Multi-omics approaches identify novel prognostic biomarkers of autophagy in uveal melanoma

Abstract

Purpose: Uveal melanoma (UVM) is a rare yet malignant ocular tumor that metastases in approximately half of all patients, with the majority of those developing metastasis typically succumbing to the disease within a year. Hitherto, no effective treatment for UVM has been identified. Autophagy is a cellular mechanism that has been suggested as an emerging regulatory process for cancer-targeted therapy. Thus, identifying novel prognostic biomarkers of autophagy may help improve future treatment.

Methods: Consensus clustering and similarity network fusion approaches were performed for classifying UVM patient subgroups. Weighted correlation network analysis was performed for gene module screening and network construction. Gene set variation analysis was used to evaluate the autophagy activity of the UVM subgroups. Kaplan-Meier survival curves (Log-rank test) were performed to analyze patient prognosis. Gene set cancer analysis was used to estimate the level of immune cell infiltration.

Results: In this study, we employed multi-omics approaches to classify UVM patient subgroups by molecular and clinical characteristics, ultimately identifying HTR2B, EEF1A2, FEZ1, GRID1, HAP1, and SPHK1 as potential prognostic biomarkers of autophagy in UVM. High expression levels of these markers were associated with poorer patient prognosis and led to reshaping the tumor microenvironment (TME) that promotes tumor progression.

Conclusion: We identified six novel potential prognostic biomarkers in UVM, all of which are associated with autophagy and TME. These findings will shed new light on UVM therapy with inhibitors targeting these biomarkers expected to regulate autophagy and reshape the TME, significantly improving UVM treatment outcomes.

Keywords: Autophagy; Biomarkers; Prognosis; Tumor microenvironment (TME); Uveal melanoma (UVM).