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Sequencing Small Non-coding RNA from Formalin-fixed Tissues and Serum-derived Exosomes from Castration-resistant Prostate Cancer Patients
Published on: November 19, 2019
A Platform-Independent Binary Gene-Pair Signature Derived from CRPC-Enriched Single-Cell Transcriptomics for
Yu He1, Boyang Li1, Zhaojie Tan1
1Department of Urology, The First Affiliated Hospital of Anhui Medical University, Institute of Urology, and Anhui Province Key Laboratory of Genitourinary Diseases, Anhui Medical University, Hefei, Anhui, People's Republic of China.
Cancer Management and Research
|August 5, 2026
Summary
A new 36-gene signature accurately predicts recurrence-free survival in prostate cancer (PCa) patients post-surgery. This tool aids in identifying high-risk individuals for better treatment strategies.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Radical prostatectomy (RP) is a key treatment for prostate cancer (PCa).
- Recurrence-free survival (RFS) is crucial for assessing RP success.
- Current methods for predicting RFS have limited accuracy.
Purpose of the Study:
- Develop a platform-independent prognostic signature to predict RFS in PCa.
- Identify early molecular indicators of advanced disease potential.
Main Methods:
- Analyzed single-cell RNA sequencing data to identify malignant epithelial subclusters.
- Applied machine learning algorithms to develop a binary gene-pair signature.
- Validated the signature in multiple external cohorts.
Main Results:
- Established a 36-gene-pair signature with robust RFS prediction (average C-index 0.725).
- Identified distinct signaling and metabolic processes between risk groups.
- High-risk patients showed immune-inflamed microenvironments and similarities to anti-PD-1 responders.
Conclusions:
- The 36-gene-pair signature offers reliable RFS risk stratification for PCa.
- High-risk patients share transcriptional similarities with anti-PD-1 therapy responders.
- CKS2 identified as a potential prognostic hub for further validation.
