A novel machine learning-driven immunogenic cell death signature for predicting ovarian cancer prognosis.

ABSTRACT:

Background

Ovarian cancer (OC) is one of the most lethal malignancies in women, primarily due to the absence of reliable predictive biomarkers and effective therapies. The complex role of immunogenic cell death (ICD) in OC remains poorly understood, despite its critical implications for enhancing immune responses against tumors. We are committed to developing and validating a novel ICD-related gene signature and producing certain guiding value for the clinical treatment of OC.

Methods

We employed single-sample gene set enrichment analysis (ssGSEA) and weighted gene coexpression network analysis (WGCNA) on The Cancer Genome Atlas (TCGA)-ovarian carcinoma dataset to identify ICD-associated genes. A combination of 10 different machine learning approaches was used to construct a

SUBMITTER: Wang Y 

PROVIDER: S-EPMC11912067 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature