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一种用于预测肺腺癌患者预后的泛素-蛋白酶体基因特征。

A Ubiquitin-Proteasome Gene Signature for Predicting Prognosis in Patients With Lung Adenocarcinoma.

作者信息

Tang Yunliang, Guo Yinhong

机构信息

Department of Rehabilitation Medicine, First Affiliated Hospital of Nanchang University, Nanchang, China.

Department of Oncology, Zhuji People's Hospital of Zhejiang Province, Zhuji, China.

出版信息

Front Genet. 2022 May 31;13:893511. doi: 10.3389/fgene.2022.893511. eCollection 2022.

Abstract

Dysregulation of the ubiquitin-proteasome system (UPS) can lead to instability in the cell cycle and may act as a crucial factor in both tumorigenesis and tumor progression. However, there is no established prognostic signature based on UPS genes (UPSGs) for lung adenocarcinoma (LUAD) despite their value in other cancers. We retrospectively evaluated a total of 703 LUAD patients through multivariate Cox and Lasso regression analyses from two datasets, the Cancer Genome Atlas ( = 477) and GSE31210 ( = 226). An independent dataset (GSE50081) containing 128 LUAD samples were used for validation. An eight-UPSG signature, including , , , , , , , and , was established. Kaplan-Meier survival analysis and time-receiver operating characteristic curves for the training and validation datasets revealed that this risk signature presented with good performance in predicting overall and relapsed-free survival. Based on the signature and its associated clinical features, a nomogram and corresponding web-based calculator for predicting survival were established. Calibration plot and decision curve analyses showed that this model was clinically useful for both the training and validation datasets. Finally, a web-based calculator (https://ostool.shinyapps.io/lungcancer) was built to facilitate convenient clinical application of the signature. An UPSG based model was developed and validated in this study, which may be useful as a novel prognostic predictor for LUAD.

摘要

泛素-蛋白酶体系统(UPS)失调可导致细胞周期不稳定,并可能在肿瘤发生和肿瘤进展中起关键作用。然而,尽管UPS基因(UPSGs)在其他癌症中具有重要价值,但目前尚无基于UPSGs的肺腺癌(LUAD)预后特征。我们通过多变量Cox和Lasso回归分析,对来自两个数据集(癌症基因组图谱,n = 477;GSE31210,n = 226)的总共703例LUAD患者进行了回顾性评估。使用包含128个LUAD样本的独立数据集(GSE50081)进行验证。建立了一个包含UBE2C、UBA52、PSMB5、PSMC4、RNF4、FBXO32、ANAPC2和CDC20的八基因UPS特征。训练集和验证集的Kaplan-Meier生存分析以及时间-接受者操作特征曲线显示,该风险特征在预测总生存期和无复发生存期方面表现良好。基于该特征及其相关临床特征,建立了用于预测生存的列线图和相应的基于网络的计算器。校准图和决策曲线分析表明,该模型对训练集和验证集均具有临床实用性。最后,构建了一个基于网络的计算器(https://ostool.shinyapps.io/lungcancer),以方便该特征在临床中的应用。本研究开发并验证了一种基于UPSGs的模型,该模型可能作为LUAD的一种新型预后预测指标。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/d9ec/9194557/2301fc3844ac/fgene-13-893511-g001.jpg

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