Integrating AI-driven single-cell analysis to decode epithelial heterogeneity: A prognostic signature and translational immunotherapy strategy targeting

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 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.