The aim of this article was to construct an accurate prognostic model by using RNA-binding proteins (RBPs) to predict overall survival (OS) for patients with clear cell renal cell carcinoma (ccRCC) as well as to reveal its associations with immune infiltration. Expression profiles based on RNA-binding proteins (RBPs) and clinical follow-up parameters were obtained from the Cancer Genome Atlas (TCGA) and the ArrayExpress databases. Through univariate COX and LASSO regression analyses, the RBPs based signature was developed. A total of six RBPs (CLK2, IGF2BP2, RNASE2, EZH2, PABPC1L, RPL22L1) were eventually used to establish a prognostic signature. Based on this signature, ccRCC patients were classified into high-risk and low-risk subgroups and significant OS was obtained in both the internal and external datasets (p<0.05). AUCs of its ROC curve were all above 0.70 and this signature was an independent prognostic factor of OS for ccRCC (p<0.05). Nomograms were also constructed to visualize the relationships among individual predictors and 1-, 3- and 5-year OS for ccRCC. Furthermore, the established RBPs based signature was strongly related to critical clinicopathologic characteristics such as grade (p=8.921e-12), stage (p=1.421e-11), M (p=1.662e-05), and T stage (p=7.907e-10). Moreover, 12 kinds of tumor-infiltrating immune cells were significantly linked to high-risk and low-risk groups classified by our constructed model (all p<0.05). Our study successfully identified six RBPs as a robust prognostic signature in ccRCC by both external and internal verification. Besides, our established model displayed significant associations with immune infiltration. In addition to original clinical parameters, our findings may further help clinicians in predicting patients' survival status and creating individualized treatment plans.

中文翻译：

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六种 RNA 结合蛋白 (RBP) 相关的预后模型可预测透明细胞肾细胞癌的总体存活率，并且与免疫浸润有关。

本文的目的是通过使用 RNA 结合蛋白 (RBP) 构建准确的预后模型来预测透明细胞肾细胞癌 (ccRCC) 患者的总生存期 (OS)，并揭示其与免疫浸润的关联。基于 RNA 结合蛋白 (RBP) 和临床随访参数的表达谱从癌症基因组图谱 (TCGA) 和 ArrayExpress 数据库中获得。通过单变量 COX 和 LASSO 回归分析，开发了基于 RBP 的签名。最终使用总共六个 RBP（CLK2、IGF2BP2、RNASE2、EZH2、PABPC1L、RPL22L1）来建立预后特征。基于这一特征，ccRCC 患者被分为高风险和低风险亚组，并且在内部和外部数据集中都获得了显着的 OS (p<0.05)。其 ROC 曲线的 AUC 均高于 0.70，该特征是 ccRCC 的 OS 的独立预后因素（p<0.05）。还构建了列线图以可视化各个预测因子与 ccRCC 的 1 年、3 年和 5 年 OS 之间的关系。此外，已建立的基于 RBP 的特征与关键的临床病理学特征密切相关，例如分级 (p=8.921e-12)、分期 (p=1.421e-11)、M (p=1.662e-05) 和 T 分期。 p=7.907e-10)。此外，12种肿瘤浸润性免疫细胞与我们构建的模型分类的高危和低危组显着相关（均p<0.05）。我们的研究通过外部和内部验证成功地将六个 RBP 确定为 ccRCC 中的稳健预后特征。此外，我们建立的模型显示出与免疫浸润的显着关联。