Li Xiaogang, Gao Zheng, Chen Jiafeng, Feng Shanru, Luo Xuanming, Shi Yinghong, Tang Zheng, Liu Weiren, Zhang Xin, Huang Ao, Gao Qiang, Ke Aiwu, Zhou Jian, Fan Jia, Fu Xiutao, Ding Zhenbin
Department of Liver Surgery and Transplantation, Liver Cancer Institute, Zhongshan Hospital, Fudan University, Shanghai, China.
Key Laboratory of Carcinogenesis and Cancer Invasion, Chinese Ministry of Education, Shanghai, China.
Front Oncol. 2023 Aug 1;13:1099385. doi: 10.3389/fonc.2023.1099385. eCollection 2023.
Various immune cell types in the tumor microenvironment (TME) of hepatocellular carcinoma (HCC) have been identified as important parameters associated with prognosis and responsiveness to immunotherapy. However, how various factors influence immune cell infiltration remains incompletely understood. Hence, we investigated the single cell multi-omics landscape of immune infiltration in HCC, particularly key gene and cell subsets that influence immune infiltration, thus potentially linking the immunotherapy response and immune cell infiltration.
We grouped patients with HCC according to immune cell infiltration scores calculated by single sample gene set enrichment analysis (ssGSEA). Differential expression analysis, functional enrichment, clinical trait association, gene mutation analysis, tumor immune dysfunction and exclusion (TIDE) and prognostic model construction were used to investigate the immune infiltration landscape through multi-omics. Stepwise regression was further used to identify key genes regulating immune infiltration. Single cell analysis was performed to explore expression patterns of candidate genes and investigate associated cellular populations. Correlation analysis, ROC analysis, Immunotherapy cohorts were used to explore and confirm the role of key gene and cellular population in predicting immune infiltration state and immunotherapy response. Immunohistochemistry and multiplexed fluorescence staining were used to further validated our results.
Patients with HCC were clustered into high and low immune infiltration groups. Mutations of CTNNB1 and TTN were significantly associated with immune infiltration and altered enrichment of cell populations in the TME. TIDE analysis demonstrated that T cell dysfunction and the T cell exclusion score were elevated in the high and low infiltration groups, respectively. Six risk genes and five risk immune cell types were identified and used to construct risk scores and a nomogram model. CXCR6 and LTA, identified by stepwise regression, were highly associated with immune infiltration. Single cell analysis revealed that LTA was expressed primarily in tumor infiltrating T lymphocytes and partial B lymphocytes, whereas CXCR6 was enriched predominantly in T and NK cells. Notably, CXCR6 CD8 T cells were characterized as tumor enriched cells that may be potential predictors of high immune infiltration and the immune-checkpoint blockade response, and may serve as therapeutic targets.
We constructed a comprehensive single cell and multi-omics landscape of immune infiltration in HCC, and delineated key genes and cellular populations regulating immune infiltration and immunotherapy response, thus providing insights into the mechanisms of immune infiltration and future therapeutic control.
肝细胞癌(HCC)肿瘤微环境(TME)中的多种免疫细胞类型已被确定为与预后及免疫治疗反应相关的重要参数。然而,各种因素如何影响免疫细胞浸润仍未完全清楚。因此,我们研究了HCC免疫浸润的单细胞多组学图谱,特别是影响免疫浸润的关键基因和细胞亚群,从而潜在地将免疫治疗反应与免疫细胞浸润联系起来。
我们根据通过单样本基因集富集分析(ssGSEA)计算出的免疫细胞浸润分数对HCC患者进行分组。采用差异表达分析、功能富集、临床特征关联、基因突变分析、肿瘤免疫功能障碍与排除(TIDE)以及预后模型构建等方法,通过多组学研究免疫浸润图谱。进一步采用逐步回归来鉴定调节免疫浸润的关键基因。进行单细胞分析以探索候选基因的表达模式并研究相关细胞群体。使用相关性分析、ROC分析、免疫治疗队列来探索和确认关键基因及细胞群体在预测免疫浸润状态和免疫治疗反应中的作用。采用免疫组织化学和多重荧光染色进一步验证我们的结果。
HCC患者被聚类为高免疫浸润组和低免疫浸润组。CTNNB1和TTN的突变与免疫浸润显著相关,并改变了TME中细胞群体的富集情况。TIDE分析表明,高浸润组和低浸润组中T细胞功能障碍和T细胞排除分数分别升高。鉴定出六个风险基因和五种风险免疫细胞类型,并用于构建风险评分和列线图模型。通过逐步回归鉴定出的CXCR6和LTA与免疫浸润高度相关。单细胞分析显示,LTA主要在肿瘤浸润性T淋巴细胞和部分B淋巴细胞中表达,而CXCR6主要在T细胞和NK细胞中富集。值得注意的是,CXCR6 CD8 T细胞被表征为肿瘤富集细胞,可能是高免疫浸润和免疫检查点阻断反应的潜在预测指标,并可能作为治疗靶点。
我们构建了HCC免疫浸润的全面单细胞和多组学图谱,描绘了调节免疫浸润和免疫治疗反应的关键基因和细胞群体,从而为免疫浸润机制和未来治疗控制提供了见解。