College of Computer and Information, Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China.
College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang Province, China.
Technol Cancer Res Treat. 2021 Jan-Dec;20:1533033820983298. doi: 10.1177/1533033820983298.
Human epidermal growth factor 2 (HER2)+ breast cancer is considered the most dangerous type of breast cancers. Herein, we used bioinformatics methods to identify potential key genes in HER2+ breast cancer to enable its diagnosis, treatment, and prognosis prediction. Datasets of HER2+ breast cancer and normal tissue samples retrieved from Gene Expression Omnibus and The Cancer Genome Atlas databases were subjected to analysis for differentially expressed genes using R software. The identified differentially expressed genes were subjected to gene ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses followed by construction of protein-protein interaction networks using the STRING database to identify key genes. The genes were further validated via survival and differential gene expression analyses. We identified 97 upregulated and 106 downregulated genes that were primarily associated with processes such as mitosis, protein kinase activity, cell cycle, and the p53 signaling pathway. Visualization of the protein-protein interaction network identified 10 key genes (, , , , , , , , , and ), all of which were upregulated. Survival analysis using PROGgeneV2 showed that , , , , and are prognosis-related key genes in HER2+ breast cancer. A nomogram showed that high expression of , , and was positively associated with the risk of death. , which has not previously been reported in HER2+ breast cancer, was associated with breast cancer development, progression, and prognosis and is therefore a potential key gene. It is hoped that this study can provide a new method for the diagnosis and treatment of HER2 + breast cancer.
人表皮生长因子 2（HER2）+乳腺癌被认为是最危险的乳腺癌类型。在此，我们使用生物信息学方法来鉴定 HER2+乳腺癌中的潜在关键基因，以实现其诊断、治疗和预后预测。从基因表达综合数据库和癌症基因组图谱数据库中检索到的 HER2+乳腺癌和正常组织样本数据集，使用 R 软件进行差异表达基因分析。鉴定出的差异表达基因进行基因本体和京都基因与基因组百科全书通路富集分析，然后使用 STRING 数据库构建蛋白质-蛋白质相互作用网络，以识别关键基因。通过生存和差异基因表达分析进一步验证这些基因。我们鉴定出 97 个上调和 106 个下调基因，这些基因主要与有丝分裂、蛋白激酶活性、细胞周期和 p53 信号通路等过程有关。蛋白质-蛋白质相互作用网络的可视化确定了 10 个关键基因（、、、、、、、、和），它们都被上调。使用 PROGgeneV2 进行的生存分析显示、、、、和与 HER2+乳腺癌的预后相关。列线图显示，、和的高表达与死亡风险呈正相关。以前在 HER2+乳腺癌中没有报道过的，与乳腺癌的发生、发展和预后有关，因此是一个潜在的关键基因。希望本研究能够为 HER2+乳腺癌的诊断和治疗提供一种新的方法。
