IDO1-mediated cellular senescence shapes the immune landscape and predicts prognosis in SCLC: from multi-omics discovery to clinical validation

Small cell lung cancer (SCLC) represents the most aggressive subtype of lung cancer, hampering clinical management due to unclear resistance mechanisms and a lack of effective therapeutic targets. Cellular senescence (CS) remodels the tumor microenvironment, drives immune evasion, and compromises therapeutic efficacy, however, its role and clinical significance in SCLC remain to be elucidated. This study aims to systematically characterize the CS landscape in SCLC, identify key regulatory molecules, and construct a CS-associated model to predict patient prognosis and therapeutic response. We integrated single-cell RNA sequencing data from the TISCH database with bulk transcriptomic data from multiple public cohorts. CS-related gene sets were curated from the MsigDB. Unsupervised clustering was performed to identify CS-associated molecular subtypes of SCLC. These subtypes were comprehensively characterized by comparing clinical pathological features, signaling pathway activities, and immune cell infiltration patterns. Based on differentially expressed genes (DEGs) identified between subtypes, a CS scoring model was constructed and validated using multiple machine learning algorithms to predict patient prognosis. Finally, the functional role and prognostic value of IDO1, a core gene in the model, were evaluated through in vitro experiments and clinical sample validation. Identification of two CS-based subtypes allowed patient stratification into high- and low- score groups using a dedicated risk model. High-score patients exhibited reduced immune infiltration, diminished chemokine and immune checkpoint expression, shorter overall survival, and poorer response to immunotherapy. Experimental validation supported IDO1 as a contributor to chemotherapy-induced senescence and an indicator of adverse outcomes, while pharmacological inhibition of IDO1 attenuated etoposide resistance in SCLC cells. We constructed a CS-derived prognostic signature through machine learning and integrative transcriptomic analysis. This signature reproducibly stratified survival outcomes and immunotherapy response in SCLC patients, with high IDO1 expression significantly associated with poor prognosis and therapeutic resistance.