Decoding the role of polyamine metabolism in lung adenocarcinoma prognosis: A triangulated approach combining transcriptome, single-cell and Mendelian randomization analyses.

Abstract

The progression of lung adenocarcinoma (LUAD) is influenced by polyamine metabolism, which modulates antitumor immunity, although the underlying mechanisms remain unclear. The present study investigates the role of polyamine metabolism-related genes (PMRGs) in LUAD using transcriptomic data, single-cell RNA sequencing (scRNA-seq) and Mendelian randomization. Differentially expressed PMRGs were identified through differential expression analysis and weighted gene co-expression network analysis. Prognostic genes were selected via Cox regression and least absolute shrinkage and selection operator regression to construct a risk model. Immune infiltration, machine learning and scRNA-seq were employed to explore molecular mechanisms whilst reverse transcription-quantitative PCR (RT-qPCR) validated gene expression in LUAD tissues. A nomogram incorporating risk scores assisted in predicting LUAD prognosis (area under the curve >0.6). Distinct immune cell profiles, particularly involving B cells and CD4⁺ T cells, were observed between high- and low-risk groups. Drug sensitivity analysis identified 15 drugs with differential responses. Epithelial cells emerged as a key cluster, with dynamic changes in calcium voltage-gated channel auxiliary subunit α2δ2 (CACNA2D2) expression during pseudotime. RT-qPCR confirmed the downregulation of prognostic genes in LUAD. A polyamine metabolism-related prognostic signature (CACNA2D2, adenoreceptor β-1, immunoglobulin superfamily member 10 and carbonic anhydrase 4) associated with the tumor microenvironment was established, offering potential for enhanced prognosis prediction in LUAD.