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

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 derived from GEO, we filtered out 1,401 DEGs, which were used in gene enrichment and building the PPI networks. Several processes were enriched. Three subtypes associated with fatty acid synthesis and tumor immunity in OC were identified based on six critical genes ( Conclusions: This study enhances our understanding of the complex molecular mechanisms underlying OC by highlighting the interplay between tumor immunity and fatty acid synthesis. The identification of three distinct subtypes based on key genes provides a new framework for categorizing OC patients, which could lead to more personalized therapeutic approaches. The prognostic model related to fatty acid synthesis not only offers potential biomarkers for predicting patient outcomes but also suggests new avenues for targeted therapies. These findings could pave the way for more effective immune-based treatments and improve the prognosis for OC patients. Future research should focus on validating these biomarkers and exploring their functional roles in OC pathogenesis and treatment response.