BAX as a Biomarker for Predicting Immunotherapeutic Efficacy in Uveal Melanoma Patients: A Comprehensive Analysis

Uveal melanoma (UVM) is the second most common type of malignant melanoma occurring in the eye, which arises from the interstitial melanocytes in the uveal tract. This study aims to identify a highly efficient biomarker for the immunotherapy against UVM. Initially, a comprehensive analysis was conducted using the transcriptional and clinical data from The Cancer Genome Atlas (TCGA) database through the immune and stromal scores to assess the composition of infiltrating immune cells in the tumor microenvironment. Further, the expression of BCL2-Associated X, Apoptosis Regulator (BAX), and its co-expression gene networks were analyzed using the weighted gene co-expression network analysis (WGCNA) to identify relevant gene modules and hub genes. The immunohistochemistry (IHC) analysis was carried out to confirm the influence of BAX on immune infiltration. In addition, the survival analysis on the hub genes, including BAX, was performed using an external dataset from the Gene Expression Omnibus (GEO) to corroborate the prognostic significance of these genes in an independent patient cohort. A nomogram integrating patients' clinical features was developed to predict the survival outcomes. Our investigations revealed that high BAX expression was associated with severe clinical characteristics and poor prognosis in UVM. Our analyses identified 12 hub genes at the intersection of differentially expressed genes categorized by BAX expression levels and a co-expression gene model. Further, the GEO database validated the prognostic significance of these hub genes. The IHC analysis established a significant correlation between BAX expression and immune infiltration. This nomogram model demonstrated robust predictive efficiency with a concordance index (C-index) of 0.909 (95% CI: 0.846-0.971), indicating excellent discriminative ability. The calibration curves for 1-year, 3-year, and 5-year overall survival (OS) rates confirmed the nomogram's accuracy, closely reflecting the actual patient outcomes. Finally, the Decision Curve Analysis (DCA) revealed that this nomogram could accurately predict OS for a majority of patients, covering a probability range of 25-95%. Our research may provide a new therapeutic regimen to benefit the UVM patients.