Dong Yanlei, Zhang Xinyuan, Zhao Jingjie, Hou Qingzhen, Yu Yunhai, Wu Yu, Shi Xing, Wang Lina, Xu Dawei
Gynecology Department, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Central Research Laboratory, The Second Hospital, Cheeloo College of Medicine, Shandong University, Jinan, China.
Front Immunol. 2025 Aug 25;16:1585647. doi: 10.3389/fimmu.2025.1585647. eCollection 2025.
Metabolic reprogramming is an important hallmark of cervical cancer (CC), and extensive studies have provided important information for translational and clinical oncology. Here we sought to determine metabolic association with molecular aberrations, telomere maintenance and outcomes in CC.
RNA sequencing data from TCGA cohort of CC was analyzed for their metabolic gene expression profile and consensus clustering was then performed to classify tumors into different groups/subtypes. The reproducibility of the classification system was further evaluated in GSE68339 CC cohort. The association of metabolic groups with clinical characteristics, telomere maintenance and somatic alterations was assessed to define molecular features of each subtype. Finally, the metabolomic analyses were carried out to directly measure metabolites in tumors and their non-tumorous adjacent tissues (NTs) from 10 CC patients using ultra performance liquid chromatography-mass spectrometry (UPLC-MS).
The analysis of 2752 metabolism-related gene expression in TCGA 304 CC tumors showed a significant expression heterogeneity of these genes. Consensus clustering of these CC tumors identified three distinct metabolic groups (MG), with MG1, MG2 and MG3 characterized by dysregulations in glycans, amino acids/carbohydrates and lipids, respectively. Patients within the MG1 subtype had the shortest disease-free survival (DFS) coupled with robust TERC overexpression. This metabolic stratification was validated in the GSE68339 CC cohort. We further developed a 3 glycan-related gene model (GRGM-3) as a predictor for patient DFS. The TCGA patients were divided into risk-Low and High groups based on their tumor GRGM-3 score using a median cutoff, and those in the risk-High group had significantly shorter DFS. When combined with TERC expression, patients in the high-risk group with high TERC levels had the shortest DFS. Finally, we analyzed metabolites in tumors and NTs from 10 CC patients and further confirmed the metabolic dysregulations identified by gene expression profiling.
Metabolic heterogeneity occurs substantially in CCs and glycan dysregulation is associated with the shortest DFS in CCs. Specifically, the combination of GRGM-3 scores with TERC expression identifies patients with the poorest outcomes, providing a potential tool for individualized risk assessment and contributing to CC precision medicine. It is worth validating our findings for potential clinical application.
代谢重编程是宫颈癌（CC）的一个重要标志，广泛的研究为转化肿瘤学和临床肿瘤学提供了重要信息。在此，我们试图确定CC中代谢与分子异常、端粒维持及预后的关联。
分析来自TCGA队列的CC患者RNA测序数据的代谢基因表达谱，然后进行一致性聚类以将肿瘤分为不同的组/亚型。在GSE68339 CC队列中进一步评估分类系统的可重复性。评估代谢组与临床特征、端粒维持和体细胞改变的关联，以确定各亚型的分子特征。最后，使用超高效液相色谱-质谱联用仪（UPLC-MS）对10例CC患者的肿瘤及其非肿瘤相邻组织（NTs）中的代谢物进行代谢组学分析，以直接测量其中的代谢物。
对TCGA队列中304例CC肿瘤的2752个代谢相关基因表达的分析显示这些基因存在显著的表达异质性。对这些CC肿瘤进行一致性聚类，确定了三个不同的代谢组（MG），MG1、MG2和MG3分别以聚糖、氨基酸/碳水化合物和脂质的失调为特征。MG1亚型的患者无病生存期（DFS）最短，同时TERC表达强烈。这种代谢分层在GSE68339 CC队列中得到验证。我们进一步开发了一个由3个聚糖相关基因组成的模型（GRGM-3）作为患者DFS的预测指标。根据肿瘤GRGM-3评分的中位数临界值，将TCGA患者分为低风险组和高风险组，高风险组患者的DFS显著缩短。当与TERC表达相结合时，TERC水平高的高风险组患者DFS最短。最后，我们分析了10例CC患者肿瘤和NTs中的代谢物，并进一步证实了基因表达谱分析所确定的代谢失调。
CC中存在显著的代谢异质性，聚糖失调与CC患者最短的DFS相关。具体而言，GRGM-3评分与TERC表达相结合可识别出预后最差的患者，为个体化风险评估提供了一个潜在工具，并有助于CC的精准医学。值得对我们的研究结果进行验证以用于潜在的临床应用。
