Analysis of Immune Gene Expression Subtypes Reveals Osteosarcoma Immune Heterogeneity.
Analysis of Immune Gene Expression Subtypes Reveals Osteosarcoma Immune Heterogeneity.
复制标题
免疫基因表达亚型分析揭示骨肉瘤免疫异质性。
DOI：
10.1155/2021/6649412
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发表时间：
2021
影响因子：
--
通讯作者：
Chen D
中科院分区：
文献类型：
--
作者：
Wan B;Wang R;Nie J;Sun Y;Zhang B;Liu W;Chen D
由于免疫系统的复杂性和肿瘤免疫微环境的细微差别，骨肉瘤（OS）患者对免疫治疗的反应很差。方法论为了深入了解OS的免疫异质性，我们根据TARGET数据库中OS患者的免疫基因表达谱确定了稳健的患者群，并在从GEO数据库收集的独立队列中评估了其重现性。用ANOVA评估综合分子表征与可再现免疫亚型的关联。此外，我们可视化的树结构的个体患者的分布基于图结构学习的降维算法。 我们发现87个OS样本可分为5种免疫亚型，每种亚型均与不同的临床结果相关。免疫亚型也表现出广泛不同的模式，在肿瘤遗传畸变，肿瘤浸润，免疫细胞组成，和细胞因子谱。OS的免疫景观揭示了每个亚型内的显著簇内异质性，并描绘了患者之间的连续免疫谱。 在我们的研究中建立的五种免疫亚型表明OS患者的免疫异质性，并可能为表现出OS的患者提供最佳的个体免疫治疗。
Osteosarcoma (OS) patients have a poor response to immunotherapy due to the sheer complexity of the immune system and the nuances of the tumor-immune microenvironment. Methodology. To gain insights into the immune heterogeneity of OS, we identified robust clusters of patients based on the immune gene expression profiles of OS patients in the TARGET database and assessed their reproducibility in an independent cohort collected from the GEO database. The association of comprehensive molecular characterization with reproducible immune subtypes was accessed with ANOVA. Furthermore, we visualized the distribution of individual patients in a tree structure by the graph structure learning-based dimensionality reduction algorithm. We found that 87 OS samples can be divided into 5 immune subtypes, and each of them was associated with distinct clinical outcomes. The immune subtypes also demonstrated widely different patterns in tumor genetic aberrations, tumor-infiltrating, immune cell composition, and cytokine profiles. The immune landscape of OS uncovered the significant intracluster heterogeneity within each subtype and depicted a continuous immune spectrum across patients. The established five immune subtypes in our study suggested immune heterogeneity in OS patients and may provide optimal individual immunotherapy for patients exhibiting OS.
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