Targeting Glycolysis in Esophageal Squamous Cell Carcinoma: Single-Cell and Multi-Omics Insights for Risk Stratification and Personalized Therapy

Yan Wang Yunjie Shi Xiao Hu Chenfang Wang ^*

• Department of Anesthesiology, First Affiliated Hospital of Chengdu Medical College`, Chengdu, China

Background: 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.Methods: 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.Results: 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.