ArticleFrontiers in endocrinology2023
Integrating multiple machine learning methods to construct glutamine metabolism-related signatures in lung adenocarcinoma.
Article in Frontiers in endocrinology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 52 papers, 1 of them a synthesis that pooled it.
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Who cites it
52 citing papers in PubMed, 1 synthesis or guideline pooled it, 56 citations in OpenAlex.
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- Artificial neural network-based immune biomarker signature predicts pathological complete response to neoadjuvant chemotherapy in HER2-negative breast cancer.Frontiers in oncology · 2026Article
- Overcoming immune resistance in ovarian cancer: checkpoint inhibitors, tumor microenvironment, and translational advances.Frontiers in oncology · 2026Review
- Identification of key genes related to glutamine metabolism in diabetic nephropathy by machine learning methods.Scientific reports · 2025Article
- Exploration and validation of the prognostic value of mitophagy and mitochondrial dynamics-related genes in cervical cancer.Scientific reports · 2025Article
- Development and validation of a nomogram model of lung metastasis in breast cancer based on machine learning algorithm and cytokines.BMC cancer · 2025Article
- Disulfidptosis as a key regulator of glioblastoma progression and immune cell impairment.Frontiers in immunology · 2025Article
- Multidisciplinary analysis of the prognosis and biological function of NUBPL in gastric cancer.Frontiers in immunology · 2025Article
- Roles and functions of tumor-infiltrating lymphocytes and tertiary lymphoid structures in gastric cancer progression.Frontiers in immunology · 2025Review
- Metabolic reprogramming and immune microenvironment characteristics in laryngeal carcinoma: advances in immunotherapy.Frontiers in immunology · 2025Review
- Metabolic reprogramming in hepatocellular carcinoma: mechanisms of immune evasion and therapeutic implications.Frontiers in immunology · 2025Review
- Acute aerobic exercise alters serum protein distribution in colorectal cancer patients.Frontiers in oncology · 2025Article
- Biological roles and molecular mechanism of circular RNAs in epithelial-mesenchymal transition of gastrointestinal malignancies.Oncology research · 2025Review
- Mechanisms of tumor-associated macrophages in breast cancer and treatment strategy.Frontiers in immunology · 2025Review
- Targeting tumor-associated macrophages in colon cancer: mechanisms and therapeutic strategies.Frontiers in immunology · 2025Review
- Uncovering the heterogeneity of NK cells on the prognosis of HCC by integrating bulk and single-cell RNA-seq data.Frontiers in oncology · 2025Article
- Circulating cell-free DNA methylation analysis of pancreatic cancer patients for early noninvasive diagnosis.Frontiers in oncology · 2025Article
- Terpinen-4-ol suppresses proliferation and motility of cutaneous squamous cell carcinoma cells by enhancing calpain-2 expression.Oncology research · 2025Article
- Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.Immunologic research · 2024Article
Corrections and comments
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Authors and funding
10 authors at 6 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Glutamine metabolism (GM) is known to play a critical role in cancer development, including in lung adenocarcinoma (LUAD), although the exact contribution of GM to LUAD remains incompletely understood. In this study, we aimed to discover new targets for the treatment of LUAD patients by using machine learning algorithms to establish prognostic models based on GM-related genes (GMRGs). Methods: We used the AUCell and WGCNA algorithms, along with single-cell and bulk RNA-seq data, to identify the most prominent GMRGs associated with LUAD. Multiple machine learning algorithms were employed to develop risk models with optimal predictive performance. We validated our models using multiple external datasets and investigated disparities in the tumor microenvironment (TME), mutation landscape, enriched pathways, and response to immunotherapy across various risk groups. Additionally, we conducted Results: We identified 173 GMRGs strongly associated with GM activity and selected the Random Survival Forest (RSF) and Supervised Principal Components (SuperPC) methods to develop a prognostic model. Our 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. Our findings also suggest that LGALS3 may be a potential therapeutic target for LUAD.
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