Decoding epithelial–fibroblast interactions in lung adenocarcinoma through single-cell and spatial transcriptomics

• Published in: Journal of cancer research and clinical oncology Vol. 151; no. 7; pp. 221 - 12
• Main Authors: Yang, Jiajin, Xu, Qiuping, Lu, Yanjun
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 24.07.2025; Springer Nature B.V; Springer
• Subjects: Adenocarcinoma; Adenocarcinoma of Lung - genetics; Adenocarcinoma of Lung - pathology; Aged; Approximation; ASCL1 protein; Biomarkers, Tumor - genetics; Cancer Research; Cell Communication; Cell differentiation; Cell interactions; Cells; Collagen (type I); Communication; Copy number; DNA Copy Number Variations; Epithelial cell; Epithelial cells; Epithelial Cells - metabolism; Epithelial Cells - pathology; Female; Fibroblast; Fibroblasts; Fibroblasts - metabolism; Fibroblasts - pathology; Gene expression; Gene Expression Profiling; Hematology; Humans; Insulin-like growth factor-binding protein 4; Internal Medicine; Lung adenocarcinoma; Lung cancer; Lung Neoplasms - genetics; Lung Neoplasms - metabolism; Lung Neoplasms - pathology; Male; Medicine; Medicine & Public Health; Metabolism; Metabolites; Middle Aged; Nomograms; Oncology; Patients; Prognosis; Risk groups; Single-Cell Analysis - methods; Single-cell transcriptomics; Stromal cells; Survival analysis; Therapeutic targets; Transcriptome; Transcriptomics; Tumor Microenvironment; Tumors; Visualization; Single-cell transcriptomics; Epithelial cell; Lung adenocarcinoma; Tumor microenvironment; Fibroblast
• ISSN: 1432-1335; 0171-5216; 1432-1335
• DOI: 10.1007/s00432-025-06250-6

Background Lung adenocarcinoma (LUAD) exhibits significant cellular heterogeneity, yet the precise interactions between epithelial and stromal cells remain unclear. This study integrates single-cell and spatial transcriptomics to delineate tumor microenvironment dynamics, aiming to uncover key cellular subpopulations and their roles in LUAD progression. Methods We analyzed single-cell RNA sequencing (scRNA-seq) data from 21 LUAD patients and performed spatial transcriptomic deconvolution. Epithelial and fibroblast subpopulations were identified using Seurat and Harmony. Cell-cell communication was inferred via CellChat, while metabolic interactions were assessed using MEBOCOST. Copy number variation (CNV) analysis distinguished malignant cells, and trajectory inference mapped differentiation states. Spatial colocalization was examined via CellTrek. Prognostic signatures were derived from Cox regression, and a six-gene MCI score was validated using survival analysis. Results We identified eight epithelial (e.g., MUC21 + Epi, ASCL1 + Epi) and nine fibroblast subpopulations (e.g., Fb_IGFBP4, Fb_COL11A1), with tumor-enriched subsets showing elevated CNVs and metabolic crosstalk. Fb_IGFBP4 correlated with poor prognosis, while MUC21 + Epi exhibited amplified COL1A1/SDC4-mediated interactions with fibroblasts. Pathway analysis highlighted tumor-specific MK and collagen signaling between fibroblasts and epithelial cells, suggesting stromal-epithelial synergy drives progression. Spatial analysis revealed colocalization of epithelial and fibroblast subclusters in tumors, contrasting with normal tissue. The MCI score, derived from six genes (e.g., ADAM10, MARVELD1), independently predicted survival and stratified high-risk patients (AUC > 0.6). Conclusion This study identifies key stromal-epithelial subset interactions in LUAD, proposing prognostic biomarkers and therapeutic targets.