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

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.