CMA-associated cellular state remodeling defines prognostic heterogeneity and identifies ARRDC3 as a protective functional gene in small cell lung cancer.

Background: Small cell lung cancer (SCLC) is a highly aggressive malignancy characterized by rapid progression, early metastasis, frequent relapse, and treatment resistance. Although chemoimmunotherapy has improved outcomes in a subset of patients, reliable molecular stratification tools reflecting tumor heterogeneity and survival risk remain limited. Chaperone-mediated autophagy (CMA) is involved in proteostasis, metabolic adaptation, and stress responses, but its cellular heterogeneity and prognostic relevance in SCLC remain unclear. Methods: This study integrated single-cell RNA sequencing data, bulk transcriptomic cohorts, and clinical information to characterize CMA-related heterogeneity in SCLC. At the single-cell level, SCLC subtypes were annotated, CMA scores were calculated, and differences among baseline, sensitive, and resistant states were evaluated. At the bulk level, tumor versus normal differential expression analysis and weighted gene co-expression network analysis were performed to identify CMA-related candidate genes. A CMA-related Risk Score was constructed through comprehensive machine learning comparison and evaluated using survival analysis, time-dependent receiver operating characteristic curves, Cox regression, nomogram analysis, functional enrichment, immune microenvironment analysis, and drug sensitivity prediction. ARRDC3 was further validated by Results: Single-cell analysis revealed marked SCLC subtype heterogeneity and nonuniform CMA activity across cellular subtypes. Significant CMA score differences between sensitive and resistant cells were observed in the SCLC-A_NR0B1/MYCL+ and SCLC_Hypoxia_glycolytic subtypes. Integration of CMA-related co-expression modules with tumor-associated differentially expressed genes identified 53 candidate genes. The machine learning-derived Risk Score effectively stratified patients into high-risk and low-risk groups across the combined cohort, GSE60052 cohort, and cBioPortal cohort, with high-risk patients showing significantly poorer overall survival. The Risk Score remained an independent prognostic factor and was associated with proliferative, cell-cycle, metabolic, immune, and drug sensitivity-related features. Conclusion: This study revealed CMA-related cellular heterogeneity in SCLC and established a robust prognostic Risk Score associated with survival, biological pathway activity, immune microenvironment features, and potential therapeutic responses. Functional validation of ARRDC3 further supports the biological relevance of this model, providing new insights into SCLC molecular risk stratification and CMA-associated resistance states.