Data_Sheet_1_Significance of Tumor Mutation Burden Combined With Immune Infiltrates in the Progression and Prognosis of Advanced Gastric Cancer.PDF

Gastric cancer (GC) is a serious malignant tumor with high mortality and poor prognosis. The prognosis and survival are much worse for advanced gastric cancer (AGC). Recently, immunotherapy has been widely promoted for AGC patients, and studies have shown that tumor mutation burden (TMB) is closely related to immunotherapy response. Here, RNA-seq data, matched clinical information, and MAF files were downloaded from the cancer genome atlas (TCGA)-STAD project in the TCGA database. The collation and visual analysis of mutation data were implemented by the “maftools” package in R. We calculated the TMB values for AGC patients and divided the patients into high- and low-TMB groups according to the median value of TMB. Then, the correlation between high or low TMB and clinicopathological parameters was calculated. Next, we examined the differences in gene expression patterns between the two groups by using the “limma” R package and identified the immune-related genes among the DEGs. Through univariate Cox regression analysis, 15 genes related to prognosis were obtained. Furthermore, the two hub genes (APOD and SLC22A17) were used to construct a risk model to evaluate the prognosis of AGC patients. ROC and survival curves and GEO data were used as a validation set to verify the reliability of this risk model. In addition, the correlation between TMB and tumor-infiltrating immune cells was examined. In conclusion, our results suggest that AGC patients with high TMB have a better prognosis. By testing the patient’s TMB, we could better guide immunotherapy and understand patient response to immunotherapy.

胃癌（GC）是一种具有高死亡率及不良预后的严重恶性肿瘤。对于晚期胃癌（AGC），其预后及生存率更为恶劣。近年来，免疫疗法在AGC患者中的应用得到了广泛推广，研究表明肿瘤突变负荷（TMB）与免疫疗法反应密切相关。本研究所涉及的数据包括从TCGA数据库中下载的癌症基因组图谱（TCGA）-STAD项目的RNA-seq数据、匹配的临床信息和MAF文件。通过R语言的“maftools”包对突变数据进行汇编和可视化分析。我们计算了AGC患者的TMB值，并根据TMB的中位数将患者分为高TMB和低TMB组。随后，我们计算了高或低TMB与临床病理参数之间的相关性。接着，利用“limma”R包分析了两组之间的基因表达模式差异，并确定了差异表达基因（DEGs）中的免疫相关基因。通过单因素Cox回归分析，获得了与预后相关的15个基因。此外，通过构建风险模型，利用枢纽基因（APOD和SLC22A17）评估AGC患者的预后。ROC曲线、生存曲线及GEO数据被用作验证集，以验证该风险模型的可靠性。此外，还考察了TMB与肿瘤浸润免疫细胞之间的相关性。总之，我们的研究结果提示，高TMB的AGC患者具有更好的预后。通过检测患者的TMB，我们能够更好地指导免疫疗法，并理解患者对免疫疗法的反应。