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.
目的 觀察下調Ras同源類似物E (RhoE)表達對人乳腺癌細胞231生物學行為的影響。 方法 蛋白質印跡技術檢測小干擾RNA（siRNA）轉染前后RhoE在乳腺癌細胞231中的表達；RhoE siRNA的細胞轉染 用lipofectamine?2000脂質體法；Cell Counting Kit-8檢測轉染細胞及對照細胞的增殖變化；損傷刮擦試驗和體外侵襲實驗（Transwell小室）分別檢測轉染細胞及對照細胞的遷移與侵襲能力。 結果 RhoE在乳腺癌細胞231中的表達較高；成功轉染RhoE siRNA的乳腺癌細胞，蛋白質印跡顯示RhoE的表達被明顯的抑制；RhoE的表達被抑制后對乳腺癌細胞的增殖、遷移和侵襲有著明顯的促進作用。 結論 下調RhoE 表達能夠明顯促進乳腺癌細胞的增殖﹑遷移和侵襲，RhoE可能在乳腺癌的發生發展中起著重要作用。
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.