Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2026
Integrating AI-driven single-cell analysis to decode epithelial heterogeneity: A prognostic signature and translational immunotherapy strategy targeting KRT18-induced PD-L1 in lung adenocarcinoma.
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Zhang Han, Zhou Jun, Liu Jiwei, Zhang Pengpeng, Li Xin, Yu Yue, Mao Wenjun, Sun Daqiang
Аннотация
OBJECTIVE: The heterogeneity of epithelial cells and their interaction with the immune microenvironment play crucial roles in tumor progression, but the underlying mechanisms remain unclear.
METHODS: We analyzed single-cell transcriptomic data from normal and tumor tissues to characterize epithelial cells and their microenvironment. Key genes were identified and used, via survival analysis and multiple machine learning methods, to construct a prognostic model termed the Epithelial Signature (EpiSig). We further validated, through a series of experiments, the critical immunological roles of the key genes incorporated into the EpiSig model.
RESULTS: Tumor tissues showed a marked increase in epithelial cells, a reduction in natural killer (NK)/T cells, and cell co-occurrence patterns distinct from normal tissues. We identified differentially expressed genes in tumor epithelial cells and integrated multiple machine-learning algorithms to construct the EpiSig model. This model effectively stratified patient prognosis, with the high-EpiSig group exhibiting significantly worse survival; receiver operator characteristic curve (ROC) and principal component analysis (PCA) analyses further supported its accuracy and robustness. Immune analyses indicated lower immune cell infiltration, decreased human leukocyte antigen (HLA) expression, and elevated programmed cell death ligand 1/programmed cell death protein 1 (PD-L1/PD-1) in the high-EpiSig group, reflecting a more pronounced immunosuppressive microenvironment. The core gene KRT18 correlated strongly with EpiSig scores (R=0.65) and promoted lung adenocarcinoma (LUAD) cell proliferation in functional assays. Further, low KRT18 tumors showed higher CD4+/CD8+ T cell and CD20+ B cell infiltration; in a mouse LLC subcutaneous tumor model, both Krt18 knockdown and PD-1 blockade suppressed tumor growth, with greater efficacy in combination. Mechanistically, KRT18 activated NF-kB/p65 to upregulate PD-L1, promoting immune exclusion and impairing T-cell effector function.
CONCLUSIONS: This study highlights the close relationship between epithelial cell heterogeneity and immune microenvironment alterations in tumors, and presents the EpiSig as a robust tool for prognostic prediction. KRT18 may serve as a promising therapeutic target in LUAD.
METHODS: We analyzed single-cell transcriptomic data from normal and tumor tissues to characterize epithelial cells and their microenvironment. Key genes were identified and used, via survival analysis and multiple machine learning methods, to construct a prognostic model termed the Epithelial Signature (EpiSig). We further validated, through a series of experiments, the critical immunological roles of the key genes incorporated into the EpiSig model.
RESULTS: Tumor tissues showed a marked increase in epithelial cells, a reduction in natural killer (NK)/T cells, and cell co-occurrence patterns distinct from normal tissues. We identified differentially expressed genes in tumor epithelial cells and integrated multiple machine-learning algorithms to construct the EpiSig model. This model effectively stratified patient prognosis, with the high-EpiSig group exhibiting significantly worse survival; receiver operator characteristic curve (ROC) and principal component analysis (PCA) analyses further supported its accuracy and robustness. Immune analyses indicated lower immune cell infiltration, decreased human leukocyte antigen (HLA) expression, and elevated programmed cell death ligand 1/programmed cell death protein 1 (PD-L1/PD-1) in the high-EpiSig group, reflecting a more pronounced immunosuppressive microenvironment. The core gene KRT18 correlated strongly with EpiSig scores (R=0.65) and promoted lung adenocarcinoma (LUAD) cell proliferation in functional assays. Further, low KRT18 tumors showed higher CD4+/CD8+ T cell and CD20+ B cell infiltration; in a mouse LLC subcutaneous tumor model, both Krt18 knockdown and PD-1 blockade suppressed tumor growth, with greater efficacy in combination. Mechanistically, KRT18 activated NF-kB/p65 to upregulate PD-L1, promoting immune exclusion and impairing T-cell effector function.
CONCLUSIONS: This study highlights the close relationship between epithelial cell heterogeneity and immune microenvironment alterations in tumors, and presents the EpiSig as a robust tool for prognostic prediction. KRT18 may serve as a promising therapeutic target in LUAD.