Single-cell and machine learning-based neural regulation signature for prognosis prediction and immunotherapy response in lung adenocarcinoma

OBJECTIVE: Lung adenocarcinoma (LUAD) molecular heterogeneity limits traditional prognostic models. Given the emerging role of neural regulation (NR) in tumor progression, we aimed to delineate NR-associated cellular phenotypes via single-cell RNA sequencing (scRNA-seq) and develop a robust machine-learning-derived signature (NR.Sig) to precisely assess prognosis and guide personalized immunotherapy. METHODS: We integrated three LUAD scRNA-seq cohorts and ten transcriptomic cohorts with immunotherapy records. Single-cell analyses (clustering, cell-cell communication, pseudotime trajectory) identified NR-enriched epithelial subpopulations. Using their prognostic marker genes, we evaluated 101 combinations from 10 machine learning algorithms via leave-one-out cross-validation. The combination yielding the highest C-index formed the NR.Sig model. Its prognostic accuracy, stability, and clinical utility in characterizing the tumor immune microenvironment (TME) and forecasting immunotherapy efficacy were comprehensively validated across multiple independent cohorts. RESULTS: "CRABP2-positive epithelial cells" were identified as a stem-like, NR-enriched malignant subpopulation correlating strongly with immune exhaustion. The random survival forest (RSF)-based NR.Sig achieved optimal modeling performance. Validation confirmed that NR.Sig high-risk patients had significantly shorter overall and progression-free survival. NR.Sig outperformed conventional clinical indicators and existing prognostic models, with FAM83A identified as the core hub gene. Crucially, high-risk scores inversely correlated with immune infiltration. Conversely, the low-risk group exhibited an "immune-hot" phenotype with enhanced cancer-immunity cycle activity and elevated checkpoint expression, translating to significantly higher immunotherapy response rates in independent clinical cohorts. CONCLUSION: By integrating scRNA-seq with an optimized machine learning framework, we developed and validated NR.Sig. This robust signature holds significant clinical translational value, serving as a precise molecular tool for LUAD risk stratification, prognostic assessment, and the guidance of personalized immunotherapy strategies.