乳腺癌患者糖酵解相关的高风险基因预后分析

Prognosis Analysis of Glycolysis Related High-Risk Genes in Patients with Breast Cancer

摘要: 目的：利用生物信息学筛选乳腺癌糖酵解相关的高风险基因，为乳腺癌寻找预后标志物或潜在治疗靶点。方法：通过TCGA数据库，下载乳腺肿瘤患者的转录组数据和临床数据，利用GSEA和R4.0.3对该数据集进行基因富集及差异分析。将得到的差异性基因与临床数据合并，构建糖酵解预后模型并进行模型验证。最后用GEPIA数据库对预测的显著差异的糖酵解基因做单基因生存分析，并用GEO数据库中三阴性乳腺癌数据集进行验证。结果：从TCGA数据库收集到乳腺肿瘤患者的转录组数据包括正常组织文件111个，肿瘤组织文件1053个。富集分析筛选后得到正常组和肿瘤组差异基因256个，其中显著差异的糖酵解基因5个，其表达量与患者预后呈负相关，与GEPIA单基因预后分析一致。GEO数据库三阴性乳腺癌数据集进行验证，证实基因PGK1、SDC1、PGK1为乳腺癌的高风险基因。结论：通过生物信息学分析发现糖酵解基因PGK1、SDC1、PGK1可能作为乳腺癌的预后指标，为乳腺肿瘤特别是三阴性乳腺癌的诊断及治疗提供了新的方向。

Abstract: Objective: Using bioinformatics to investigate high-risk genes of breast cancer (BC) glycolysis to find prognostic markers or potential therapeutic targets. Methods: The mRNA expression profiles and clinical information of patients with BC were obtained from The Cancer Genome Atlas (TCGA) data-base. Glycolysis-related differential genes were obtained by Gene Set Enrichment Analysis (GSEA) and R4.0.3, which be used to construct the prognosis model. We verified the accuracy of the model. Finally, we performed a single gene survival analysis using Gene Expression Profiling Interactive Analysis (GEPIA), and we used the triple negative breast cancer (TNBC) dataset from the GEO data-base to verify the prognosis of high-risk genes. Results: Profiling of mRNA expression was carried out in samples of patients with TCGA BC (tumour = 1053, nomal = 111). GSEA has been undertaken to get differential genes 256, among them, 5 genes were significantly different and they are a nega-tive correlation with the prognosis of BC patients. Survival analysis of five genes was performed us-ing the GEPIA database, and the results were consistent with the prediction. The TNBC dataset from GEO database verified that genes PGK1, SDC1 and PGK1 were high-risk genes for BC. Conclusion: Glycolytic genes PGK1, SDC1 and PGK1 may be used as prognostic indicators of breast tumors, which provides a new direction for the diagnosis and treatment of BC, especially TNBC.