School of Life Sciences, Zhengzhou University, Zhengzhou, China.
Sci Rep. 2021 Oct 21;11(1):20799. doi: 10.1038/s41598-021-00268-9.
Breast cancer is the most common cancer and the leading cause of cancer-related deaths in women. Increasing molecular targets have been discovered for breast cancer prognosis and therapy. However, there is still an urgent need to identify new biomarkers. Therefore, we evaluated biomarkers that may aid the diagnosis and treatment of breast cancer. We searched three mRNA microarray datasets (GSE134359, GSE31448 and GSE42568) and identified differentially expressed genes (DEGs) by comparing tumor and non-tumor tissues using GEO2R. Functional and pathway enrichment analyses of the DEGs were performed using the DAVID database. The protein-protein interaction (PPI) network was plotted with STRING and visualized using Cytoscape. Module analysis of the PPI network was done using MCODE. The associations between the identified genes and overall survival (OS) were analyzed using an online Kaplan-Meier tool. The redundancy analysis was conducted by DepMap. Finally, we verified the screened HUB gene at the protein level. A total of 268 DEGs were identified, which were mostly enriched in cell division, cell proliferation, and signal transduction. The PPI network comprised 236 nodes and 2132 edges. Two significant modules were identified in the PPI network. Elevated expression of the genes Discs large-associated protein 5 (DLGAP5), aurora kinase A (AURKA), ubiquitin-conjugating enzyme E2 C (UBE2C), ribonucleotide reductase regulatory subunit M2(RRM2), kinesin family member 23(KIF23), kinesin family member 11(KIF11), non-structural maintenance of chromosome condensin 1 complex subunit G (NCAPG), ZW10 interactor (ZWINT), and denticleless E3 ubiquitin protein ligase homolog(DTL) are associated with poor OS of breast cancer patients. The enriched functions and pathways included cell cycle, oocyte meiosis and the p53 signaling pathway. The DEGs in breast cancer have the potential to become useful targets for the diagnosis and treatment of breast cancer.
乳腺癌是最常见的癌症,也是女性癌症相关死亡的主要原因。越来越多的分子靶标已被发现用于乳腺癌的预后和治疗。然而,仍然迫切需要鉴定新的生物标志物。因此,我们评估了可能有助于乳腺癌诊断和治疗的生物标志物。我们检索了三个 mRNA 微阵列数据集(GSE134359、GSE31448 和 GSE42568),并使用 GEO2R 比较肿瘤和非肿瘤组织来鉴定差异表达基因(DEGs)。使用 DAVID 数据库对 DEGs 进行功能和通路富集分析。使用 STRING 绘制蛋白质-蛋白质相互作用(PPI)网络,并使用 Cytoscape 可视化。使用 MCODE 对 PPI 网络进行模块分析。使用在线 Kaplan-Meier 工具分析鉴定基因与总生存期(OS)的相关性。通过 DepMap 进行冗余分析。最后,我们在蛋白质水平上验证了筛选出的 HUB 基因。共鉴定出 268 个 DEGs,它们主要富集在细胞分裂、细胞增殖和信号转导中。PPI 网络包含 236 个节点和 2132 个边。在 PPI 网络中鉴定出两个显著的模块。基因 Discs large-associated protein 5(DLGAP5)、aurora kinase A(AURKA)、ubiquitin-conjugating enzyme E2 C(UBE2C)、ribonucleotide reductase regulatory subunit M2(RRM2)、kinesin family member 23(KIF23)、kinesin family member 11(KIF11)、non-structural maintenance of chromosome condensin 1 complex subunit G(NCAPG)、ZW10 相互作用因子(ZWINT)和 denticleless E3 ubiquitin protein ligase homolog(DTL)的高表达与乳腺癌患者的不良 OS 相关。富集的功能和途径包括细胞周期、卵母细胞减数分裂和 p53 信号通路。乳腺癌中的 DEGs 有可能成为乳腺癌诊断和治疗的有用靶点。