Journal of the Egyptian National Cancer Institute · 2026
Unlocking new therapeutic horizons through integrative bioinformatics and transcriptomics for drug repositioning in breast cancer therapy.
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Adikusuma Wirawan, Irham Lalu Muhammad, Satria Rahmat Dani, Nurlaila Ika, Dewi Dyah Laksmi, Firdayani Firdayani, Syafarina Inna
Аннотация
BACKGROUND: Breast cancer (BRCA) remains one of the most frequently diagnosed malignancies and a leading cause of cancer-related mortality among women worldwide. Its molecular heterogeneity and limited therapeutic options for aggressive subtypes highlight the need for novel treatment strategies. Drug repositioning offers a promising approach by identifying new therapeutic uses for existing drugs with established safety profiles.
METHODS: We applied an integrative transcriptomic and bioinformatics framework to identify candidate drug targets and repurposed drugs for BRCA. Differentially expressed genes (DEGs) were identified from four Gene Expression Omnibus (GEO) microarray datasets using the limma package with thresholds of |log2 fold change| > 1 and false discovery rate (FDR) < 0.05. Overlapping DEGs were expanded through protein-protein interaction analysis using the STRING database. Functional annotation across ten biological evidence categories was performed using WebGestalt to prioritize BRCA risk genes through a multi-criteria scoring approach. Drug-gene interactions were then analyzed using the Drug-Gene Interaction Database (DGIdb), and tissue-specific gene expression was evaluated using the GTEx database.
RESULTS: Twenty-eight consistently dysregulated genes were identified and expanded into a 77-gene interaction network. Functional prioritization yielded 18 BRCA risk genes, including five druggable targets associated with 11 candidate drugs. ITGB7 emerged as a promising biomarker and therapeutic target, with vedolizumab identified as the top candidate drug.
CONCLUSIONS: This study highlights the potential of integrative transcriptomic analysis to identify biomarkers and drug repositioning candidates in BRCA, providing a foundation for further experimental validation.
METHODS: We applied an integrative transcriptomic and bioinformatics framework to identify candidate drug targets and repurposed drugs for BRCA. Differentially expressed genes (DEGs) were identified from four Gene Expression Omnibus (GEO) microarray datasets using the limma package with thresholds of |log2 fold change| > 1 and false discovery rate (FDR) < 0.05. Overlapping DEGs were expanded through protein-protein interaction analysis using the STRING database. Functional annotation across ten biological evidence categories was performed using WebGestalt to prioritize BRCA risk genes through a multi-criteria scoring approach. Drug-gene interactions were then analyzed using the Drug-Gene Interaction Database (DGIdb), and tissue-specific gene expression was evaluated using the GTEx database.
RESULTS: Twenty-eight consistently dysregulated genes were identified and expanded into a 77-gene interaction network. Functional prioritization yielded 18 BRCA risk genes, including five druggable targets associated with 11 candidate drugs. ITGB7 emerged as a promising biomarker and therapeutic target, with vedolizumab identified as the top candidate drug.
CONCLUSIONS: This study highlights the potential of integrative transcriptomic analysis to identify biomarkers and drug repositioning candidates in BRCA, providing a foundation for further experimental validation.