通过生物信息学分析鉴定与乳腺癌脑转移相关的关键基因
Identification of Critical Genes Related to Breast Cancer with Brain Metastasis Through Bioinformatics Analysis.
作者信息
Tang Mingjun, Su Xi, Zhao Zipin, Ma Jun, Zhang Ning
机构信息
State Key Laboratory of Advanced Medical Materials and Devices, Medical School, Tianjin University, Tianjin, China.
出版信息
Curr Med Chem. 2025 Jul 16. doi: 10.2174/0109298673397130250708114435.
INTRODUCTION
Distant metastasis accounts for the majority of Breast Cancer (BC)-related mortality. The brain is one of the most common regions of metastasis. However, the underlying molecular mechanisms remain uncertain.
METHODS
In this study, gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Datasets GSE100534 and GSE52604, containing 16 primary brain tumor samples and 38 breast cancer brain metastasis samples, were used to identify the Differentially Expressed Genes (DEGs). The Metascape database was used to analyze enriched Gene Ontology (GO) entries and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway entries in DEGs. The STRING database was then used to construct a Protein-Protein Interaction (PPI) network, and the Cytoscape platform was employed to visualize the network. Furthermore, the Kaplan-Meier curve was used to analyze the Relapse-Free Survival (RFS) among the hub genes. Finally, the iRegulon plugin was used to construct a regulatory network to find the transcription factors (TFs) that regulate the expression of the hub genes.
RESULTS
A total of 344 DEGs, including 182 up-regulated and 162 down-regulated genes, were identified by using the limma package in R. A module with 18 nodes and 9 hub genes was selected from the PPI network by using the plugins MCODE and Cyto- Hubba, respectively. KEGG pathway analysis demonstrated that brain metastasis in BC was closely related to the oocyte cell cycle. The Kaplan-Meier curve showed that high expression of these 9 hub genes was associated with poor RFS in BC patients. TFs' analysis showed that E2F4, SIN3A, FOXM1, and TFDP1 interacted with these hub genes.
DISCUSSION
This study revealed that Breast Cancer Brain Metastasis (BCBM) may have a promoting effect on the cell cycle of oocytes and affect the maturation and division of oocytes through the KEGG and GO analyses of 344 DEGs. The selected 9 hub genes (ASPM, BUB1, BUB1B, CCNA2, CCNB1, CDK1, NDC80, NCAPG, and TOP2A) and 4 transcription factors (E2F4, SIN3A, FOXM1, TFDP1) may play a critical role in brain metastasis of BC.
CONCLUSION
The results of this study may aid in the early diagnosis and suggest potential targets for the treatment of BCBM.
引言
远处转移是乳腺癌（BC）相关死亡的主要原因。脑是最常见的转移部位之一。然而，其潜在的分子机制仍不确定。
方法
在本研究中，从基因表达综合数据库（GEO）下载基因表达谱。使用数据集GSE100534和GSE52604（分别包含16个原发性脑肿瘤样本和38个乳腺癌脑转移样本）来鉴定差异表达基因（DEG）。使用Metascape数据库分析DEG中富集的基因本体论（GO）条目和京都基因与基因组百科全书（KEGG）通路条目。然后使用STRING数据库构建蛋白质-蛋白质相互作用（PPI）网络，并采用Cytoscape平台可视化该网络。此外，使用Kaplan-Meier曲线分析枢纽基因中的无复发生存率（RFS）。最后，使用iRegulon插件构建调控网络，以找到调节枢纽基因表达的转录因子（TF）。
结果
使用R语言中的limma软件包共鉴定出344个DEG，其中包括182个上调基因和162个下调基因。分别使用插件MCODE和Cyto-Hubba从PPI网络中选择了一个包含18个节点和9个枢纽基因的模块。KEGG通路分析表明，BC中的脑转移与卵母细胞细胞周期密切相关。Kaplan-Meier曲线显示，这9个枢纽基因的高表达与BC患者的RFS较差相关。TF分析表明，E2F4、SIN3A、FOXM1和TFDP1与这些枢纽基因相互作用。
讨论
本研究通过对344个DEG进行KEGG和GO分析，揭示了乳腺癌脑转移（BCBM）可能对卵母细胞的细胞周期有促进作用，并影响卵母细胞的成熟和分裂。所选的9个枢纽基因（ASPM、BUB1、BUB1B、CCNA2、CCNB1、CDK1、NDC80、NCAPG和TOP2A）和4个转录因子（E2F4、SIN3A、FOXM1、TFDP1）可能在BC的脑转移中起关键作用。
结论
本研究结果可能有助于早期诊断，并为BCBM的治疗提出潜在靶点。
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