Early detection of urological tumors based on genomic characteristics of cell-free DNA fragments: a multi-center study

Huiyong Zhang; Caihong Huang; Chunmeng Wei; Rongbin Zhou; Chengbang Wang; Wenhao Lu; Zuheng Wang; Xiao Li; Shaohua Chen; Dianyu Wang; Jin Ji; Yuexiang Li; Rirong Yang; Junyi Chen; Yanling Hu; Fubo Wang

doi:10.1038/s41698-025-01130-1

Early detection of urological tumors based on genomic characteristics of cell-free DNA fragments: a multi-center study

NPJ Precis Oncol. 2025 Nov 17;9(1):352. doi: 10.1038/s41698-025-01130-1.

Authors

Huiyong Zhang^#^{1

2}, Caihong Huang^#³, Chunmeng Wei^#^{1

4}, Rongbin Zhou¹, Chengbang Wang^{1

4}, Wenhao Lu¹, Zuheng Wang⁴, Xiao Li⁵, Shaohua Chen^{6

7}, Dianyu Wang⁸, Jin Ji^{9

10}, Yuexiang Li¹, Rirong Yang¹, Junyi Chen¹¹, Yanling Hu^{12

13}, Fubo Wang^{14

15

16

17}

Affiliations

¹ Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, Nanning, Guangxi, China.
² School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China.
³ Guangxi Academy of Medical Sciences, the People's Hospital of Guangxi Zhuang Autonomous Region, Nanning, Guangxi, China.
⁴ Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China.
⁵ School of Life Sciences, Guangxi Medical University, Nanning, Guangxi, China.
⁶ Department of Urology, Affiliated Tumor Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China.
⁷ School of Public Health, Guangxi Medical University, Nanning, Guangxi, China.
⁸ Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China.
⁹ Department of Urology, Shanghai Changhai Hospital, Naval Medical University, Shanghai, China.
¹⁰ Department of Urology, Naval Medical Center, Naval Medical Univiersiy, Shanghai, China.
¹¹ Department of Urology, The Second Affiliated Hospital of Fujian Medical University, Quanzhou, Fujian, China. chenjunyidoctor@163.com.
¹² Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, Nanning, Guangxi, China. huyanling@gxmu.edu.cn.
¹³ School of Life Sciences, Guangxi Medical University, Nanning, Guangxi, China. huyanling@gxmu.edu.cn.
¹⁴ Center for Genomic and Personalized Medicine, Guangxi Key Laboratory for Genomic and Personalized Medicine, University Engineering Research Center of Digital Medicine and Healthcare, Guangxi Medical University, Nanning, Guangxi, China. wangfubo@gxmu.edu.cn.
¹⁵ Department of Urology, the First Affiliated Hospital of Guangxi Medical University, Guangxi Medical University, Nanning, Guangxi, China. wangfubo@gxmu.edu.cn.
¹⁶ School of Life Sciences, Guangxi Medical University, Nanning, Guangxi, China. wangfubo@gxmu.edu.cn.
¹⁷ School of Public Health, Guangxi Medical University, Nanning, Guangxi, China. wangfubo@gxmu.edu.cn.

^# Contributed equally.

Abstract

Cell-free DNA (cfDNA) has shown potential in distinguishing cancer patients from healthy individuals. This study investigates cfDNA fragmentomics-fragmentation patterns, end motifs (EDMs), and breakpoint motifs (BPMs)-to develop an early detection method for bladder urothelial carcinoma (BLCA), prostate adenocarcinoma (PRAD), and clear cell renal cell carcinoma (ccRCC). Using low-coverage whole genome sequencing (lcWGS) on plasma samples from 758 participants (including BLCA, PRAD, ccRCC, benign prostatic hyperplasia patients, and healthy controls), we analyzed cfDNA features. Machine learning models (logistic regression, support vector machine, random forest, XGBoost, Stacking) distinguished urological tumors from non-tumor cases with AUCs of 96% (BLCA), 99% (ccRCC), 92% (PRAD), and 89% (pan-cancer). Key discriminators included 6-bp EDMs and BPMs. A proposed two-tier screening strategy combining pan-cancer and cancer-specific features offers a cost-effective, non-invasive approach for early detection with strong clinical potential.

Abstract

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