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<p>Screening and molecular characterization of human intestinal pathologies such as colorectal cancer (CRC) currently depends on colonoscopy, an invasive procedure associated with risks and poor adherence. A non-invasive method that captures host molecular changes could improve early detection and monitoring of intestinal diseases. Transcriptomic profiling of shed intestinal cells in stool has shown potential in neonates but is limited in adults by the dominance of bacterial RNA. To address this, we combined microbial ribosomal RNA (rRNA) depletion with unique molecular identifier (UMI)-based RNA sequencing to enrich and quantify human transcripts in stool. Applying this method to samples from 54 CRC patients and 24 healthy controls, we profiled thousands of human genes per sample. Stool-derived gene expression distinguished CRC from control samples with high accuracy (AUC = 0.86) and strongly correlated with matched tumor tissue signatures. Notably, stool transcriptomes reverted to control-like patterns after tumor resection. Our approach offers a powerful, non-invasive alternative to current CRC diagnostics and enables molecular insights into tumor biology. This method could complement or replace existing screening tools and may be applicable to other gastrointestinal diseases.</p>

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Stool shed cell transcriptomics mirrors tumor biology and enables colorectal cancer diagnosis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月2日

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  • Keren Bahar Halpern,
  • Ilan Kent,
  • Oran Yakubovsky,
  • Shani Ben-Moshe,
  • Tal Barkai,
  • Michal Fine,
  • Roy Novoselsky,
  • Alon Israeli,
  • Ido Nachmany,
  • Shalev Itzkovitz

摘要

Screening and molecular characterization of human intestinal pathologies such as colorectal cancer (CRC) currently depends on colonoscopy, an invasive procedure associated with risks and poor adherence. A non-invasive method that captures host molecular changes could improve early detection and monitoring of intestinal diseases. Transcriptomic profiling of shed intestinal cells in stool has shown potential in neonates but is limited in adults by the dominance of bacterial RNA. To address this, we combined microbial ribosomal RNA (rRNA) depletion with unique molecular identifier (UMI)-based RNA sequencing to enrich and quantify human transcripts in stool. Applying this method to samples from 54 CRC patients and 24 healthy controls, we profiled thousands of human genes per sample. Stool-derived gene expression distinguished CRC from control samples with high accuracy (AUC = 0.86) and strongly correlated with matched tumor tissue signatures. Notably, stool transcriptomes reverted to control-like patterns after tumor resection. Our approach offers a powerful, non-invasive alternative to current CRC diagnostics and enables molecular insights into tumor biology. This method could complement or replace existing screening tools and may be applicable to other gastrointestinal diseases.