AUTHOR=Zhang Pengpeng , Pei Shengbin , Wu Leilei , Xia Zhijia , Wang Qi , Huang Xufeng , Li Zhangzuo , Xie Jiaheng , Du Mingjun , Lin Haoran 

TITLE=Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma

JOURNAL=Frontiers in Endocrinology

VOLUME=Volume 14 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/endocrinology/articles/10.3389/fendo.2023.1196372

DOI=10.3389/fendo.2023.1196372

ISSN=1664-2392

ABSTRACT=Abstract
Background: Glutamine metabolism (GM) has been widely acknowledged to play a critical role in cancer development. However, the exact contribution of GM in lung adenocarcinoma (LUAD) remains incompletely understood. In this study, we employed machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs) with the objective of discovering new targets for the treatment of LUAD patients.
Methods: The AUCell and WGCNA algorithms, together with single-cell and bulk RNA-seq data, were used to identify the most prominent GM-related genes. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance, which were subsequently validated using multiple external datasets. Furthermore, we investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Finally, we conducted in vitro and in vivo experiments to confirm the role of LGALS3 in LUAD.
Results: We identified 173 genes that were strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. This model's performance was validated using multiple external datasets. Our analysis revealed that the low-risk group had higher immune cell infiltration and increased expression of immune checkpoints, indicating that this group may be more receptive to immunotherapy. Moreover, our experimental results confirmed that LGALS3 promoted the proliferation, invasion, and migration of LUAD cells.
Conclusion: Our study established a prognostic model based on GMRGs that can predict the effectiveness of immunotherapy and provide novel approaches for the treatment of LUAD.