An Integrative Machine Learning Approach Identifies a Cancer- Associated Fibroblast–Driven Predictive Model for Early-Stage Lung Squamous Cell Carcinoma

Background Lung squamous cell carcinoma (LUSC) remains associated with unfavorable clinical outcomes, even in early-stage disease. Increasing evidence indicates that the tumor microenvironment (TME), particularly cancer-associated fibroblasts (CAFs), plays a crucial role in tumor progression and therapeutic resistance. However, the prognostic and therapeutic implications of CAF-related molecular signatures in early-stage LUSC remain insufficiently defined. Methods Transcriptomic profiles and corresponding clinical data of patients with early-stage LUSC were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. Stromal and CAF infiltration levels were quantified, and key CAF-associated modules were identified using weighted gene co-expression network analysis (WGCNA). A CAF-related prognostic signature was subsequently constructed using multivariate Cox regression analysis. Functional enrichment analysis was performed to explore underlying biological mechanisms. The predictive value of the signature for chemotherapy and immune checkpoint blockade response was evaluated. In addition, FSTL3, a core gene within the model, was selected for in vitro validation to assess its effects on cell proliferation, colony formation, and apoptosis in LUSC cells. Results We established a four-gene CAF-related prognostic signature that effectively stratified patients into high- and low-risk groups with significantly different overall survival outcomes. Multivariate analysis confirmed that the CAF-based risk score served as an independent prognostic factor in early-stage LUSC. Enrichment analysis demonstrated that epithelial–mesenchymal transition (EMT) was prominently activated in the high-risk group. Furthermore, risk stratification based on the CAF signature was associated with differential predicted responses to chemotherapy and immune checkpoint inhibitors. Functional experiments revealed that FSTL3 knockdown significantly inhibited cell proliferation and colony formation while promoting apoptosis, supporting the biological relevance of the identified signature. Conclusions We established a robust CAF-derived four-gene prognostic model that effectively predicted patient survival and therapeutic response in early stage LUSC. Experimental validation highlighted the biological and clinical relevance of the CAF-associated signature and identified FSTL3 as a potential therapeutic target for early stage LUSC.