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食管鳞状细胞癌免疫细胞浸润图谱的特征分析

Characterization of the Immune Cell Infiltration Landscape in Esophageal Squamous Cell Carcinoma.

作者信息

Sui Zhilin, Wu Xianxian, Du Longde, Wang Han, Yuan Lijuan, Zhang Jian V, Yu Zhentao

机构信息

Departments of Esophageal Cancer, Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin's Clinical Research Center for Cancer, Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.

Center for Energy Metabolism and Reproduction, Shenzhen Institute of Advanced Technology,Chinese Academy of Sciences; Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences; Shenzhen Key Laboratory of Metabolic Health, Shenzhen, China.

出版信息

Front Oncol. 2022 Jul 7;12:879326. doi: 10.3389/fonc.2022.879326. eCollection 2022.

Abstract

BACKGROUND

Immunotherapy has achieved remarkable efficacy in treating oesophageal squamous cell carcinoma (ESCC). However, this treatment has limited efficacy in some patients. An increasing number of evidence suggested that immune cells within the tumour microenvironment (TME) are strongly related to immunotherapy response and patient prognosis. Thus, the landscape of immune cell infiltration (ICI) in ESCC needs to be mapped.

METHODS

In the study, the ICI pattern in 206 cases of ESCC was characterised by two algorithms, namely, CIBERSORT and single-sample gene set enrichment analysis (ssGSEA). The ICI score of each specimen was calculated by principal component analysis (PCA) according to ICI signature genes A (ICISGA) and B (ICISGB). The prognostic difference was evaluated by using the Kaplan-Meier method. The related pathways of ICI score were investigated by applying gene set enrichment analysis (GSEA). The R packages of 'regplot', 'timeROC' and 'rms' were applied for the construction of nomogram model.

RESULT

Three TME subtypes were identified with no prognostic implication. A total of 333 differentially expressed genes (DEGs) among immune subtypes were determined, among which ICISGA and ICISGB were identified. Finally, ICI scores were constructed, and the patients were grouped into high or low ICI score group. Compared with the low ICI score group, the high ICI score group had better prognosis. GSEA revealed that the high ICI score group referred to multiple signalling pathways, including B cell receptor, Fc gamma R-mediated phagocytosis, NOD-like receptor and TGF-β signalling pathways. In addition, the nomogram model was constructed to evaluate 1-, 3- and 5-year probability of death in an ESCC patient. The ROC and calibration curves indicated that the model has a good discrimination ability.

CONCLUSION

We depicted a comprehensive ICI landscape in ESCC. ICI score may be used as a predictor of survival rate, which may be helpful for guiding immunotherapy in the future.

摘要

背景

免疫疗法在治疗食管鳞状细胞癌(ESCC)方面取得了显著疗效。然而,这种治疗方法在某些患者中的疗效有限。越来越多的证据表明,肿瘤微环境(TME)中的免疫细胞与免疫治疗反应和患者预后密切相关。因此,需要绘制ESCC中免疫细胞浸润(ICI)的图谱。

方法

在本研究中,采用两种算法,即CIBERSORT和单样本基因集富集分析(ssGSEA),对206例ESCC的ICI模式进行了表征。根据ICI特征基因A(ICISGA)和B(ICISGB),通过主成分分析(PCA)计算每个样本的ICI得分。采用Kaplan-Meier法评估预后差异。应用基因集富集分析(GSEA)研究ICI得分的相关通路。应用“regplot”“timeROC”和“rms”等R包构建列线图模型。

结果

确定了三种TME亚型,无预后意义。确定了免疫亚型之间总共333个差异表达基因(DEG),其中鉴定出ICISGA和ICISGB。最后,构建了ICI得分,并将患者分为高ICI得分组或低ICI得分组。与低ICI得分组相比,高ICI得分组预后更好。GSEA显示,高ICI得分组涉及多个信号通路,包括B细胞受体、FcγR介导的吞噬作用、NOD样受体和TGF-β信号通路。此外,构建了列线图模型以评估ESCC患者1年、3年和5年的死亡概率。ROC和校准曲线表明该模型具有良好的区分能力。

结论

我们描绘了ESCC中全面的ICI图谱。ICI得分可作为生存率的预测指标,可能有助于未来指导免疫治疗。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/725f/9300817/c2536b3060af/fonc-12-879326-g001.jpg

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