的细胞转染 用lipofectamine?2000脂质体法；Cell Counting Kit-8检测转染细胞及对照细胞的增殖变化；损伤刮擦试验和体外侵袭实验（Transwell小室）分别检测转染细胞及对照细胞的迁移与侵袭能力。 结果 RhoE在乳腺癌细胞231中的表达较高；成功转染RhoE siRNA的乳腺癌细胞，蛋白质印迹显示RhoE的表达被明显的抑制；RhoE的表达被抑制后对乳腺癌细胞的增殖、迁移和侵袭有着明显的促进作用。 结论 下调RhoE 表达能够明显促进乳腺癌细胞的增殖﹑迁移和侵袭，RhoE可能在乳腺癌的发生发展中起着重要作用。
The aim of this article is to study the regulatory feedback loop between β-catenin and IQ motif containing GTPase activating protein 1 (IQGAP1), as well as the effect of this regulation loop in colon cancer cell proliferation. Western blot was used to detect the expression of IQGAP1 and β-catenin after changing their expression respectively by transfection in SW1116 cells. CCK-8 cell proliferation assay was used to detect the effect of IQGAP1 involved in the proliferation of SW1116 cells promoted by β-catenin. The results of Western blot indicated that β-catenin could positively regulate IQGAP1, while IQGAP1 silencing could up-regulate β-catenin, forming a negative feedback loop. The results of CCK-8 showed that IQGAP1 silencing inhibited β-catenin-mediated proliferation in SW1116 cells. In conclusion, our research reveals a negative regulatory feedback loop between β-catenin and IQGAP1 which has a remarkable effect on the proliferation ability of colon cancer cells.
To evaluate the differential expression profiles of the lncRNAs, miRNAs, mRNAs and ceRNAs, and their implication in the prognosis in clear cell renal cell carcinoma (CCRCC), the large sample genomics analysis technologies were used in this study. The RNA and miRNA sequencing data of CCRCC were obtained from The Cancer Genome Atlas (TCGA) database, and R software was used for gene expression analysis and survival analysis. Cytoscape software was used to construct the ceRNA network. The results showed that a total of 1 570 lncRNAs, 54 miRNAs, and 17 mRNAs were differentially expressed in CCRCC, and most of their expression levels were up-regulated (false discovery rate < 0.01 and absolute log fold change > 2). The ceRNA regulatory network showed the interaction between 89 differentially expressed lncRNAs and 9 differentially expressed miRNAs. Further survival analysis revealed that 38 lncRNAs (including COL18A1-AS1, TCL6, LINC00475, UCA1, WT1-AS, HOTTIP, PVT1, etc.) and 2 miRNAs (including miR-21 and miR-155) were correlated with the overall survival time of CCRCC (P < 0.05). Together, this study provided us several new evidences for the targeted therapy and prognosis assessment of CCRCC.