Evidence map›Paper›PMID 40676798›Full record

ArticleCurrent medicinal chemistry2026

Identification of Critical Genes Related to Breast Cancer with Brain Metastasis Through Bioinformatics Analysis.

Mingjun Tang, Xi Su, Zipin Zhao, Jun Ma, Ning Zhang

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Article in Current medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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5 · Who and what money

Authors and funding

5 authors.

Mingjun TangState Key Laboratory of Advanced Medical Materials and Devices, Medical School, Tianjin University, Tianjin, China.ORCID 0009-0004-8706-2622
Xi SuState Key Laboratory of Advanced Medical Materials and Devices, Medical School, Tianjin University, Tianjin, China.
Zipin ZhaoState Key Laboratory of Advanced Medical Materials and Devices, Medical School, Tianjin University, Tianjin, China.
Jun MaState Key Laboratory of Advanced Medical Materials and Devices, Medical School, Tianjin University, Tianjin, China.
Ning ZhangState Key Laboratory of Advanced Medical Materials and Devices, Medical School, Tianjin University, Tianjin, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDistant 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.

methodsIn 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.

resultsA 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.

conclusionThe results of this study may aid in the early diagnosis and suggest potential targets for the treatment of BCBM.

Indexed as

Brain NeoplasmsBreast NeoplasmsComputational BiologyDatabases, GeneticFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticGene Regulatory NetworksHumansProtein Interaction MapsTranscription FactorsTranscription FactorsBreast cancer brain metastasis (BCBM)cell cycleKEGG pathwayprognosisprotein-protein interaction (PPI)regulatory network

Identifiers

PMID40676798

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.