-related tumor immune microenvironment in non-small cell lung cancer: a novel signature to predict hot and cold tumor.

BACKGROUND

At present, non-small cell lung cancer (NSCLC) remains a great threat to the health of people worldwide. Immune checkpoint inhibitors (ICIs) have shown positive results in the treatment of advanced NSCLC. However, the treatment response of ICIs is not stable and unpredictable. We used a bioinformatics analysis to determine a novel signature to diagnose the hot and cold tumor in NSCLC which may guide the programmed cell death protein 1/programmed cell death 1 ligand 1 () therapeutic strategy.

METHODS

The RNA-seq dataset and clinical data of 485 lung adenocarcinoma (LUAD) and 473 lung squamous cell carcinoma (LUSC) samples from The Cancer Genome Atlas (TCGA) database. Tumor infiltrating immune cells was calculated by CIBERSORT algorithm and ConsensusClusterPlus was used to classify the hot and cold tumor. Least absolute shrinkage and selection operator (LASSO) regression, Support Vector Machine (SVM) and Gaussian Mixture Model (GMM) were performed to determine the diagnostic area under curve (AUC) of novel signature of ICIs treatment. Overall survival (OS) analysis was based on the Kaplan-Meier statistical method.

RESULTS

In this study, we found that the expression of is associated with () expression. We identified novel signatures [, , , , , , , combined diagnostic (AUC) =0.838], in order to diagnose the hot and cold tumor subtype to indicate the treatment response of inhibitor in NSCLC. Furthermore, we found that in hot tumor subtype, high expression group had worse OS than low expression group (P=0.047); high expression group had worse OS than low SH2D3C expression group either (P=0.003). was correlated to expression in NSCLC samples (R=0.49, P<0.001). We speculated that likely plays a crucial role in -related immunotherapy in NSCLC patients. Pathway enrichment showed that the focal adhesion (P=0.005) and actin cytoskeleton (P=0.022) pathways were associated with OS.

CONCLUSIONS

This study aimed to identify the classification of hot and cold tumors, and develop a novel signature to predict the ICI treatments response for high expression NSCLC patients.