The landscape and prognostic value of tumor-infiltrating immune cells in gastric cancer
The landscape and prognostic value of tumor-infiltrating immune cells in gastric cancer
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作者：
Li, Linhai;Ouyang, Yiming;Zhu, Yu
背景。胃癌（GC）是世界上第四大最常诊断的恶性肿瘤，也是导致癌症相关死亡的第二大原因。肿瘤微环境，特别是肿瘤浸润免疫细胞（TIICs），在促进和抑制肿瘤生长方面发挥着至关重要的作用。本研究的目的是评估TIICs的情况，并制定GC的预后图。材料与方法。从癌症基因组图谱（TCGA）数据集中获得的基因表达谱通过CIBERSORT算法量化22个TIICs在GC中的比例。采用LASSO回归分析和多变量Cox回归分析选择最佳生存相关TIICs，并制定免疫评分公式。基于免疫评分和临床信息，构建预后nomogram，并通过受试者工作特征曲线（ROC）曲线下面积（AUC）和校正图评价其预测准确性。此外，国际癌症基因组联盟（ICGC）数据集的数据验证了nomogram。在GC样品中，巨噬细胞（25.3%）、静息记忆CD4 T细胞（16.2%）和CD8 T细胞（9.7%）在22个TIICs中含量最多。筛选出7个TIICs并用于开发免疫评分公式。TCGA组预后nomogram AUC为0.772，与ICGC组（0.730）和整体组（0.748）相似，明显优于单纯分期TNM组（0.591）。校正图显示预测值与实际观测值具有较好的一致性。生存分析显示，高免疫评分组胃癌患者的临床预后较差。多因素分析结果显示免疫评分是一个独立的预后因素。免疫评分可增强TNM分期系统的临床预后预测能力，为胃癌患者的风险评估和治疗选择提供便捷的工具。
Background. Gastric cancer (GC) is the fourth most frequently diagnosed malignancy and the second leading cause of cancer-associated mortality worldwide. The tumor microenvironment, especially tumor-infiltrating immune cells (TIICs), exhibits crucial roles both in promoting and inhibiting cancer growth. The aim of the present study was to evaluate the landscape of TIICs and develop a prognostic nomogram in GC.Materials and Methods. A gene expression profile obtained from a dataset from The Cancer Genome Atlas (TCGA) was used to quantify the proportion of 22 TIICs in GC by the CIBERSORT algorithm. LASSO regression analysis and multivariate Cox regression were applied to select the best survival-related TIICs and develop an immunoscore formula. Based on the immunoscore and clinical information, a prognostic nomogram was built, and the predictive accuracy of it was evaluated by the area under the curve (AUC) of the receiver operating characteristic curve (ROC) and the calibration plot. Furthermore, the nomogram was validated by data from the International Cancer Genome Consortium (ICGC) dataset.Results. In the GC samples, macrophages (25.3%), resting memory CD4 T cells (16.2%) and CD8 T cells (9.7%) were the most abundant among 22 TIICs. Seven TIICs were filtered out and used to develop an immunoscore formula. The AUC of the prognostic nomogram in the TCGA set was 0.772, similar to that in the ICGC set (0.730) and whole set (0.748), and significantly superior to that of TNM staging alone (0.591). The calibration plot demonstrated an outstanding consistency between the prediction and actual observation. Survival analysis revealed that patients with GC in the high-immunoscore group exhibited a poor clinical outcome. The result of multivariate analysis revealed that the immunoscore was an independent prognostic factor.Discussion. The immunoscore could be used to reinforce the clinical outcome prediction ability of the TNM staging system and provide a convenient tool for risk assessment and treatment selection for patients with GC.