Analyses of multi-omics differences between patients with high and low PD1/PDL1 expression in lung squamous cell carcinoma

Abstract

Background: Immunotherapy has achieved excellent results in patients with lung squamous cell carcinoma. However, in which population it can exert the greatest effect is still unknown. Some studies have suggested that its effect is related to the expression level of PD1. Analyzing the relationship between PD1 expression level and genetic differences in lung squamous cell carcinoma patients will be helpful in understanding the underlying causes of this immunotherapy effect and provide a reference for clinical practice.

Methods: In this study, we used RNA-seq, miRNA-seq, methylation array, mutation profiles, and copy number variation data from the TCGA database and RNA-seq data from the GEO database to analyze the distinctive genomic patterns associated with PD1 and PDL1 expression. RNA-seq data from 44 LUSC patients who underwent surgery at Zhongshan Hospital were also included in the study.

Results: After grouping LUSC patients according to the expression levels of PD1 and PDL1, we found no significant difference in survival between the two groups. However, 178 genes, including IL-21, KLRC3, and KLRC4, were significantly upregulated in both the TCGA and GEO databases in the high expression group, and there was a precise correlation between gene expression and epigenetic changes in the two groups. At the same time, the overall level of somatic mutations was not significantly different between the two groups. It is worth noting that the gene enrichment results showed that the differential pathways were mainly enriched in immune regulation, especially T cell-related immune activities.

Conclusion: We found that the differences in gene expression between the two groups were related to immunity, which may affect the effectiveness of immunotherapy. We hope our results can provide a reference for further research and help in finding other targets to improve the efficacy of immunotherapy.

Keywords: Bioinformatics; Lung squamous cell carcinoma; Multiomics; Programmed death 1; Programmed death-ligand 1.