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Related Concept Videos

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Combining two or more treatment methods increases the life span of cancer patients while reducing damage to vital organs or tissue from the overuse of a single treatment. Combination therapy also targets different cancer-inducing pathways, thus reducing the chances of developing resistance to treatment.
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Related Experiment Video

Updated: Jan 15, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
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Protocol for tumor prognostic prediction, molecular stratification, and target discovery using a machine

Jiang Li1, Xiaoning Hong1, Zhongxu Zhu2

  • 1Clinical Big Data Research Center, The Seventh Affiliated Hospital, Sun Yat-Sen University, Shenzhen, China.

STAR Protocols
|October 8, 2025
PubMed
Summary

This study introduces a machine learning method to analyze gastric tumor prognosis and molecular subtypes based on gene expression. It helps identify potential drug targets for precision oncology.

Keywords:
BioinformaticsCancerComputer sciencesHealth SciencesSystems biology

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Area of Science:

  • Oncology
  • Bioinformatics
  • Computational Biology

Background:

  • Precision oncology requires accurate tumor prognosis and understanding of molecular heterogeneity.
  • Identifying druggable targets is crucial for developing effective cancer therapies.

Purpose of the Study:

  • To present a protocol for analyzing aging-associated prognosis and molecular heterogeneity in gastric tumors.
  • To develop a machine learning-driven approach using transcriptome data.

Main Methods:

  • Developing a prognostic model with a robust gene signature.
  • Identifying molecular subtypes within gastric tumors.
  • Building a machine learning classifier for accurate subtype prediction.
  • Inferring subtype-specific regulatory networks and prioritizing druggable transcription factors via drug sensitivity analysis.

Main Results:

  • The protocol enables the development of a prognostic model for gastric tumors.
  • It facilitates the identification of distinct molecular subtypes.
  • The approach allows for the prioritization of potential therapeutic targets.

Conclusions:

  • This transcriptome-based, machine learning approach provides a framework for advancing precision oncology.
  • The protocol aids in understanding tumor heterogeneity and identifying actionable therapeutic targets in gastric cancer.