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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年11月17日

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  • 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

摘要

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.