ArticleMedical oncology (Northwood, London, England)2026
Integrative transcriptomic and network analysis elucidates the multi-target molecular mechanisms of carboplatin in melanoma.
Article in Medical oncology (Northwood, London, England), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Melanoma is an aggressive malignancy with high metastatic potential and frequent resistance to standard therapies, highlighting the need for robust biomarkers and improved mechanistic understanding of effective treatments. This study integrates transcriptomic profiling and network-based analyses to identify key regulatory genes and clarify how clinically used agents may counteract melanoma-specific molecular dysregulation. Comparative evaluation of three independent gene expression datasets yielded 194 overlapping differentially expressed genes (DEGs) enriched in pathways central to tumor progression. A protein-protein interaction network was constructed, and hub genes were prioritized using the Maximal Clique Centrality algorithm, revealing critical nodes involved in melanoma biology. Drug-gene interaction analysis via DGIdb identified carboplatin as a multi-target compound interacting with CDKN1A, BRCA1, PTEN, and AKT1. Although carboplatin, often combined with paclitaxel, is already utilized clinically for advanced or treatment-refractory melanoma, its multi-target molecular effects in relation to melanoma transcriptomic alterations remain insufficiently defined. Experimental validation in B16F0 melanoma cells demonstrated dose- and time-dependent cytotoxicity, with an IC₅₀ of approximately 17 µM at 48 h. RT-qPCR analysis showed increased expression of CDKN1A, BRCA1, and PTEN, alongside reduced AKT1 expression, indicating activation of DNA damage response pathways and reinforcement of tumor-suppressive signaling. Overall, this study identifies key molecular determinants of melanoma progression and provides mechanistic evidence explaining carboplatin's therapeutic activity through modulation of dysregulated transcriptomic hubs. The combined computational and experimental framework offers a valuable strategy for biomarker discovery and mechanistic evaluation of clinically used agents in melanoma research.
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