ArticleCancer reports (Hoboken, N.J.)2025
Bioinformatic Characterization of Genes That Are Correlated to the Progression of Breast Cancer to Breast Cancer Brain Metastasis.
Article in Cancer reports (Hoboken, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
1 citing paper in PubMed.
- Bioinformatic Characterization of Genes That Are Correlated to the Progression of Breast Cancer to Breast Cancer Brain Metastasis.Cancer reports (Hoboken, N.J.) · 2025Article
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe 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.
aimsIt 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
resultsThe 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.
conclusionThe 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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