Abstract
Lung adenocarcinoma (LUAD), a common type of non-small cell lung cancer, is associated with low survival rates and challenges in early detection. Therefore, identifying prognostic biomarkers is crucial for improving patient outcomes. This study utilized 2 datasets - the Cancer Genome Atlas-Lung Adenocarcinoma (TCGA-LUAD) and GSE72094 - along with 182 immune escape-related genes and 597 cancer-associated fibroblast-related genes. Weighted gene co-expression network analysis was used to identify module genes. Differential expression analysis of TCGA-LUAD data revealed LUAD-associated differentially expressed genes, which were then intersected with module genes to identify LUAD-specific differentially expressed immune escape-cancer fibroblast-related genes. To identify potential biomarkers and develop a risk model, univariate Cox regression, least absolute shrinkage and selection operator analysis, and multivariate Cox regression were performed. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes databases were used for enrichment analysis. Immune infiltration and immune cell-biomarker correlations were assessed using CIBERSORT, and the pRRophetic tool was employed to predict LUAD chemotherapeutic sensitivities. Reverse transcription-quantitative polymerase chain reaction was used to validate the expression of prognostic genes in non-small cell lung cancer. The results showed that 183 differentially expressed immune escape-cancer fibroblast-related genes were identified by intersecting 1460 module genes with 5439 differentially expressed genes. Six genes (KRT8, S100A16, COL4A3, SMAD9, MAP3K8, and CCDC146) were selected as potential biomarkers for risk modeling. Gene Ontology enrichment analysis highlighted the involvement of glucose metabolism, ion channel complexes, and channel activity-related genes. Kyoto Encyclopedia of Genes and Genomes analysis revealed pathways related to morphine addiction and protein digestion/absorption. Immune infiltration analysis identified significant differences in 9 immune cell types, including memory B cells and CD8 T cells, between risk groups. Sensitivity to chemotherapeutics, such as AZD6482, ABT-263, A-770041, and BMS-536924, was observed in LUAD. Reverse transcription-quantitative polymerase chain reaction validation results demonstrated that KRT8 and S100A16 were significantly upregulated in tumor tissues, while COL4A3 and SMAD9 expression was downregulated, which was consistent with the TCGA-LUAD database analysis. In conclusion, 6 genes (KRT8, S100A16, COL4A3, SMAD9, MAP3K8, and CCDC146) were identified as potential biomarkers, offering valuable insights into LUAD pathogenesis and therapeutic strategies.