Identification of differentially expressed genes in lung adenocarcinoma cells using single-cell RNA sequencing not detected using traditional RNA sequencing and microarray.
Zhencong ChenMengnan ZhaoMing LiQihai SuiYunyi BianJiaqi LiangZhengyang HuYuansheng ZhengTao LuYiwei HuangCheng ZhanWei JiangQun WangLijie TanPublished in: Laboratory investigation; a journal of technical methods and pathology (2020)
Lung adenocarcinoma (LUAD) is the leading cause of cancer-related deaths worldwide. Traditional RNA sequencing data fails to detect the exact cellular and molecular changes in tumor cells as they make up only a small proportion of tumor tissue. 10× genomics single-cell RNA sequencing (10× scRNA-seq) and gene expression data of LUAD patients was obtained from the Department of Thoracic Surgery, Zhongshan Hospital, Fudan University, ArrayExpress, TCGA, and GEO databases. Differentially expressed genes (DEGs) were identified in LUAD and alveolar cells (DEGs-scRNA-cancer_cell), tumor- and normal tissue-derived cells (DEGs-scRNA-sample), and normal and LUAD patients (DEGs-Bulk). Flow cytometry and qRT-PCR were performed to validate the significantly differentially expressed ligand-receptor pairs. We selected 159,219 cells and 594 samples in the scRNA-seq data and traditional RNA sequencing, respectively. A total of 1042 DEGs-scRNA-cancer_cell, 788 DEGs-scRNA-sample, and 2510 DEGs-Bulk were identified in this study. We also identified 57 DEGs that were only detected in DEGs-scRNA-cancer_cell (only-DEGs-scRNA-cancer_cell). To explore the relationship between only-DEGs-scRNA-cancer_cell and survival in LUAD, 14 and 22 only-DEGs-scRNA-cancer_cell, which were closely related with survival in TCGA and GEO cohorts were identified. Functional enrichment analyses showed these DEGs-scRNA-cancer_cells were mainly related to cell proliferation and immunoregulation. Our study detected and compared DEGs at different levels and revealed genes that may regulate tumor development. Our results provide a potential new protocol to determine the contribution of DEGs to cancer progression and to help identify potential therapeutic targets.
Keyphrases
- single cell
- rna seq
- induced apoptosis
- gene expression
- end stage renal disease
- cell proliferation
- cell cycle arrest
- genome wide
- high throughput
- newly diagnosed
- chronic kidney disease
- big data
- flow cytometry
- randomized controlled trial
- squamous cell carcinoma
- signaling pathway
- cell death
- young adults
- prognostic factors
- machine learning
- risk assessment
- peritoneal dialysis
- dna methylation
- binding protein
- artificial intelligence
- acute care
- genome wide analysis