Wang Mingwei, Ying Qiaohui, Xing Yuncan, Dai Shuchang, Wang Jue, Liu Zhong
Institute of Blood Transfusion, Chinese Academy of Medical Sciences and Peking Union Medical College, Chengdu, 610052, Sichuan, China.
Institute of Oral Basic Research, School and Hospital of Stomatology, Cheeloo College of Medicine, Shandong University, Jinan, 250012, China.
Sci Rep. 2025 Jul 24;15(1):26956. doi: 10.1038/s41598-025-12350-7.
Ovarian cancer (OC) is a highly fatal gynecological malignancy primarily attributable to late-stage detection and restricted treatment options. Aberrant glycolysis, exemplified by the Warburg effect, facilitates tumor development, immunological evasion, and alteration of the microenvironment. Identifying glycolysis-related biomarkers could provide novel insights into prognosis and potential therapeutic targets for OC.The transcriptomic and clinical information of OC patients were obtained from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases. Differentially expressed glycolysis-related genes (GRGs) were identified and analyzed for their prognostic significance. Consensus clustering was employed to identify glycolysis subtypes, followed by pathway enrichment and immune infiltration analyses. A ten-gene GRG signature was developed with LASSO-Cox regression and verified in various cohorts. Single-cell RNA sequence and drug susceptibility analysis were performed to explore tumor microenvironment heterogeneity and potential therapeutic agents.A total of 457 differentially expressed GRGs were discovered, of which 30 were substantially linked with OC prognosis. Three molecular subtypes were characterized, with cluster C exhibiting the worst prognosis and activation of tumor-associated pathways. A prognostic model comprising ten genes (LMCD1, L1CAM, MYCN, GALT, IDO1, RPL18, XBP1, LPAR3, RUNX3, PLCG1) was developed and validated, demonstrating robust predictive efficacy across various cohorts. Immune analysis revealed substantial disparities in immune infiltration among risk groups, whereas single-cell analysis identified several critical genes essential for metabolism, proliferation, and interactions within the tumor microenvironment.This work highlights the prognostic and therapeutic significance of GRGs in OC. The ten-gene GRG signature serves as a reliable framework for risk assessment and the formulation of individualized treatment regimens. Nonetheless, further experimental validation and extensive clinical research are necessary to enable the application of these findings in clinical practice. These results highlight the potential of targeting glycolytic pathways as a promising approach to improve the management and treatment outcomes of OC.
卵巢癌(OC)是一种高度致命的妇科恶性肿瘤,主要归因于晚期检测和有限的治疗选择。以Warburg效应为代表的异常糖酵解促进肿瘤发展、免疫逃逸和微环境改变。识别糖酵解相关生物标志物可为OC的预后和潜在治疗靶点提供新见解。从癌症基因组图谱(TCGA)、基因型-组织表达(GTEx)和基因表达综合数据库(GEO)获取OC患者的转录组和临床信息。识别差异表达的糖酵解相关基因(GRGs)并分析其预后意义。采用一致性聚类识别糖酵解亚型,随后进行通路富集和免疫浸润分析。通过LASSO-Cox回归建立了一个十基因GRG特征,并在不同队列中进行验证。进行单细胞RNA序列和药物敏感性分析以探索肿瘤微环境异质性和潜在治疗药物。共发现457个差异表达的GRGs,其中30个与OC预后密切相关。鉴定出三种分子亚型,C簇预后最差且肿瘤相关通路激活。建立并验证了一个包含十个基因(LMCD1、L1CAM、MYCN、GALT、IDO1、RPL18、XBP1、LPAR3、RUNX3、PLCG1)的预后模型,在不同队列中显示出强大的预测效力。免疫分析揭示了风险组间免疫浸润的显著差异,而单细胞分析确定了肿瘤微环境中代谢、增殖和相互作用所必需的几个关键基因。这项工作突出了GRGs在OC中的预后和治疗意义。十基因GRG特征作为风险评估和制定个体化治疗方案的可靠框架。然而,需要进一步的实验验证和广泛的临床研究,以使这些发现能够应用于临床实践。这些结果突出了靶向糖酵解途径作为改善OC管理和治疗结果的有前景方法的潜力。
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