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

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.