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Related Experiment Video

Updated: Jan 22, 2026

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Immune Predictors of Radiotherapy Outcomes in Cervical Cancer.

Linghao Wang1, Jie Zhu2, Zequn Ding1

  • 1Med-X Research Institute & School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China.

Advanced Science (Weinheim, Baden-Wurttemberg, Germany)
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Summary
This summary is machine-generated.

This study reveals how the tumor immune microenvironment changes after radiotherapy, identifying key immune cells and interactions that predict patient response. These findings offer new biomarkers for improving cervical cancer radiotherapy effectiveness.

Keywords:
cervical cancercomplement pathwaysmachine learningmacrophage polarizationradiotherapy outcomes

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Area of Science:

  • Oncology
  • Immunology
  • Radiotherapy Research

Background:

  • The tumor immune microenvironment significantly impacts radiotherapy (RT) sensitivity, but resistance mechanisms are not fully understood.
  • Identifying immune predictors is crucial for optimizing RT outcomes and developing personalized treatment strategies.

Purpose of the Study:

  • To investigate the dynamic changes in the immune microenvironment following radiotherapy.
  • To identify immune cell subsets and molecular interactions that predict patient response to RT.
  • To develop a predictive model for stratifying patients based on RT outcomes.

Main Methods:

  • Integration of single-cell transcriptomics and machine learning algorithms.
  • Comprehensive analysis of immune cell populations, activation states, and intercellular communication (CellChat).
  • Validation in preclinical murine models and development of a predictive prognostic model (CCRTIM).

Main Results:

  • Radiotherapy alters the tumor microenvironment, characterized by reduced epithelial cells, increased apoptosis, and inflammation.
  • Accumulation of M1-like HSPA1B+ macrophages with enhanced antigen-presenting capacity was observed post-RT.
  • Increased T and NK cell cytotoxicity alongside exacerbated exhaustion markers (PDCD1, TIGIT) were noted.
  • Epithelial-myeloid crosstalk via the C3/C3AR1 axis was identified as critical; C3AR1 antagonism reduced RT efficacy.
  • An 8-feature model (CCRTIM) accurately predicted prognosis (AUC=0.76) and enabled risk stratification.

Conclusions:

  • Radiotherapy induces significant immune remodeling within the tumor microenvironment.
  • The C3/C3AR1 axis and specific macrophage subsets are key players in RT response.
  • The developed CCRTIM model provides actionable biomarkers for enhancing precision radiotherapy in cervical cancer.