Abstract
With the highest case-fatality rate among women, the molecular pathological alterations of ovarian cancer (OV) are complex, depending on the diversity of genomic alterations. Increasing evidence supports that immune infiltration in tumors is associated with prognosis. Therefore, we aim to assess infiltration in OV using multiple methods to capture genomic signatures regulating immune events to identify reliable predictions of different outcomes. A dataset of 309 ovarian serous cystadenocarcinoma patients with overall survival >90 days from The Cancer Genome Atlas (TCGA) was analyzed. Multiple estimations and clustering methods identified and verified two immune clusters with component differences. Functional analyses pointed out immune-related alterations underlying internal genomic variables potentially. After extracting immune genes from a public database, the LASSO Cox regression model with 10-fold cross-validation was used for selecting genes associated with overall survival rate significantly, and a risk score model was then constructed. Kaplan-Meier survival and Cox regression analyses among cohorts were performed systematically to evaluate prognostic efficiency among the risk score model and other clinical pathological parameters, establishing a predictive ability independently. Furthermore, this risk score model was compared among identified signatures in previous studies and applied to two external cohorts, showing better prediction performance and generalization ability, and also validated as robust in association with immune cell infiltration in bulk tissues. Besides, a transcription factor regulation network suggested upper regulatory mechanisms in OV. Our immune risk score model may provide gyneco-oncologists with predictive values for the prognosis and treatment management of patients with OV.