Integrated Analysis of Single-cell and Bulk RNA-Sequencing Identifies a Signature Based on Cancer-related Fibroblast Marker Genes to Predict Prognosis and Therapy Response in Lung Adenocarcinoma.

Cancer-related fibroblasts (CAFs), crucial in the tumor microenvironment, significantly influence tumorigenesis and extracellular matrix shaping. This study aimed to analyze the expression of CAF marker genes in lung adenocarcinoma (LUAD) and create a prognostic signature. We included 716 LUAD patients from different cohorts, conducting a comprehensive analysis of single-cell RNA sequencing data from the Gene Expression Omnibus (GEO) database, identifying 227 CAF marker genes. Using the Cancer Genome Atlas (TCGA) LUAD cohort, we developed a 3-gene prognostic signature, categorizing patients into high-risk and low-risk groups. The signature's predictive capability was validated across clinical subgroups and GEO cohorts. It was determined as an independent prognostic factor via univariate and multivariate analyses, leading to the construction of a nomogram for clinical prognosis prediction. Immune profile analysis indicated that high-risk patients exhibited immunosuppression and immune cell infiltration, while the tumor immune dysfunction and exclusion score suggested higher immunotherapy sensitivity in the low-risk group. In addition, high-risk patients showed greater sensitivity to several first-line chemotherapeutic drugs. The expression of hub genes was validated using quantitative real-time reverse transcription polymerase chain reaction (qRT-PCR) and the Human Protein Atlas (HPA). In conclusion, this study presented a novel prognostic signature for LUAD patients based on CAF marker genes, demonstrating strong predictive power for prognosis and treatment response.