Zhang Jingyuan, Liu Xinkui, Huang Zhihong, Wu Chao, Zhang Fanqin, Han Aiqing, Stalin Antony, Lu Shan, Guo Siyu, Huang Jiaqi, Liu Pengyun, Shi Rui, Zhai Yiyan, Chen Meilin, Zhou Wei, Bai Meirong, Wu Jiarui
School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
School of Management, Beijing University of Chinese Medicine, Beijing, 100029, China.
Comput Biol Med. 2023 Jan;152:106460. doi: 10.1016/j.compbiomed.2022.106460. Epub 2022 Dec 21.
T cells are present in all stages of tumor formation and play an important role in the tumor microenvironment. We aimed to explore the expression profile of T cell marker genes, constructed a prognostic risk model based on these genes in Lung adenocarcinoma (LUAD), and investigated the link between this risk model and the immunotherapy response.
We obtained the single-cell sequencing data of LUAD from the literature, and screened out 6 tissue biopsy samples, including 32,108 cells from patients with non-small cell lung cancer, to identify T cell marker genes in LUAD. Combined with TCGA database, a prognostic risk model based on T-cell marker gene was constructed, and the data from GEO database was used for verification. We also investigated the association between this risk model and immunotherapy response.
Based on scRNA-seq data 1839 T-cell marker genes were identified, after which a risk model consisting of 9 gene signatures for prognosis was constructed in combination with the TCGA dataset. This risk model divided patients into high-risk and low-risk groups based on overall survival. The multivariate analysis demonstrated that the risk model was an independent prognostic factor. Analysis of immune profiles showed that high-risk groups presented discriminative immune-cell infiltrations and immune-suppressive states. Risk scores of the model were closely correlated with Linoleic acid metabolism, intestinal immune network for IgA production and drug metabolism cytochrome P450.
Our study proposed a novel prognostic risk model based on T cell marker genes for LUAD patients. The survival of LUAD patients as well as treatment outcomes may be accurately predicted by the prognostic risk model, and make the high-risk population present different immune cell infiltration and immunosuppression state.
T细胞存在于肿瘤形成的各个阶段，并在肿瘤微环境中发挥重要作用。我们旨在探索T细胞标志物基因的表达谱，基于这些基因构建肺腺癌（LUAD）的预后风险模型，并研究该风险模型与免疫治疗反应之间的联系。
我们从文献中获取LUAD的单细胞测序数据，并筛选出6个组织活检样本，包括来自非小细胞肺癌患者的32108个细胞，以鉴定LUAD中的T细胞标志物基因。结合TCGA数据库，构建基于T细胞标志物基因的预后风险模型，并使用GEO数据库的数据进行验证。我们还研究了该风险模型与免疫治疗反应之间的关联。
基于scRNA-seq数据鉴定出1839个T细胞标志物基因，之后结合TCGA数据集构建了一个由9个基因特征组成的预后风险模型。该风险模型根据总生存期将患者分为高风险和低风险组。多变量分析表明，该风险模型是一个独立的预后因素。免疫图谱分析表明，高风险组呈现出有区别的免疫细胞浸润和免疫抑制状态。该模型的风险评分与亚油酸代谢、IgA产生的肠道免疫网络和药物代谢细胞色素P450密切相关。
我们的研究为LUAD患者提出了一种基于T细胞标志物基因的新型预后风险模型。该预后风险模型可以准确预测LUAD患者的生存情况以及治疗结果，并使高风险人群呈现出不同的免疫细胞浸润和免疫抑制状态。
