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一种与免疫及上皮-间质转化相关的II级和III级胶质瘤风险模型及免疫治疗策略。

An immune and epithelial-mesenchymal transition-related risk model and immunotherapy strategy for grade II and III gliomas.

作者信息

Luo Wei, Quan Qi, Jiang Jiaxin, Peng Roujun

机构信息

Department of VIP Section, State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, Guangzhou, China.

出版信息

Front Genet. 2023 Jan 4;13:1070630. doi: 10.3389/fgene.2022.1070630. eCollection 2022.

DOI:10.3389/fgene.2022.1070630
PMID:36778912
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC9909968/
Abstract

Grade II and III gliomas are heterogeneous and aggressive diseases. More efficient prognosis models and treatment methods are needed. This study aims to construct a new risk model and propose a new strategy for grade II and III gliomas. The data were downloaded from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), gene set enrichment analysis (GSEA), and the EMTome website for analysis. The Human Cell Landscape website and the Genomics of Drug Sensitivity in Cancer website were used for single-cell analysis and drug susceptibility analysis. Gene set enrichment analysis, gene function enrichment analysis, univariate and multivariate Cox regression analyses, Pearson's correlation analysis, log-rank test, Kaplan-Meier survival analysis, and ROC analysis were performed. We constructed an immune-related prognostic model associated with the isocitrate dehydrogenase 1 (IDH1) mutation status. By analyzing the immune microenvironment of patients with different risk scores, we found that high-risk patients were more likely to have an inflammatory immune microenvironment and a higher programmed death ligand-1 (PD-L1) expression level. Epithelial-mesenchymal transition (EMT)-related gene sets were significantly enriched in the high-risk group, and the epithelial-mesenchymal transition phenotype was associated with a decrease in CD8 T cells and an increase in M2 macrophages. Transforming growth factor-β (TGF-β) signaling was the most important signaling in inducing epithelial-mesenchymal transition, and TGFB1/TGFBR1 was correlated with an increase in CD8 T cytopenia and M2 macrophages. Survival analysis showed that simultaneous low expression of TGFBR1 and PD-L1 had better survival results. Through single-cell analysis, we found that TGFB1 is closely related to microglia and macrophages, especially M2 macrophages. Finally, we discussed the sensitivity of TGFB1 inhibitors in gliomas using cell line susceptibility data. These results demonstrated a potential immunotherapy strategy in combination with the TGFB1/TGFBR1 inhibitor and PD-1/PD-L1 inhibitor for grade II and III gliomas.

摘要

II级和III级胶质瘤是异质性侵袭性疾病。需要更有效的预后模型和治疗方法。本研究旨在构建一种新的风险模型，并为II级和III级胶质瘤提出一种新策略。数据从癌症基因组图谱（TCGA）、基因表达综合数据库（GEO）、基因集富集分析（GSEA）以及EMTome网站下载进行分析。利用人类细胞图谱网站和癌症药物敏感性基因组学网站进行单细胞分析和药物敏感性分析。进行了基因集富集分析、基因功能富集分析、单变量和多变量Cox回归分析、Pearson相关性分析、对数秩检验、Kaplan-Meier生存分析和ROC分析。我们构建了一个与异柠檬酸脱氢酶1（IDH1）突变状态相关的免疫相关预后模型。通过分析不同风险评分患者的免疫微环境，我们发现高风险患者更有可能具有炎性免疫微环境和更高的程序性死亡配体1（PD-L1）表达水平。上皮-间质转化（EMT）相关基因集在高风险组中显著富集，上皮-间质转化表型与CD8 T细胞减少和M2巨噬细胞增加相关。转化生长因子-β（TGF-β）信号是诱导上皮-间质转化中最重要的信号，TGFB1/TGFBR1与CD8 T细胞减少和M2巨噬细胞增加相关。生存分析表明，TGFBR1和PD-L1同时低表达具有更好的生存结果。通过单细胞分析，我们发现TGFB1与小胶质细胞和巨噬细胞密切相关，尤其是M2巨噬细胞。最后，我们利用细胞系敏感性数据讨论了TGFB1抑制剂在胶质瘤中的敏感性。这些结果证明了一种联合TGFB1/TGFBR1抑制剂和PD-1/PD-L1抑制剂用于II级和III级胶质瘤的潜在免疫治疗策略。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/ea9c98b9a827/fgene-13-1070630-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/4a671cea75e3/fgene-13-1070630-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/f185f63578e2/fgene-13-1070630-g008.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/ea9c98b9a827/fgene-13-1070630-g010.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/4a671cea75e3/fgene-13-1070630-g001.jpg
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https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/ca2c4d039bc0/fgene-13-1070630-g005.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/363d13d4c7b9/fgene-13-1070630-g006.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/a8ba60a8f8ec/fgene-13-1070630-g007.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/f185f63578e2/fgene-13-1070630-g008.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/c7f286f3be29/fgene-13-1070630-g009.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/ad58/9909968/ea9c98b9a827/fgene-13-1070630-g010.jpg

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[1]
Explainable Machine Learning Models for Glioma Subtype Classification and Survival Prediction.

Cancers (Basel). 2025-8-9

[2]
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[3]
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