Integrated machine learning and single-cell analysis reveal the prognostic and therapeutic potential of SUMOylation-related genes in ovarian cancer.

ABSTRACT:

Introduction

Ovarian cancer (OC) exhibits high mortality and chemoresistance rates, underscoring the urgent need for precise prognostic biomarkers and novel therapeutic targets. SUMOylation, crucial in cellular stress responses, is frequently dysregulated in various cancers. This study aims to characterize SUMOylation and its regulators in OC and identify potential biomarkers and therapeutic targets.

Methods

In this study, using multi-omics data, we characterized the unique features of SUMOylation in OC and revealed the association between SUMOylation-related genes (SRGs) and OC malignancy. We conducted integrated machine learning and single-cell RNA sequencing data analysis to identify key SRGs and explored their functional characteristics. The prognostic potential of these

SUBMITTER: Deng Z 

PROVIDER: S-EPMC12174102 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature