ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 16 - 2025 | doi: 10.3389/fimmu.2025.1601982
This article is part of the Research TopicHarnessing Molecular Insights for Enhanced Drug Sensitivity and Immunotherapy in CancerView all 34 articles
Risk scoring model for lung adenocarcinoma based on PD-L1 related signature reveals prognostic predictability and correlation with tumor immune microenvironment. genes was constructed
Provisionally accepted
- 1Department of Thoracic Surgery, Ningbo First Hospital, Ningbo, China
- 2Department of Psychology, College of Liberal Arts, Kean University-Wenzhou, Wenzhou, Zhejiang Province, China
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Immunotherapy has recently become a hot topic in the field of oncology, with PD -L1 playing a crucial role in this area. However, the research on PD-L1 correlation prediction models is not fully understood. The aim of our study was to investigate the role of PD-L1-related genes in lung adenocarcinoma immunity. Firstly, we collected a valid dataset of 562 cases of TCGA lung adenocarcinoma, including 503 of tumor tissue and 59 of normal tissue were collected. The dataset was analyzed using the DESeq2 package of R. language, and 1,251 high-expression genes and 285 low-expression genes were obtained. The tumor samples were divided into CD274-high and CD274low CD274 expression samples based on CD274 (PD-L1) high and low expression, and 873 genes were up-regulated and 1,010 genes were down-regulated between CD274high and CD274-low samples. Subsequently, the intersection of 1,251 and 873 was taken to obtain 110 genes that were both highly expressed genes in tumors and CD274 high-expression samples. Survival analysis of 110 genes yielded 5 meaningful genes including GPR115, MF12, GREB1L, SPRR1B, and LIPK (p < 0.001). These five genes were used to construct PD-L1 risk predictors. Cytokine-cytokine receptor interaction and IL-17 signaling pathway were involved in the regulation of this risk model factorsrisk factors to lung adenocarcinoma. The level of effector memory CD4 T cells and Type 2 T helper cells were correlated with the risk model factorrisk factor.Importantly, the PD-L1 risk prediction model could effectively predict the prognosis of patients. In conclusion, the construction of PD-L1 risk model was of great significance for the treatment of lung adenocarcinoma.
Keywords: Lung Adenocarcinoma, Programmed death ligand-1, bioinformatics, survival analysis, machine learning
Received: 28 Mar 2025; Accepted: 09 May 2025.
Copyright: © 2025 Lifei, Ren, Wang, Zhao, Chen and Hu. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Wentao Hu, Department of Thoracic Surgery, Ningbo First Hospital, Ningbo, China
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