Effects of RNA Binding Proteins on the Prognosis and Malignant Progression in Prostate Cancer.

Xiaoliang Hua,Sheng Tai,Shengdong Ge,Li Zhang,Juan Chen,Chaozhao Liang

doi:10.3389/fgene.2020.591667

Abstract

Prostate cancer (PCa) is a common lethal malignancy in men. RNA binding proteins (RBPs) have been proven to regulate the biological processes of various tumors, but their roles in PCa remain less defined. In the present study, we used bioinformatics analysis to identify RBP genes with prognostic and diagnostic values. A total of 59 differentially expressed RBPs in PCa were obtained, comprising 28 upregulated and 31 downregulated RBP genes, which may play important roles in PCa. Functional enrichment analyses showed that these RBPs were mainly involved in mRNA processing, RNA splicing, and regulation of RNA splicing. Additionally, we identified nine RBP genes (EXO1, PABPC1L, REXO2, MBNL2, MSI1, CTU1, MAEL, YBX2, and ESRP2) and their prognostic values by a protein–protein interaction network and Cox regression analyses. The expression of these nine RBPs was validated using immunohistochemical staining between the tumor and normal samples. Further, the associations between the expression of these nine RBPs and pathological T staging, Gleason score, and lymph node metastasis were evaluated. Moreover, these nine RBP genes showed good diagnostic values and could categorize the PCa patients into two clusters with different malignant phenotypes. Finally, we constructed a prognostic model based on these nine RBP genes and validated them using three external datasets. The model showed good efficiency in predicting patient survival and was independent of other clinical factors. Therefore, our model could be used as a supplement for clinical factors to predict patient prognosis and thereby improve patient survival.

Highlights

Prostate cancer (PCa), one of the most common and lethal neoplasms in the urologic system, results in approximately 260,000 annual deaths in men worldwide (Siegel et al, 2020)
The results showed that the high-risk patients had higher proportions of high Gleason score (P < 0.0001), lymph node metastasis (P < 0.0001), high pathological T staging (P < 0.0001), advanced age (P < 0.05), FIGURE 6 | Consensus clustering based on the nine survival-related RNA binding proteins. (A) Consensus clustering cumulative distribution function (CDF) for k = 2 to k = 10. (B) The relative change in area under the CDF curve for k = 2 to k = 10. (C) The Kaplan–Meier curve for prostate cancer patients to evaluate disease-free survival. (D) The Gene Set Enrichment Analysis showed that several oncogenic pathways were significantly enriched in cluster 2
The results revealed that the high-risk PCa patients tended to have advanced stage, high Gleason score, high ratio of lymph node metastasis and recurrence, and poor prognosis, suggesting that our model was closely associated with traditional clinical variables

Summary

Introduction

Prostate cancer (PCa), one of the most common and lethal neoplasms in the urologic system, results in approximately 260,000 annual deaths in men worldwide (Siegel et al, 2020). The main monitoring indicators of PCa include serum prostate-specific antigen (PSA) levels and pathological stage identification. New biomarkers are needed to aid in the diagnosis and timely treatment of PCa. With advances in medical research, the disease-free survival of PCa patients has improved. Approximately 30% of PCa patients experience recurrence and metastasis after undergoing surgical resection (Tomita et al, 2020). While androgen deprivation therapy is an effective therapeutic method employed in the initial stage of treatment, many PCa patients eventually develop aggressive castration-resistant PCa (CRPC; Graham et al, 2008; Wong et al, 2014). The identification of valuable molecular markers and construction of a more effective and specific stratification model are of great significance to guide clinical treatment and improve the prognosis and diagnosis of PCa patients

Methods

Results

Discussion

Conclusion