Identification of an Immunologic gene signature in prediction the prognosis and therapeutic responses of ovarian cancer

Background The poor prognosis of ovarian cancer is largely due to the high risk of recurrence and drug resistance. Current treatment of ovarian cancer limits in surgery, chemotherapy and immunotherapy but the response rate of each patient varies. Methods Transcriptome data of ovarian cancer were collected from The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium. Least absolute shrinkage and selection operator (LASSO) was applied to select the candidate genes and calculate the risk value to establish the model. A medium risk score was set to divide the cohort into high- or low-risk group. The predictive reliability was validated respectively in the validation and the whole cohorts. Results Differential immune genes were identified from GTEx database In the training cohort, five genes were applied to establish the model via a LASSO regression model. Based on the risk assessment model, the overall survival rate, recurrence rate and diseases progression rate in high-risk cohort was significantly lower than the low-risk cohort. The risk score (AUC = 0.681) of a time-ROC curve had a higher prognostic value than pathological grade, age or grade. Besides, the risk assessment model could also predict the sensitivity of chemotherapy and immunotherapy for ovarian cancer patients. Conclusion The model constructed by five immune-related genes has a significant value in prediction of the overall survival and the response to chemotherapy and immunotherapy. With better performance in evaluating the disease progression and prognosis, the model could further discern the immune characteristics of ovarian cancer patients and thus improve the poor outcomes.