Integrating bulk and single-cell transcriptomic data to construct a risk model for histidine metabolism-related...

The rising incidence and mortality of lung adenocarcinoma (LUAD) present a significant public health challenge. Histidine, an essential amino acid, plays a pivotal role in metabolic processes, yet its specific contribution to LUAD pathogenesis remains to be elucidated. This study obtained bulk and single-cell RNA sequencing (scRNA-seq) data for LUAD from UCSC Xena and Code Ocean platforms, respectively. By integrating differential expression analysis, univariate/multivariate Cox analysis, and LASSO regression analysis, prognostic genes for LUAD were identified, and a prognostic risk model was constructed. Algorithms including ESTIMATE, ssGSEA, and CIBERSORT were employed to investigate immune heterogeneity across different groups. Furthermore, molecular subtypes of LUAD were identified through consensus clustering. This study, through the integration of bulk and scRNA-seq data, identified epithelial cells as the key effector cell population in LUAD, which can be further subdivided into four functionally heterogeneous subpopulations. Seven histidine metabolism-related epithelial cell-specific genes with prognostic significance in LUAD were identified (WIF1, GATA2, CD69, ID1, C4BPA, WFDC2, and CCL20), enabling the construction of a robust prognostic risk model. Immune infiltration analysis revealed that low-risk patients exhibited more robust immune infiltration and activity. Furthermore, cross-cancer exploratory evidence suggested potential sensitivity to CTLA-4 and PD-L1 inhibitors in this group. Furthermore, consensus clustering analysis successfully partitioned LUAD into two molecular subtypes exhibiting immune heterogeneity. The prognostic model constructed based on epithelial cell-specific genes associated with histidine metabolism effectively distinguishes LUAD patients and their immune characteristics, revealing epithelial cells as a key cell population regulating LUAD histidine metabolism.