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A Qualitative Transcriptional Signature for Predicting the Recurrence Risk of Prostate Cancer Patients After Radical Prostatectomy

36 Pages Posted: 18 Jun 2019

See all articles by Xiang Li

Xiang Li

Fujian Medical University - Department of Bioinformatics

Haiyan Huang

Fujian Medical University - Department of Bioinformatics

Jiahui Zhang

Fujian Medical University - Department of Bioinformatics

Fengle Jiang

Fujian Medical University - Department of Bioinformatics

Yating Guo

Fujian Medical University - Department of Bioinformatics

Yidan Shi

Fujian Medical University - Department of Bioinformatics

Zheng Guo

Fujian Medical University - Department of Bioinformatics

Lu Ao

Fujian Medical University - Department of Bioinformatics; Key Laboratory of Medical Bioinformatics; Fujian Medical University - Fujian Key Laboratory of Tumor Microbiology

More...

Abstract

Background: Previously reported transcriptional signatures for predicting the recurrence risk of prostate cancer (PCa) patients after radical prostatectomy are mainly based on risk threshold values of quantitative expression measurements summarized from the signature genes, which are vulnerable to the measurement variation from different experimental batches and the quality of clinical samples. The within-sample relative expression orderings (REOs) of genes, which are the qualitative transcriptional characteristics, are highly robust against batch effects and sample quality variations.

Methods: Gene pairs with REOs significantly correlated with the biochemical recurrence free survival (BFS) were identified from 131 PCa samples in the training dataset. A qualitative transcriptional REOs-based signature were selected from these gene pairs .

Findings: A signature consisting of 74 gene pairs was developed for predicting the recurrence risk of PCa patients after radical prostatectomy. A sample was assigned as high risk when at least 37 gene pairs voted for high risk; otherwise, low risk. The signature was validated in six independent datasets, including 660 fresh-frozen samples and 106 formalin-fixed paraffin-embedded samples. The Kaplan-Meier survival analysis showed that the average BFS of the low-risk groups were significantly better than that of the high-risk groups. Analyses of multi-omics data of PCa samples from TCGA suggested that both the epigenomic and genomic alternations could cause the reproducible transcriptional differences between the two different prognostic groups.

Interpretation: The proposed qualitative transcriptional signature can robustly stratify PCa patients after radical prostatectomy into two groups with different recurrence risk and distinct multi-omics characteristics.

Funding Statement: This work was supported by the National Natural Science Foundation of China (Grant Nos: 81602738 and 81872396), young and middle-aged backbone training project in the health system of Fujian province (Grant No. 2017-ZQN-56), outstanding youth scientific research personnel training program in Fujian Province University (Grant No. 2017B018).

Declaration of Interests:The authors declare no competing interests.

Ethics Approval Statement: Not required.

Keywords: Prostate Cancer, Biochemical Recurrence-Free Survival, Qualitative Signature, Relative Expression Orderings

Suggested Citation

Li, Xiang and Huang, Haiyan and Zhang, Jiahui and Jiang, Fengle and Guo, Yating and Shi, Yidan and Guo, Zheng and Ao, Lu, A Qualitative Transcriptional Signature for Predicting the Recurrence Risk of Prostate Cancer Patients After Radical Prostatectomy (June 17, 2019). Available at SSRN: https://ssrn.com/abstract=3405547 or http://dx.doi.org/10.2139/ssrn.3405547

Xiang Li

Fujian Medical University - Department of Bioinformatics ( email )

Fuzhou, 350122
China

Haiyan Huang

Fujian Medical University - Department of Bioinformatics ( email )

Fuzhou, 350122
China

Jiahui Zhang

Fujian Medical University - Department of Bioinformatics ( email )

Fuzhou, 350122
China

Fengle Jiang

Fujian Medical University - Department of Bioinformatics ( email )

Fuzhou, 350122
China

Yating Guo

Fujian Medical University - Department of Bioinformatics ( email )

Fuzhou, 350122
China

Yidan Shi

Fujian Medical University - Department of Bioinformatics ( email )

Fuzhou, 350122
China

Zheng Guo

Fujian Medical University - Department of Bioinformatics ( email )

Fuzhou, 350122
China

Lu Ao (Contact Author)

Fujian Medical University - Department of Bioinformatics ( email )

Fuzhou, 350122
China

Key Laboratory of Medical Bioinformatics ( email )

Fuzhou
China

Fujian Medical University - Fujian Key Laboratory of Tumor Microbiology ( email )

Fuzhou
China

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