http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Cao Qingfei,Ma Jiaji,Li Weitao,Hong Peng,Shen Tong,Tong Ming 한국유전학회 2023 Genes & Genomics Vol.45 No.1
Background Guanine nucleotide-binding protein 2 (GNBP2) is a GTPase that has critical roles in host immunity and some types of cancer, but its function in clear cell renal cell carcinoma (ccRCC) is not fully understood. Objective This work explored the role of GNBP2 in ccRCC progression and the underlying molecular mechanism. Methods Two public human cancer databases TNMplot and TISIDB were employed to analyze the expression pattern of GNBP2 during ccRCC progression and the correlation between GNBP2 expression and clinical features of ccRCC patients. GNBP2 functions in ccRCC cells were determined by EdU staining, flow cytometry, scratch wound assay, transwell assay, and xenograft model. Gene expression was evaluated using qPCR, Western blot, immunofluorescence staining, and immunohistochemical staining. Results GNBP2 expression was significantly elevated in ccRCC tissues and increased gradually with the increasing tumor grades. Patients with higher GNBP2 expression had shorter overall survival times. Knockdown of GNBP2 suppressed tumor cell proliferation and cell cycle progression and reduced the capability of migration and invasion, while GNBP2 overexpression exhibited protumor effects. GNBP2 silencing by RNA interference significantly inhibited the tumor growth of tumor-bearing nude mice and decreased the proliferation marker Ki67. Mechanistically, GNBP2 downregulation suppressed the STAT3 signaling transduction, as it reduced the phosphorylation of STAT3 and modulated the expression of the target genes, including c-Myc, MMP2, N-cadherin, and E-cadherin. Conclusion These findings reveal that GNBP2 promotes ccRCC progression by regulating STAT3 signaling transduction, indicating that GNBP2 might be a promising molecular target for ccRCC therapy.
Research of Resource Scheduling Strategy in Cloud Computing
Ying Gao,Guang Yang,Yanglin Ma,Mu Lei,Jiajie Duan 보안공학연구지원센터 2015 International Journal of Grid and Distributed Comp Vol.8 No.3
To solve the cloud computing resource scheduling problem in IaaS platform, a scheduling model based on ant colony algorithm was proposed. In this model, pheromone changes dynamically according to the best route searched by ants. This model automatically updates pheromones and guides ants to search the global best route. Experiment results show that the proposed model is of better ability in energy consumption in the IaaS cloud computing platform.