Table11_TGFβ-Associated Signature Predicts Prognosis and Tumor Microenvironment Infiltration Characterization in Gastric Carcinoma.XLSX

Background: Gastric carcinoma (GC) is a carcinoma with a high incidence rate, and it is a deadly carcinoma globally. An effective tool, that is, able to predict different survival outcomes for GC patients receiving individualized treatments is deeply needed.
Methods: In total, data from 975 GC patients were collected from TCGA-STAD, GSE15459, and GSE84437. Then, we performed a comprehensive unsupervised clustering analysis based on 54 TGFβ-pathway-related genes and correlated these patterns with tumor microenvironment (TME) cell-infiltrating characteristics. WGCNA was then applied to find the module that had the closest relation with these patterns. The least absolute shrinkage and selection operator (LASSO) algorithm was combined with cross validation to narrow down variables and random survival forest (RSF) was used to create a risk score.
Results: We identified two different TGFβ regulation patterns and named them as TGFβ Cluster 1 and Cluster 2. TGFβ Cluster 1 was linked to significantly poorer survival outcomes and represented an inflamed TME subtype of GC. Using WGCNA, a module (magenta) with the closest association with the TGFβ clusters was identified. After narrowing down the gene list by univariate Cox regression analysis, the LASSO algorithm and cross validation, four of the 243 genes in the magenta module were applied to build a risk score. The group with a higher risk score exhibited a considerably poorer survival outcome with high predictive accuracy. The risk score remained an independent risk factor in multivariate Cox analysis. Moreover, we validated this risk score using GSE15459 and GSE84437. Furthermore, we found that the group with a higher risk score represented an inflamed TME according to the evidence that the risk score was remarkably correlated with several steps of cancer immunity cycles and a majority of the infiltrating immune cells. Consistently, the risk score was significantly related to immune checkpoint genes and T cell–inflamed gene expression profiles (GEPs), indicating the value of predicting immunotherapy.
Conclusions: We have developed and validated a TGFβ-associated signature, that is, capable of predicting the survival outcome as well as depicting the TME immune characteristics of GC. In summary, this signature may contribute to precision medicine for GC.

背景：胃癌（Gastric carcinoma, GC）是一种高发恶性肿瘤，在全球范围内具有较高的致死率。临床上亟需一款能够预测接受个体化治疗的胃癌患者不同生存结局的有效工具。
方法：本研究从TCGA-STAD、GSE15459以及GSE84437三个数据集中共收集了975例胃癌患者的临床数据。基于54个转化生长因子β（Transforming Growth Factor β, TGFβ）通路相关基因，我们开展了全面的无监督聚类分析，并将得到的聚类模式与肿瘤微环境（Tumor microenvironment, TME）的细胞浸润特征进行关联分析。随后采用加权基因共表达网络分析（Weighted Gene Co-expression Network Analysis, WGCNA）筛选与上述聚类模式相关性最高的基因模块。结合交叉验证的最小绝对收缩和选择算子（LASSO）算法用于筛选变量，同时采用随机生存森林（Random Survival Forest, RSF）构建风险评分模型。
结果：本研究鉴定出两种不同的TGFβ调控模式，并分别命名为TGFβ聚类1型与聚类2型。其中TGFβ聚类1型与较差的生存结局显著相关，且对应胃癌的炎性肿瘤微环境亚型。通过WGCNA分析，我们筛选得到与TGFβ聚类相关性最高的基因模块（洋红色模块）。经单因素Cox回归分析、LASSO算法及交叉验证筛选基因列表后，从洋红色模块的243个基因中选取4个基因构建风险评分模型。风险评分较高的患者组生存结局显著更差，且该模型具有较高的预测准确性。多因素Cox回归分析显示，风险评分是独立的预后危险因素。此外，本研究利用GSE15459与GSE84437数据集对该风险评分模型进行了验证。进一步分析发现，风险评分较高的患者组对应炎性肿瘤微环境：该风险评分与癌症免疫循环的多个环节以及大多数浸润免疫细胞均显著相关。同时，风险评分与免疫检查点基因及T细胞炎性基因表达谱（Gene Expression Profiles, GEPs）显著相关，提示该模型在预测免疫治疗疗效方面具有应用价值。
结论：本研究开发并验证了一款与TGFβ通路相关的基因特征模型，该模型既可以预测胃癌患者的生存结局，也能够反映其肿瘤微环境的免疫特征。综上，该基因特征模型可为胃癌的精准医疗提供参考依据。