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

被引:18
作者
Hua, Xiaoliang [1 ,2 ,3 ]
Ge, Shengdong [1 ,2 ,3 ]
Chen, Juan [4 ]
Zhang, Li [1 ,2 ,3 ]
Tai, Sheng [1 ,2 ,3 ]
Liang, Chaozhao [1 ,2 ,3 ]
机构
[1] Anhui Med Univ, Dept Urol, Affiliated Hosp 1, Hefei, Peoples R China
[2] Anhui Med Univ, Anhui Prov Key Lab Genitourinary Dis, Hefei, Peoples R China
[3] Anhui Med Univ, Inst Urol, Hefei, Peoples R China
[4] Chongqing Med Univ, Minist Educ, Key Lab Clin Diagnost, Sch Lab Med, Chongqing, Peoples R China
关键词
prostate cancer; RNA binding protein; bioinformatics; biomarker; prognostic model; GENE-EXPRESSION; STABILITY;
D O I
10.3389/fgene.2020.591667
中图分类号
Q3 [遗传学];
学科分类号
071007 ; 090102 ;
摘要
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.
引用
收藏
页数:15
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