Intercellular Communication-Related Molecular Subtypes and a Gene Signature Identified by the Single-Cell RNA Sequencing Combined with a Transcriptomic Analysis.

Abstract

Background The tumor microenvironment (TME) of lung adenocarcinoma (LUAD) comprise various cell types that communicate with each other through ligand-receptor interactions. This study focused on the identification of cell types in LUAD by single-cell RNA sequencing (scRNA-seq) data and screening of intercellular communication-related genes. Methods The Gene Expression Omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo) provided the RNA-seq data of LUAD patients in the GSE149655, GSE31210, and GSE72094 datasets. Quality control of the scRNA-seq data in GSE149655 was performed by the Seurat package (http://seurat.r-forge.r-project.org) for identifying highly variable genes for principal component analysis (PCA) and cell clustering. The CellPhoneDB (http://www.cellphonedb.org) was used for filtering intercellular communication-related ligand-receptor pairs. According to ligand and receptor expressions, LUAD samples were clustered using ConsensusClusterPlus (https://www.bioconductor.org/packages/release/bioc/html/ConsensusClusterPlus). Additionally, the identification of prognosis-related ligand and receptor genes was conducted along with the development of a risk prediction model by the least absolute shrinkage and selection operator (LASSO) Cox regression analysis. Results This study identified twelve cell types in 8170 cells of LUAD tissues along with 219 ligand and receptor genes. LUAD was classified into three different molecular subtypes, among which cluster 3 (C3) had the longest overall survival (OS) time and cluster (C1) had the shortest OS time. In comparison with the other two molecular subtypes, it was observed that C1 had a higher rate of somatic mutations and lower levels of infiltrating immune cells and immune scores. Ten genes were screened from the total ligand and receptor genes to construct a risk model, which showed a strong prediction power in the prognosis of patients with LUAD. Conclusion The results of this study revealed cell types specific to LUAD, which were classified into different molecular subtypes according to intercellular communication-related genes. A novel prognostic risk model was developed in this study, providing new insights into prognostic assessment models for LUAD.