Hebei University of Technology, 300400 Tianjin, China.
Hebei University of Technology, 300400 Tianjin, China.
Gene. 2020 Aug 5;750:144757. doi: 10.1016/j.gene.2020.144757. Epub 2020 May 6.
Breast cancer is a very serious disease that threatens human health. The identification of co-expression modules is conducive to revealing the interaction mechanism between genes. The potential biomarkers identified from the co-expression modules have profound implications for the diagnosis and treatment of breast cancer. According to the clinical staging information, the gene expression data of breast cancer was divided into different stages and analyzed separately. The co-expression modules for each stage were identified by WGCNA. The pathways involved in the co-expression modules of each stage were revealed by KEGG enrichment analysis. Combined with clinical information, 81 core genes were screened from the co-expression modules of each stage. By constructing a support vector machine, it was confirmed that these core genes can effectively distinguish breast cancer samples. The biological functions involved in these core genes are revealed by GO enrichment analysis. Survival analysis showed that the expression of 11 genes had significant effects on the survival of breast cancer patients. These results may provide a reference for the mechanism study of breast cancer.
乳腺癌是一种严重威胁人类健康的疾病。共表达模块的鉴定有助于揭示基因之间的相互作用机制。从共表达模块中鉴定出的潜在生物标志物对乳腺癌的诊断和治疗具有深远的意义。根据临床分期信息,将乳腺癌的基因表达数据分为不同阶段并分别进行分析。通过 WGCNA 鉴定每个阶段的共表达模块。通过 KEGG 富集分析揭示每个阶段共表达模块中涉及的途径。结合临床信息,从每个阶段的共表达模块中筛选出 81 个核心基因。通过构建支持向量机,证实这些核心基因可以有效地区分乳腺癌样本。通过 GO 富集分析揭示这些核心基因所涉及的生物学功能。生存分析表明,11 个基因的表达对乳腺癌患者的生存有显著影响。这些结果可能为乳腺癌的机制研究提供参考。