Peng Peng, Chen Zi-Rong, Zhang Xiao-Lin, Guo Dong-Sheng, Zhang Bin, He Xi-Miao, Wan Feng
Department of Neurosurgery, Xiangyang Central Hospital, Affiliated Hospital of Hubei University of Arts and Science, Xiangyang, 441021, China.
Department of Neurosurgery, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, 430030, China.
Curr Med Sci. 2023 Feb;43(1):156-165. doi: 10.1007/s11596-022-2694-1. Epub 2023 Mar 3.
To construct and verificate an RNA-binding protein (RBP)-associated prognostic model for gliomas using integrated bioinformatics analysis.
RNA-sequencing and clinic pathological data of glioma patients from The Cancer Genome Atlas (TCGA) database and the Chinese Glioma Genome Atlas database (CGGA) were downloaded. The aberrantly expressed RBPs were investigated between gliomas and normal samples in TCGA database. We then identified prognosis related hub genes and constructed a prognostic model. This model was further validated in the CGGA-693 and CGGA-325 cohorts.
Totally 174 differently expressed genes-encoded RBPs were identified, containing 85 down-regulated and 89 up-regulated genes. We identified five genes-encoded RBPs (ERI1, RPS2, BRCA1, NXT1, and TRIM21) as prognosis related key genes and constructed a prognostic model. Overall survival (OS) analysis revealed that the patients in the high-risk subgroup based on the model were worse than those in the low-risk subgroup. The area under the receiver operator characteristic curve (AUC) of the prognostic model was 0.836 in the TCGA dataset and 0.708 in the CGGA-693 dataset, demonstrating a favorable prognostic model. Survival analyses of the five RBPs in the CGGA-325 cohort validated the findings. A nomogram was constructed based on the five genes and validated in the TCGA cohort, confirming a promising discriminating ability for gliomas.
The prognostic model of the five RBPs might serve as an independent prognostic algorithm for gliomas.
通过综合生物信息学分析构建并验证一种用于胶质瘤的RNA结合蛋白（RBP）相关预后模型。
从癌症基因组图谱（TCGA）数据库和中国胶质瘤基因组图谱数据库（CGGA）下载胶质瘤患者的RNA测序和临床病理数据。在TCGA数据库中研究胶质瘤与正常样本之间异常表达的RBP。然后我们鉴定出与预后相关的枢纽基因并构建了一个预后模型。该模型在CGGA - 693和CGGA - 325队列中进一步验证。
共鉴定出174个差异表达的编码RBP的基因，其中85个下调基因和89个上调基因。我们鉴定出5个编码RBP的基因（ERI1、RPS2、BRCA1、NXT1和TRIM21）作为与预后相关的关键基因并构建了一个预后模型。总生存（OS）分析显示，基于该模型的高风险亚组患者的预后比低风险亚组患者差。预后模型在TCGA数据集中的受试者工作特征曲线（AUC）面积为0.836，在CGGA - 693数据集中为0.708，表明这是一个良好的预后模型。CGGA - 325队列中5种RBP的生存分析验证了这些发现。基于这5个基因构建了一个列线图并在TCGA队列中进行了验证，证实其对胶质瘤具有良好的鉴别能力。
5种RBP的预后模型可能作为胶质瘤的一种独立预后算法。
