Diagnostic accuracy of salivary nucleic acid biomarkers in gastric cancer detection: a systematic review and meta-analysis

Gastric cancer (GC) remains a leading cause of cancer-related mortality worldwide. Endoscopy, although accurate, is invasive and impractical for population-based screening. Salivary nucleic acid biomarkers have emerged as a promising non-invasive alternative, but their diagnostic accuracy has not been systematically quantified.This systematic review and meta-analysis followed PRISMA-DTA guidelines and was registered in PROSPERO (CRD420261395418). Databases, including MEDLINE, Embase, Web of Science, Scopus, Cochrane Library, and Google Scholar, were searched. Studies evaluating salivary nucleic acid biomarkers for histologically confirmed GC were included. Methodological quality was assessed using QUADAS-2. Bivariate random-effects models were used to pool sensitivity, specificity, diagnostic odds ratio (DOR), and area under the summary ROC curve (AUC). Analyses were stratified by diagnostic model: single biomarker, multiple-biomarker panel, and integrated biomarker model. Eleven records were included in this systematic review, and six were eligible for meta-analysis. Single salivary biomarkers showed modest diagnostic performance (sensitivity 0.70, specificity 0.75, DOR 7.33, and AUC 0.79). Multiple-biomarker panels improved accuracy (sensitivity 0.74, specificity 0.81, DOR 8.83, and AUC 0.81). Integrated models in the validation phase, combining salivary biomarkers with demographic or other biological variables, showed promising diagnostic performance (sensitivity 0.81, specificity 0.81, and DOR of 18.20), although between-study heterogeneity was high for sensitivity. Salivary nucleic acid biomarkers, particularly within validation-phase integrated models, represent a promising non-invasive approach for GC detection. However, current evidence is limited by case-control designs, methodological heterogeneity, spectrum bias, and high risk of bias in patient selection. Standardized collection protocols and large-scale multicenter prospective validation are essential before clinical implementation.