UriBLAD

Autor: Hu Linyi, Qinghua Xu, Qifeng Wang, Wenyong Ma, Kaibing Song, Zhipeng Meng, Dahong Zhang, Deshuang Huang, Dingwei Ye, Xiao Zhang, Wu Yiwang, Chen Jinying, Peng Li, Yingjia Wang, Yijun Shen, Wanli Ren, Yifeng Sun, Yangyang Lu, Sheng Wu
Rok vydání: 2021
Předmět:
Zdroj: The Journal of Molecular Diagnostics. 23:61-70
ISSN: 1525-1578
DOI: 10.1016/j.jmoldx.2020.10.005
Popis: Bladder cancer is the most common urinary system neoplasm, with approximately 550,000 new cases per year worldwide. Current methods for diagnosis and monitoring of bladder cancer are often invasive and/or lack sensitivity and specificity. In this study, the authors aimed to develop an accurate, noninvasive urine-based gene expression assay for the detection of bladder cancer. Urine specimens were collected at five Chinese hospitals from patients with bladder cancer, and from healthy and other control subjects. The expression levels of 70 genes were characterized by quantitative RT-PCR in a training cohort of 211 samples. Machine learning approaches were used to identify a 32-gene signature to classify cancer status. The performance of this gene signature was further validated in a multicenter, prospective cohort of 317 samples. In the blind validation set, the 32-gene signature achieved encouraging performance of 90% accuracy, 83% sensitivity, and 95% specificity. The area under the receiver operating characteristic curve reached 0.93. Importantly, the 32-gene signature performed well in the detection of non-muscle invasive tumor and low-grade tumor with sensitivities of 81.6% and 81%, respectively. In conclusion, we present a novel gene expression assay using urine samples that can accurately discriminate patients with bladder cancer from controls. The results might prompt further development of this gene expression assay into an in vitro diagnostic test amenable to routine clinical practice.
Databáze: OpenAIRE
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