Integrating Radiosensitivity Index and Radiation Resistance Related Index Improves Prostate Cancer Outcome Prediction

Qi-Qiao Wu ^1,2, Zhao-Sheng Yin ³, Yi Zhang ⁴

⁰Clinical Research Center for Precision Medicine of Abdominal Tumor of Fujian Province, China.

Advances in Radiation Oncology +

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March 4, 2025

View abstract on PubMed

Summary

A novel nomogram combining gene signatures predicts prostate cancer recurrence. This tool aids in assessing radiation therapy effectiveness and patient outcomes.

Area Of Science

Oncology
Genomics
Radiotherapy

Background

Prostate cancer (PCa) recurrence after treatment poses a significant clinical challenge.
Accurate prediction of recurrence is crucial for optimizing treatment strategies and improving patient outcomes.
Existing predictive biomarkers require refinement for enhanced accuracy in PCa.

Purpose Of The Study

To develop and validate a predictive nomogram for prostate cancer recurrence.
To integrate 31-gene signature (31-GS), radiosensitivity index (RSI), and radiation-resistance-related gene index (RRRI) for improved prediction.
To identify novel gene signatures with superior predictive performance in PCa.

Main Methods

Utilized transcriptome data from public databases (GEO, TCGA) for PCa patients.
Analyzed and compared four PCa-associated radiosensitivity predictive indices: 14Genes, RSI, RRRI, and 20Genes.
Employed Cox analysis, LASSO regression, WGCNA, and functional enrichment analysis.
Constructed a nomogram to enhance recurrence prediction capability.

Main Results

The 14Genes signature demonstrated the most promising predictive performance and discriminative capacity among the evaluated indices.
Genes within the 14Genes model's key module were significantly enriched in radiation therapy-related cell death pathways.
The 14Genes signature exhibited the highest area under the ROC curve and decision tree variable importance in both TCGA and GEO cohorts.

Conclusions

A radiosensitivity-related nomogram was successfully established with excellent performance in predicting PCa recurrence.
The 20Genes and RRRI models can predict recurrence-free survival in patients undergoing radiation therapy.
The 20Genes model shows specificity for radiation therapy but requires further external validation.

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