Development and validation of a novel survival model for head and neck squamous cell carcinoma based on autophagy-related genes
Development and validation of a novel survival model for head and neck squamous cell carcinoma based on autophagy-related genes
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DOI：
10.1016/j.ygeno.2020.11.017
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发表时间：
2021-01-25
期刊：
影响因子：
4.4
通讯作者：
Hu, Xuegang
中科院分区：
文献类型：
--
作者：
Ren, Ziying;Zhang, Long;Hu, Xuegang
背景资料：鉴于自噬相关基因（autophagy-related genes，ARGs）在包括肿瘤在内的多种疾病的发病机制中的重要作用，本研究旨在鉴定和评价ARGs在头颈部鳞状细胞癌（HNSCC）中的潜在价值。癌症基因组图谱（TCGA）中的RNA测序和临床数据通过单因素考克斯回归分析和Lasso考克斯回归分析模型建立了13个新的自噬相关预后基因，用于建立预后风险模型。多因素考克斯比例回归模型和生存分析用于评估预后风险模型。同时，基于TCGA数据库和基因表达数据库（GEO）的数据，通过受试者工作特征（ROC）曲线分析，验证了预测风险模型的有效性。结果：在HNSCC患者中共鉴定出13个具有预后价值的ARG（GABARAPL 1、ITGA 3、USP 10、ST 13、MAPK 9、PRKN、FADD、IKBKB、ITPR 1、TP 73、MAP 2K 7、CDKN 2A和EEF 2K）。根据13个ARG建立预后风险模型，将HNSCC患者按总生存期（OS）分为高、低风险组（HR = 0.379，95% CI：0.289-0.495，p < 0.0001）。多因素考克斯分析显示，该模型是独立的预后因素（HR =1.506，95% CI = 1.330-1.706，P < 0.001）。TCGA和GEO的ROC曲线下面积（AUC）均具有显著性，AUC分别为0.685和0.928。通过基因集变异分析（GSVA）和基因集富集分析（GSEA），功能注释显示该模型显著富集了许多与肿瘤发生相关的关键通路，包括p53通路、IL 2 STAT 5信号通路、TGF β信号通路、PI 3 K Ak mTOR信号通路。此外，我们开发了一个诺模图显示一些临床网络可以作为临床决策的参考。结论：总的来说，我们开发和验证了一种新的强大的13个基因的签名HNSCC预后预测。13种ARG可作为HNSCC患者独立、可靠的预后指标和治疗靶点。
Background: In view of the critical role of autophagy-related genes (ARGs) in the pathogenesis of various diseases including cancer, this study aims to identify and evaluate the potential value of ARGs in head and neck squamous cell carcinoma (HNSCC).Methods: RNA sequencing and clinical data in The Cancer Genome Atlas (TCGA) were analyzed by univariate Cox regression analysis and Lasso Cox regression analysis model established a novel 13autophagy related prognostic genes, which were used to build a prognostic risk model. A multivariate Cox proportional regression model and the survival analysis were used to evaluate the prognostic risk model. Moreover, the efficiency of prognostic risk model was tested by receiver operating characteristic (ROC) curve analysis based on data from TCGA database and Gene Expression Omnibus (GEO). Besides, the other independent datasets from Human Protein Atlas dataset (HPA) also applied.Results: 13 ARGs (GABARAPL1, ITGA3, USP10, ST13, MAPK9, PRKN, FADD, IKBKB, ITPR1, TP73, MAP2K7, CDKN2A, and EEF2K) with prognostic value were identified in HNSCC patients. Subsequently, a prognostic risk model was established based on 13 ARGs, and significantly stratified HNSCC patients into highand low-risk groups in terms of overall survival (OS) (HR = 0.379,95% CI: 0.289-0.495, p < 0.0001). The multivariate Cox analysis revealed that this model was an independent prognostic factor (HR =1.506, 95% CI = 1.330-1.706, P < 0.001). The areas under the ROC curves (AUC) were significant for both the TCGA and GEO, with AUC of 0.685 and 0.928 respectively. Functional annotation revealed that model significantly enriched in many critical pathways correlated with tumorigenesis, including the p53 pathway, IL2 STAT5 signaling, TGF beta signaling, PI3K Ak mTOR signaling by gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA). In addition, we developed a nomogram shown some clinical net could be used as a reference for clinical decision making.Conclusions: Collectively, we developed and validated a novel robust 13-gene signatures for HNSCC prognosis prediction. The 13 ARGs could serve as an independent and reliable prognostic biomarkers and therapeutic targets for the HNSCC patients.