Alterations of commensal microbiota are associated with pancreatic cancer.

Background: Dysbiosis commonly occurs in pancreatic cancer, but its specific characteristics and interactions with pancreatic cancer remain obscure.
Materials and Methods: The 16S rRNA sequencing method was used to analyze multisite (oral and gut) microbiota characteristics of pancreatic cancer, chronic pancreatitis, and healthy controls. Differential analysis was used to identify the pancreatic cancer-associated genera and pathways. A random forest algorithm was adopted to establish the diagnostic models for pancreatic cancer.
Results: The chronic pancreatitis group exhibited the lowest microbial diversity, while no significant difference was found between the pancreatic cancer group and healthy controls group. Diagnostic models based on the characteristics of the oral (area under the curve (AUC) 0.916, 95% confidence interval (CI) 0.832-1) or gut (AUC 0.856; 95% CI 0.74, 0.972) microbiota effectively discriminate the pancreatic cancer samples in this study, suggesting saliva as a superior sample type in terms of detection efficiency and clinical compliance. Oral pathogenic genera ( Granulicatella , Peptostreptococcus , Alloprevotella , Veillonella , etc.) and gut opportunistic genera ( Prevotella , Bifidobacterium , Escherichia/Shigella , Peptostreptococcus , Actinomyces , etc.), were significantly enriched in pancreatic cancer. The 16S function prediction analysis revealed that inflammation, immune suppression, and barrier damage pathways were involved in the course of pancreatic cancer.
Conclusion: This study comprehensively described the microbiota characteristics of pancreatic cancer and suggested potential microbial markers as non-invasive tools for pancreatic cancer diagnosis.