Abnormal expression of RNA binding proteins (RBPs) has been reported across various cancers. However, the potential role of RBPs in colorectal cancer (CRC) remains unclear. In this study, we performed a systematic bioinformatics analysis of RBPs in CRC. We downloaded CRC data from The Cancer Genome Atlas (TCGA) database. Our analysis identified 242 differentially expressed RBPs between tumor and normal tissues, including 200 upregulated and 42 downregulated RBPs. Next, we found eight RBPs (RRS1, PABPC1L, TERT, SMAD6, UPF3B, RP9, NOL3, and PTRH1) related to the prognoses of CRC patients. Among these eight prognosis-related RBPs, four RBPs (NOL3, PTRH1, UPF3B, and SMAD6) were selected to construct a prognostic risk score model. Furthermore, our results indicated that the prognostic risk score model accurately predicted the prognosis of CRC patients [area under the receiver operating characteristic curve (AUC)for 3- and 5-year overall survival (OS) and was 0.645 and 0.672, respectively]. Furthermore, we developed a nomogram based on a prognostic risk score model. The nomogram was able to demonstrate the wonderful performance in predicting 3- and 5-year OS. Additionally, we validated the clinical value of four risk genes in the prognostic risk score model and identified that these risk genes were associated with tumorigenesis, lymph node metastasis, distant metastasis, clinical stage, and prognosis. Finally, we used the TIMER and Human Protein Atlas (HPA)database to validate the expression of four risk genes at the transcriptional and translational levels, respectively, and used a clinical cohort to validate the roles of NOL3 and UPF3B in predicting the prognosis of CRC patients. In summary, our study demonstrated that RBPs have an effect on CRC tumor progression and might be potential prognostic biomarkers for CRC patients.

已经报道了各种癌症中RNA结合蛋白（RBP）的异常表达。然而，RBPs在大肠癌（CRC）中的潜在作用仍不清楚。在这项研究中，我们对CRC中的RBP进行了系统的生物信息学分析。我们从癌症基因组图谱（TCGA）数据库下载了CRC数据。我们的分析鉴定了肿瘤和正常组织之间的242个差异表达的RBP，包括200个上调和42个下调的RBP。接下来，我们发现了8个与CRC患者的预后相关的RBP（RRS1，PABPC1L，TERT，SMAD6，UPF3B，RP9，NOL3和PTRH1）。在这8个与预后相关的RBP中，选择了4个RBP（NOL3，PTRH1，UPF3B和SMAD6）来构建预后风险评分模型。此外，我们的结果表明，预后风险评分模型可以准确预测CRC患者的预后[3年和5年总生存期（OS）的受试者工作特征曲线（AUC）下的面积，分别为0.645和0.672]。此外，我们基于预后风险评分模型开发了列线图。诺模图能够证明在预测3年和5年OS方面的出色表现。此外，我们在预后风险评分模型中验证了四个风险基因的临床价值，并确定了这些风险基因与肿瘤发生，淋巴结转移，远处转移，临床分期和预后相关。最后，我们使用TIMER和人类蛋白质图谱（HPA）数据库分别在转录和翻译水平上验证了四个风险基因的表达，并通过临床队列验证了NOL3和UPF3B在预测CRC患者预后中的作用。总之，我们的研究表明，RBPs对CRC肿瘤的进展有影响，并且可能是CRC患者的潜在预后生物标志物。