Table7_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）持续性感染中扮演着至关重要的角色，并与宫颈癌鳞状细胞癌（CSCC）的发生与发展密切相关。本研究通过对免疫基因进行整合生物信息学分析，旨在构建一个免疫基因特征及免疫相关列线图，以预测CSCC患者的预后。研究方法：从ImmPort数据库中检索到与免疫相关的基因列表。通过癌症基因组图谱（TCGA）网站获取CSCC患者的基因和临床信息。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GEO数据库中的GSE44001队列。在此基础上，结合建立的基因特征和具有相应临床信息的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CSCC的5个免疫基因预后特征。ICOS、ISG20的低表达以及ANGPTL4、SBDS、LTBR的高表达是CSCC预后的风险因素，表明总生存期（OS）较差。基于此特征，高风险组患者的OS显著低于低风险组（p值<0.001），1年、3年、5年的OS曲线下面积（AUCs）分别为0.784、0.727和0.715。构建了包含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年的OS AUC值分别为0.811、0.717和0.712。该列线图表现出良好的校准，并且相较于5个免疫基因特征和临床阶段单独预测，具有更高的净收益。研究结论：5个免疫基因特征可能成为CSCC患者预后预测的一个新颖且独立的预测因子。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，相较于特征和临床阶段单独预测，提高了预测性能。