Affiliations: [a] Department of Cardiothoracic Surgery, Ningde Municipal Hospital Affiliated to Ningde Normal University, Ningde, Fujian, China | [b] Department of Cardiothoracic Surgery, School of Clinical Medicine, Fujian Medical University, Ningde Municipal Hospital, Ningde, Fujian, China
Correspondence:
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Corresponding author: Changjin He, Department of Cardiothoracic Surgery, School of Clinical Medicine, Fujian Medical University, Ningde Municipal Hospital, No. 13 East Mindong Road, Dongqiao Economic and Technological Development Zone, Ningde, Fujian 352100, China. E-mail: zhuotianyi@126.com.
Abstract: BACKGROUND: Although it has been established that cancer-associated fibroblasts (CAFs) facilitate tumor development, the relationship between CAFs and the prognosis of patients with lung adenocarcinoma (LUAD) has not been extensively explored. OBJECTIVE: This study was formulated to investigate the prognostic value of CAF-related genes in LUAD. METHODS: Differential analysis was carried out with TCGA-LUAD dataset as the training set. By overlapping differentially expressed genes (DEGs) with genes associated with CAF, CAF-related DEGs specific to LUAD were obtained. A prognostic risk model was constructed by Lasso and Cox regression analysis, and samples were grouped according to median risk score. The efficacy of the model was accessed through survival curve and receiver operating characteristic curve (ROC) analyses, with the validation set for verification. Risk score combined with clinical factors was utilized for Cox analysis to verify the independence of the model, and a nomogram was drawn. GSEA was performed on different risk groups. Immunologic infiltration and tumor mutational burden were assessed in different risk groups. RESULTS: Eleven feature genes including DLGAP5, KCNE2, UPK2, NPAS2, ARHGAP11A, ANGPTL4, ANLN, DKK1, SMUG1, C16orf74, and ACAD8 were identified, based on which a prognostic model was constructed. Risk score could predict the prognosis of LUAD patients and could be an independent prognostic factor for LUAD patients. GSEA outcomes displayed significant enrichment of genes in the high-risk group in the P53 SIGNALING PATHWAY. In comparison to the low-risk group, the high-risk group exhibited a decreased degree of immune infiltration and an elevated level of tumor mutational burden. CONCLUSION: An 11-gene model was constructed based on CAF-related genes to predict LUAD prognosis. This model represented an independent prognostic factor for LUAD.
