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Related Experiment Video

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Machine learning-based multimodal radiomics and transcriptomics models for predicting radiotherapy sensitivity and

Chengyu Ye1, Hao Zhang1, Zhou Chi1

  • 1The Affiliated Cancer Hospital of Wenzhou Medical University, Wenzhou Central Hospital, Wenzhou, PR China.

The Journal of Biological Chemistry
|May 17, 2025
PubMed
Summary

This study uses machine learning to predict radiotherapy response in esophageal cancer. STUB1 gene enhances treatment efficacy by targeting SRC, offering a new therapeutic strategy for better patient outcomes.

Keywords:
SEResNet101SRC ubiquitinationSTUB1esophageal cancerprognostic risk modelradiotherapy sensitivity

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Area of Science:

  • Oncology
  • Genetics
  • Radiotherapy

Background:

  • Radiotherapy is crucial for esophageal cancer but response varies.
  • Predicting individual patient outcomes remains a challenge.

Purpose of the Study:

  • To develop predictive models for radiotherapy sensitivity and prognosis in esophageal cancer.
  • To integrate machine learning with multimodal radiomics and transcriptomics data.

Main Methods:

  • Applied SEResNet101 deep learning model to imaging and transcriptomic data from UCSC Xena and TCGA databases.
  • Identified prognosis-associated genes including STUB1, PEX12, and HEXIM2.
  • Constructed a prognostic risk model using Lasso regression and Cox analysis.

Main Results:

  • Developed a prognostic risk model that stratifies patients by survival probability.
  • Identified STUB1 as an E3 ubiquitin ligase that enhances radiotherapy sensitivity by degrading SRC.
  • Confirmed that STUB1 overexpression or SRC silencing improves radiotherapy response in vitro and in vivo.

Conclusions:

  • Multimodal data integration predicts radiotherapy response and prognosis in esophageal cancer.
  • STUB1 is a potential therapeutic target for improving radiotherapy efficacy.
  • Findings support individualized radiotherapy planning for esophageal cancer patients.