Targeting regulated cell death pathways in lung cancer: mechanisms, therapeutic strategies, and clinical translation

Lung cancer is the leading cause of global cancer mortality, with treatment efficacy limited by high heterogeneity, drug resistance, and an immunosuppressive tumor microenvironment. Focusing primarily on non-small cell lung cancer (NSCLC), this review systematically analyzes eight key regulated cell death (RCD) pathways in lung cancer. These pathways are apoptosis, autophagy, necroptosis, ferroptosis, cuproptosis, pyroptosis, immunogenic cell death (ICD), and lysosome-dependent cell death (LDCD). Mechanistic dissection reveals complex crosstalk and a dynamic equilibrium among these pathways. For instance, apoptosis escape via EGFR/PI3K/Akt/mTOR signaling promotes survival, while autophagy exhibits a context-dependent dual role regulated by factors such as RBBP4 and the AURKA-CXCL5 axis. Importantly, several RCD pathways exert potent immunomodulatory functions. Necroptosis activates T cells by releasing damage-associated molecular patterns (DAMPs), while ferroptosis enhances NK cell cytotoxicity through GPX4 inactivation. Regarding therapeutic advances, synergistic strategies show promise, such as berberine with EGFR-TKIs inducing apoptosis via EGFR degradation, and (-)-Guaiol triggering ICD to synergize with PD-1/PD-L1 inhibitors. Novel inducers, including Auranofin (ferroptosis), TMEM100 agonists (necroptosis), and cuproptosis nanomedicines (e.g., DE-Cu 4 O 3 NPs), demonstrate preclinical potential. Prognostic models based on RCD-related genes (e.g., LDCD signatures) can predict immune features and response to immune checkpoint inhibitors (ICIs). However, clinical translation faces bottlenecks, including intricate pathway crosstalk, difficulties in remodeling the immunosuppressive niche, low ICI response in EGFR-mutant patients, and a lack of standardized biomarkers and optimized delivery systems. Future research should prioritize coordinated targeting of multiple death pathways, utilize advanced computational tools integrated with multi-omics data to decipher RCD network complexity and optimize treatment prediction, and strengthen interdisciplinary translational efforts. Ultimately, a deep understanding of the RCD network paves the way for a paradigm shift toward precision therapy in lung cancer.