Abstract

Background Non-small-cell lung cancer (NSCLC) exhibits pronounced molecular heterogeneity, and current predictive models rarely incorporate mitochondrial quality-control programs such as mitophagy. We hypothesize that an artificial intelligence model based on mitophagy-related genes (MRGs) at single-cell resolution could improve prediction of survival and immunotherapy benefit. Methods We analyzed single-cell RNA sequencing data from treatment-naïve NSCLC tumors to evaluate the activity of MRGs and identify genes exhibiting differential expression between cells with high versus low mitophagy levels. These differentially expressed genes were then cross-referenced with mitophagy gene sets to pinpoint candidate prognostic markers. Using LASSO regression combined with multiple machine learning classifiers, we constructed a risk model, which was validated in both internal and external cohorts, including a clinical immunotherapy trial. We further examined the relationship between the risk model, immune cell infiltration, and drug sensitivity in silico. The key MRGs were then experimentally validated in A549 cells using qRT-PCR, Western blotting, immunofluorescence, and functional assays for cell migration and wound healing. Results We quantified the mitochondrial autophagy activity of 18,167 single cells. Differential expression yielded 1,668 genes; intersection with the MRG list produced 39 candidates. A six-gene panel (FOS, CANX, EIF4G1, CALCOCO2, HSP90AB1, and PRKAR1A) emerged from LASSO. Gradient boosting machine (GBM) achieved the optimal cross-validated performance (testing set: AUC = 0.80, validation set: AUC = 0.72). SHAP analysis ranked PRKAR1A and CALCOCO2 as the top risk contributors. Patients classified into the high-MRG-score group exhibited consistently shorter overall survival (OS) across all datasets (HR = 3.66, 95% CI 1.72 − 7.81, P < 0.001).