髓母细胞瘤中预后标志物的加权基因共表达网络筛选

WGCNA screening of prognostic markers in medulloblastoma

目的探讨应用加权基因共表达网络分析(WGCNA)方法寻找髓母细胞瘤中特异表达的基因模块,筛选可能诊断和治疗髓母细胞瘤的标记基因。方法采用WGCNA对髓母细胞瘤中与生存相关的基因模块进行鉴定。利用Cytoscape软件构建共表达网络。采用Kaplan Meier(KM)分析方法对核心基因进行生存分析。结果根据WGCNA分析结果发现绿色模块与生存性状显著相关。对绿色模块基因进行分析结果显示,利用cytoscape软件筛选出了与生存性状相关性最大的核心基因UBE2G1,并进行了鉴定。结论UBE2G1可能作为一个候选的诊断性生物标志物和一个有希望的治疗靶点。

Objective 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.Methods 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.Results 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.Conclusions Our results indicate that UBE2G1 may be served as a candidate diagnostic biomarker and a promising therapeutic target for Medulloblastoma.