Integrated analyses identify potential prognostic markers for uveal melanoma

Area of Science:

• Oncology
• Genomics
• Bioinformatics

Background:

• Uveal melanoma (UM) is the most common primary intraocular malignancy in adults, often leading to metastasis, vision loss, and death.
• Identifying prognostic markers is crucial for early detection and effective management of UM.

Purpose of the Study:

• To identify novel prognostic biomarkers for uveal melanoma (UM) by analyzing gene expression data.
• To develop predictive signatures for overall survival (OS) and recurrence-free survival (RFS) in UM patients.

Main Methods:

• Weighted gene co-expression network analysis (WGCNA) was used to construct gene co-expression modules from TCGA data.
• Cox regression and LASSO Cox regression models were applied to identify significant genes and construct predictive signatures.
• Hub genes were validated using an external Gene Expression Omnibus (GEO) dataset.

Main Results:

• Eight co-expression modules were identified, with blue and yellow modules associated with clinical stage.
• Multiple modules (blue, yellow, green, brown, pink) showed significant associations with overall survival (OS) and recurrence-free survival (RFS).
• Validated hub genes from key modules formed predictive signatures that effectively distinguished between low- and high-risk UM patient groups.

Conclusions:

• Co-expression network analysis provides a framework for understanding UM pathogenesis.
• Identified biomarkers and developed predictive signatures can significantly improve prognosis prediction for UM patients.
• These findings contribute to better patient management and potential therapeutic strategies for uveal melanoma.