ArticleIranian journal of basic medical sciences2025
Bioinformatics analysis identifies dysregulation of miR-548F-3p and its hub gene in triple-negative breast cancer.
Article in Iranian journal of basic medical sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- KIF23 in disease pathogenesis and therapeutics : from molecular mechanisms to clinical translation.Cancer cell international · 2026Review
- KIF23 in disease pathogenesis and its therapeutic and diagnostic potential.Discover oncology · 2026Review
- Albumin-bound paclitaxel drives a cytotoxic CD8Frontiers in immunology · 2026Article
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3 authors.
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Abstract
Objectives: Triple-negative breast cancer (TNBC), which affects 15-20% of cases, lacks targeted therapies and poses challenges in treatment. MicroRNAs (miRNAs) are potential biomarkers and therapeutic targets in breast cancer. To unravel its unique regulatory role, this study focused on miRNA microarray analysis, particularly miR-548F-3p, in TNBC samples. Materials and Methods: Using the GSE76275 dataset, gene expression profiles were analyzed using the Affymetrix Human Genome U133 Plus 2.0 Array. Differentially expressed genes (DEGs) were identified using robust preprocessing. Weighted gene co-expression network analysis (WGCNA) explored gene modules and identified hub genes co-expressed with miR-548F-3p. Functional enrichment and protein-protein interaction (PPI) network analyses were conducted. Survival analysis was used to assess the prognostic impact of the identified genes. Results: The study found 224 up-regulated DEGs, with miR-548F-3p exhibiting significant down-regulation. MultimiR identified 400 genes that were targeted by miR-548F-3p. WGCNA revealed a blue co-expression module, with 356 genes targeted by miR-548F-3p. A Venn diagram identified common genes, including VANGL2, BRCC3, ANP32E, and ANLN. Functional enrichment highlighted crucial pathways in TNBC pathogenesis, including mitotic spindle organization, spindle assembly checkpoint signaling, cell cycle, and amino acid (serine) metabolism. PPI network analysis identified hub genes, including FOXM1, KIF23, and CDC20. VANGL2, BRCC3, ANP32E, and ANLN were significantly associated with patient outcomes in survival analysis. Conclusion: This analysis highlighted TNBC's molecular landscape, emphasizing miR-548F-3p's regulatory role. The identified genes, VANGL2, BRCC3, ANP32E, and ANLN, offer insights into TNBC pathogenesis and potential therapeutic targets, laying the foundation for understanding their clinical implications in the intricate landscape of TNBC.
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