Identifying immune cells-related phenotype to predict immunotherapy and clinical outcome in gastric cancer.
Identifying immune cells-related phenotype to predict immunotherapy and clinical outcome in gastric cancer.
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识别免疫细胞相关表型以预测胃癌的免疫治疗和临床结果
DOI：
10.3389/fimmu.2022.980986
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发表时间：
2022
影响因子：
7.3
通讯作者：
中科院分区：
文献类型：
--
作者：
肿瘤微环境主要由肿瘤浸润性免疫细胞(TIICs)、成纤维细胞、细胞外基质和分泌因子组成。TIICs通常与免疫治疗的敏感性和多发性癌症的预后有关，然而单个细胞对肿瘤预后的预测作用有限。方法在单样本基因集浓缩分析的基础上，结合3个基因表达总表(GEO)队列，建立TIIC模型进行风险分层和预后预测。使用我们的临床队列和TCGA队列验证了TIIC模型的性能。为了评估TIIC模型对免疫治疗的预测能力，我们绘制了IMURITY 210和GSE135222队列的接受者操作特征曲线。结果TIIC组在趋化因子、肿瘤浸润性免疫细胞和免疫调节剂等方面存在差异。TIIC模型对于预测免疫治疗的结果至关重要。在我们的临床样本中，我们验证了低TIIC评分组的PD-1和PD-L1的表达水平高于高TIIC评分组，无论是在肿瘤还是间质中都是如此。结论TIIC模型可为胃癌免疫细胞靶向治疗策略提供新的思路，并可预测患者的生存结局。
Background The tumor microenvironment is mainly composed of tumor-infiltrating immune cells (TIICs), fibroblast, extracellular matrix, and secreted factors. TIICs are often associated with sensitivity to immunotherapy and the prognosis of multiple cancers, yet the predictive role of individual cells on tumor prognosis is limited. Methods Based on single-sample gene set enrichment analysis, we combined three Gene Expression Omnibus (GEO) cohorts to build a TIIC model for risk stratification and prognosis prediction. The performance of the TIIC model was validated using our clinical cohort and the TCGA cohort. To assess the predictive power of the TIIC model for immunotherapy, we plotted the receiver operating characteristic curve with the IMvigor210 and GSE135222 cohorts. Results Chemokines, tumor-infiltrating immune cells, and immunomodulators differed between the two TIIC groups. The TIIC model was vital for predicting the outcome of immunotherapy. In our clinical samples, we verified that the expression levels of PD-1 and PD-L1 were higher in the low TIIC score group than in the high TIIC score group, both in the tumor and stroma. Conclusions Collectively, the TIIC model could provide a novel idea for immune cell targeting strategies in gastric cancer and predict the survival outcome of patients.
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影响因子：
10.9
作者：
Wang P;Chen Y;Long Q;Li Q;Tian J;Liu T;Wu Y;Ding Z
通讯作者：
Ding Z
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影响因子：
7.3
作者：
Jiang S;Zhang Y;Zhang X;Lu B;Sun P;Wu Q;Ding X;Huang J
通讯作者：
Huang J
影响因子：
16.6
作者：
Desbois M;Udyavar AR;Ryner L;Kozlowski C;Guan Y;Dürrbaum M;Lu S;Fortin JP;Koeppen H;Ziai J;Chang CW;Keerthivasan S;Plante M;Bourgon R;Bais C;Hegde P;Daemen A;Turley S;Wang Y
通讯作者：
Wang Y
影响因子：
16.6
作者：
Huang, Yu-Kuan;Wang, Minyu;Boussioutas, Alex
通讯作者：
Boussioutas, Alex
影响因子：
15.9
作者：
Fakih, Marwan;Ouyang, Ching;Lee, Peter P.
通讯作者：
Lee, Peter P.