Multi-omics study of prognostic models and molecular networks related to ovarian cancer.

ABSTRACT:

Objective

Ovarian cancer (OV) is considered the most lethal gynecological cancer in women. Despite significant advancements in treatment and risk-stratification methods, these approaches remain far from ideal. This study aims to leverage large-scale public cohorts to identify differentially expressed prognostic genes between OV and normal ovarian tissue and evaluate their impact on patient survival.

Methods

Utilizing data from extensive public cohorts and machine learning methods, we conducted a comprehensive screening to identify genes differentially expressed in ovarian cancer compared to normal ovarian tissue. We also developed a risk score for each patient based on these genes. Subsequent analyses explored the immunological profiles and genomic alterations associated wit

SUBMITTER: Bian P 

PROVIDER: S-EPMC12595189 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature