Microbial DNA in Blood Used to Track Liver Cancer Origin

Determining whether a cancerous tumor started in the liver or spread there from colorectal cancer is crucial for diagnosis and treatment decisions. Researchers at the University of California San Diego have discovered a microbial DNA signature in blood plasma that can distinguish between primary liver cancer and metastatic colorectal cancer in the liver with high accuracy.

The study analyzed cell-free DNA (cfDNA)—fragments of DNA released by dead cells—isolated from the blood plasma of 16 patients with primary liver cancer and 11 patients with metastatic colorectal cancer. Through metagenomic analysis, the team developed a microbial cfDNA classifier that identified the tumor type with 90% accuracy.

Distinct microbial species characterized each cancer type. Primary liver cancer patients had microbes such as Pseudomonas aeruginosa, Corynebacterium accolens, and C. glucuronolyticum, which are commonly associated with immunocompromised conditions, liver transplant complications, and antimicrobial defense. On the other hand, patients with metastatic colorectal cancer showed microbes including Acinetobacter tandoii, A. tianfuensis, A. septicus, A. parvus, Pseudomonas asiatica, and Bifidobacterium faecale, species known to be linked to hospital-acquired infections, bloodstream infections, and gastrointestinal inflammation.

This microbial profiling approach could provide a new diagnostic method, especially valuable when standard imaging techniques are unclear or unavailable. As Amir Zarrinpar, senior author of the paper published in eGastroenterology, noted, “The use of microbial DNA signatures could open up a new diagnostic pathway, especially in situations where radiographic imaging is ambiguous or not accessible.”

While larger patient studies are required, this finding supports increasing evidence that microbes play a role in cancer biology. Importantly, this study is among the first to demonstrate the potential of microbial DNA circulating in blood to non-invasively identify tumor origin without relying on machine learning or artificial intelligence techniques.

Clinical applications of this research could include earlier cancer detection, improved monitoring of patients at high risk, and development of microbial DNA-based biomarkers for precise diagnosis. Additionally, understanding the microbial involvement in cancers may guide new microbiome-based therapeutic strategies.