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An MGRN1-Based Biomarker Combination Accurately Predicts Melanoma Patient Survival

GSE305457 Homo sapiens Expression profiling by high throughput sequencing 8 samples Submitted 2025/09/14 Platform GPL16791
Summary
With ever-increasing incidence and high metastatic potential, cutaneous melanoma is the deadliest skin cancer. Risk prediction based on the Tumor-Node-Metastasis (TNM) staging system has medium accuracy with intermediate IIB-IIIB stages, as roughly 25% of patients with low-medium-grade TNM, and hence a favorable prognostic, undergo an aggressive disease with short survival and around 15% of deaths arise from metastases of thin, low-risk lesions. Therefore, reliable prognostic biomarkers are required. We used genomic and clinical information of melanoma patients from the TCGA-SKCM cohort and two GEO studies for discovery and validation of potential biomarkers, respectively. Neither mutation nor overexpression of major melanoma driver genes provided significant prognostic information. Conversely, expression of MGRN1 and the melanocyte-specific genes MLANA, PMEL, and TYRP1 provided a simple 4-gene signature identifying with high-sensitivity (>80%), low-medium TNM patients with adverse outcomes. Transcriptomic analysis of tumors with this signature, or from low-medium-grade TNM patients with poor outcomes, revealed comparable dysregulation of an inflammatory response, cell cycle progression, and DNA damage/repair programs. A functional analysis of MGRN1- knockout cells confirmed these molecular features. Therefore, the simple MGRN1-MLANAPMEL- TYRP1 combination of biomarkers complemented TNM staging prognostic accuracy and pointed to the dysregulation of immunological responses and genomic stability as determinants of a melanoma outcome.
Published in
An MGRN1-Based Biomarker Combination Accurately Predicts Melanoma Patient Survival
Sánchez-Beltrán J, Soler Díaz J, Herraiz C et al. · International journal of molecular sciences 2025 · PMID 40004203 · doi:10.3390/ijms26041739
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Also filed as BioProject PRJNA1306010 and SRA study SRP608905. Searching any of these in the dataset finder brings you back here.

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