Du Yuanyuan, Miao Zefeng, Li Peng, Feng Dan, Liu Mulin, Ji Aifang, Li Shijun
College of Laboratory Medicine, Dalian Medical University, Dalian, 116044, Liaoning, China.
Department of Laboratory Medicine, Heping Hospital Affiliated to Changzhi Medical College, Changzhi, 046000, Shanxi, China.
Med Oncol. 2025 Aug 30;42(10):458. doi: 10.1007/s12032-025-03007-6.
As one of the most prevalent malignancies worldwide, colorectal cancer (CRC) exhibits a strong metabolic dependency on glycolysis, which fuels tumor expansion and shapes an immunosuppressive microenvironment. Despite its clinical significance, the regulatory landscape and cellular diversity of glycolytic metabolism in CRC require systematic exploration. Multi-omics datasets (bulk/scRNA-seq and spatial transcriptomics) were analyzed to quantify glycolytic signatures. Core regulatory genes were selected via integrated pathway mapping and a machine learning framework incorporating five-feature selection algorithms. Cellular subpopulations were delineated by metabolic profiles, with niche interactions modeled through ligand-receptor network analysis. Findings were validated across multicenter cohorts. Our analyses identified a tumor subpopulation characterized by a High Glycolytic State (HGS), displaying elevated glycolytic signature alongside stem-like properties. Spatial profiling demonstrated relative enrichment of HGS cells in central tumor regions, potentially reflecting adaptation to nutrient-limited conditions. Among the molecular features associated with HGS maintenance, five candidate regulators (PFKP, ERO1A, FKBP4, HDLBP, HSPA5) showed correlation with unfavorable clinical outcomes. Our study characterizes the metabolic heterogeneity of CRC and suggests a potential role for HGS cells in shaping the tumor microenvironment. The molecular features identified here may offer insights into metabolic dependencies that could be explored for future therapeutic targeting.
作为全球最常见的恶性肿瘤之一，结直肠癌（CRC）对糖酵解表现出强烈的代谢依赖性，糖酵解为肿瘤扩张提供能量并塑造免疫抑制微环境。尽管其具有临床意义，但CRC中糖酵解代谢的调控格局和细胞多样性仍需要系统探索。分析多组学数据集（批量/单细胞RNA测序和空间转录组学）以量化糖酵解特征。通过整合通路映射和包含五种特征选择算法的机器学习框架选择核心调控基因。通过代谢谱描绘细胞亚群，通过配体-受体网络分析对生态位相互作用进行建模。研究结果在多中心队列中得到验证。我们的分析确定了一个以高糖酵解状态（HGS）为特征的肿瘤亚群，其糖酵解特征升高并具有干细胞样特性。空间分析表明HGS细胞在肿瘤中心区域相对富集，这可能反映了对营养受限条件的适应。在与HGS维持相关的分子特征中，五个候选调节因子（PFKP、ERO1A、FKBP4、HDLBP、HSPA5）与不良临床结果相关。我们的研究描述了CRC的代谢异质性，并表明HGS细胞在塑造肿瘤微环境中可能发挥的作用。此处确定的分子特征可能为代谢依赖性提供见解，可为未来的治疗靶点探索提供参考。
