肺腺癌转移相关的免疫特征开发及潜在中药预测

本研究旨在确定具有免疫相关基因的可靠预后特征,该特征可以预测预后并对肺腺癌(lung adenocarcinoma, LUAD)患者的个体化管理提供帮助。从癌症基因组图谱(The Cancer Genome Atlas, TCGA)数据库下载LUAD患者的mRNA表达谱和相应的临床数据;使用单因素COX和LASSO模型来构建预后模型;使用基于风险评分的方法开发预后特征;通过Kaplan-Meier分析比较高风险患者和低风险患者之间的总生存期(overall survival, OS), OS的独立预测因子通过单变量和多变量COX分析确定;单样本基因集富集分析(single sample gene set enrichment analysis, ssGSEA)用于评估免疫细胞浸润程度;通过LASSO和COX回归分析构建生存预后特征。根据预后特征,在OS方面将患者显著分层为高风险组和低风险组,与低风险组相比,高风险组的LUAD患者OS显著降低。通过ROC曲线分析证实了预后基因标记的预测能力。多因素COX分析显示,风险评分是OS的独立预测因子。通过免疫分析,发现了肺腺癌转移组与未转移组的不...     

联合TCGA,GEO数据库分析THY1在胃癌中的表达及临床意义

为了研究THY1 (THYmocyte differentiation antigen 1)在胃癌中的表达情况,预测并探讨THY1参与肿瘤发生发展的可能机制及临床价值。本研究从GEO (Gene expression omnibus)数据库中选择GSE33335、GSE56807、GSE63089 3个芯片的数据,利用"limma"、"RobustRankAggreg".R语言包,找到3个芯片中共同的差异基因,并通过DAVID网站对差异基因进行功能通路富集分析,利用"ggplots".R语言包进行可视化分析。通过Kmplotter在线网站筛选跟胃癌生存预后相关的差异基因。利用Oncomine数据库探究THY1基因在不同癌症及胃癌中的差异表达。利用癌症基因组图谱TCGA (Cancer genome atlas)数据库获取胃癌数据集,随后以THY1的表达水平进行患者的生存分析和基因集富集分析(gene set enrichment analysis, GSEA),以期挖掘THY1的潜在临床意义及其分子机制。结果本研究发现THY1的表达水平与胃癌患者的生存预后相关,THY1高表达的患者总生存期明显短于低表达的患者(p0.001) THY1高表达样本富集了细胞黏附、细胞因子受体互作通路、ECM受体通路、粘着斑通路、骨架蛋白调控、癌症通路、TGF-β通路等基因集。研究结果表明,在胃癌中,THY1高表达是一种预后不良因素,可以作为预测患者转移发生、判断预后的有效生物标志物。     

基于生物信息学分析的非小细胞肺癌诊断预后相关基因的筛选

为了从基因层面探讨非小细胞肺癌(non-small cell lung cancer, NSCLC)发生发展的内在机制,筛选与NSCLC诊断、预后相关的基因,为NSCLC分子机制的进一步研究提供生物信息学依据,利用生物信息学方法对GEO数据库和TCGA数据库的数据集进行合并分析,筛选NSCLC组织与正常肺组织之间的差异表达基因(differentially expressed genes, DEGs),并对所取交集的DEGs进行基因集富集分析(gene set enrichment analysis, GSEA)、基因本体论(gene ontology, GO)分析、KEGG (kyoto encyclopedia of genes and genomes)通路富集分析、蛋白质相互作用(protein-protein interaction, PPI)分析、ROC曲线诊断效能分析及LASSO生存分析。文中共筛选出240个DEGs,主要涉及核分裂、染色体分离等生物学过程。GSEA分析结果显示,富集的通路主要涉及DNA修复和细胞周期。从PPI网络中筛选出20个hub基因, ROC结果显示, UBE2C (AUC=0.939)、TOP2A(AUC=0.927)、RRM2 (AUC=0.927)、CCNB1 (AUC=0.928)、MKI67 (AUC=0.930)、AURKA (AUC=0.931)、MELK(AUC=0.950)相对具有较高的诊断价值, LASSO COX回归结果则显示IL6、KIAA0101、MKI67、TPX2、AURKA、CDKN3及CDCA5与NSCLC患者的预后强相关。本研究结果表明, ZWINT、KIF2C、MELK、CDCA5可能在NSCLC中发挥着重要的作用,为阐明NSCLC的分子机制提供了新思路。     

丝氨酸蛋白酶抑制剂(SERPIN)家族相关基因在结肠腺癌中预后模型的建立及应用

应用生物信息学方法,构建结肠腺癌(COAD)丝氨酸蛋白酶抑制剂(SERPIN)家族相关基因预后模型。从TCGA数据库和GEO数据库下载结肠腺癌(COAD)转录组和临床数据,根据数据中SERPINs家族基因的表达量对COAD患者进行一致性聚类分析；将数据随机均分为训练集(Train)组和验证集(Test)组,基于两个亚型的差异基因,利用Train组进行COX回归和Lasso回归构建预后模型,根据模型风险评分中位值将样本分为高、低风险两组,绘制高低风险组患者生存曲线；通过ROC曲线评价模型预测能力；利用Test组数据验证模型；构建列线图,评估患者生存率模型预测值与实际值的一致性；并利用利用ESTIMATE算法和CIBERSORT算法评估风险评分和肿瘤微环境(TME)以及免疫浸润的相关性。通过34个SERPIN基因确定了两个亚型,基于2个亚型筛选出了436个预后相关分型差异基因,通过Lasso回归确定出了11个预后相关基因参与风险模型的构建,根据模型评分区分的高低风险组具有明显的生存差异,列线图可以准确预测1、3和5年生存率。肿瘤微环境分析和免疫浸润分析显示高风险评分组患者免疫活性差。SERPIN家族相关基因构建的风险评分模型能够预测COAD的预后,有利于进一步指导临床对COAD的诊治,提高患者生存率。     

基于单细胞测序构建胶质母细胞瘤预后模型

[目的]基于单细胞测序筛选胶质母细胞瘤特征基因并构建预后模型。[方法]分析GEO数据库单细胞RNA测序数据集GSE84465,筛选出GBM细胞分化相关的差异基因。下载TCGA数据库GBM的基因表达谱和临床数据,采用Lasso回归、Cox回归分析筛选出特征基因构建预后模型,根据独立预后因素构建列线图,GSE83300作为外部验证集。基于风险评分中位数将患者分组,比较两组生存差异。[结果]通过scRNA-seq得到492个分化差异基因,经过回归分析得到基于6个基因(PLAUR、RARRES2、G0S2、MDK、SERPINE2、CD81)的预后模型。其1、3、5年ROC曲线下面积均大于0.7;KM分析显示高低风险组预后存在差异(P<0.001),GSE83300验证结果与TCGA一致。多因素Cox回归分析表明年龄和风险评分可以作为独立影响因素(P<0.01);C-Index(0.679)、校准图显示列线图预测模型有良好的拟合度。GSEA分析示高低风险组差异基因集参与细胞因子受体相互作用、抗原处理与提呈等通路。[结论]由PLAUR、RARRES2、G0S2、MDK、SERPINE...     

急性髓系白血病铁死亡相关基因筛选及预后模型的建立

基于急性髓系白血病(Acute Myeloid Leukemia,AML)临床大数据及多组学数据库探讨铁死亡相关基因在AML中的作用,并建立铁死亡基因表达相关预后模型。整合TCGA数据库中151例AML患者和GTEx数据库中337例正常人外周血的临床和转录组数据。将Wilcoxon检验和单因素Cox分析结果取交集,筛选出预后相关差异表达基因(Differential Expression Genes, DEGs),使用Lasso回归建立基因标志物预后模型,利用受试者工作特征曲线(Receiver Operating Characteristic Curve,ROC曲线)评价预测价值,Kaplan-Meier法进行生存分析,对AML患者临床数据进行单因素和多因素Cox回归分析,使用差异基因表达分析等方法比较高、低风险患者间的组学差异,最后,利用BeatAML数据库对基因标志物进行验证。将差异基因表达分析和单因素分析结果取交集,得到13个预后相关DEGs。构建了8个基因标志物的预后评分模型,并将患者分为高、低风险两组;ROC曲线分析证实了模型良好的预测性能;生存分析提示高、低风险组患者的生存率具有显著差异;单因素分析显示年龄和风险评分与患者整体生存显著相关,多因素分析显示,年龄和风险评分是独立预后指标。在2个风险组之间筛选出384个DEGs,GO富集分析结果显示,富集的基因大多与中性粒细胞和白细胞的趋化与迁移等免疫相关分子和通路显著相关,KEGG富集通路主要与TNF信号通路、细胞因子与细胞因子受体相互作用相关。BeatAML数据库验证结果显示,5个基因与预后显著相关。铁死亡相关基因在AML中显著表达,且高风险患者预后较差,该研究对AML铁死亡相关潜在生物标志物的发现和应用奠定了一定的基础。     

联合TCGA和GEO数据库筛选乳腺癌易感基因

本研究是利用公共基因芯片数据库筛选乳腺癌的预后基因,预测和探索这些基因在乳腺癌进展中的可能机制和临床价值.首先,我们筛选了公共基因芯片数据库(gene expression omnibus,GEO)GSE22820和癌症基因组图谱(the cancer genome atlas,TCGA)乳腺癌数据库的重叠差异表达基因,联合R语言分析乳腺癌组织与癌旁正常组织差异表达的基因;其次,基于STRING数据库及Cytoscape软件构建蛋白质相互作用网络图,分析并识别了中枢基因和前3个模块;之后进行了更多的功能分析,包括基因本体(gene ontology,GO)和京都基因与基因组百科全书(kyoto encyclopedia of genes and genomes,KEGG)通路分析以及基因集富集分析(gene set enrichment analysis,GSEA),以研究这些基因的作用以及潜在的潜在机制;最后进行了Kaplan-Meier分析和Cox比例风险分析,以阐明这些基因的诊断和预后效果.相关数据分析表明15个基因的表达水平与生存预后相关,高表达基因患者的总生存时间短于低表达患者(P＜0.05);Cox比例风险分析表明UBE2T、ER-CC6L和RAD51这3个基因是预后生存的独立因素(P＜0.05);GSEA分析表明在UBE2T、ERCC6L和RAD51基因中细胞周期、基础转录因子和卵母细胞减数分裂明显富集.最终,我们得出结论,这3种基因标志物的高表达是乳腺癌预后不良因素,可作为预测乳腺癌患者转移和预后的有效生物标志物.     

基于生物信息学探讨疏肝降脂颗粒调节铁死亡治疗非酒精性脂肪性肝病的作用

本研究旨在运用生物信息学方法探讨疏肝降脂颗粒调节铁死亡治疗非酒精性脂肪性肝病(non-alcoholic fatty liver disease, NAFLD)的潜在分子机制。通过网络药理学方法获取“疏肝降脂颗粒-铁死亡”潜在的交集靶点,基于GSE89632数据集筛选“疏肝降脂颗粒-NAFLD-铁死亡”交集靶点的差异表达基因(differentially expressed genes, DEGs),并运用R4.2.2软件进行相关性分析、富集分析和免疫浸润分析。对GSE89632数据集的样本进行共识聚类,构建机器学习模型,筛选关键DEGs,构建列线图模型并验证;对关键基因进行临床相关性分析。共获得18个“疏肝降脂颗粒-NAFLD-铁死亡”DEGs,富集在Th17细胞分化、甲状腺激素信号通路、 ErbB信号通路、 IL-17信号通路等方面。随机森林(random forest, RF)模型为最优机器学习模型,AURKA、SREBF1、HMOX1、MYC和JUN是RF模型中重要性排名前五的基因,HMOX1、JUN与体重指数(body mass index, BMI)呈正相关。列线图模型可以...     

DNA甲基化驱动的转录表达特征作为肝癌预后预测标志物的价值

肝细胞癌(hepatocellular carcinoma,简称肝癌)是最常见的恶性肿瘤之一。DNA甲基化的异常是恶性肿瘤的特征之一,并被发现在肝癌等肿瘤的发生发展中发挥重要作用。为了能为肝癌患者提供新的临床预后预测标志物,本研究首先采用整合组学分析策略在全基因组范围内鉴定与肝癌患者预后相关的DNA甲基化驱动的差异表达基因;然后,采用LASSO (least absolute shrinkage and selection operator)分析建立了10个最优基因组合的预后预测模型。Cox比例风险回归分析显示,在校正临床特征参数后,此预测模型高风险评分与患者不良预后显著相关,表明该模型具有潜在的独立预后价值。受试者工作特征(receiver operating characteristic,ROC)曲线分析显示该风险评分模型在预测患者短期和长期预后方面优于其他已被报道的肝癌预后预测模型。基因集富集分析(gene set enrichment analysis, GSEA)表明,高风险评分与细胞周期和DNA损伤修复通路相关。以上结果表明,本研究构建了一个基于10个DNA甲基化驱动基因的预后风险评分模型,该模型可作为肝癌患者的潜在预后生物标志物,有助于肝癌患者的生存预后评估和治疗策略的指导。     

基于生物信息学分析的宫颈鳞状细胞癌预后相关基因的筛选

为了分析宫颈鳞状细胞癌(cervical squamous cell carcinoma, CESC)与正常组织中的差异表达基因(differentially expressed genes, DEGs),鉴定与CESC预后相关的关键基因,从GEO和TCGA数据库下载CESC的基因表达谱数据,利用R软件筛选CESC组织与正常组织中的DEGs,并对这些DEGs开展功能和通路富集分析;然后构建蛋白质-蛋白质相互作用(protein-protein interaction, PPI)网络,筛选出关键(hub)基因;最后对hub基因进行LASSO COX回归及总体生存率(overall survival, OS)分析。研究共筛选出167个DEGs,这些基因主要涉及染色体分离、DNA复制等生物过程,介导染色质结合、G蛋白偶联受体结合等分子功能,富集于染色体区域、纺锤体和MCM复合体。GSEA分析结果显示,富集的通路主要涉及DNA复制和细胞周期信号通路。此外,从PPI网络中筛选出20个hub基因, LASSO COX回归结果显示MAD2L1、ZWINT、RRM2、TTK、CDC6、PBK、TOP2A、KIF11、KIF20A、NCAPG、NUSAP1、CCNB1及CDK1与CESC患者的预后相关; Kaplan-Meier曲线显示, ZWINT、DTL、CCNB1、CDC6、TOP2A、CDK1、PBK、RFC4及NUSAP1的m RNA表达水平与CESC患者生存预后相关。本研究结果表明, ZWINT、CDC6、PBK、TOP2A、NUSAP1、CCNB1和CDK1为CESC的预后关键基因,为阐明CESC的分子机制提供了理论依据。     

Revealing the link between macrophage in microenvironment of osteosarcoma and poor prognosis by utilizing the Integrated analysis

Objectives:Osteosarcoma (OS) is the most common type of primary malignant bone tumor, The effect of tumor microenvironment components on OS oncogenesis remains unknown.Methods:To investigate the function of immune cells in osteosarcoma, we provided a text-based GMT (Gene Matrix Transposed) file in which each line defines one of lm22 with their markers. We used STRING to draw DEG’s PPI network and selected hub genes and modules. Then, survival analysis was conducted to hub genes. We identified 10,390 common genes, and identified 218 DEGs based on the combined t-value and Z scores.Results:The KEGG and GSEA enrichment analysis showed that macrophages are significantly activated in osteosarcoma. PPI network analysis revealed that hub gene CD163 molecule. We found that the expression of CD163 was negatively associated with the OS of osteosarcoma patients. These results suggest that macrophages are a risk factor in patients with osteosarcoma.Conclusions:This study has systematically validated results of the studies carried out previously and filled up the gap in the field of OS on large-scaled meta-analysis. In addition, for the hub gene (CD163) and the macrophage cell capable of being used as a novel biomarker in promoting early diagnosis and development of therapeutic approaches.     

肺腺癌诊断标志物筛选及免疫细胞浸润分析

用生物信息学方法筛选肺腺癌(Lung adenocarcinoma,LUAD)的诊断生物标志物,并分析肺腺癌中免疫细胞浸润情况。从GEO和TCGA数据库下载肺腺癌的表达数据集,利用R软件筛选肺腺癌与正常肺组织间的差异表达基因(DEGs),使用DAVID网站对DEGs进行GO及KEGG富集分析,使用STRING及Cytoscape等工具对DEGs构建蛋白相互作用网络并筛选hub基因;利用Kaplan-Meier法对DEGs进行生存分析,并对hub基因进行ROC分析筛选诊断生物标志物,利用GSEA预测有预后价值的基因参与的信号通路;并用Cibersort软件反卷积算法分析肺腺癌中免疫细胞浸润情况。共得到肺腺癌的234个DEGs,这些基因主要参与信号转导、物质代谢、免疫反应等相关信号通路;构建PPI网络筛选出的20个hub基因中8个存在预后价值(CCNA2、DLGAP5、HMMR、MMP1、MMP9、MMP13、SPP1、TOP2A),ROC分析中DLGAP5、SPP1值分别是0.703、0.706;DLGAP5、SPP1基因表达水平与肺腺癌组织浆细胞、未活化的CD4⁺记忆细胞、调节T细胞、巨噬细胞M0、M1、M2及中性粒细胞浸润密切相关(P＜0.05)。肺腺癌中DLGAP5、SPP1具有较高诊断价值且参与肺腺癌组织免疫细胞浸润;DLGAP5、SPP1基因可作为肺腺癌诊断的生物标志物,可为肺腺癌的靶向治疗研究提供新思路。     

Prognostic implication of cuproptosis related genes associates with immunity in Ewing's sarcoma

Growing evidence demonstrated that cuproptosis play critical roles in human cancers. We aimed to identify the roles of cuproptosis related genes (CRGs) in prognosis and immunity of Ewing's sarcoma.The data of GSE17674 and GSE63156 were obtained from GEO. The expression of 17 CRGs and immune cells were explored, then correlation was analyzed. Based on CRGs, two molecular clusters were identified by consensus clustering algorithm. KM survival and IME features including immune cells, immune response, checkpoint genes between clusters were evaluated. NFE2L2, LIAS, and CDKN2A were screened out as prognostic signatures by univariate, LASSO and step regression. A risk model was established, and validated by KM method with p = 0.0026, and perfect AUC values. The accuracy of risk model was also well validated in external dataset. A nomogram was constructed and evaluated by calibration curves and DCA. Low level of immune cells, immune response, and enriched checkpoint genes were found in high-risk group. GSEA of signatures and GSVA of ES-related pathways revealed the potential molecular mechanism involved in ES progression. Several drugs showed sensitivity to ES samples. DEGs between risk groups were screened out, and function enrichment was conducted. Finally, scRNA analysis of GSE146221 was done. NFE2L2, and LIAS played crucial role in the evolution of ES by pesudotime and trajectory methods. Our study provided new aspects for further research in ES.     

Identification and validation of autophagy-related prognostic signature for head and neck squamous cell carcinoma

BackgroundMany studies have demonstrated that autophagy plays a significant role in regulating tumor growth and progression. However, the effect of autophagy-related genes (ARGs) on the prognosis have rarely been analyzed in head and neck squamous cell carcinoma (HNSCC).MethodsWe obtained differentially expressed ARGs from HNSCC mRNA data in The Cancer Genome Atlas (TCGA) database. And then we performed gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses to explore the autophagy-related biological functions. The overall survival (OS)-related and disease specific survival (DSS)-related ARGs were identified by univariate Cox regression analyses. With these genes, we established OS-related and DSS-related risk signature by LASSO regression method, respectively. We validated the reliability of the risk signature with receiver operating characteristic (ROC) analysis, Kaplan-Meier survival curves, clinical correlation analysis, and nomogram. Then we analyzed relationships between risk signature and immune cell infiltration.ResultsWe established the prognostic signatures based on 14 ARGs for OS and 12 ARGs for DSS. The ROC curves, survival analysis, and nomogram validated the predictive accuracy of the models. Clinic correlation analysis showed that the risk group was closely related to Stage, pathological T stage, pathological N stage and human papilloma virus (HPV) subtype. Cox regression demonstrated that the risk score was an independent predictor for the prognosis of HNSCC patients. Furthermore, patients in low-risk score group exhibited higher immunescore and distinct immune cell infiltration than high-risk score group. And we further analysis revealed that the copy number alterations (CNAs) of ARGs-based signature affected the abundance of tumor-infiltrating immune cells.ConclusionIn this study, we identified novel autophagy-related signature for the prediction of OS and DSS in patients with HNSCC. Meanwhile, our study provides a novel sight to understand the role of autophagy and elucidate the important role of autophagy in tumor immune microenvironment (TIME) of HNSCC.     

Identification of biomarkers correlated with hypertrophic cardiomyopathy with co-expression analysis

Hypertrophic cardiomyopathy (HCM) is reported to be the most common genetic heart disease. To identify key module and candidate biomarkers correlated with clinical prognosis of patients with HCM, we carried out this study with co-expression analysis. To construct a co-expression network of hub genes correlated with HCM, the Weighted Gene Co-expression Network Analysis (WGCNA) was performed. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed by Database for Annotation, Visualization and Integrated Discovery (DAVID). The protein-protein interaction network analysis of central genes was performed to recognize the interactions of central genes. Gene set enrichment analyses were carried out to discover the possible mechanisms involved in the pathways promoted by hub genes. To validate the hub genes, quantitative real-time polymerase chain reaction (RT-PCR) was performed. Based on the results of topological overlap measure based clustering, 2,351 differentially expressed genes (DEGs) were identified. Those genes were included in six different modules. Of these modules, the yellow and the blue modules showed a pivotal correlation with HCM. DEGs were enriched in immune system procedure associated GO terms and KEGG pathways. We identified nine hub genes (TYROBP, STAT3, CSF1R, ITGAM, SYK, ITGB2, LILRB2, LYN, and HCK) affected the immune system significantly. Among the genes we validated with RT-PCR, TYROBP, CSF1R, and SYK showed significant increasing expression levels in model HCM rats. In conclusion, we identified two modules and nine hub genes, which were prominently associated with HCM. We found that immune system may play a crucial role in the HCM. Accordingly, those genes and pathways might become therapeutic targets with clinical usefulness in the future.     

Novel prognostic model established for patients with head and neck squamous cell carcinoma based on pyroptosis-related genes

We aimed at establishing a risk – score model using pyroptosis-related genes to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC). A total of 33 pyroptosis-related genes were selected. We then evaluated the data of 502 HNSCC patients and 44 normal patients from TCGA database. Gene expression was then profiled to detect differentially expressed genes (DEGs). Using the univariate, the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we generated a risk – score model. Tissue samples from neoplastic and normal sites of 44 HNSCC patients were collected. qRT-PCR were employed to analyze the mRNA level of the samples. Kaplan-Meier method was used to evaluate the overall survival rate (OS). Enrichment analysis was performed to elucidate the underlying mechanism of HNSCC patient's differentially survival status from the perspective of tumor immunology. 17 genes were categorized as DEGs. GSDME, IL-6, CASP8, CASP6, NLRP1 and NLRP6 were used to establish the risk – score model. Each patient's risk score in the TCGA cohort was calculated using the risk – score formula. The risk score was able to independently predict the OS of the HNSCC patients (P = 0.02). The OS analysis showed that the risk score model (P < 0.0001) was more reliable than single gene, a phenomenon verified by practical patient cohort. Additionally, enrichment analysis indicated more active immune activities in low-risk group than high-risk group. In conclusion, our risk – score model has provided novel strategy for the prediction of HNSCC patients’ prognosis.     

Novel prognostic model established for patients with head and neck squamous cell carcinoma based on pyroptosis-related genes

We aimed at establishing a risk – score model using pyroptosis-related genes to predict the prognosis of patients with head and neck squamous cell carcinoma (HNSCC). A total of 33 pyroptosis-related genes were selected. We then evaluated the data of 502 HNSCC patients and 44 normal patients from TCGA database. Gene expression was then profiled to detect differentially expressed genes (DEGs). Using the univariate, the least absolute shrinkage and selection operator (LASSO) Cox regression analyses, we generated a risk – score model. Tissue samples from neoplastic and normal sites of 44 HNSCC patients were collected. qRT-PCR were employed to analyze the mRNA level of the samples. Kaplan-Meier method was used to evaluate the overall survival rate (OS). Enrichment analysis was performed to elucidate the underlying mechanism of HNSCC patient's differentially survival status from the perspective of tumor immunology. 17 genes were categorized as DEGs. GSDME, IL-6, CASP8, CASP6, NLRP1 and NLRP6 were used to establish the risk – score model. Each patient's risk score in the TCGA cohort was calculated using the risk – score formula. The risk score was able to independently predict the OS of the HNSCC patients (P = 0.02). The OS analysis showed that the risk score model (P < 0.0001) was more reliable than single gene, a phenomenon verified by practical patient cohort. Additionally, enrichment analysis indicated more active immune activities in low-risk group than high-risk group. In conclusion, our risk – score model has provided novel strategy for the prediction of HNSCC patients’ prognosis.