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Detection of Tissue-resident Bacteria in Bladder Biopsies by 16S rRNA Fluorescence In Situ Hybridization

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Urine IRF4/PENK/PXDN Methylation Signatures Enable Machine Learning-Driven Bladder Cancer Detection and

Yi He^1,2,3, Wenhua Xie^1,2,3, Wei Chen^1,2,3

¹Department of Urology, The Affiliated Hospital of Jiaxing University, Jiaxing, China.

Technology in Cancer Research & Treatment

|June 11, 2026

Summary

This study introduces a urine test using DNA methylation and machine learning for early bladder cancer (BC) detection. The BladderCando model accurately identifies BC, improving upon current diagnostic methods.

Keywords:

DNA methylation bladder cancer immune cell infiltration tumor-microenvironment urine

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Cell-Free DNA Integrity Analysis in Urine Samples

Cell-Free DNA Integrity Analysis in Urine Samples

Published on: January 5, 2017

Area of Science:

Oncology
Genetics
Bioinformatics

Background:

Cystoscopy for bladder cancer (BC) diagnosis and surveillance presents challenges.
Early detection of BC is crucial for effective treatment and improved patient outcomes.

Purpose of the Study:

To develop and validate a cost-effective, urine-based assay for early BC detection using DNA methylation and machine learning.
To identify robust molecular signatures for BC detection and understand tumor-microenvironment interactions.

Main Methods:

Prospective cohort study with 155 participants.
Targeted next-generation sequencing of urine DNA to quantify methylation at 44 CpG sites in IRF4, PENK, and PXDN.
Development of a random forest classifier (BladderCando model) integrating methylation data and systems analyses.

Main Results:

Significantly increased methylation of IRF4, PENK, and PXDN in BC urine samples (P < 0.0001).
The BladderCando model achieved high diagnostic accuracy (AUC = 0.9783, F1 = 0.9773), outperforming urine cytology for low-grade BC.
Epigenetic alterations correlated with gene expression, immune cell infiltration, and tumor-microenvironment characteristics.

Conclusions:

Urine DNA methylation of IRF4, PENK, and PXDN serves as a reliable biomarker for BC detection.
Machine learning integration significantly enhances diagnostic precision for bladder cancer.
These findings offer potential for improved early surveillance and therapeutic strategy development.