Identification of multiple prognostic biomarker sets for risk stratification in SKCM.

ABSTRACT:

Introduction

The majority of available transcriptomics-related cancer prognosis studies strive to define one collection of biomarkers that can be used to predict high-risk patients. However, using a single biomarker profile could restrict its strength and applicability to diverse groups of patients. In order to fill this gap, we discuss the prospect of determining several, discrete sets of prognostic biomarkers in Skin Cutaneous Melanoma (SKCM). Our search identifies various genes including CREG1, PCGF5 and VPS13C whose expression pattern depicts significant correlations with overall survival (OS) in SKCM patients.

Methods

We developed machine learning-based prognostic models using SKCM gene expression data to predict 1-, 3-, and 5-year overall survival. Advanced feature sel

SUBMITTER: Malik S 

PROVIDER: S-EPMC12819672 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature