bioRxivGeneticsPreprint
Unveiling Novel Molecular Drivers in Breast Cancer Brain Metastasis: Multi-Omics Integration Identifies Downregulation of VCAN and Emerging Roles of ASCL2/GRAMD1A as Prognostic Biomarkers and Therapeutic Vulnerabilities
Hadith RastadiD, Mahin Seifi Alan, Azadeh Jalilian, Sanaz Seifi-Alan, Sedighe Hoseini, Haniyeh Rashidi, Kobra Hosseini, Mahnaz Seifi AlaniD
Abstract
PurposeBreast cancer brain metastasis (BCBM) presents a major clinical challenge, driven by molecular mechanisms that remain poorly characterized. Patients and methodsThree RNA-seq datasets (GSE110590, GSE193103, GSE209998) were analyzed to identify BCBM-associated genes. Survival outcomes (2,976 primary tumors) were assessed via Kaplan-Meier (KM Plotter), genetic alterations via cBioPortal, pathways/networks via GeneMANIA/SIGNOR, and miRNA-mRNA interactions via miRNet. Drug candidates were prioritized using the CTD. ResultsTNFRSF9 and VCAN were downregulated (log2FC: -1.18 to -2.63), while GRAMD1A, ASCL2, TACC3, and PFKFB4 were upregulated (log2FC: +1.02 to +1.70). High PFKFB4 (HR=1.71) and TACC3 (HR=1.46) predicted poor survival, with VCAN suppression (Fold change (Fc) =0.24) and GRAMD1A elevation (Fc=1.31) confirmed in metastases. Pathways included ECM remodeling (VCAN), metabolic rewiring (PFKFB4), and mitotic instability (TACC3). miR-210-3p (hypoxia) and miR-27a-3p (angiogenesis) drove BCBM, countered by miR-335/34a. Drug candidates: Valproic Acid (TACC3/ASCL2), Vorinostat (VCAN), and CDK4/6 inhibitors. ConclusionThis study identifies TNFRSF9, VCAN, GRAMD1A, ASCL2, TACC3, and PFKFB4 as key drivers of BCBM, with dysregulation linked to immune evasion, metabolic adaptation, and mitotic instability. Prioritized miRNAs (e.g., miR-210-3p) and repurposed drugs (e.g., Valproic Acid, Vorinostat) offer actionable therapeutic strategies. These findings advance precision approaches for BCBM, pending preclinical validation to translate targets into clinical practice.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- research focus: drug response / drug discovery
- approach: statistical model
- approach: machine learning (classical)
- Citations
- 0
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