Multi-omics and single-cell analysis reveals machine learning-based pyrimidine metabolism-related signature in the prognosis of patients with lung adenocarcinoma

Background: Pyrimidine metabolism is a hallmark of tumor metabolic reprogramming, while its significance in the prognostic and therapeutic implications of patients with lung adenocarcinoma (LUAD) still remains unclear. Methods: In this study, an integrated framework of various machine learning and deep learning algorithms was used to develop the pyrimidine metabolism-related signature (PMRS). Its efficacy in genomic stability, chemotherapy and immunotherapy resistance was evaluated through comprehensive multi-omics analysis. The single-cell landscape of patients between PMRS subgroups was also elucidated. Subsequently, the biological functions of LYPD3, the most important coefficient factor in the PMRS model, were experimentally validated in LUAD cell lines. Results: The PMRS model with "random survival forest" algorithm exhibited the best performance and was utilized for further analysis. It displayed excellent accuracy and stability in various model evaluation assays. Compared to the PMRS-high subgroup, patients with lower PMRS scores had better survival outcomes, more stable genomic characteristics and higher sensitivity to immunotherapy. Single-cell analysis indicated that as PMRS increased, epithelial cells gradually exhibited malignant phenotypes with enhanced pyrimidine metabolism, while PMRS-high patients showed an inhibitory status of tumor immune microenvironment. Further experiments indicated that LYPD3 promoted the malignant progression in LUAD cell lines. Conclusion: Our study constructed the PMRS model, highlighting its potential value in the treatment and prognosis of LUAD patients and providing new insights into the individualized precision treatment for LUAD patients.