Identification and validation of an immune-related programmed cell death signature for predicting prognosis and immunotherapy in large-scale multicenter cohorts for lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) is a major subtype of lung cancer with a 5-year survival rate of less than 20%. While immunotherapy has revolutionized cancer treatment, only 10-20% of cases show durable responses to immune checkpoint blockade. Thus, developing accurate methods to predict prognosis and response to immune checkpoint inhibitors (ICIs) is crucial. Programmed cell death (PCD) plays a significant role in maintaining tissue homeostasis and responding to various physiological or pathological conditions. Increasing evidence suggests that PCD is involved in tumor initiation, development, prognosis, and response to immunotherapy. To provide reliable LUAD clinical tools, we developed an immune-related programmed cell death signature (IRPCDS) and validated its ability to predict prognosis and ICI response for precision medicine. Methods: In this study, we integrated 18 PCD signatures to develop an IRPCDS. We employed 10 machine learning algorithms and 101 algorithm combinations to assess the performance of the IRPCDS. The signature was validated across multiple cohorts to ensure its robustness in predicting clinical outcomes for LUAD patients. Results: The IRPCDS demonstrated strong performance in predicting the clinical prognosis of LUAD patients, effectively stratifying them into different risk groups for targeted interventions. Notably, the IRPCDS outperformed traditional clinicopathological factors and previously published 52 signatures in predicting overall survival (OS). Patients classified in the low-risk group exhibited high levels of immune infiltration and favorable responses to ICIs, while those in the high-risk group showed a higher overall mutation burden and an increased frequency of mutations in driver genes associated with LUAD. Additionally, we validated the expression of the IRPCDS genes at both the transcriptional and protein levels across multiple datasets and clinical specimens. Conclusions: The IRPCDS serves as a robust and promising tool for enhancing clinical outcomes and precision medicine for individual LUAD patients. By integrating PCD signatures, this approach provides valuable insights into the prognostic landscape of LUAD, paving the way for more effective immunotherapeutic strategies.