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

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.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-10-02
DOI
https://doi.org/10.1038/s41598-025-17411-5
Primary Topic
Genetic factors in colorectal cancer
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

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

Ido Nachmany, Shani Ben‐Moshe, Ilan Kent, Shalev Itzkovitz et al.
Scientific Reports
Genetic factors in colorectal cancer
article

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

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

Abstract

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.

Scientific ReportsVol. 15(1)
Tel Aviv University (IL), Sheba Medical Center (IL), Weizmann Institute of Science (IL)
European Commission, Minerva Foundation
Openalex Percentile: Top 62%
Genetic factors in colorectal cancer
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.