Oral and fecal microbiota as accurate non-invasive tools for detection of pancreatic cancer in the Chinese population.

Pancreatic cancer (PCA), a leading cause of cancer-related deaths, has limited non-invasive diagnostic methods. We aimed to identify oral and fecal microbiome biomarkers and construct diagnostic classifiers. Oral and fecal samples from 97 PCA patients and 90 healthy controls underwent 16S rRNA sequencing. Samples were randomly divided into training and validation cohorts in a 7:3 ratio. Random forest models were constructed using training cohort and validated internally and externally in Chinese, Japanese, and Spanish populations. Results revealed significant dysbiosis of the oral and fecal microbiota of PCA patients. Most of the differential taxa shared between oral and fecal samples showed similar changes. Relative abundances of Streptococcus in oral samples, and of Bifidobacterium, Klebsiella and Akkermansia in fecal samples, were enriched in PCA. The fecal Firmicutes to Bacteroidota ratio was higher in PCA patient samples. Oral and fecal microbiome classifiers based on the top 20 contributing genera were constructed, and internal validation showed that the area under the curve (AUC) values were 0.963 and 0.890, respectively. The fecal microbiome classifier performed well in the external Chinese population, with an AUC of 0.878, but poorly in the Japanese and Spanish populations. Furthermore, fecal microbiomes could predict metastasis status in PCA patients, with an AUC of 0.804. In conclusion, oral and fecal microbiota were dysbiotic in PCA patients. Fecal microbiome classifier provides a feasible, non-invasive, and cost-effective tool with high precision for PCA screening in China; oral microbiome classifier requires further validation in external populations sampled with the same simple and convenient methods.