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.

前列腺癌（PCa）是男性常见的致命恶性肿瘤。RNA 结合蛋白 (RBP) 已被证明可以调节各种肿瘤的生物过程，但它们在 PCa 中的作用仍不清楚。在本研究中，我们利用生物信息学分析来鉴定具有预后和诊断价值的 RBP 基因。共获得59个PCa中差异表达的RBP基因，其中28个上调RBP基因和31个下调RBP基因，这些基因可能在PCa中发挥重要作用。功能富集分析表明，这些RBP主要参与mRNA加工、RNA剪接和RNA剪接的调控。此外，我们通过蛋白质-蛋白质相互作用网络和 Cox 回归分析确定了 9 个 RBP 基因（EXO1、PABPC1L、REXO2、MBNL2、MSI1、CTU1、MAEL、YBX2 和 ESRP2）及其预后价值。使用肿瘤和正常样本之间的免疫组织化学染色来验证这九种 RBP 的表达。此外，还评估了这九种RBP的表达与病理T分期、格里森评分和淋巴结转移之间的关联。此外，这9个RBP基因显示出良好的诊断价值，可以将PCa患者分为具有不同恶性表型的两类。最后，我们基于这九个 RBP 基因构建了一个预后模型，并使用三个外部数据集对其进行了验证。该模型在预测患者生存方面表现出良好的效率，并且独立于其他临床因素。因此，我们的模型可以作为临床因素的补充来预测患者的预后，从而提高患者的生存率。