Table_2_Diagnosis and prognosis prediction model for digestive system tumors based on immunologic gene sets.xlsx

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年全球癌症统计报告，消化系统肿瘤（DST）的发病率与死亡率均位列全球首位。本研究系统性地探究了免疫基因集（IGS），以发掘高效的诊断与预后生物标志物。研究采用基因集变异分析（GSVA），计算了消化系统肿瘤患者体内4872个免疫基因集的富集得分。借助机器学习算法XGBoost构建了可区分正常样本与癌组织样本的分类器，该分类器在验证集与全数据集上均展现出优异的特异性与敏感性（受试者工作特征曲线下面积[AUC]：验证集=0.993，全数据集=0.999）。本研究通过一致性聚类方法构建了基于免疫基因集的消化系统肿瘤亚型（IGTS）。采用最小绝对收缩和选择算子（LASSO）法构建风险预测模型。将消化系统肿瘤划分为三种亚型：亚型1预后最佳，亚型3次之，亚型2预后最差。基于9个基因集构建的预后模型可有效预测患者预后情况。该预后模型与肿瘤突变负荷（TMB）、肿瘤免疫微环境（TIME）、免疫检查点及体细胞突变均存在显著相关性。本研究基于风险评分与患者临床信息构建了整合列线图，其校准曲线拟合度良好（AUC=0.762）。我们进一步利用基因表达汇编（Gene Expression Omnibus, GEO）数据库中的其他队列，验证了诊断与预后模型的可靠性与有效性。本研究鉴定出的基于免疫基因集的诊断与预后模型，可为消化系统肿瘤的早期诊断与精准治疗提供坚实的理论依据。