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

Jingyuan Zhang; Xinkui Liu; Zhihong Huang; Chao Wu; Fanqin Zhang; Aiqing Han; Antony Stalin; Shan Lu; Siyu Guo; Jiaqi Huang; Pengyun Liu; Rui Shi; Yiyan Zhai; Meilin Chen; Wei Zhou; Meirong Bai; Jiarui Wu

doi:10.1016/j.compbiomed.2022.106460

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

Comput Biol Med. 2023 Jan:152:106460. doi: 10.1016/j.compbiomed.2022.106460. Epub 2022 Dec 21.

Authors

Jingyuan Zhang¹, Xinkui Liu¹, Zhihong Huang¹, Chao Wu¹, Fanqin Zhang¹, Aiqing Han², Antony Stalin³, Shan Lu¹, Siyu Guo¹, Jiaqi Huang¹, Pengyun Liu¹, Rui Shi¹, Yiyan Zhai¹, Meilin Chen¹, Wei Zhou⁴, Meirong Bai⁵, Jiarui Wu⁶

Affiliations

¹ School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China.
² School of Management, Beijing University of Chinese Medicine, Beijing, 100029, China.
³ Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, 610054, China.
⁴ Pharmacy Department, China-Japan Friendship Hospital, Beijing, 100029, China. Electronic address: weizhou19940530@163.com.
⁵ Key Laboratory of Mongolian Medicine Research and Development Engineering, Ministry of Education, Tongliao, 028000, China. Electronic address: baimeirong@126.com.
⁶ School of Chinese Materia Medica, Beijing University of Chinese Medicine, Beijing, 100029, China. Electronic address: exogamy@163.com.

PMID: 36565482
DOI: 10.1016/j.compbiomed.2022.106460

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.

Keywords: Bulk RNA sequencing; Lung adenocarcinoma; Prognostic risk model; Single-cell RNA sequencing; T cell; Tumor immune environment.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Adenocarcinoma of Lung* / genetics
Carcinoma, Non-Small-Cell Lung* / genetics
Humans
Lung Neoplasms* / genetics
Prognosis
Sequence Analysis, RNA
T-Lymphocytes
Tumor Microenvironment / genetics