Development of a novel disulfidptosis-correlated m6A/m1A/m5C/m7G gene signature to predict prognosis and therapeutic response for lung adenocarcinoma patients by integrated machine-learning

Abstract

Abstract Background Lung adenocarcinoma (LUAD) represents a significant global health burden, necessitating advanced prognostic tools for improved patient management. RNA modifications (m6A, m1A, m5C, m7G), and disulfidptosis, a novel cell death mechanism, have emerged as promising biomarkers and therapeutic targets in cancer. Methods We systematically compiled disulfidptosis-correlated genes and RNA modification-related genes from existing literature. A novel disulfidptosis-correlated m6A/m1A/m5C/m7G riskscore was computed using integrated machine-learning algorithms. Transcriptomic data from TCGA and GEO databases were downloaded analyzed. Single-cell RNA-sequencing data from the TISCH database was processed using the Seurat package. Genes’ protein–protein interaction network was constructed using the String database. Functional phenotype analysis was performed using GSVA, ClusterProfiler, and IOBR packages. Consensus clustering divided patients into two distinct groups. Drug sensitivity predictions were obtained from the GDSC1 database and predicted using the Oncopredict package. Results The disulfidptosis-correlated m6A/m1A/m5C/m7G risk score effectively stratified LUAD patients into prognostically distinct groups, demonstrating superior predictive accuracy compared to conventional clinical parameters. Patients in different risk groups exhibited significant molecular and clinical differences. Subsequent analyses identified two molecular subtypes associated with RNA modification and disulfidptosis, revealing differences in immune infiltration and prognosis. Functional enrichment analyses highlighted pathways involving RNA modification and disulfidptosis, underscoring their roles in LUAD pathogenesis. Single-cell analysis revealed distinct features between high- and low-risk status cells. Conclusion This study introduces a novel disulfidptosis-correlated m6A/m1A/m5C/m7G risk score as a robust prognostic tool for LUAD, integrating insights from RNA modifications and cell death mechanisms. The risk score enhances prognostic stratification and identifies potential targets for personalized therapeutic strategies in LUAD. This comprehensive approach emphasizes the critical roles of RNA modifications and disulfidptosis in LUAD biology, paving the way for future research and clinical applications aimed at improving patient outcomes.

Keywords

• Disulfidptosis
• m6A/m1A/m5C/m7G
• Machine-learning
• Lung adenocarcinoma
• Prognosis