Hypermethylation Loci of ZNF671, IRF8, and OTX1 as Potential Urine-Based Predictive Biomarkers for Bladder Cancer

Author:

Jiang Yuan-Hong1ORCID,Liu Yu-Shu23,Wei Yu-Chung4ORCID,Jhang Jia-Fong1,Kuo Hann-Chorng12ORCID,Huang Hsin-Hui23,Chan Michael W. Y.567ORCID,Lin Guan-Ling567,Cheng Wen-Chi8,Lin Shu-Chuan23ORCID,Wang Hung-Jung8910

Affiliation:

1. Department of Urology, Hualien Tzu Chi Hospital, Tzu Chi University, Hualien 970374, Taiwan

2. Guzip Biomarkers Corporation, Hsinchu City 302041, Taiwan

3. Phalanx Biotech, Hsinchu City 302041, Taiwan

4. Graduate Institute of Statistics and Information Science, National Changhua University of Education, Changhua City 500207, Taiwan

5. Department of Biomedical Sciences, National Chung Cheng University, Minhsiung, Chiayi 621301, Taiwan

6. Epigenomics and Human Disease Research Center, National Chung Cheng University, Minhsiung, Chiayi 621301, Taiwan

7. Center for Innovative Research on Aging Society (CIRAS), National Chung Cheng University, Min-Hsiung, Chiayi 621301, Taiwan

8. Institute of Medical Sciences, Tzu Chi University, Hualien 970374, Taiwan

9. Doctoral Degree Program in Translational Medicine, Tzu Chi University and Academia Sinica, Hualien 97004, Taiwan

10. Department of Biomedical Sciences and Engineering, Tzu Chi University, Hualien 970374, Taiwan

Abstract

Bladder cancer (BCa) is a significant health issue and poses a healthcare burden on patients, highlighting the importance of an effective detection method. Here, we developed a urine DNA methylation diagnostic panel for distinguishing between BCa and non-BCa. In the discovery stage, an analysis of the TCGA database was conducted to identify BCa-specific DNA hypermethylation markers. In the validation phase, DNA methylation levels of urine samples were measured with real-time quantitative methylation-specific PCR (qMSP). Comparative analysis of the methylation levels between BCa and non-BCa, along with the receiver operating characteristic (ROC) analyses with machine learning algorithms (logistic regression and decision tree methods) were conducted to develop practical diagnostic panels. The performance evaluation of the panel shows that the individual biomarkers of ZNF671, OTX1, and IRF8 achieved AUCs of 0.86, 0.82, and 0.81, respectively, while the combined yielded an AUC of 0.91. The diagnostic panel using the decision tree algorithm attained an accuracy, sensitivity, and specificity of 82.6%, 75.0%, and 90.9%, respectively. Our results show that the urine-based DNA methylation diagnostic panel provides a sensitive and specific method for detecting and stratifying BCa, showing promise as a standard test that could enhance the diagnosis and prognosis of BCa in clinical settings.

Funder

Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation

National Science and Technology Council

Publisher

MDPI AG

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