Abstract
Background: Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. However, it cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data. Methods: Bulk RNA-seq data were downloaded from The Cancer Genome Atlas (TCGA) database. LUAD scRNA-seq data were acquired from Gene Expression Omnibus (GEO) database. The uniform manifold approximation and projection (UMAP) was used for dimensionality reduction and cluster identification. Weighted Gene Correlation Network Analysis (WGCNA) was utilized to identify key modules and differentially expressed genes (DEGs). The non-negative Matrix Factorization (NMF) algorithm was used to identify different subtypes based on DEGs. The Cox regression analysis was used to develop the prognostic model. The characteristics of mutation landscape, immune status, and immune checkpoint inhibitors (ICIs) related genes between different risk groups were also investigated. Results: scRNA-seq data of four samples were integrated to identify 13 clusters and 9cell types. After applying differential analysis, NK cells, bladder epithelial cells, and bronchial epithelial cells were identified as significant cell types. Overall, 329 DEGs were selected for prognostic model construction through differential analysis and WGCNA. Besides, NMF identified two clusters based on DEGs in the TCGA cohort, with distinct prognosis and immune characteristics being observed. We developed a prognostic model based on the expression levels of six DEGs. A higher risk score was significantly correlated with poor survival outcomes but was associated with a more frequent TP53 mutation rate, higher tumor mutation burden (TMB), and up-regulation of PD-L1. Two independent external validation cohorts were also adopted to verify our results, with consistent results observed in them. Conclusion: This study constructed and validated a prognostic model for LUAD by integrating 10× scRNA-seq and bulk RNA-seq data. Besides, we observed two distinct subtypes in this population, with different prognosis and immune characteristics.
Highlights
Lung cancer is one of the most common incident cancers and the leading cause of cancer-related death worldwide (Chen et al, 2016)
GSE31210 and GSE13213 cohorts were acquired from the Gene Expression Omnibus (GEO) database to serve as independent external cohorts for risk model validation
10× scRNA-seq data of two lung adenocarcinoma (LUAD) and two normal samples were downloaded from the GSE149655 dataset
Summary
Lung cancer is one of the most common incident cancers and the leading cause of cancer-related death worldwide (Chen et al, 2016). With the rapid development of cancer genomics in recent decades, more and more gene alteration has been identified as an effective treatment target for LUAD. The majority of LUAD patients with driver gene mutation can benefit from molecular targeted therapy, such as epidermal growth factor receptor (EGFR)- tyrosine kinase inhibitors (TKIs), anaplastic lymphoma kinase (ALK)-TKIs (Yi et al, 2021a), and recently KRAS (Uras et al, 2020) and c-MET (Zhang et al, 2018) inhibitors. Lung adenocarcinoma (LUAD) remains a lethal disease worldwide, with numerous studies exploring its potential prognostic markers using traditional RNA sequencing (RNA-seq) data. It cannot detect the exact cellular and molecular changes in tumor cells. This study aimed to construct a prognostic model for LUAD using single-cell RNA-seq (scRNA-seq) and traditional RNA-seq data
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