Salivary metabolites for pancreatic cancer diagnosis: a systematic review and meta-analysis.

BACKGROUND: Pancreatic cancer (PC), a malignancy with poor prognosis, demands innovative diagnostic strategies for early detection. Current methods, such as endoscopic ultrasound and blood-based biomarkers, face limitations in accessibility, invasiveness, and accuracy, particularly for early-stage disease. Saliva, a non-invasive biofluid enriched with metabolites and proteins, offers promise as a diagnostic medium. METHODS: A systematic search was conducted across databases, including MEDLINE (PubMed), Scopus, Web of Science, LIVIVO, Embase, Cochrane Library, Ovid, and the Google Scholar search engine. The search had no restrictions on language or publication date. The database search was performed in September 2024 and updated in December 2024 using PRISMA-DTA guidelines. Methodological quality was assessed using Quality Assessment Tool for Diagnostic Accuracy Studies-2 (QUADAS-2). Statistical analysis was performed, and pooled sensitivity, specificity, and diagnostic odds ratio (DOR) calculated. RESULTS: This systematic review and meta-analysis include five studies published between 2010 and 2024. 7,799 records were screened, yielding five studies (4,328 subjects: 854 patients with PC, 3,474 controls) for inclusion in our analysis. Pooled sensitivity and specificity for salivary metabolites were 0.784 (95% CI: 0.769–0.797) and 0.793 (95% CI: 0.764–0.819), respectively, with a diagnostic odds ratio (DOR) of 15.79 (95% CI: 12.61–19.76) and an AUC of 0.867. Multi-metabolite panels significantly outperformed single biomarkers (DOR: 101.68, 95% CI: 24.79-417.08 vs. 14.29, 95% CI: 11.59-17.62), underscoring the value of combinatorial approaches. Subgroup analyses revealed pathway-specific variations, with nervous system-related pathways showing the highest DOR (24.58, 95% CI: 12.41-48.69) and polyamine pathways the lowest (8.96, 95% CI: 5.36-14.98). CONCLUSION: This study highlights saliva’s potential as a complementary tool in PC diagnosis and in bridging the gap between experimental biomarkers and clinical utility.