A clinically relevant SLC2A1-associated malignant epithelial cell state predicts prognosis and immunotherapy response...

Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality, with therapeutic resistance largely driven by unresolved malignant epithelial heterogeneity within the tumor microenvironment. However, the epithelial cell states that underlie poor prognosis and immunotherapy resistance remain incompletely defined. We performed an integrative multi-omics analysis combining large-scale single-cell RNA sequencing, spatial transcriptomics, and bulk transcriptomic data with clinical outcomes. The Scissor algorithm was applied to identify prognosis-associated epithelial cell states, followed by construction of a risk score model. External validation was conducted across multiple independent cohorts, including immunotherapy-treated datasets. We identified a prognostically relevant Scissor⁺ malignant epithelial cell state associated with adverse survival. This state was characterized by activation of MYC, epithelial-mesenchymal transition, hypoxia, and NF-κB signaling, and was linked to an immunosuppressive tumor microenvironment. Based on this state, we developed a Scissor⁺ epithelial cell-derived risk score (SERS), which demonstrated robust and reproducible prognostic performance across multiple cohorts and was associated with reduced responsiveness to immunotherapy. Further analyses identified SLC2A1 as a key gene associated with this malignant epithelial state. Functional experiments confirmed that SLC2A1 promotes tumor cell proliferation, migration, and invasion. In addition, cell-cell communication analysis suggested a potential SLC2A1-CAF-collagen signaling axis linking epithelial cell states with stromal interactions. This study defines a clinically relevant malignant epithelial cell state in LUAD and establishes a framework linking cell states, molecular features, and microenvironmental interactions. These findings provide potential biomarkers for prognostic stratification and immunotherapy response prediction in LUAD.