【摘要】 目的 基于生物信息学方法筛选乳腺癌的关键生物标志物，并分析其预后价值。方法 利用TCGA和GEO数据库筛选乳腺癌的共有差异表达基因；DAVID数据库对差异基因进行GO和KEGG富集分析；单因素和多因素Cox分析获得具有显著预后价值的基因；利用UALCAN在线工具对关键基因进行病理特征分析。结果 从TCGA和GEO数据库中分别获得1566和231个差异基因；通过共表达分析得到140个共有的差异表达基因；单因素和多因素Cox回归分析中发现S100B和TFF1在乳腺癌中具有独立预后价值；临床病理特征分析结果显示，S100B和TFF1在乳腺癌组织中的表达与其患者的性别、分期、淋巴结转移、TP53突变等均有显著差异。结论 S100B和TFF1可作为关键生物标志物影响乳腺癌的发生和发展。更多还原

【Abstract】 Objective Screening and analysis of key biomarkers of breast cancer based on bioinformatics approach for their prognostic value.Methods The TCGA and GEO databases were used to screen for differential expression genes in breast cancer; the DAVID database was used for GO and KEGG enrichment analysis of differential genes; univariate and multifactorial Cox analyses were performed to obtain genes with prognostic value; and the UALCAN online tool was used for pathological characterization of key genes.Results We obtained 1566 and 231 differential genes from TCGA and GEO databases, respectively; 140 common differential genes were obtained by co-expression analysis; S100B and TFF1 were found to have independent prognostic value in breast cancer by univariate and multifactorial Cox analysis; the results of clinicopathological characteristics analysis showed that the expression of S100B and TFF1 was significant difference in breast cancer with the gender, age, stage, node metastasis, and TP53 mutation of the patients.Conclusion S100B and TFF1 may serve as key biomarkers to influence the development and progression of breast cancer.更多还原