Abstract
Ovarian cancer (OV) is a malignant tumor that ranks first among gynecological cancers, thus posing a significant threat to women's health. Immunogenic cell death (ICD) can regulate cell death by activating the adaptive immune system. Here, we aimed to comprehensively characterize the features of ICD-associated genes in ovarian cancer, and to investigate their prognostic value and role in the response to immunotherapy. After analyzing datasets from The Cancer Genome Atlas, we utilized weighted gene coexpression network analysis to screen for hub genes strongly correlated with ICD genes in OV, which was subsequently validated with OV samples from the Gene Expression Omnibus (GEO) database. A prognostic risk model was then constructed after combining univariate, multivariate Cox regression and LASSO regression analysis to recognize nine ICD-associated molecules. Next, we stratified all OV patients into two subgroups according to the median value. The multivariate Cox regression analysis showed that the risk model could predict OV patient survival with good accuracy. The same results were also found in the validation set from GEO. We then compared the degree of immune cell infiltration in the tumor microenvironment between the two subgroups of OV patients, and revealed that the high-risk subtype had a higher degree of immune infiltration than the low-risk subtype. Additionally, in contrast to patients in the high-risk subgroup, those in the low-risk subgroup were more susceptible to chemotherapy. In conclusion, our research offers an independent and validated model concerning ICD-related molecules to estimate the prognosis, degree of immune infiltration, and chemotherapy susceptibility in patients with OV.