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结肠癌RNA结合蛋白相关预后模型的构建与验证

Development And Validation of An RNA Binding Protein-Associated Prognostic Model for Colon Adenocarcinoma.

作者信息

Yu Xiajing, Guo Daixin, Gao Jie, Hu Jialing, Zhang Wenyige, Yang Qijun, Wang Jingyi, He Yingcheng, Liao Kaili, Wang Xiaozhong

机构信息

Jiangxi Province Key Laboratory of Immunology and Inflammation, Jiangxi Provincial Clinical Research Center for Laboratory Medicine, Department of Clinical Laboratory, The Second Affiliated Hospital, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

School of Public Health, Jiangxi Medical College, Nanchang University, Nanchang, Jiangxi, China.

出版信息

J Cancer. 2025 May 18;16(8):2537-2552. doi: 10.7150/jca.103477. eCollection 2025.

DOI:10.7150/jca.103477
PMID:40535805
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC12170993/
Abstract

We aimed to identify prognostic RNA-binding proteins (RBP) in colon cancer, analyze their biological functions, and develop predictive models for patient prognosis. We downloaded COAD's RNA sequencing data from the Cancer Genome Atlas (TCGA) database, and the expression and prognostic value of these RBPs in COAD were systematically evaluated. Differential expression, KEGG, and GO enrichment analyses were then performed. Cytoscape was used to visualize the protein-protein interaction network, and Cox regression was used to establish a predictive model. Finally, the expression of RBP was verified by the HPA database and immunohistochemical staining. A total of 472 differentially expressed RBPs were detected, including 321 up-regulated RBPs and 151 down-regulated RBPs. Four RBPs (MSI2, EZH2, NCL, TERT) were identified as key prognostic genes and used to construct prognostic models, based on this model, the overall survival (OS) of patients in high-risk subgroup was worse than that of patients in the low-risk subgroup. The area under the curve of time-dependent receiver operator characteristic curve of TCGA training set and Gene Expression Omnibus (GEO) validation set was 0.607 and 0.638 respectively, which confirmed that the prognosis model was good, it showed a good ability to identify COAD. In general, our prognostic model is based on 4 RBPs encoding genes, which greatly reduces the cost of sequencing and is more conducive to clinical applications.

摘要

我们旨在鉴定结肠癌中的预后性RNA结合蛋白(RBP),分析其生物学功能,并开发患者预后的预测模型。我们从癌症基因组图谱(TCGA)数据库下载了结肠癌(COAD)的RNA测序数据,并系统评估了这些RBP在COAD中的表达和预后价值。随后进行了差异表达、KEGG和GO富集分析。使用Cytoscape可视化蛋白质-蛋白质相互作用网络,并使用Cox回归建立预测模型。最后,通过人类蛋白质图谱(HPA)数据库和免疫组织化学染色验证RBP的表达。共检测到472个差异表达的RBP,包括321个上调的RBP和151个下调的RBP。四个RBP(MSI2、EZH2、NCL、TERT)被鉴定为关键预后基因并用于构建预后模型,基于该模型,高风险亚组患者的总生存期(OS)比低风险亚组患者差。TCGA训练集和基因表达综合数据库(GEO)验证集的时间依赖性受试者工作特征曲线下面积分别为0.607和0.638,证实预后模型良好,显示出良好的识别COAD的能力。总体而言,我们的预后模型基于4个RBP编码基因,大大降低了测序成本,更有利于临床应用。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/219f/12170993/b551f849c5d3/jcav16p2537g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/219f/12170993/6bbc94b40051/jcav16p2537g001.jpg
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