Machine learning-based analysis identifies glucose metabolism-related genes ADPGK as potential diagnostic biomarkers for clear cell renal cell carcinoma.

IF 3.5 3区 医学 Q2 ONCOLOGY
Frontiers in Oncology Pub Date : 2025-09-16 eCollection Date: 2025-01-01 DOI:10.3389/fonc.2025.1559887
Tie Li, Shijin Wang, Guandu Li, Xiaochen Qi, Guangzhen Wu, Xiangyu Che
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引用次数: 0

Abstract

Introduction: Clear cell renal cell carcinoma, with its high morbidity and mortality, is one of the more difficult diseases in the world and still lacks an effective therapeutic target. The primary way they break down glucose is through aerobic glycolysis, which leads to energy acquisition and synthesis of the material base required for cell growth. Although targeting glucose metabolism has driven the development of a variety of tumour therapies, the specific regulatory mechanisms remain unclear. Therefore, based on machine learning analysis algorithms, we analysed the correlation between glycometabolic pathways and ccRCC in the REACTOME database and verified the impact of the key gene ADPGK on the prognosis of ccRCC.

Methods: We utilised a total of 89 gene collections of glucose metabolism pathways from the REACTOME (https://reactome.org/) database as the data base for our study. To uncover potential therapeutic target genes, we adopt three machine learning algorithms (LASSO, RF, and Boruta). We reassigned the 7 screened genes based on gene expression differences between cancer and paracancerous tissues, and applied an unsupervised consensus clustering algorithm to establish a typology based on the expression of glucose metabolism-related genes (ADPGK). We then validated the link between ADPGK and cancer cell invasion and metastasis by in vitro experiments on ccRCC cell lines.

Results: We identified ADPGK as a key gene for the glucose metabolism pathway and suggested that it may promote invasion and metastasis of ccRCC. In addition, based on the results of immune infiltration, ADPGK was observed to significantly affect the immune response in ccRCC. Our results suggest that the implementation of therapeutic strategies based on key genes of glucose metabolism may bring new hope for ccRCC patients.

Discussion: Our results suggest that targeting the glucose metabolism pathway can kill ccRCC cells. ADPGK, a gene related to glucose metabolism, may be an important biomarker for the diagnosis and characterization of ccRCC. However, whether ADPGK affects glycolysis in ccRCC, and the mechanism by which glycolysis is regulated is not clear. This is the direction of further research in the future.

基于机器学习的分析确定葡萄糖代谢相关基因ADPGK作为透明细胞肾细胞癌的潜在诊断生物标志物。
透明细胞肾细胞癌发病率高、死亡率高,是目前世界上治疗难度较大的疾病之一,目前仍缺乏有效的治疗靶点。它们分解葡萄糖的主要方式是通过有氧糖酵解,从而获得能量并合成细胞生长所需的物质基础。虽然靶向葡萄糖代谢已经推动了多种肿瘤治疗的发展,但具体的调节机制尚不清楚。因此,我们基于机器学习分析算法,在REACTOME数据库中分析糖代谢途径与ccRCC的相关性,验证关键基因ADPGK对ccRCC预后的影响。方法:我们利用REACTOME (https://reactome.org/)数据库中共89个葡萄糖代谢途径基因集合作为我们研究的数据库。为了发现潜在的治疗靶基因,我们采用了三种机器学习算法(LASSO, RF和Boruta)。我们根据肿瘤和癌旁组织之间的基因表达差异对筛选的7个基因进行重新分配,并应用无监督共识聚类算法建立基于葡萄糖代谢相关基因(ADPGK)表达的分型。然后,我们通过体外实验验证了ADPGK与ccRCC细胞系的癌细胞侵袭和转移之间的联系。结果:我们发现ADPGK是葡萄糖代谢途径的关键基因,并提示其可能促进ccRCC的侵袭和转移。此外,根据免疫浸润的结果,我们观察到ADPGK显著影响ccRCC的免疫应答。我们的研究结果表明,基于糖代谢关键基因的治疗策略的实施可能为ccRCC患者带来新的希望。讨论:我们的研究结果表明,靶向葡萄糖代谢途径可以杀死ccRCC细胞。ADPGK是一个与糖代谢相关的基因,可能是ccRCC诊断和表征的重要生物标志物。然而,ADPGK是否影响ccRCC中的糖酵解,以及糖酵解被调节的机制尚不清楚。这是今后进一步研究的方向。
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来源期刊
Frontiers in Oncology
Frontiers in Oncology Biochemistry, Genetics and Molecular Biology-Cancer Research
CiteScore
6.20
自引率
10.60%
发文量
6641
审稿时长
14 weeks
期刊介绍: Cancer Imaging and Diagnosis is dedicated to the publication of results from clinical and research studies applied to cancer diagnosis and treatment. The section aims to publish studies from the entire field of cancer imaging: results from routine use of clinical imaging in both radiology and nuclear medicine, results from clinical trials, experimental molecular imaging in humans and small animals, research on new contrast agents in CT, MRI, ultrasound, publication of new technical applications and processing algorithms to improve the standardization of quantitative imaging and image guided interventions for the diagnosis and treatment of cancer.
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