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整合多组学分析鉴定和验证喉鳞状细胞癌中的免疫浸润表型。

Identification and Validation of Immune Infiltration Phenotypes in Laryngeal Squamous Cell Carcinoma by Integrative Multi-Omics Analysis.

机构信息

Department of Radiation Oncology, Eye & ENT Hospital, Fudan University, Shanghai, China.

Department of Otolaryngology, Eye & ENT Hospital, Fudan University, Shanghai, China.

出版信息

Front Immunol. 2022 Feb 24;13:843467. doi: 10.3389/fimmu.2022.843467. eCollection 2022.

Abstract

BACKGROUND

Laryngeal squamous cell carcinoma (LSCC) is one of the world's most common head and neck cancer. However, the immune infiltration phenotypes of LSCC have not been well investigated.

METHODS

The multi-omics data of LSCC were obtained from the TCGA (n=111) and GEO (n=57) datasets. The infiltrations of the 24 immune cell populations were calculated using the GSVA method. Then LSCC samples with different immune cell infiltrating patterns were clustered, and the multi-omics differences were investigated.

RESULTS

Patients were clustered into the high-infiltration and low-infiltration groups. The infiltration scores of most immune cells were higher in the high-infiltration group. Patients with high-infiltration phenotype have high N and TNM stages but better survival, as well as less mutated COL11A1 and MUC17. Common targets of immunotherapies such as PD1, PDL1, LAG3, and CTLA4 were significantly up-regulated in the high-infiltration group. The differentially expressed genes were mainly enriched in several immune-related GOs and KEGG pathways. Based on the genes, miRNAs, and lncRNAs differentially expressed in both the TCGA and GEO cohorts, we built a ceRNA network, in which BTN3A1, CCR1, miR-149-5p, and so on, located at the center. A predictive model was also constructed to calculate a patient's immune infiltration phenotype using 16 genes' expression values, showing excellent accuracy and specificity in the TCGA and GEO cohorts.

CONCLUSIONS

In this study, the immune infiltration phenotypes of LSCC and the corresponding multi-omics differences were explored. Our model might be valuable to predicting immunotherapy's outcome.

摘要

背景

喉鳞状细胞癌(LSCC)是世界上最常见的头颈部癌症之一。然而,LSCC 的免疫浸润表型尚未得到很好的研究。

方法

从 TCGA(n=111)和 GEO(n=57)数据集获得 LSCC 的多组学数据。使用 GSVA 方法计算 24 种免疫细胞群体的浸润情况。然后,根据不同免疫细胞浸润模式对 LSCC 样本进行聚类,并研究多组学差异。

结果

患者被聚类为高浸润组和低浸润组。高浸润组中大多数免疫细胞的浸润评分较高。高浸润表型患者的 N 分期和 TNM 分期较高,但生存情况较好,且 COL11A1 和 MUC17 的突变较少。免疫治疗的常见靶点如 PD1、PDL1、LAG3 和 CTLA4 在高浸润组中显著上调。差异表达基因主要富集在几个免疫相关的 GO 和 KEGG 通路中。基于 TCGA 和 GEO 队列中差异表达的基因、miRNAs 和 lncRNAs,我们构建了一个 ceRNA 网络,其中 BTN3A1、CCR1、miR-149-5p 等位于网络的中心。还构建了一个预测模型,使用 16 个基因的表达值来计算患者的免疫浸润表型,在 TCGA 和 GEO 队列中均表现出优异的准确性和特异性。

结论

本研究探讨了 LSCC 的免疫浸润表型及其相应的多组学差异。我们的模型可能对预测免疫治疗的疗效有价值。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/a22a/8907422/de74fe955829/fimmu-13-843467-g001.jpg

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