Abstract
BackgroundProstate cancer (PCa) is the second leading cause of cancer death in men in 2018. Thus, the evaluation of prognosis is crucial for clinical treatment decision of human PCa patients. We aim to establishing an effective and reliable model to predict the outcome of PCa patients.MethodsWe first identified differentially expressed genes between prostate cancer and normal prostate in TCGA-PRAD and then performed WGCNA to initially identify the candidate Gleason score related genes. Then, the candidate genes were applied to construct a LASSO Cox regression analysis model. Numerous independent validation cohorts, time-dependent receiver operating characteristic (ROC), univariate cox regression analysis, nomogram were used to test the effectiveness, accuracy and clinical utility of the prognostic model. Furthermore, functional analysis and immune cells infiltration were performed.ResultsGleason score-related differentially expressed candidates were identified and used to build up the outcome model in TCGA-PRAD cohort and was validated in MSKCC cohort. We found the 3-gene outcome model (CDC45, ESPL1 and RAD54L) had good performance in predicting recurrence free survival, metastasis free survival and overall survival of PCa patients. Time-dependent ROC and nomogram indicated an ideal predictive accuracy and clinical utility of the outcome model. Moreover, outcome model was enriched in 28 pathways by GSVA and GSEA. In addition, the risk score was positively correlated with memory B cells, native CD4 T cells, activated CD4 memory T cells and eosinophil, and negatively correlated with plasma cells, resting CD4 memory T cells, resting mast cells and neutrophil.ConclusionsIn summary, our outcome model proves to be an effective prognostic model for predicting the risk of prognosis in PCa.
Highlights
Prostate cancer (PCa) is the second leading cause of cancer death in men in 2018
We combined the modules with different clinical features (overall survival (OS) time, overall survival status, disease free survival (DFS) time, disease free survival status, age, biochemical recurrence, clinical M stage, clinical T stage, total Gleason score, laterality, number of positive lymphonode, pathological T stage, pathological N stage, prostate specific antigen (PSA) value, radiation therapy and targeted molecular therapy), we could identify the key genes related to total Gleason score
Construction of outcome model Excluding the samples with incomplete disease free survival data, we used 436 TCGA-PRAD patients as the training set
Summary
The evaluation of prognosis is crucial for clinical treatment decision of human PCa patients. We aim to establishing an effective and reliable model to predict the outcome of PCa patients. Prostate cancer (PCa) is the second leading cause of cancer death in men in 2018 [1]. The 5-year relative survival rate of distant PCa is only 30% [2]. Despite decades of efforts in research, the standard treatment options and guidelines for PCa patients diagnosed with metastatic progression have remained unchanged [3, 4]. The Gleason scoring system has been widely used for assessment of prognosis of PCa based on histology [5]. In patients with metastatic PCa, metastatic biopsies are rarely performed [6].
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
