International forum of allergy & rhinology · 2025
Molecular Profiling and Tumor Microenvironment in Sinonasal Mucosal Melanoma as Biomarkers for Enhanced Prognostic Stratification.
СтатьяПоддержка исследования: вне правительства СШАНазвание и аннотация: Перевод в очереди
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Molina-Garcia Manuel, Rojas-Lechuga Maria Jesus, Langdon Cristobal, Mateu Judit, Bague Jaume, Torres Teresa, de Souza Vinicius G, Bernal-Sprekelsen Manuel, Alobid Isam, da Silva-Júnior Rui Milton Patricio, Puig Susana
Аннотация
BACKGROUND: Sinonasal mucosal melanoma (SNMM) is a rare and aggressive melanoma subtype with a notably poor prognosis. Despite molecular characterization advances, SNMM remains clinically challenging, highlighting the need for detailed molecular profiling. This study aimed to identify the molecular features of SNMM, elucidate its prognostic implications, and provide insights for improved therapies.
METHODS: Sixteen SNMM tumors were retrospectively analyzed at the Hospital Clinic of Barcelona. Next-generation sequencing targeted 1392 immuno-oncology-related probes. Log-rank test, hierarchical clustering analysis (HCA), Cox regression, differentially expressed genes, gene set enrichment analysis, and the xCell algorithm were performed. Statistical analyses comprised descriptive statistics, clinical variable associations, and survival analyses.
RESULTS: Among 16 tumors, 107 genes significantly correlated with melanoma-specific survival (MSS) (p < 0.05). HCA based on these genes revealed two clusters: Cluster B showed poorer prognosis (median MSS: 11.73 months; Q1: 8.74, Q3: 30.78) compared to Cluster A (median MSS: 81.74 months; Q1: 32.82, Q3: 92.44; p = 0.0051). Cox regression identified staging and molecular clustering as independent MSS predictors, with Cluster B exhibiting a hazard ratio of 13.23 (95% confidence intervals [CI]: 1.503-116.48, p = 0.02). Cluster B tumors displayed upregulated cell cycle genes and reduced infiltration of CD4+, CD8+, Th1, and B cells.
CONCLUSIONS: Molecular profiling in SNMM provides prognostic information beyond standard clinical parameters. Cell cycle and immune-related gene expression patterns, together with decreased infiltration of CD4+, CD8+, Th1, and B cells, correlate with poorer MSS. Integrating molecular profiling with clinical staging could improve risk assessment and help identify high-risk patients for tailored therapeutic approaches.
METHODS: Sixteen SNMM tumors were retrospectively analyzed at the Hospital Clinic of Barcelona. Next-generation sequencing targeted 1392 immuno-oncology-related probes. Log-rank test, hierarchical clustering analysis (HCA), Cox regression, differentially expressed genes, gene set enrichment analysis, and the xCell algorithm were performed. Statistical analyses comprised descriptive statistics, clinical variable associations, and survival analyses.
RESULTS: Among 16 tumors, 107 genes significantly correlated with melanoma-specific survival (MSS) (p < 0.05). HCA based on these genes revealed two clusters: Cluster B showed poorer prognosis (median MSS: 11.73 months; Q1: 8.74, Q3: 30.78) compared to Cluster A (median MSS: 81.74 months; Q1: 32.82, Q3: 92.44; p = 0.0051). Cox regression identified staging and molecular clustering as independent MSS predictors, with Cluster B exhibiting a hazard ratio of 13.23 (95% confidence intervals [CI]: 1.503-116.48, p = 0.02). Cluster B tumors displayed upregulated cell cycle genes and reduced infiltration of CD4+, CD8+, Th1, and B cells.
CONCLUSIONS: Molecular profiling in SNMM provides prognostic information beyond standard clinical parameters. Cell cycle and immune-related gene expression patterns, together with decreased infiltration of CD4+, CD8+, Th1, and B cells, correlate with poorer MSS. Integrating molecular profiling with clinical staging could improve risk assessment and help identify high-risk patients for tailored therapeutic approaches.