bioRxivCancer BiologyPreprint
Meta-analysis of the melanoma miRNome identifies consensus signatures of progression and prognosis regulating metabolic plasticity and stress resistance
B. Gomez-Cabanes, H. Gomez-Martinez, C. Galiana-Rosello, Z. Andreu, A. Lopez-Cerdan, N. M. Eskow, J. A. Lopez-Guerrero, B. Dolader-Rabinad, R. A. Hernandez-Ramirez, I. Soler-Saez, M. R. Hidalgo, E. Hernando, F. Garcia-Garcia
Abstract
Cutaneous melanoma is the deadliest form of skin cancer, and accurate risk stratification remains challenging because of the molecular heterogeneity underlying disease progression. The transition from benign nevi to malignant melanoma and the development of the high-risk ulcerated phenotype require novel biomarkers to improve diagnosis and prognosis. We aimed to integrate miRNA-level evidence to generate consensus signatures associated with these processes. We performed a robust meta-analysis of six independent transcriptomic studies, overcoming inter-study heterogeneity, combined with extensive in silico characterization and validated miRNA-mRNA interactome construction. In the diagnostic setting, we identified a consensus signature of 24 miRNAs. Network topology analysis highlighted hsa-miR-142-5p as a candidate regulatory hub targeting CDK6, SIRT1, and TGFBR2, potentially contributing to the disruption of Oncogene-Induced Senescence barriers. In the prognostic setting, we identified a stress-adaptive signature of 23 miRNAs, with hsa-miR-223-3p emerging as a candidate driver of invasiveness through predicted RHOB suppression, while loss of hsa-miR-200c may facilitate epithelial-mesenchymal transition and EGFR-mediated survival pathways. Overall, our findings support a sequential model in which distinct miRNA alterations first promote senescence escape and later favor adaptation to the ulcerated microenvironment. These candidate regulatory axes warrant experimental validation as potential biomarkers and therapeutic targets.
- organism: human
- assay: bulk RNA-seq
- research focus: differential expression
- research focus: gene regulatory network inference
- approach: statistical model
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