Du B S, Yuan L, Sun L G, Zhang Z Y
Second Department of Neurosurgery, Xinxiang Central Hospital, Xinxiang 453003, China.
Sanquan Medical College of Xinxiang Medical University, Xinxiang 453003, China.
Zhonghua Yi Xue Za Zhi. 2020 Feb 18;100(6):460-464. doi: 10.3760/cma.j.issn.0376-2491.2020.06.013.
In this study, we used the Weighted gene co-expression network analysis (WGCNA) analysis to find the gene module that are specifically expressed in Medulloblastoma and screened the marker genes that may diagnose and treat Medulloblastoma. WGCNA was used to identify the gene modules that are specifically associated with suvival in Medulloblastoma. Cytoscape software was used to construct Co-expression Network. Survival analysis of hub genes using Kaplan Meier (KM) analysis method. Based on the predicted co-expression network, we found that green module significantly associated with survival traits. Green module genes were analyzed and we identified the hub gene UBE2G1 by cytoscape software which have the most correlation with survival trait. Our results indicate that UBE2G1 may be served as a candidate diagnostic biomarker and a promising therapeutic target for Medulloblastoma.
在本研究中,我们使用加权基因共表达网络分析(WGCNA)来寻找在髓母细胞瘤中特异性表达的基因模块,并筛选出可能用于诊断和治疗髓母细胞瘤的标记基因。WGCNA用于识别与髓母细胞瘤生存特异性相关的基因模块。使用Cytoscape软件构建共表达网络。使用Kaplan-Meier(KM)分析方法对枢纽基因进行生存分析。基于预测的共表达网络,我们发现绿色模块与生存特征显著相关。对绿色模块基因进行分析,通过Cytoscape软件鉴定出与生存特征相关性最高的枢纽基因UBE2G1。我们的结果表明,UBE2G1可能作为髓母细胞瘤的候选诊断生物标志物和有前景的治疗靶点。