Unveiling critical genes and molecular subtypes in ovarian cancer: insights into tumor immunity and carbohydrate-lipid metabolism.

ABSTRACT:

Background

Ovarian cancer (OC) has the highest mortality rate among all gynecological cancers, yet its pathogenesis remains unclear. This study aims to use integrated bioinformatics methods to identify important biomarkers and subtypes closely related to tumor immunity and fatty acid synthesis in OC.

Methods

RNA sequencing data offered with the Gene Expression Omnibus (GEO) were processed. Differentially expressed genes (DEGs) were screened and annotated via Gene Ontology (GO) Enrichment Analysis, Gene Set Variation Analysis (GSVA), and Gene Set Enrichment Analysis (GSEA). Besides, the critical DEGs were used in building the protein-protein interaction (PPI) networks, screening significant subtypes, and constructing risk models.

Results

After processing the raw data d

SUBMITTER: Zhang Y 

PROVIDER: S-EPMC12605307 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature