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September 19, 2025Open MedicineOpen Access

A machine learning-based prognostic model integrating mRNA stemness index, hypoxia, and glycolysis‑related biomarkers for colorectal cancer

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Authors

DLDan LiuMZM. ZhangYNYing Nie

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Overview

This analysis reveals a machine learning model predicting outcomes in colorectal cancer, integrating hypoxia and glycolysis biomarkers.

Key Points

  • The prognostic model accurately predicts survival in colorectal cancer patients based on selected biomarkers.
  • High-risk CRC patients showed poor outcomes and responses to immunotherapy in the TCGA dataset.
  • The novel model utilizes gene expression data from established cancer databases for validation and accuracy.
  • This approach emphasizes the importance of understanding cancer stemness and metabolic factors in prognosis.

Cite This Study

Liu et al. (2025) studied this question.

synapsesocial.com/papers/68d464ea31b076d99fa63e8bhttps://doi.org/10.1515/med-2025-1247
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  4. 4Novel hypoxia- and lactate metabolism-related molecular subtyping and prognostic signature for colorectal cancer2024 · 58 citations
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