Identification of CXCL13/CXCR5 axis's crucial and complex effect in human lung adenocarcinoma
Identification of CXCL13/CXCR5 axis's crucial and complex effect in human lung adenocarcinoma
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鉴定CXCL13/CXCR5轴在人肺腺癌中的关键和复杂作用
DOI：
10.1016/j.intimp.2021.107416
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发表时间：
2021-03-03
影响因子：
5.6
通讯作者：
Liu, Li
中科院分区：
文献类型：
--
作者：
Tian, Chen;Li, Chang;Liu, Li
免疫逃逸和对免疫治疗的低反应性是目前肺癌治疗面临的关键挑战。在本研究中，我们基于CXCL13/CXCR5构建了一个新的免疫相关分类器，CXCL13/CXCR5是一个重要的肿瘤微环境成分，与肿瘤微环境中三级淋巴结构(TLSS)的形成密切相关。利用该分类器，我们将患者分为两个主要类，每个类又进一步分为亚类(A1、A2、B1、B2、B3)。在后面的分析中，我们注意到B3亚群的患者在生存时间和免疫渗透方面比A1亚群的患者有明显的优势，这表明对免疫治疗的反应更有利。此外，我们还证明了与亚簇相关的遗传和表观遗传调控，并恢复了四个关键的差异表达基因(ERBB4，GRIN2A，IL2RA，CCND2)。通过多项实验，我们验证了CCND2在肿瘤转移和T细胞凋亡中的独特作用。CCND2过表达可显著损害癌细胞的迁移和侵袭能力，下调PD-1/PD-L1信号通路，这可能是T细胞凋亡率减少的原因。最后，我们构建了一个能够成功预测ICI反应的回归风险模型。综上所述，我们的研究建立了新的分层模型，可以成功地预测患者的生存和对ICI的反应。利用多组学数据的综合分析，发现了四个关键基因，其中CCND2基因因其在肿瘤转移和T细胞凋亡中的作用而被确定为潜在的治疗靶点。
Immune escape and low response to immunotherapy are crucial challenges in present lung cancer treatment. In this study, we constructed a new immune-related classifier based on CXCL13/CXCR5, an important tumor microenvironment component and strongly related with the formation of tertiary lymphoid structures (TLSs) in tumor microenvironment. With the classifier, we divided patients into two main clusters and each cluster was further divided into subcluster (A1, A2, B1, B2, B3). In the later analysis, we noticed that patients in subcluster B3 had a distinct advantage over patients in A1 in survival time and immune infiltration, suggesting a more favorable response to immunotherapy. Moreover, we demonstrated the genetic and epigenetic regulation related to the subclusters and recovered four key differentially expressed genes (ERBB4, GRIN2A, IL2RA, CCND2). With several experiments, we verified the unique role of CCND2 in tumor metastasis and T cell apoptosis. Overexpressing CCND2 could significantly impair cancer cell abilities of migration and invasion and downregulate PD-1/PD-L1 signaling, which may be the cause of T cell apoptosis reduction. In the end, we constructed a regression risk model that could successfully predict ICI response. To sum up, our study established new stratification models that can successfully predict patient survival and response to ICI. And using integrative analysis of multi-omics data, four key DEGs were noticed, and CCND2, one of the four genes, was identified as a potential treatment target because of its effect in tumor metastasis and T cell apoptosis.