Evidence map›Paper›PMID 40115255›Full record

ArticleFrontiers in pharmacology2025

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

Abstract read
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Article in Frontiers in pharmacology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Yan Wang *Department of Anesthesia, First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.
Yunjie Shi *Department of Anesthesia, First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.
Xiao HuDepartment of Anesthesia, First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.
Chenfang WangDepartment of Anesthesia, First Affiliated Hospital of Chengdu Medical College, Chengdu, Sichuan, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

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. Conclusion: 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.

Indexed as

esophageal squamous cell carcinomaglycolysisimmune microenvironmentrisk score modeltumor microenvironment

Identifiers

PMID40115255
PMCPMC11922847

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