A mast cell-related prognostic model for non-small cell lung cancer

Yan Yang; Weiwei Qian; Jian Zhou; Xianming Fan

doi:10.21037/jtd-23-362

A mast cell-related prognostic model for non-small cell lung cancer

J Thorac Dis. 2023 Apr 28;15(4):1948-1957. doi: 10.21037/jtd-23-362. Epub 2023 Apr 26.

Authors

Yan Yang^{1

2

3}, Weiwei Qian⁴, Jian Zhou^{5

6}, Xianming Fan¹

Affiliations

¹ Department of Respiratory and Critical Care Medicine, Affiliated Hospital of Southwest Medical University, Luzhou, China.
² Department of Respiratory and Critical Care Medicine, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China.
³ Chinese Academy of Sciences Sichuan Translational Medicine Research Hospital, Chengdu, China.
⁴ Department of Emergency, Shangjinnanfu Hospital, West China Hospital, Sichuan University, Chengdu, China.
⁵ Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, School of Biomedical Engineering, Shenzhen University Medical School, Shenzhen, China.
⁶ Department of Immunology, International Cancer Center, Shenzhen University Health Science Center, Shenzhen, China.

Abstract

Background: The immune microenvironment of non-small cell lung cancer (NSCLC) plays a critical role in its treatment. Mast cells (MCs) appear to play a key role in the tumor microenvironment, and studies are needed to further elucidate the diagnosis and treatment of NSCLC.

Methods: Data was collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets. Univariate Cox and Least Absolute Shrinkage and Selection Operator (LASSO) regression analyses constructed a resting mast cell related genes (RMCRGs) risk model. Differences in the immune infiltration levels of diverse immune infiltrating cells between the high- and low-risk groups were identified by CIBERSORT. We analyzed the enrichment terms in the entire TCGA cohort using Gene Set Enrichment Analysis (GSEA) software version 4.1.1. We used Pearson correlation analysis to identify the relationships between risk scores, immune checkpoint inhibitors (ICIs), and tumor mutation burden (TMB). Finally, the half-maximal inhibitory concentration (IC50) values for chemotherapy in the high- and low-risk populations were evaluated via the R "oncoPredict" package.

Results: We found 21 RMCRGs that were significantly associated with resting MCs. Gene ontology (GO) analysis showed that the 21 RMCRGs were enriched in regulating angiotensin blood levels and angiotensin maturation. An initial univariate Cox regression analysis was performed on the 21 RMCRGs, four of which were identified as significantly related to prognostic risk in NSCLC. Then, LASSO regression was carried out to construct a prognostic model. We found a positive correlation between the expression of the four RMCRGs with resting mast cell infiltration in NSCLC; the higher the risk score, the less resting mast cell infiltration and immune checkpoint inhibitor (ICI) expression. A drug sensitivity analysis showed a difference in drug sensitivity between the high- and low-risk groups.

Conclusions: We constructed a predictive prognostic risk model for NSCLC containing four RMCRGs. We hope this risk model will provide a theoretical basis for future investigations on NSCLC mechanisms, diagnosis, treatment, and prognosis.

Keywords: Resting mast cells (Resting MCs); non-small cell lung cancer (NSCLC); prognostic model.