Table6_An Immune-Gene-Based Classifier Predicts Prognosis in Patients With Cervical Squamous Cell Carcinoma.XLS

Objective: Immunity plays a vital role in the human papilloma virus (HPV) persistent infection, and closely associates with occurrence and development of cervical squamous cell carcinoma (CSCC). Herein, we performed an integrated bioinformatics analysis to establish an immune-gene signature and immune-associated nomogram for predicting prognosis of CSCC patients.Methods: The list of immunity-associated genes was retrieved from ImmPort database. The gene and clinical information of CSCC patients were obtained from The Cancer Genome Atlas (TCGA) website. The immune gene signature for predicting overall survival (OS) of CSCC patients was constructed using the univariate Cox-regression analysis, random survival forests, and multivariate Cox-regression analysis. This signature was externally validated in GSE44001 cohort from Gene Expression Omnibus (GEO). Then, based on the established signature and the TCGA cohort with the corresponding clinical information, a nomogram was constructed and evaluated via Cox regression analysis, concordance index (C-index), receiver operating characteristic (ROC) curves, calibration plots and decision curve analyses (DCAs).Results: A 5-immune-gene prognostic signature for CSCC was established. Low expression of ICOS, ISG20 and high expression of ANGPTL4, SBDS, LTBR were risk factors for CSCC prognosis indicating poor OS. Based on this signature, the OS was significantly worse in high-risk group than in low-risk group (p-value < 0.001), the area under curves (AUCs) for 1-, 3-, 5-years OS were, respectively, 0.784, 0.727, and 0.715. A nomogram incorporating the risk score of signature and the clinical stage was constructed. The C-index of this nomogram was 0.76. AUC values were 0.811, 0.717, and 0.712 for 1-, 3-, 5-years OS. The nomogram showed good calibration and gained more net benefits than the 5-immune-gene signature and the clinical stage.Conclusion: The 5-immune-gene signature may serve as a novel, independent predictor for prognosis in patients with CSCC. The nomogram incorporating the signature risk score and clinical stage improved the predictive performance than the signature and clinical stage alone for predicting 1-year OS.

研究目标：免疫在人类乳头瘤病毒（HPV）的持续感染中发挥着至关重要的作用，并与宫颈鳞状细胞癌（CSCC）的发生和发展密切相关。本研究通过整合生物信息学分析，建立了一套免疫基因特征和免疫相关列线图，以预测CSCC患者的预后。研究方法：从ImmPort数据库中检索了免疫相关基因列表。CSCC患者的基因和临床信息从The Cancer Genome Atlas（TCGA）网站获取。利用单变量Cox回归分析、随机生存森林和多变量Cox回归分析构建了预测CSCC患者总生存期（OS）的免疫基因特征。该特征在外部验证了Gene Expression Omnibus（GEO）中的GSE44001队列。然后，基于建立的基因特征和相应的临床信息TCGA队列，构建并评估了列线图，通过Cox回归分析、一致性指数（C-index）、受试者工作特征（ROC）曲线、校准图和决策曲线分析（DCAs）进行评估。研究结果：建立了一个包含5个免疫基因的CSCC预后特征。ICOS、ISG20的低表达以及ANGPTL4、SBDS、LTBR的高表达是CSCC预后的风险因素，表明预后不良。基于此特征，高风险组的OS明显差于低风险组（p值<0.001），1年、3年、5年OS的曲线下面积（AUCs）分别为0.784、0.727和0.715。构建了一个结合特征风险评分和临床阶段的列线图。该列线图的C-index为0.76。1年、3年、5年OS的AUC值分别为0.811、0.717和0.712。该列线图表现出良好的校准性，且比5个免疫基因特征和临床阶段单独预测1年OS时具有更高的净收益。研究结论：5个免疫基因特征可能作为CSCC患者预后预测的一个新颖且独立的指标。结合特征风险评分和临床阶段的列线图在预测1年OS方面，比单独使用特征和临床阶段具有更好的预测性能。