Metabolic reprogramming signature predicts prognosis and immune landscape in small cell lung cancer: MOCS2 validation and implications for personalized therapy.

Introduction: Small cell lung cancer (SCLC) remains a leading cause of cancer mortality worldwide, characterized by rapid progression and poor clinical outcomes, and the function of metabolic reprogramming remains unclear in SCLC. Methods: We performed multi-omics analysis using public SCLC datasets, analyzing single-cell RNA sequencing to identify metabolic reprogramming patterns between chemotherapy-resistant and sensitive samples. Bulk RNA sequencing from GSE60052 and cBioportal cohorts was used to identify metabolism-related gene modules through WGCNA and develop a Gradient Boosting Machine prognostic model. Functional validation of MOCS2, the top-ranked gene in our model, was conducted through siRNA knockdown experiments in SCLC cell lines. Results: Single-cell analysis revealed distinct metabolic reprogramming patterns between chemotherapy-resistant and sensitive samples. WGCNA identified a turquoise module strongly correlated with metabolic reprogramming (cor = 0.56, P < 0.005). The GBM-based prognostic model demonstrated excellent performance (C-index = 0.915) with MOCS2, USP39, SMYD2, GFPT1, and PRKRIR identified as the most important variables. Kaplan-Meier analysis confirmed significant survival differences between high-risk and low-risk groups in both validation cohorts (P < 0.001). Conclusion: Our study established a robust metabolism-based prognostic model for SCLC that effectively stratifies patients into risk groups with distinct survival outcomes, immune profiles, and drug sensitivity patterns. Functional validation experiments confirmed MOCS2 as an important regulator of SCLC cell proliferation and migration, providing valuable insights for treatment selection and prognosis prediction in SCLC.