Background: Ovarian cancer (OV) is a heterogeneous gynecologic malignancy with limited and patient-specific responses to immunotherapy. This study aimed to develop and validate a CD40LG-centered 6-gene signature based on immune-modulatory genes (IMGs) for prognostic stratification and prediction of immunotherapy response in OV patients.

Methods: Transcriptomic data and corresponding clinical annotations for normal and malignant ovarian tissues were retrieved from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) database, respectively. The IMG-based prognostic signature (IMPS) was generated by identifying and integrating differentially expressed IMGs using univariate Cox regression and least absolute shrinkage and selection operator (LASSO)-Cox analysis. Tumor microenvironment (TME) characteristics, survival outcomes, and immunotherapy responses were analyzed with CIBERSORT, ESTIMATE, and Tumor Immune Dysfunction and Exclusion (TIDE) algorithms, and major IMGs were verified by quantitative real-time PCR (qRT-PCR).

Results: The IMPS, including CD40LG, HMGB3, IL27RA, TNFRSF8, BTLA, and HLA-DOB, exhibited significant prognostic ability. Kaplan-Meier curves demonstrated a survival advantage in the low-risk group (p < 0.0001), which was successfully cross-validated using the International Cancer Genome Consortium (ICGC) dataset. The low-risk group exhibited an anti-tumor immune phenotype (increased M1 macrophage infiltration, enriched antigen-processing pathways, activated T-cell signaling), whereas the high-risk group had higher immune-evasion potential (TIDE: r = 0.206, p < 0.0001). Analysis of the IMvigor210 trial indicated that individuals within the low-risk category exhibited a more favorable response to PD-1/PD-L1 blockade therapy. CD40LG expression was downregulated in OV tissues and associated with sensitivity to immunotherapy.

Conclusions: The CD40LG-centered 6-gene IMPS enables precise stratification of OV patients according to clinical outcomes and immunotherapy responsiveness, serving as a promising tool for guiding personalized immunotherapy strategies.