3158 - Integrating Radiosensitivity Index and Radiation Resistance Related Index Improves Prostate Cancer Outcome Prediction

05:00pm - 06:00pm PT

Hall F

Screen: 18

POSTER

Presenter(s)

Qi-Qiao Wu, MD, PhD Candidate - Fudan Univerisity Zhongshan Hospital(Xiamen), Xiamen, Fujian

Q. Q. Wu¹, B. F. Tang², P. Yang³, S. Du^2,4, and Z. C. Zeng³; ¹Fudan Univerisity Zhongshan Hospital(Xiamen), Xiamen, China, ²Zhongshan Hospital Fudan University, Shanghai, China, ³Department of Radiation Oncology, Zhongshan Hospital, Fudan University, Shanghai, China, ⁴Fudan University, Zhongshan Hospital, Shanghai, China

Purpose/Objective(s):

This study aimed to establish a nomogram combining 31-gene signature (31-GS), radiosensitivity index (RSI), and radiation-resistance-related gene index (RRRI) to predict recurrence in prostate cancer (PCa) patients.

Materials/Methods:

Transcriptome data of PCa were obtained from GEO and TCGA to validate the predictive potential of three sets of published biomarkers, namely, 31-GS, RSI, and RRRI. To adjust these markers for the characteristics of PCa, we analyzed four PCa- associated radiosensitivity predictive indices based on 31-GS, RSI, and RRRI by the Cox analysis and least absolute shrinkage and selection operator regression analysis. Time-dependent receiver operating characteristic (ROC) curves, decision curve analyses, integrated discrimination improvement, net reclassification improvement and decision tree model construction were used to compare the radiosensitivity predictive ability of these four gene signatures. Key modules and associated functions were identified through a weighted gene co-expression network analysis (WGCNA) and Gene function enrichment analysis. A nomogram was built to improve the recurrence-prediction capability.

Results:

We validated and compared the predictive potential of two published predictive indices. Based on the 31-GS, RSI, and RRRI, we analyzed four PCa-associated radiosensitivity predictive indices: 14Genes, RSI, RRRI, and 20Genes. Among them, 14Genes showed the most promising predictive performance and discriminative capacity. Genes in the key module defined by the 14Genes model were significantly enriched in radiotherapy-related cell death pathways. The area under ROC curve and Decision Tree variable Importance for 14Genes was the highest in the TCGA and GSE cohorts.

Conclusion:

This study successfully established a radiosensitivity-related nomogram, which had excellent performance in predicting recurrence in patients with PCa. For patients who received radiation therapy, 20Genes and RRRI model can be used to predict recurrence-free survival, whereas 20Genes is more radiotherapy-specific but needs further external validation.