Integrating bulk and single-cell transcriptomic data to construct a risk model for histidine metabolism-related epithelial cell features in lung adenocarcinoma, predicting prognosis and immune landscape.
OpenAlex 토픽 ·
Ferroptosis and cancer prognosis
Cancer Immunotherapy and Biomarkers
Single-cell and spatial transcriptomics
[BACKGROUND] The rising incidence and mortality of lung adenocarcinoma (LUAD) present a significant public health challenge.
APA
Chaofan Mao, Lin Zhou (2026). Integrating bulk and single-cell transcriptomic data to construct a risk model for histidine metabolism-related epithelial cell features in lung adenocarcinoma, predicting prognosis and immune landscape.. RNA biology. https://doi.org/10.1080/15476286.2026.2662721
MLA
Chaofan Mao, et al.. "Integrating bulk and single-cell transcriptomic data to construct a risk model for histidine metabolism-related epithelial cell features in lung adenocarcinoma, predicting prognosis and immune landscape.." RNA biology, 2026.
PMID
42003422
Abstract
[BACKGROUND] 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.
[METHODS] 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.
[RESULTS] 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.
[CONCLUSION] 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.
[METHODS] 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.
[RESULTS] 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.
[CONCLUSION] 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.
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