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Integrative Bioinformatic Analysis Identifies Key Genes Driving Breast Cancer Brain Metastasis.

Overview

Authors: Wei-Yi Ting1, Yueh-Hsun Lu1,2, Che-Ming Lin1,2
ORCID iDs: Wei-Yi Ting, Yueh-Hsun Lu, Che-Ming Lin
  1. Department of Radiology, Shuang-Ho Hospital, Taipei Medical University, New Taipei City 23561, Taiwan; (W.-Y.T.); (Y.-H.L.)
  2. Department of Radiology, School of Medicine, College of Medicine, Taipei Medical University, New Taipei City 23561, Taiwan
Institutions: Taipei Medical University-Shuang Ho Hospital (Taiwan); Taipei Medical University (Taiwan)
Journal: Diagnostics (Basel, Switzerland), volume 16, issue 8, article 1149
Dates: received 18 March 2026; accepted 7 April 2026; published online 13 April 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/diagnostics16081149 · PMID 42072775 · PMCID PMC13115110 · OpenAlex W7153972491
Open access: gold, a free copy (OpenAlex)
Status: data only
Categories: other condition (population)
Methods: Statistics, Connectivity
Keywords: breast cancer, brain metastasis, bioinformatics, biomarker, cell cycle, metabolism
Topic: 14-3-3 protein interactions (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Citations: not cited yet (Europe PMC); 35 references in the paper

Abstract

Background/Objectives: Brain metastasis (BM) represents a significant clinical challenge in advanced breast cancer, yet the molecular mechanisms driving breast cancer brain metastasis (BCBM) remain incompletely characterized. This study aims to identify key molecular pathways and hub genes specifically associated with BCBM through comprehensive bioinformatic analyses. Methods: Gene Set Enrichment Analysis (GSEA), differential gene expression analysis, and weighted gene co-expression network analysis (WGCNA) were performed using two independent GEO datasets (GSE191230 and GSE43837). Protein–protein interaction (PPI) networks were constructed to visualize functional interconnections among dysregulated genes. Survival analyses were conducted using the Kaplan–Meier Plotter database to evaluate the prognostic significance of identified hub genes. Results: GSEA revealed significant upregulation of metabolic pathways (mTORC1 signaling, glycolysis, oxidative phosphorylation) and downregulation of immune-related pathways in BCBM compared to primary tumors. Integrative analysis identified 34 consistently dysregulated genes across datasets, from which 12 hub genes were validated. Among these, RRM2, CDCA8, CCNB1, LMNB2, FANCI, NCAPH, YWHAZ, and ESPL1 demonstrated brain-specific over-expression compared to other metastatic sites. Functional enrichment analysis highlighted cell cycle dysregulation as a critical mechanism in BCBM, and all hub genes showed significant association with poor prognosis in breast cancer patients. Conclusions: This study identifies a unique molecular profile of BCBM characterized by cell cycle dysregulation, metabolic reprogramming, and immune microenvironment alterations. The brain-specific expression patterns of these hub genes represent potential biomarkers for BCBM risk assessment and novel therapeutic targets, providing a basis for precision medicine development.

Reproduced under the paper's license (CC BY), from the paper cited above.

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Data

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Data Availability Statement

The original contributions presented in this study are included in the article/Supplementary Material. Further inquiries can be directed to the corresponding author.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 1, 29 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 3 authors, 6 keywords, 35 references.

Cite

This paper

Ting, W.-Y., Lu, Y.-H., & Lin, C.-M. (2026). Integrative Bioinformatic Analysis Identifies Key Genes Driving Breast Cancer Brain Metastasis. Diagnostics (Basel, Switzerland), 16(8), 1149. https://doi.org/10.3390/diagnostics16081149

BibTeX

@article{ting2026integrative,
author = {Ting, Wei-Yi and Lu, Yueh-Hsun and Lin, Che-Ming},
title = {{Integrative Bioinformatic Analysis Identifies Key Genes Driving Breast Cancer Brain Metastasis}},
journal = {Diagnostics (Basel, Switzerland)},
year = {2026},
month = apr,
volume = {16},
number = {8},
pages = {1149},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2075-4418},
doi = {10.3390/diagnostics16081149},
url = {https://doi.org/10.3390/diagnostics16081149},
pmid = {42072775},
pmcid = {PMC13115110}
}

RIS

TY - JOUR
AU - Ting, Wei-Yi
AU - Lu, Yueh-Hsun
AU - Lin, Che-Ming
TI - Integrative Bioinformatic Analysis Identifies Key Genes Driving Breast Cancer Brain Metastasis
T2 - Diagnostics (Basel, Switzerland)
J2 - Diagnostics (Basel)
PY - 2026
DA - 2026/04/13
VL - 16
IS - 8
SP - 1149
SN - 2075-4418
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/diagnostics16081149
UR - https://doi.org/10.3390/diagnostics16081149
LA - en
ER -

CSL-JSON

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