Research Paper Volume 15, Issue 20 pp 11571—11587
Metabolic and senescence characteristics associated with the immune microenvironment in non-small cell lung cancer: insights from single-cell RNA sequencing
- 1 Department of Thoracic Surgery, The Yuebei People’s Hospital of Shaoguan, Shaoguan, Guangdong 512025, China
- 2 College of Physical Education and Health, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China
- 3 Department of Nursing Medical College, Shaoguan University, Shaoguan, Guangdong 512005, China
- 4 Department of Gynaecology and Obstetrics, The Qujiang District Maternal and Child Health Care Hospital, Shaoguan, Guangdong, China
Received: June 12, 2023 Accepted: October 6, 2023 Published: October 26, 2023
https://doi.org/10.18632/aging.205146How to Cite
Copyright: © 2023 Liao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Abstract
Non-small lung cancer (NSCLC) has been defined as a highly life-threatening heterogeneous disease, with high mortality and occurrence. Recent research has indicated that tumor-infiltrating lymphocytes play a key determinant role in cancer progression. Emerging single-cell RNA sequencing (also termed scRNA-seq) has been extensively applied to depict the baseline landscape of the cell composition and function phenotype in the tumor environment (TME). Herein, we dissected the cell types in NSCLC samples (including tissue and blood) and identified three types of cell marker genes including cancer cells, T cells, and macrophages by integrating two NSCLC-associated scRNA-seq datasets in GEO. Survival analysis indicated that 17 marker genes were related to tumor prognosis. Function annotation was used to scrutinize the molecular mechanism of these marker genes in different cells. Besides, we investigated the developmental trajectory and T cell receptor repertoire diversity of tumor-infiltrating T cells. Our analysis will help further understand the complexity of cell components and the heterogeneity of TME in NSCLC.