A novel signature predicts prognosis and immunotherapy in lung adenocarcinoma based on cancer-associated fibroblasts - Ren, Qianhe; Zhang, Pengpeng; Lin, Haoran; Feng, Yanlong; Chi, Hao; Zhang, Xiao; Xia, Zhijia; Cai, Huabao; Yu, Yue (Frontiers in Immunology, 2023)
Abstract Background: Extensive research has established significant correlations between cancer-associated fibroblasts (CAFs) and various stages of cancer development, including initiation, angiogenesis, progression, and resistance to therapy. In this study, we aimed to investigate the characteristics of CAFs in lung adenocarcinoma (LUAD) and develop a risk signature based on CAFs to predict the prognosis of patients with LUAD. Methods: We obtained single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq data from the GEO and TCGA database. The Seurat R package was used to process the scRNA-seq data and identify CAF clusters based on several CAF markers. Pearson correlation analysis was then applied to determine differentially expressed genes (DEGs) associated with CAF clusters. CAF-related prognostic genes were further identified using univariate Cox regression analysis. To reduce the number of genes, Lasso regression was performed, and a risk signature was established. A novel nomogram that incorporated the risk signature and clinicopathological features was developed to predict the clinical applicability of the model. Additionally, we conducted an immune landscape and immunotherapy responsiveness analysis. Finally, we performed in vitro experiments to verify the functions of EXO1 in LUAD. Results: We identified 5 CAF clusters in LUAD using scRNA-seq data, of which 3 clusters were significantly associated with prognosis in LUAD. A total of 492 genes were found to be significantly linked to CAF clusters from 1731 DEGs and were used to construct a risk signature consisting of four genes. Moreover, our immune landscape exploration revealed that the risk signature was significantly related to immune scores, and its ability to predict responsiveness to immunotherapy was confirmed. Furthermore, a novel nomogram incorporating the risk signature and clinicopathological features showed excellent clinical applicability. Finally, we verified the functions of EXP1 in LUAD through in vitro experiments. Conclusions: The novel risk signature based on CAFs has proven to be an excellent predictor of LUAD prognosis, and its clinical applicability has been confirmed as favorable. The comprehensive characterization of LUAD based on the CAF signature can predict the response of LUAD to immunotherapy and provide novel insights into the management of LUAD patients.