摘要
目的基于生物信息数据库信息探讨糖酵解相关基因GDP-L-岩藻糖合酶(GFUS)对三阴性乳腺癌(TNBC)预后情况的预测价值。方法从癌症基因组图谱(TCGA)数据库获取TNBC基因表达信息,应用生物信息学方法分析GFUS在TNBC中的差异表达及预后差异,筛选与GFUS共表达基因建立预测TNBC预后模型并验证。结果从TCGA数据库下载160例TNBC组织和113例正常组织的基因转录组数据,验证了GFUS在TNBC中的高表达与不良预后相关。基于GFUS共表达基因,通过COX回归分析得到3个与TNBC预后相关基因,以此构建模型。根据预后模型将外部数据集训练样本分为高风险组(45例)和低风险组(62例)。Kaplan-Meier生存分析显示,高风险组总生存情况低于低风险组,差异有统计学意义(P<0.05)。结论基于糖酵解相关基因GFUS建立的模型可有效预测TNBC预后情况,并为个体化治疗提供新思路。
Objective To explore the predictive value of glycolysis-related gene GDP-L-amylose synthase(GFUS)on the prognostic profile of triple-negative breast cancer(TNBC)based on bioinformatics database information.Methods The gene expression information of TNBC was obtained from the Cancer Genome Atlas(TCGA)database.The differential expression and prognostic differences of GFUS in TNBC were analyzed by bioinformatics methods.The co-expressed genes with GFUS were screened to establish a prognostic model for TNBC and verified.Results The gene transcriptome data of 160 TNBC tissues and 113 normal tissues were downloaded from the TCGA database,which verified that the high expression of GFUS in TNBC was associated with poor prognosis.Based on GFUS co-expressed genes,three genes related to TNBC prognosis were obtained by COX regression analysis to construct the model.According to the prognostic model,the external data set training samples were divided into high-risk group(45 cases)and low-risk group(62 cases).Kaplan-Meier survival analysis showed that the total survival of the high-risk group was lower than that of the low-risk group,and the difference was statistically significant(P<0.05).Conclusion The model based on the glycolysis-related gene GFUS can effectively predict the prognosis of TNBC and provide new ideas for individualized treatment.
作者
章海斌
王练
ZHANG Hai-bin;WANG Lian(Department of Medical Oncology,the First Affiliated Hospital of Bengbu Medical College,Bengbu 233000,Anhui,China;Department of Traditional Chinese Medicine,the First Affiliated Hospital of Bengbu Medical College,Bengbu 233000,Anhui,China)
出处
《医学信息》
2024年第1期22-28,34,共8页
Journal of Medical Information
基金
蚌埠医学院自然科学重点项目(编号:2021byzd095)。
