ArticleScientific reports2024
Genetic and molecular characterization of metabolic pathway-based clusters in esophageal squamous cell carcinoma.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
What it found
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Who cites it
5 citing papers in PubMed, 5 citations in OpenAlex.
- CT-based deep learning signatures associated with transcriptomic heterogeneity and combined with nutritional biomarkers improve prediction of 3-year overall survival in esophageal squamous cell carcinoma.Insights into imaging · 2026Article
- Mast cell driven immunometabolism as a therapeutic entry point in ESCC.Frontiers in cell and developmental biology · 2026Review
- MALMPS: A Machine Learning-Based Metabolic Gene Prognostic Signature for Stratifying Clinical Outcomes and Molecular Heterogeneity in Stage II/III Colorectal Cancer.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Fusobacterium periodonticum BCT protein targeting glucose metabolism to promote the epithelial-mesenchymal transition of esophageal cancer cells by lactic acid.Journal of translational medicine · 2024Article
- Cancer metabolic reprogramming and precision medicine-current perspective.Frontiers in pharmacology · 2024Review
Corrections and comments
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Authors and funding
10 authors at 3 institutions in 1 country.
Funding
Abstract
Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive types of squamous cell carcinoma and represents a significant proportion of esophageal cancer. Metabolic reprogramming plays a key role in the occurrence and development of ESCC. Unsupervised clustering analysis was employed to stratify ESCC samples into three clusters: MPC1-lipid type, MPC2-amino acid type, and MPC3-energy type, based on the enrichment scores of metabolic pathways extracted from the Reactome database. The MPC3 cluster exhibited characteristics of energy metabolism, with heightened glycolysis, cofactors, and nucleotide metabolism, showing a trend toward increased aggressiveness and poorer survival rates. On the other hand, MPC1 and MPC2 primarily involved lipid and amino acid metabolism, respectively. In addition, liquid chromatography‒mass spectrometry-based metabolite profiles and potential therapeutic agents were explored and compared among ESCC cell lines with different MPCs. MPC3 amplified energy metabolism markers, especially carnitines. In contrast, MPC1 and MPC2 predominantly had elevated levels of lipids (primarily triacylglycerol) and amino acids, respectively. Furthermore, MPC3 demonstrated a suboptimal clinical response to PD-L1 immunotherapy but showed increased sensitivity to the doramapimod chemotherapy regimen, as evident from drug sensitivity evaluations. These insights pave the way for a more personalized therapeutic approach, potentially enhancing treatment precision for ESCC patients.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.