Zhou Lin, Wang Chunyu
School of Information Science and Technology, University of Science and Technology of China, Hefei, Anhui, China.
School of Biological and Environmental Engineering, Chaohu University, Chaohu, Anhui, China.
Front Oncol. 2023 Mar 3;13:1107532. doi: 10.3389/fonc.2023.1107532. eCollection 2023.
According to 2020 global cancer statistics, digestive system tumors (DST) are ranked first in both incidence and mortality. This study systematically investigated the immunologic gene set (IGS) to discover effective diagnostic and prognostic biomarkers. Gene set variation (GSVA) analysis was used to calculate enrichment scores for 4,872 IGSs in patients with digestive system tumors. Using the machine learning algorithm XGBoost to build a classifier that distinguishes between normal samples and cancer samples, it shows high specificity and sensitivity on both the validation set and the overall dataset (area under the receptor operating characteristic curve [AUC]: validation set = 0.993, overall dataset = 0.999). IGS-based digestive system tumor subtypes (IGTS) were constructed using a consistent clustering approach. A risk prediction model was developed using the Least Absolute Shrinkage and Selection Operator (LASSO) method. DST is divided into three subtypes: subtype 1 has the best prognosis, subtype 3 is the second, and subtype 2 is the worst. The prognosis model constructed using nine gene sets can effectively predict prognosis. Prognostic models were significantly associated with tumor mutational burden (TMB), tumor immune microenvironment (TIME), immune checkpoints, and somatic mutations. A composite nomogram was constructed based on the risk score and the patient's clinical information, with a well-fitted calibration curve (AUC = 0.762). We further confirmed the reliability and validity of the diagnostic and prognostic models using other cohorts from the Gene Expression Omnibus database. We identified diagnostic and prognostic models based on IGS that provide a strong basis for early diagnosis and effective treatment of digestive system tumors.
根据2020年全球癌症统计数据，消化系统肿瘤（DST）的发病率和死亡率均位居首位。本研究系统地调查了免疫基因集（IGS），以发现有效的诊断和预后生物标志物。基因集变异（GSVA）分析用于计算消化系统肿瘤患者中4872个IGS的富集分数。使用机器学习算法XGBoost构建区分正常样本和癌症样本的分类器，其在验证集和整体数据集上均显示出高特异性和敏感性（受试者操作特征曲线下面积[AUC]：验证集=0.993，整体数据集=0.999）。基于IGS的消化系统肿瘤亚型（IGTS）采用一致性聚类方法构建。使用最小绝对收缩和选择算子（LASSO）方法开发了风险预测模型。DST分为三个亚型：亚型1预后最佳，亚型3次之，亚型2最差。使用九个基因集构建的预后模型可以有效预测预后。预后模型与肿瘤突变负荷（TMB）、肿瘤免疫微环境（TIME）、免疫检查点和体细胞突变显著相关。基于风险评分和患者临床信息构建了综合列线图，校准曲线拟合良好（AUC=0.762）。我们使用来自基因表达综合数据库的其他队列进一步证实了诊断和预后模型的可靠性和有效性。我们基于IGS确定了诊断和预后模型，为消化系统肿瘤的早期诊断和有效治疗提供了有力依据。
