Identification of a deubiquitinating gene-related signature in ovarian cancer using integrated transcriptomic analysis and machine learning framework.

ABSTRACT:

Background

Ovarian carcinoma represents an aggressive malignancy with poor prognosis and limited therapeutic efficacy. While deubiquitinating (DUB) genes are known to regulate crucial cellular processes and cancer progression, their specific roles in ovarian carcinoma remain poorly understood.

Methods

We conducted an integrated analysis of single-cell RNA sequencing and bulk transcriptome data from public databases. DUB genes were identified through Genecard database. Using the Seurat package, we performed cell clustering and differential expression analysis. Cell-cell communications were analyzed using CellChat. A DUB-related risk signature (DRS) was developed using machine learning approaches through integration of GEO and TCGA datasets. The prognostic value and immune cha

SUBMITTER: Hu S 

PROVIDER: S-EPMC11985714 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature