Li Weidong, Zhong Qixing, Deng Naisheng, Wang Haitao, Ouyang Jun, Guan Zhifen, Zhou Xinhao, Li Kai, Sun Xueying, Wang Yao
Department of Gastrointestinal Surgery, Zhongshan City People's Hospital, Zhongshan, 528400, Guangdong, China.
Department of Molecular Medicine & Pathology, Faculty of Medical and Health Sciences, the University of Auckland, Auckland, 1142, New Zealand.
Heliyon. 2024 Sep 19;10(19):e37985. doi: 10.1016/j.heliyon.2024.e37985. eCollection 2024 Oct 15.
Glutamine metabolism presents a promising avenue for cancer prevention and treatment, but the underlying mechanisms in gastric cancer (GC) progression remain elusive.
The TCGA-STAD and GEO GSE62254 datasets, containing gene expression, clinical information, and survival outcomes of GC, were meticulously examined. Differential expression analysis and weighted gene co-expression network analysis (WGCNA) were employed to excavate a key module (MEturquoise), which was used to intersect with glutamine metabolism-related genes (GMRGs) and differentially expressed genes (DEGs) to identify differentially expressed GMRGs (DE-GMRGs). LASSO and Cox Univariate analyses were implemented to determine risk model genes. Correlation of the risk model with clinical parameters, pathways, and tumor immune microenvironments, was analyzed, and its prognostic independence was validated by Cox analyses. Finally, reverse transcription-quantitative polymerase chain reaction (RT-qPCR) was performed to validate the expression levels of MYB, LRFN4, LMNB2, and SLC1A5 in GC and para-carcinoma tissue.
The excavation of 4521 DEGs led to the discovery of the key MEturquoise module, which exhibited robust correlations with GC traits. The intersection analysis identified 42 DE-GMRGs, among which six genes showed consistency. Further LASSO analysis established , , , and as pivotal risk model genes. The risk model demonstrated associations with oncogenic and metabolism-related pathways, inversely correlating with responses to immune checkpoint blockade therapies. This risk model, together with "age", was validated to be an independent prognostic factor for GC. RT-qPCR result indicated that , , , and expressions were remarkably up-regulated in GC tissues comparison with para-carcinoma tissue.
The present study has generated a novel risk module containing four DE-GMRGs for predicting the prognosis and the response to immune checkpoint blockade treatments for GC. This risk model provides new insights into the involvement of glutamine metabolism in GC, warranting further investigation.
谷氨酰胺代谢是癌症预防和治疗的一个有前景的途径，但胃癌（GC）进展的潜在机制仍不清楚。
仔细检查了包含GC基因表达、临床信息和生存结果的TCGA-STAD和GEO GSE62254数据集。采用差异表达分析和加权基因共表达网络分析（WGCNA）挖掘关键模块（MEturquoise），该模块与谷氨酰胺代谢相关基因（GMRGs）和差异表达基因（DEGs）进行交集分析以鉴定差异表达的GMRGs（DE-GMRGs）。进行LASSO和Cox单因素分析以确定风险模型基因。分析风险模型与临床参数、通路和肿瘤免疫微环境的相关性，并通过Cox分析验证其预后独立性。最后，进行逆转录定量聚合酶链反应（RT-qPCR）以验证MYB、LRFN4、LMNB2和SLC1A5在GC和癌旁组织中的表达水平。
4521个DEGs的挖掘导致关键MEturquoise模块的发现，该模块与GC特征表现出强相关性。交集分析鉴定出42个DE-GMRGs，其中6个基因表现出一致性。进一步的LASSO分析确定MYB、LRFN4、LMNB2和SLC1A5为关键风险模型基因。该风险模型显示与致癌和代谢相关通路有关，与免疫检查点阻断疗法的反应呈负相关。该风险模型与“年龄”一起被验证为GC的独立预后因素。RT-qPCR结果表明，与癌旁组织相比，MYB、LRFN4、LMNB2和SLC1A5在GC组织中的表达明显上调。
本研究生成了一个包含四个DE-GMRGs的新型风险模块，用于预测GC的预后和对免疫检查点阻断治疗的反应。该风险模型为谷氨酰胺代谢参与GC提供了新的见解，值得进一步研究。
