Abstract
OBJECTIVE: This study aims to mine the TCGA database for differentially expressed genes in recurrent lung cancer tissues, determine the relationship between these recurrent genes and lung cancer at the single-cell level, and identify potential targets for lung cancer treatment. METHODS: Data for lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) were obtained from the TCGA database and grouped based on clinical recurrence information. Single-cell data from GSE131907 were downloaded from the GEO database. R was utilized to screen for differentially expressed genes (DEGs), followed by weighted gene co-expression network analysis (WGCNA) of these DEGs. Additionally, the GSEA database was employed to visualize differential pathways and identify key genes. The relationship between the expression of these key genes and lung cancer recurrence was validated using the GSE131907 single-cell dataset. RESULTS: A total of 2,239 differentially expressed genes were identified in the LUAD dataset, while 3,404 differentially expressed genes were found in the LUSC dataset. WGCNA revealed that the lapis lazuli module gene set was associated with recurrence. Validation at the single-cell level indicated that the FOXI1, FOXB1, and KCNA7 genes were linked to lung cancer progression. CONCLUSION: The differentially expressed genes primarily influence NSCLC recurrence through involvement in biological processes related to metabolism and hormone secretion pathways. Notably, the KCNA7 and FOX gene families were identified as critical for NSCLC recurrence. This study highlights specific genes within proliferation and cell cycle pathways as key therapeutic targets for managing NSCLC recurrence.