Identification of Prognostic Biomarkers Associated with Cancer Stem Cell Features in Prostate Adenocarcinoma
Dehua Zhang, Muchun Zhang, Qifu Zhang, Zhiyi Zhao, Yong Nie
Department of Urology, Yiling Hospital of Yichang, Yichang, Hubei, China (mainland)
Med Sci Monit 2020; 26:e924543
Available online: 2020-05-30
Prostate adenocarcinoma (PRAD) is the second most common malignancy in males and the fifth leading cause of cancer mortality. Cancer stem cells (CSCs) play an important role in the occurrence and development of PRAD, however, the prognostic biomarkers associated with CSC features have not been identified in PRAD.
MATERIAL AND METHODS: In order to identify the prognostic stemness-related genes (SRGs) of PRAD, the RNA sequencing data of patients with PRAD were retrieved from The Cancer Genome Atlas (TCGA) databases. The mRNA expression-based stemness index (mRNAsi) and the differential expressed genes (DEGs) were evaluated and identified. The associations between the mRNAsi and tumorigenesis, overall survival (OS), prostate-specific antigen (PSA) value, and Gleason score were also established by nonparametric test and Kaplan-Meier survival analysis. The SRGs were identified as the overlapped DEGs of PRAD-associated DEGs and the mRNAsi-associated DEGs. Based on the prognostic SRGs, the predict model was constructed. Its accuracy was tested by the area under the curve (AUC) of the receiver operator characteristic (ROC) curve and the risk score.
RESULTS: A total of 6005 PRAD-associated DEGs and 2462 mRNAsi-associated DEGs were identified. The mRNAsi was significantly upregulated in PRAD and associated with the PSA value and Gleason score. A total of 1631 SRGs were identified, with 36 prognostic SRGs screened by the univariate Cox analysis. Based on the prognostic SRGs, the predict model was constructed with the AUC of 0.986. Moreover, the risk score of the model was proved to be an independent prognostic factor, indicating its significant applicability.
CONCLUSIONS: Our data demonstrate the mRNAsi as a reliable index for the tumorigenesis, PSA value, and Gleason score of PRAD. Additionally, this study provides a well-applied model for predicting the OS for patients with PRAD based on prognostic SRGs.
Keywords: carcinogenesis, Decision Support Techniques, Neoplastic Stem Cells, Prostatic Neoplasms