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Article Abstract
Background: In recent years, the clinical application of targeted therapies and immunotherapy has significantly improved survival outcomes for patients with lung adenocarcinomas(LUAD). However, due to fewer mutations, lung squamous cell carcinomas(LUSC) shows limited efficacy with targeted and immunotherapy, resulting in a notably lower 5-year survival rate compared to lung adenocarcinoma. The m7G modification plays an important role in tumorigenesis, progression, immune evasion, and therapeutic response. This study aims to develop a novel scoring system based on m7G modification and immune status to clinically predict the prognosis of patients with LUSC and to provide new therapeutic targets.
Methods: In this study, we utilized RNA-seq data from the TCGA-LUSC database as the training set and GSE50081 from the GEO database as the validation set. Immunotherapy data were obtained from the IMMPORT database, and m7G data from previous research. Using bioinformatics, we developed a prognostic model for LUSC based on m7G pathway-related immune gene characteristics. We analyzed the correlation between the prognostic model and clinical pathological features of LUSC, as well as the model's independent prognostic capability. Subsequently, patients were divided into high-risk and low-risk groups, and we examined the differences in enriched pathways, immune cell infiltration correlations, and drug sensitivity between the two groups.
Results: The m7G immune-related genes FGA, CSF3R, and ORM1 increase the survival risk in patients with lung squamous cell carcinoma, whereas NTS exerts a protective effect. The prognostic risk model for lung squamous cell carcinoma (LUSC) based on m7G immune-related gene expression demonstrates that the overall survival of the high-risk group is significantly poorer than that of the low-risk group.
Conclusion: The risk model developed based on m7G immune-related genes can help predict the clinical prognosis of LUSC patients and guide treatment decisions.
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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11911325 | PMC |
| http://dx.doi.org/10.3389/fimmu.2025.1515838 | DOI Listing |
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