T cell-related prognostic risk model and tumor immune environment modulation in lung adenocarcinoma based on single-cell and bulk RNA sequencing. (January 2023)

• Record Type: Journal Article
• Authors: 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
• Is Part Of: Computers in biology and medicine. Volume 152(2023)
• Publication Date: 2023-01
• Subjects: Single-cell RNA sequencing -- Bulk RNA sequencing -- Lung adenocarcinoma -- Tumor immune environment -- Prognostic risk model -- T cell; Medicine -- Data processing -- Periodicals; Biology -- Data processing -- Periodicals; 610.285
• DOI: 10.1016/j.compbiomed.2022.106460
• Languages: English
• ISSNs: 0010-4825

Abstract

Abstract: Background: 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. Methods: 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. Results: 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. Conclusion: 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. Highlights: T cells are present in all stages of tumor formation and play an important role in the tumor microenvironment. 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 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.