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August 25, 2025Open Access

Molecular Subtypes of Esophageal Squamous Cell Carcinoma Driven by N- Glycosylation and Construction of a Machine Learning-Based Prognostic Model

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Authors

CYCai YueHHHaifeng HuangWLWenqian Li

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Overview

Multi-omics analysis identifies molecular subtypes in esophageal squamous cell carcinoma, suggesting new machine learning prognostic models might improve patient outcomes.

Key Points

  • Higher risk scores correlate with poorer survival rates in esophageal squamous cell carcinoma patients, indicating significant clinical implications.
  • N-glycosylation plays a vital role in the molecular subtypes stratification, with active and inactive pathways influencing tumor progression.
  • Utilizing machine learning, a five-gene prognostic model was established, aiding in the identification of drug sensitivity in ESCC.
  • The findings support tailored approaches in precision oncology, emphasizing the need for targeted therapeutic strategies based on molecular subtypes.

Cite This Study

Yue et al. (2025) studied this question.

synapsesocial.com/papers/68af7b2a7567bf4f94ff3264https://doi.org/10.21203/rs.3.rs-7391512/v1
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