Oral and fecal microbiome alterations in pancreatic cancer: insights into potential diagnostic biomarkersOriginal paper
What was studied?
This study measured five specific oral and gut bacteria in pancreatic cancer patients versus healthy controls, testing them as potential non-invasive diagnostic markers. Microbial DNA was extracted from saliva and stool. Real-time quantitative PCR targeted 16S rRNA genes of the five chosen species. Results were expressed as log10 colony-forming units. Principal component analysis and logistic regression assessed how well the bacteria distinguished cancer from control.
Who was studied?
The study enrolled 20 newly diagnosed, untreated pancreatic cancer patients and 20 healthy controls, all aged 20 to 70, recruited at Taleghani Hospital in Tehran, Iran. Controls had no history of pancreatic cancer, inflammatory bowel disease, or oral health issues. The cancer group was significantly older, at a mean of 67 years versus 32.6 in controls. Lifestyle, clinical, and laboratory data were collected for both groups.
What were the most important findings?
Salivary Granulicatella adiacens rose to a median of 7.35 log10 CFU per mL in cancer patients versus 2.43 in controls. Fecal Fusobacterium nucleatum rose to 4.37 versus 1.20. Both differences reached p below 0.001. Three species fell significantly in cancer patients: salivary Neisseria elongata (2.37 versus 5.63), fecal Roseburia intestinalis (2.34 versus 5.07), and fecal Bifidobacterium bifidum (3.45 versus 4.34), all at p below 0.001. Principal component analysis separated the two groups. Fusobacterium nucleatum reached an area under the curve of 1.00, though hemoglobin and hematocrit gave the highest accuracy.
What are the greatest implications of this study?
These microbial shifts suggest saliva and stool profiles could support non-invasive screening for pancreatic cancer, a disease usually caught too late for cure. The pattern of elevated pathobionts and depleted protective bacteria fits prior international reports. The authors stress limits. The age gap between patients and controls may confound results, the cross-sectional design cannot show causation, and the small single-center sample needs validation in larger, diverse cohorts.