Combination of Immune-Related Genomic Alterations Reveals Immune Characterization and Prediction of Different Prognostic Risks in Ovarian Cancer - Zhao, Xibo; Cong, Shanshan; Guo, Qiuyan; Cheng, Yan; Liang, Tian; Wang, Jing; Zhang, Guangmei (Frontiers in Cell and Developmental Biology, 2021)
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 the immune infiltration in tumors is associated with prognosis. Therefore, we aim to assess infiltration in OV applied by multiple methods to capture the genomic signatures regulating immune events for identifying 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, LASSO Cox regression model with ten-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 the 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 the previous studies and applied to two external cohorts, showing better predict performance and generalization ability, and also validated as robustness in association with immune cells infiltration in bulk tissues. Besides, a transcription factors regulation network suggested upper regulatory mechanisms in OV. Our immune risk score model may provide gynaeco-oncologists with predictive values for prognosis and treatment management in patients with OV.