Integrated analysis of scRNA-seq and bulk RNA-seq identifies matrisome-related biomarkers for prognostic stratification and immune landscape in lung adenocarcinoma

Lung adenocarcinoma (LUAD) displays significant biological heterogeneity, with matrisome-related genes (MRGs) playing key roles in tumor progression and immune regulation. Understanding the interplay between MRGs, the tumor microenvironment, and host immunity is critical for mechanistic insights. LUAD transcriptomic and clinical data were sourced from TCGA, GEO (GSE31210), and single-cell data (GSE189357). MRGs were analyzed <i>via</i> limma, with prognostic genes identified using univariate Cox. A LASSO-multivariate Cox model was built and validated using Kaplan-Meier, receiver operating characteristic, and external datasets. Functional enrichment (GO/KEGG/GSEA), immune infiltration (ssGSEA/ESTIMATE/CIBERSORT), tumor mutational burden, immunotherapy response, and drug sensitivity were assessed. Consensus clustering defined molecular subtypes. Single-cell analysis (Seurat/SCISSOR) identified risk-associated cells, with AUCell and CellChat evaluating activity and cell communication. A 7-gene risk model (ANGPTL4, C1QTNF6, CCL20, CLEC3B, FCN1, LAMA3, PRELP) stratified LUAD patients into distinct survival groups (area under the curve > 0.7). Low-risk patients showed higher immune infiltration, lower TIDE scores (suggesting better immunotherapy response), and reduced sensitivity to Axitinib/Gefitinib. Single-cell analysis implicated fibroblasts and myeloid cells in high-risk profiles, with activated MIF/TGF-β pathways. This integrated transcriptomic and single-cell model predicts LUAD prognosis and immune landscape, guiding personalized therapy.