Development and Validation of an Individualized Immune Prognostic Signature for Recurrent Prostate Cancer.

Abstract

Immune-related genes possess promising prognostic potential in multiple cancer types. Here, we describe the development of an immune-related prognostic signature for predicting prostate cancer recurrence. Prostate cancer gene expression profiles for 477 prostate cases, as well as accompanying follow-up information were downloaded from The Cancer Genome Atlas (TCGA) and GEO. The samples were divided into 3 groups and immune gene sets significantly associated with prognosis were identified by evaluating the relationship between the expression of 1039 immune genes and prognosis in the training set. Relative expression levels of these genes were used to identify prognostic gene pairs. LASSO was used for feature selection and robust biomarkers selected. Finally, the identified immune prognostic markers were validated using dataset and GEO validation dataset and their performance compared with existing prognostic models. In total, 87 immune genes, significantly associated with prognosis, were identified and 2447 immune gene pairs (IRGPs) established. Univariate survival analysis identified 641 prognosis-associated immune gene pairs. 8-IRGPs were obtained via LASSO feature selection and an 8-IRGPs signature established. The 8-IRGPs signature exhibited an independent prognosis value in prostate cancer of the training set, test set, and external validation set (p = <0.001). The 5- year survival AUC in both the training set and the validation set was >0.7. The 8-IRGPs outperformed clinical tumor classification features, including T, N, radiation therapy (RT) and targeted molecular therapy (TMT) (p <0.01). In addition, we compared the prognostic characteristics of 8-IRGPs with 3 reported prostate cancers and found that 8-IRGPs achieved a high C index (0.85) and had the highest predictive performance within 10 years of follow-up (HR: 10.5). Finally, we integrated T, N, RT, TMT, and 8-IRGPs and generated a novel alignment chart to aid the prediction of prostate cancer recurrence in individual patients (p <0.01). Here, we identified an 8-IRGP novel prognostic signature for the prediction of prostate cancer recurrence.