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Integrated bioinformatics screening and experimental validation: construction of a LUAD prediction model based on Treg-related genes

T. Zhao,Yan Yao,Yan Sun,Qingliang Lv,Changgang Sun,Yining Cheng,Chundi Gao,Zhuang Jing

Published 2025 in PeerJ

ABSTRACT

Background The prognosis of lung adenocarcinoma (LUAD) is poor, and clinical treatment mainly comprises a combination of traditional therapy and immunotherapy. However, the role and mechanism of tumor-infiltrating regulatory T cells (Tregs) in immunotherapy remain controversial. Therefore, we aimed to determine the role of Tregs in LUAD and to construct a relevant prognostic model for future clinical treatment. Methods A LUAD dataset was downloaded from the Gene Expression Omnibus (GEO) database, screened, integrated, and divided into test and validation datasets. CIBERSORT and weighted correlation network analysis (WGCNA) algorithms were combined to screen for Treg cell-related modules. Minimum absolute contraction and selection operator (LASSO) and univariate and multivariate Cox regression analyses were used to screen genes in the key modules and construct Treg-related prognostic models. Then, the expression differences of genes in the prognostic model were analyzed, and the results were verified by Western blotting. Result Among all cluster modules, the correlation between the brown module and Treg s (R2 = 0.43, P = 1e − 30) was the highest. After LASSO and univariate and multivariate Cox regression screening, six genes (ADARB2, B3GALT, FER, LTB4R2, N6AMT1, and SCN9A) were selected to construct the prognosis model, and the prognosis of low-risk patients was found to be better than that of high-risk patients. Finally, the SCN9A gene with the highest correlation with the model was selected and verified using Western blot analysis. The results showed that the expression of Treg surface markers in LUAD cells was increased, and the expression of SCN9A was decreased compared with that in normal lung epithelial cells. Conclusion We identified the role of Treg-related genes in LUAD, constructed and verified a related prognostic model, and explored a potential therapeutic target, SCN9A, to provide a new perspective for the clinical treatment of LUAD.

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