Bioinformatic Characterization of Genes That Are Correlated to the Progression of Breast Cancer to Breast Cancer Brain Metastasis

IF 1.9 Q4 ONCOLOGY

Cancer reports Pub Date : 2025-10-07 DOI:10.1002/cnr2.70360

Mageshree Pillay, Oliver Tendayi Zishiri

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Abstract

Background

The incidence of breast cancer is escalating into millions of cases annually all over the world with hundreds of thousands of deaths recorded each year. It has been well established that breast cancer is caused by both genetic and non-genetic factors. However, there is a paucity of information on breast cancer that metastasizes to the brain. The molecular process of carcinogenesis in breast cancer brain metastasis (BCBM) is yet to be fully characterized.

Aims

It is crucial to identify genes linked with breast cancer brain metastasis development and prognosis. This study sought out to decipher putative pathogenic and predictive genes in BCBM using bioinformatic analysis of public datasets.

Methods and Results

The bioinformatic analysis utilized the GSE125989, GSE191230 and GSE52604 datasets. GEO2R was used for the identification of DEGs. Venn was employed to identify the common up-regulated and down-regulated genes. The STRING website was used to create the protein-protein interaction (PPI) network of the DEGs, which was then represented using Cytoscape. A Kaplan–Meier (KM) plotter was used to conduct the hub gene survival analysis. Validation of the hub genes was carried out using UALCAN. The heat map was then visualized using Fun Rich. The tumor infiltrating analysis was carried out using TIMER. Using DAVID, the GO and KEGG analyses were conducted. The structure of the hub genes was obtained from the human protein atlas. A total of 4 DEGs was identified. A PPI network was developed, one significant module was identified, and 3 clusters were selected. Ten hub genes were discovered using Cytoscape‘s MCC ranking technique. Ten hub genes (IL6, INS, TNF, PPARG, PPARA, SLC2A4, PPARGC1A, IRS1, LEP and ADIPOQ) were all associated with the progression of BCBM.

Conclusion

The study‘s findings revealed that the hub genes investigated could be possibly vital genes in determining the molecular mechanism of BCBM.

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与乳腺癌进展到乳腺癌脑转移相关基因的生物信息学特征。

背景：乳腺癌的发病率正在上升，全世界每年有数百万例病例，每年有数十万人死亡。众所周知，乳腺癌是由遗传和非遗传因素引起的。然而，关于乳腺癌转移到大脑的信息却很少。乳腺癌脑转移（BCBM）发生癌变的分子过程尚未完全明确。目的：确定与乳腺癌脑转移发展和预后相关的基因是至关重要的。本研究试图通过对公共数据集的生物信息学分析来破译BCBM中假定的致病和预测基因。方法和结果：生物信息学分析使用GSE125989、GSE191230和GSE52604数据集。采用GEO2R对deg进行鉴定。采用Venn法鉴定常见的上调和下调基因。STRING网站用于创建deg的蛋白质-蛋白质相互作用（PPI）网络，然后使用Cytoscape表示。使用Kaplan-Meier （KM）绘图仪进行枢纽基因存活分析。使用UALCAN对中心基因进行验证。然后使用Fun Rich将热图可视化。采用TIMER进行肿瘤浸润分析。使用DAVID进行GO和KEGG分析。枢纽基因的结构从人蛋白图谱中得到。共鉴定出4个deg。建立了一个PPI网络，确定了一个重要模块，并选择了3个集群。利用Cytoscape的MCC排序技术发现了10个枢纽基因。10个中心基因（IL6、INS、TNF、PPARG、PPARA、SLC2A4、PPARGC1A、IRS1、LEP和ADIPOQ）均与BCBM的进展相关。结论：本研究结果表明，所研究的枢纽基因可能是决定BCBM分子机制的重要基因。

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