Dissecting T Cell Exhaustion in Non-Small Cell Lung Cancer: Single-Cell and Spatial Transcriptomics Reveal Prognostic Signatures and Therapeutic Implications

Background: Non-small cell lung cancer (NSCLC) represents 85% of lung cancers and remains a leading cause of cancer death. Immunotherapy advancements have improved treatment, but many patients develop resistance due to T cell exhaustion. Understanding this mechanism, aided by single-cell RNA sequencing, is vital for creating personalized therapies. Methods: Single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from NSCLC patients and normal tissues were collected from multiple databases. Batch effects were corrected, and scRNA-seq data were processed using Seurat for dimensionality reduction and clustering. Exhausted T cell subpopulations were identified, and transcriptional and spatial analyses were conducted using SCENIC and pseudotime analysis. Additionally, a 20-T-ExhauRs prognostic model was developed using machine learning algorithms, and immune infiltration and drug sensitivity analyses were performed. Statistical analyses were conducted using R and Python software. Results: The study identified exhausted T cell subpopulations in NSCLC using dimensionality reduction and clustering, revealing 25 subpopulations and significant differences between normal and NSCLC groups. Pseudotime and transcription factor analysis showed the evolution of exhausted T cell subpopulations. Spatial transcriptomics and metabolic pathway enrichment revealed heterogeneity in the tumor microenvironment. The 20-T-ExhauRs prognostic model was developed using machine learning and demonstrated strong survival prediction accuracy. Immune infiltration analysis revealed weaker immune responses in high-risk groups, while drug sensitivity analysis indicated reduced effectiveness of certain chemotherapies. The study offers insights into immune regulation, tumor progression, and therapeutic strategies for NSCLC. Conclusion: This study identified exhausted T cell subpopulations in NSCLC, revealing their roles in tumor progression. The 20-T-ExhauRs model accurately predicted survival outcomes. Spatial transcriptomics and immune infiltration analyses highlighted tumor heterogeneity, suggesting potential therapeutic strategies to improve NSCLC treatment and patient prognosis.