- Today
- Holidays
- Birthdays
- Reminders
- Cities
- Atlanta
- Austin
- Baltimore
- Berwyn
- Beverly Hills
- Birmingham
- Boston
- Brooklyn
- Buffalo
- Charlotte
- Chicago
- Cincinnati
- Cleveland
- Columbus
- Dallas
- Denver
- Detroit
- Fort Worth
- Houston
- Indianapolis
- Knoxville
- Las Vegas
- Los Angeles
- Louisville
- Madison
- Memphis
- Miami
- Milwaukee
- Minneapolis
- Nashville
- New Orleans
- New York
- Omaha
- Orlando
- Philadelphia
- Phoenix
- Pittsburgh
- Portland
- Raleigh
- Richmond
- Rutherford
- Sacramento
- Salt Lake City
- San Antonio
- San Diego
- San Francisco
- San Jose
- Seattle
- Tampa
- Tucson
- Washington
Birmingham Today
By the People, for the People
Scientists Uncover Gut Signals That Could Detect Cancers Early
New AI-powered analysis reveals shared biomarkers across gastrointestinal diseases, paving the way for less invasive diagnostics.
Apr. 5, 2026 at 3:08am
Got story updates? Submit your updates here. ›
Researchers have identified a set of gut bacteria and metabolites that could significantly improve how gastrointestinal diseases like gastric cancer, colorectal cancer, and inflammatory bowel disease are detected and treated. Using advanced AI tools, the team discovered that biomarkers linked to one condition can often predict others, suggesting these diseases are more interconnected than previously thought. This cross-disease insight could lead to faster diagnoses without invasive procedures.
Why it matters
Current diagnostic methods like endoscopies and biopsies can be invasive, expensive, and sometimes miss diseases at early stages. These new gut biomarkers offer a promising path toward non-invasive, cross-disease screening that could lead to earlier detection and more personalized treatments for a range of gastrointestinal conditions.
The details
The researchers used machine learning and AI to analyze microbiome and metabolome data from patients with gastric cancer (GC), colorectal cancer (CRC), and inflammatory bowel disease (IBD). They found that models trained on data from one disease could often accurately predict biomarkers for another, indicating shared underlying biological mechanisms. For example, GC-based models were able to identify IBD biomarkers, while CRC models could predict GC-related markers. The team also highlighted distinct microbial and metabolic patterns for each disease, along with important overlaps. Simulations further supported the role of these gut biomarkers in differentiating healthy and diseased states.
- The research was published in April 2026 in the Journal of Translational Medicine.
The players
Dr. Animesh Acharjee
Lead co-author of the study and researcher at the University of Birmingham Dubai, part of the Health Data Science MSc Programme.
University of Birmingham
One of the institutions that carried out the research, along with the University of Birmingham Dubai and University Hospitals Birmingham NHS Foundation Trust.
What they’re saying
“Current diagnostic methods like endoscopy and biopsies are effective but can be invasive, expensive, and sometimes miss diseases at early stages.”
— Dr. Animesh Acharjee, Lead co-author
“Our analysis offers a better understanding of the underlying mechanisms driving disease progression and identifies key biomarkers for targeted therapies. These biomarkers could help identify diseases earlier and more accurately, leading to better, more personalised treatment.”
— Dr. Animesh Acharjee, Lead co-author
What’s next
The researchers plan to explore how these findings can be applied in clinical settings, including developing non-invasive diagnostic tests and more targeted therapies based on the identified biomarkers. They also intend to validate their models using larger and more diverse patient groups, and investigate whether these biomarkers could help predict additional related diseases in the future.
The takeaway
This innovative cross-disease analysis of gut biomarkers could revolutionize the diagnosis and treatment of multiple gastrointestinal conditions, leading to earlier detection and more personalized care without invasive procedures.


