Department of Laboratory Medicine, The First People's Hospital of Kashi, Kashi City, China.
The First People's Hospital of Kashi, Kashi City, China.
Medicine (Baltimore). 2024 Sep 13;103(37):e39639. doi: 10.1097/MD.0000000000039639.
Construction of a prognostic model for esophageal cancer (ESCA) based on prognostic RNA-binding proteins (RBPs) and preliminary evaluation of RBP function.
RNA-seq data of ESCA was downloaded from The Cancer Genome Atlas database and mRNA was extracted to screen differentially expressed genes using R. After screening RBPs in differentially expressed genes, R packages clusterProfiler and pathview were used to analyze the RBPs for Gene Ontology enrichment and Kyoto Encyclopedia of Genes and Genomes pathway. Based on the prognosis-related RBPs, COX regression was used to establish the prognostic risk model of ESCA. Risk model predictive ability was assessed using calibration analysis, receiver operating characteristic curves, Kaplan-Meier curves, decision curve analysis, and Harrell consistency index (C-index). A nomogram was established by combining the risk model with clinicopathological features.
A total of 105 RBPs were screened from ESCA. A prognostic risk model consisting of 6 prognostic RBPs (ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1) was constructed by COX regression analysis. The prognosis was worse in the high-risk group, and the receiver operating characteristic curve showed (area under the curve = 0.90) that the model better predicted patients' 5-year survival. In addition, 6 prognostic RBPs had good diagnostic power for ESCA. In addition, a total of 39 mRNAs were identified as predicted target molecules for DKC1.
ARHGEF28, BOLL, CIRBP, DKC1, SNRPB, and TRIT1, as RBPs, are associated with the prognosis of ESCA, which may provide new ideas for targeted therapy of ESCA.
基于预后 RNA 结合蛋白（RBPs）构建食管癌（ESCA）预后模型，并初步评估 RBP 功能。
从癌症基因组图谱数据库下载 ESCA 的 RNA-seq 数据，并提取 mRNA，使用 R 筛选差异表达基因。筛选差异表达基因中的 RBPs 后，使用 R 包 clusterProfiler 和 pathview 分析 RBPs 的基因本体论富集和京都基因与基因组百科全书通路。基于与预后相关的 RBPs，使用 COX 回归建立 ESCA 的预后风险模型。通过校准分析、接受者操作特征曲线、Kaplan-Meier 曲线、决策曲线分析和 Harrell 一致性指数（C 指数）评估风险模型的预测能力。通过结合风险模型和临床病理特征建立列线图。
从 ESCA 中筛选出 105 个 RBPs。通过 COX 回归分析构建了由 6 个预后 RBPs（ARHGEF28、BOLL、CIRBP、DKC1、SNRPB 和 TRIT1）组成的预后风险模型。高风险组的预后较差，接受者操作特征曲线显示（曲线下面积=0.90），该模型能更好地预测患者的 5 年生存率。此外，6 个预后 RBPs 对 ESCA 具有良好的诊断能力。此外，共鉴定出 39 个 mRNAs 作为 DKC1 的预测靶分子。
ARHGEF28、BOLL、CIRBP、DKC1、SNRPB 和 TRIT1 作为 RBPs 与 ESCA 的预后相关，这可能为 ESCA 的靶向治疗提供新的思路。
