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

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.