Abstract
Ovarian cancer (OC) remains one of the deadliest gynecological malignancies. Immune checkpoint blockade (ICB) inhibitors efficacy in OC has been minimal, highlighting the need for a deeper understanding of the immune microenvironment in OC. Recent studies suggest that DNA methylation and transcription factors may influence the response to immunotherapy. This study aims to classify ovarian cancer into distinct immune subtypes by integrating DNA methylation and transcription factor data through comprehensive bioinformatics analysis. Using data from The Cancer Genome Atlas (TCGA), we identified twelve differentially methylated genes (DMGs) associated with transcription factors and categorized OC into two immune subtypes, C1 and C2.The C1 subtype exhibited higher levels of immune infiltration and better prognosis, characteristic of immune "hot" tumors, whereas the C2 subtype was associated with lower immune infiltration and poorer prognosis, indicative of immune "cold" tumors. A prognostic prediction model based on four key genes-KRT81, PAPPA2, FGF10, and FMO2-was developed using the least absolute shrinkage and selection operator (LASSO) and Cox regression analyses. This model effectively stratified the TCGA OC cohort into high- and low-risk groups and was validated by predicting patient survival outcomes. Additionally, drug sensitivity analysis revealed potential therapeutic targets for different risk groups, offering new avenues for precision treatment in ovarian cancer. Immunohistochemical tests confirmed the potential of KRT81 as a prognostic marker for ovarian cancer. Our findings enhance the understanding of the molecular characteristics of the OC immune microenvironment, propose novel biomarkers for prognosis, which may potentially improve the prognosis of OC.