5 Gleason score-associated gene signatures serve as novel biomarkers for identifying early recurring events and contributing to early diagnosis for Prostate Adenocarcinoma

Abstract

Background: Prostate cancer (PCa) recurrence leads to much higher mortality than those without recurring events. Early and accurate laboratory diagnosis is particularly important for early identification of patients at high risk of recurrence and to benefit from additional systemic intervention. This study aimed to develop efficient and accurate Prostate Cancer diagnostic and prognostic biomarkers for the identification of initial tumor new events. Methods: Gene Expression Omnibus (GEO) datasets and The Cancer Genome Atlas (TCGA) data portal were utilized to obtain differentially expressed genes (DEGs) and clinical trait information in PCa. WGCNA analysis obtained the most relevant clinical traits and genes enriched in several modules. Univariate Cox regression analysis and multivariate Cox proportional hazards (Cox-PH) model was employed to candidate gene signatures related to Disease-Free Interval (DFI). Internal and external cohort was utilized to test and validate the validity, accuracy, and clinical utility of prognostic models.Results: We constructed and optimized a valid and credible model for predicting patient outcomes, based on 5 Gleason score-associated gene signatures (ZNF695, CENPA, TROAP, BIRC5, KIF20A). Furthermore, ROC and Kaplan-Meier analysis revealed higher diagnostic efficiency for PCa and predictive effectiveness in tumor recurrence and metastasis. Calibration curve also revealed high prediction accuracy in internal TCGA cohort and external GEO cohort. The model was prognostically significant in the stratified cohort, including TNM classification and Gleason score, and was deemed to be an independent PCa prognostic factor, and superioring to other clinicopathological characteristics. On the other hand, we also measured the correlation between gene signatures’ expression and inflammation landscape. 5 gene signatures were significantly positively correlated with tumor purity and negatively correlated with the immersion levels of CD8+ T cells. Conclusions: Our study identified and validated 5 gene signatures as biomarkers for prostate cancer diagnosis, providing an assessment of DFI while predicting tumor progression, possibly providing novel theories for the treatment of prostate cancer.