Construction of a prognostic model based on memory CD4+ T cell–associated genes for lung adenocarcinoma and its applications in immunotherapy

Autor: Yong Li, Xiangli Ye, Huiqin Huang, Rongxiang Cao, Feijian Huang, Limin Chen
Jazyk: angličtina
Rok vydání: 2024
Předmět:
Zdroj: CPT: Pharmacometrics & Systems Pharmacology, Vol 13, Iss 5, Pp 837-852 (2024)
Druh dokumentu: article
ISSN: 2163-8306
DOI: 10.1002/psp4.13122
Popis: Abstract The association between memory CD4+ T cells and cancer prognosis is increasingly recognized, but their impact on lung adenocarcinoma (LUAD) prognosis remains unclear. In this study, using the cell‐type identification by estimating relative subsets of RNA transcripts algorithm, we analyzed immune cell composition and patient survival in LUAD. Weighted gene coexpression network analysis helped identify memory CD4+ T cell–associated gene modules. Combined with module genes, a five‐gene LUAD prognostic risk model (HOXB7, MELTF, ABCC2, GNPNAT1, and LDHA) was constructed by regression analysis. The model was validated using the GSE31210 data set. The validation results demonstrated excellent predictive performance of the risk scoring model. Correlation analysis was conducted between the clinical information and risk scores of LUAD samples, revealing that LUAD patients with disease progression exhibited higher risk scores. Furthermore, univariate and multivariate regression analyses demonstrated the model independent prognostic capability. The constructed nomogram results demonstrated that the predictive performance of the nomogram was superior to the prognostic model and outperformed individual clinical factors. Immune landscape assessment was performed to compare different risk score groups. The results revealed a better prognosis in the low‐risk group with higher immune infiltration. The low‐risk group also showed potential benefits from immunotherapy. Our study proposes a memory CD4+ T cell–associated gene risk model as a reliable prognostic biomarker for personalized treatment in LUAD patients.
Databáze: Directory of Open Access Journals
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