ArticlePeerJ2025
Integrated bioinformatics screening and experimental validation: construction of a LUAD prediction model based on Treg-related genes.
Article in PeerJ, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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8 authors.
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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 ( 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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