Wang Yan, Shi Yunjie, Hu Xiao, Wang Chenfang
Department of Anesthesia, First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.
School of Clinical Medicine, Chengdu Medical College, Chengdu, Sichuan, China.
Front Pharmacol. 2025 Mar 6;16:1559546. doi: 10.3389/fphar.2025.1559546. eCollection 2025.
Esophageal squamous cell carcinoma (ESCC) is closely linked to aberrant glycolytic metabolism, a hallmark of cancer progression, immune evasion, and therapy resistance. This study employs single-cell transcriptomics and multi-omics approaches to unravel glycolysis-mediated mechanisms in ESCC, with a focus on risk stratification and therapeutic opportunities.
Data from TCGA and GEO databases were integrated with single-cell RNA sequencing, bulk RNA sequencing, as well as clinical datasets to investigate glycolysis-associated cell subtypes and their clinical implications in ESCC. Analytical approaches encompassed cell subtype annotation, cell-cell communication network analysis, and gene regulatory network modeling. A glycolysis-related risk score model was built via non-negative matrix factorization (NMF) and Cox regression, and then experimentally verified through Western blotting. Drug sensitivity analyses were carried out to explore potential therapeutic strategies.
Single-cell analysis identified epithelial cells as the dominant glycolysis-active subtype, and tumor tissues showed significantly higher glycolytic activity than adjacent normal tissues. Among malignant epithelial subpopulations, IGFBP3+Epi (IGFBP3-expressing epithelial cells) and LHX9+Epi (LHX9-expressing epithelial cells) had elevated glycolysis levels, which correlated with poor prognosis, immune suppression, and changes in the tumor microenvironment. The seven-gene glycolysis-based risk score model divided patients into high- and low-risk groups, demonstrating strong prognostic performance. Drug sensitivity analysis showed high-risk patients were more responsive to Navitoclax as well as Rapamycin, but low-risk ones were more sensitive to Afatinib and Erlotinib, highlighting the model's usefulness in guiding personalized treatment.
This research emphasizes the crucial role of glycolysis in ESCC progression a well as immune modulation, offering a novel glycolysis-related risk score model with significant prognostic and therapeutic implications. These findings provide a basis for risk-based stratification and tailored therapeutic strategies, advancing precision medicine in ESCC.
食管鳞状细胞癌（ESCC）与异常糖酵解代谢密切相关，这是癌症进展、免疫逃逸和治疗耐药的一个标志。本研究采用单细胞转录组学和多组学方法来揭示ESCC中糖酵解介导的机制，重点关注风险分层和治疗机会。
将来自TCGA和GEO数据库的数据与单细胞RNA测序、批量RNA测序以及临床数据集相结合，以研究ESCC中与糖酵解相关的细胞亚型及其临床意义。分析方法包括细胞亚型注释、细胞间通信网络分析和基因调控网络建模。通过非负矩阵分解（NMF）和Cox回归建立了一个与糖酵解相关的风险评分模型，然后通过蛋白质印迹法进行实验验证。进行药物敏感性分析以探索潜在的治疗策略。
单细胞分析确定上皮细胞是主要的糖酵解活性亚型，肿瘤组织的糖酵解活性明显高于相邻正常组织。在恶性上皮亚群中，IGFBP3 + Epi（表达IGFBP3的上皮细胞）和LHX9 + Epi（表达LHX9的上皮细胞）的糖酵解水平升高，这与预后不良、免疫抑制和肿瘤微环境变化相关。基于糖酵解的七基因风险评分模型将患者分为高风险和低风险组，显示出强大的预后性能。药物敏感性分析表明，高风险患者对Navitoclax和雷帕霉素更敏感，而低风险患者对阿法替尼和厄洛替尼更敏感，突出了该模型在指导个性化治疗方面的有用性。
本研究强调了糖酵解在ESCC进展以及免疫调节中的关键作用，提供了一个具有重要预后和治疗意义的新型糖酵解相关风险评分模型。这些发现为基于风险的分层和量身定制的治疗策略提供了基础，推动了ESCC的精准医学发展。
