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MiR-371 promotes proliferation and metastasis in hepatocellular carcinoma by targeting PTEN
( Hao Wang ),( Yi Zhao ),( Tingsong Chen ),( Guofang Liu ),( Nan He ),( Heping Hu ) 생화학분자생물학회(구 한국생화학분자생물학회) 2019 BMB Reports Vol.52 No.5
Hepatocellular carcinoma (HCC) is the leading cause of cancer-related mortality worldwide. MiR-371 has recently emerged as an important regulator in tumorigenesis, and may serve as a biomarker for malignant tumors. We transfected miR-371 or its inhibitor in two human HCC cell lines, then used 3-(4,5-Dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide, soft agar colony formation, and transwell migration assays to evaluate the effects on cell proliferation, migration, and invasion. We found that miR-371 was positively correlated with HCC metastasis and poor prognosis in the inflicted patients, and the high expression of miR-371 was promoted, whereas a low level of miR-371 depressed cell proliferation and invasion. We found PTEN to be a direct target of miR-371. The overexpression or knockdown of PTEN exhibited the opposite effects from those of miR-371 on cell proliferation and migration. Our study demonstrates that miR-371 promotes proliferation and metastasis in HCC by targeting PTEN. [BMB Reports 2019; 52(5): 312-317]
Guo Zhao,Shulin Li,Wanqing Zuo,Haoran Song,Heping Zhu,Wenjie Hu 전력전자학회 2023 JOURNAL OF POWER ELECTRONICS Vol.23 No.9
To address the problem where the different operating conditions of hydropower units have a large influence on the parameters of the trend prediction model of the operating condition indicators, a support vector regression machine prediction model based on parameter adaptation is proposed in this paper. First, the Aquila optimizer (AO) is improved, and a sine chaotic map is introduced to influence the population initialization process. An improved adaptive weight factor is used to balance the local search and global search capabilities. Second, according to the power and the head, the operating conditions of the unit are refined into several typical sets of operating conditions. On this basis, an SVR model is established using the improved AO search algorithm proposed in this paper, and the prediction parameters under each of the operating condition are optimized to establish the data of the operating conditions and optimal parameters. Then a neural network is used to fit the working condition and the optimal prediction parameters. In addition, the nonlinear function mapping of the complex relationship between the two is constructed. Finally, the constructed mapping relationship is added to the traditional SVR, and an adaptive SVR prediction model suitable for changes in the working conditions of hydropower units is realized. Simulation results show that when compared to the traditional SVR prediction model, the adaptive SVR prediction model designed in this paper can automatically adjust the prediction parameters according to changes in the working conditions and achieve the goal of maintaining optimal prediction performance under different working conditions. In addition, it has the ability to accurately predict the development trend of the unit operating state index within a certain time scale.