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Purpose <p>To investigate glycosphingolipid biosynthesis (GSB) dysregulation in uveal melanoma (UVM) and develop a machine learning-driven prognostic signature bridging GSB activity, tumor microenvironment, and clinical outcomes.</p> Methods <p>Using TCGA and GEO cohorts, GSB activity was quantified via Gene Set Variation Analysis (GSVA). Differential expression analysis, least absolute shrinkage and selection operator regression, and Cox regression were used to identify prognostic GSB-related genes and establish the glycosphingolipid biosynthetic risk score (GBRS). Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and C-index were used to evaluate GBRS. Immune infiltration was assessed using ssGSEA and ESTIMATE algorithms. Drug sensitivity (IC50) correlations were evaluated via oncoPredict.</p> Results <p>We uncovered pervasive GSB dysregulation across cancer types, with GSB score showing superior prognostic accuracy to traditional clinical variables in UVM. An eight-gene prognostic signature was identified and refined to a three-gene GBRS comprising CA12, SLC44A3, and BHLHA15. GBRS independently predicted overall survival and demonstrated predictive value for disease-specific survival, progression-free interval, and metastasis-free survival. GBRS correlated positively with CD8<sup>+</sup> T cell and M2-like macrophage infiltration. Meta-analysis confirmed CA12 as a significant prognostic risk factor, with sphingolipid/glycosphingolipid synthesis pathways enriched in its co-expressed genes.</p> Conclusions <p>GBRS is a robust prognostic biomarker for UVM, reflecting immune dysregulation and therapeutic vulnerabilities.</p>

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Machine learning identifies glycosphingolipid signature linking immune dysregulation and clinical prognosis in uveal melanoma

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年1月27日

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  • Haoran Guo,
  • Bai Li,
  • Zhi Zhang,
  • Junjie Chen

摘要

Purpose

To investigate glycosphingolipid biosynthesis (GSB) dysregulation in uveal melanoma (UVM) and develop a machine learning-driven prognostic signature bridging GSB activity, tumor microenvironment, and clinical outcomes.

Methods

Using TCGA and GEO cohorts, GSB activity was quantified via Gene Set Variation Analysis (GSVA). Differential expression analysis, least absolute shrinkage and selection operator regression, and Cox regression were used to identify prognostic GSB-related genes and establish the glycosphingolipid biosynthetic risk score (GBRS). Kaplan-Meier survival analysis, receiver operating characteristic (ROC) curve analysis, and C-index were used to evaluate GBRS. Immune infiltration was assessed using ssGSEA and ESTIMATE algorithms. Drug sensitivity (IC50) correlations were evaluated via oncoPredict.

Results

We uncovered pervasive GSB dysregulation across cancer types, with GSB score showing superior prognostic accuracy to traditional clinical variables in UVM. An eight-gene prognostic signature was identified and refined to a three-gene GBRS comprising CA12, SLC44A3, and BHLHA15. GBRS independently predicted overall survival and demonstrated predictive value for disease-specific survival, progression-free interval, and metastasis-free survival. GBRS correlated positively with CD8+ T cell and M2-like macrophage infiltration. Meta-analysis confirmed CA12 as a significant prognostic risk factor, with sphingolipid/glycosphingolipid synthesis pathways enriched in its co-expressed genes.

Conclusions

GBRS is a robust prognostic biomarker for UVM, reflecting immune dysregulation and therapeutic vulnerabilities.