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Identification and validation of soft tissue sarcoma-specific transcriptomic model for predicting radioresistance.

Jae Yun Moon1, Jae Berm Park2, Kyo Won Lee2

  • 1Molecular Science and Technology Research Center, Ajou University, Suwon, Republic of Korea.

International Journal of Radiation Biology
|January 10, 2025
PubMed
Summary

Researchers identified transcriptomic signatures in soft tissue sarcoma (STS) to develop a predictive model for radioresistance. The new STS-specific radioresistance index (STS-RRI) outperforms existing methods in predicting patient response to radiotherapy.

Keywords:
Radiotherapygene expression profilingin vitroresponsesarcoma

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Area of Science:

  • Oncology
  • Genomics
  • Radiotherapy research

Background:

  • Soft tissue sarcoma (STS) exhibits variable responses to radiotherapy.
  • Identifying predictive biomarkers for radioresistance is crucial for optimizing treatment strategies.

Purpose of the Study:

  • To identify transcriptomic signatures associated with radioresistance in STS.
  • To develop and validate a predictive model for radioresistance in STS patients.

Main Methods:

  • Whole transcriptomic sequencing was performed on STS cell lines to identify differentially expressed genes (DEGs) between radiosensitive and radioresistant groups.
  • A predictive model, the STS-specific radioresistance index (STS-RRI), was developed using overlapping DEGs from cell line data and The Cancer Genome Atlas (TCGA) patient cohort.
  • The performance of STS-RRI was compared with the radiosensitivity index (RSI) in predicting radiotherapy response and progression-free survival.

Main Results:

  • Thirteen overlapping DEGs were identified, and seven were used to establish the STS-RRI formula.
  • STS-RRI demonstrated superior performance in stratifying responders and non-responders in the TCGA cohort compared to RSI (p = .002).
  • STS-RRI significantly discriminated progression-free survival in the TCGA cohort (p = .013), whereas RSI did not (p = .241).

Conclusions:

  • The developed STS-RRI is an effective tool for predicting radioresistance in STS patients.
  • STS-RRI shows improved predictive accuracy for radiotherapy response and survival outcomes compared to the existing RSI.