Background: Non-small cell lung cancer (NSCLC) represents the predominant pathological subtype of lung cancer in China. Amidst the advent of precision medicine, immunotherapy has emerged as a pivotal approach in managing malignant neoplasms, substantially improving patient prognosis and survival rates. However, the efficacy of immunotherapy remains limited, primarily attributed to the development of resistance among advanced-stage patients. This study used bioinformatics methodologies to analyze and identify potential key genes governing immune resistance in NSCLC, offering novel insights into therapeutic avenues. Methods: Gene expression datasets (GSE126044 and GSE135222) encompassing NSCLC cases with immunotherapy resistance and control groups were retrieved from the Gene Expression Omnibus (GEO) repository. Differential gene expression analysis was conducted using Gene Expression Omnibus 2 R (GEO2R) with criteria set at |log FC (fold change)| ≥1 and p < 0.05. Subsequent analyses involved Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, Gene Ontology (GO) functional annotation, Protein-Protein Interaction (PPI) network construction, and Gene Set Enrichment Analysis (GSEA). The findings were visualized through volcano plots and box plots in the R program. Candidate genes were cross-validated with Genecard database entries and scrutinized against existing literature for clinical relevance. The association between key genes, immune cells, and immune-associated gene expressions was analyzed using the Tumor Immune Estimation Resource (TIMER) database. Immunohistochemistry assays were employed to assess the differentially expressed genes (DEGs) in lung cancer tissues. Results: Sixty-four upregulated DEGs were obtained from datasets GSE126044 and GSE135222. PPI network analysis identified one cluster and twelve candidate genes, further corroborated through module examination of common DEGs. Integration with Genecard database entries and literature confirmed Fas Ligand (FASLG) as a pivotal gene. KEGG and GSEA pathway analyses unveiled potential mechanisms predominantly related to the interaction between immune cell functions and cytokines, especially T cells. Analysis in the TIMER database revealed a significant positive correlation between FASLG expression and six types of infiltrating immune cells, as well as specific immune cell subsets, alongside three immune checkpoint-associated molecules: Cluster of Differentiation 274 (CD274), Cytotoxic T-Lymphocyte-Associated protein 4 (CTLA-4), and Programmed Cell Death Protein 1 (PDCD1) (p-value < 0.05). Furthermore, in The Cancer Genome Atlas (TCGA) database, FASLG was strongly associated with T cell gene markers and regulatory factors associated with T cell exhaustion, demonstrating statistical significance (p-value < 0.05). Immunohistochemical results preliminarily confirmed the significant upregulation of FASLG in lung cancer tissues. Conclusion: The identification of key genes and associated signaling cascades deepens our understanding of the molecular mechanisms governing immunotherapy resistance in NSCLC. Notably, FASLG is a potential facilitator of immune escape in NSCLC tumor cells by promoting T cell exhaustion, highlighting NSCLC as a viable target for anticancer interventions.