Diagnostic performances of pepsinogens and gastrin-17 for atrophic gastritis and gastric cancer in Mongolian subjects

In Mongolia, gastric cancer morbidity and mortality are high, and more than 80 percent of cases are diagnosed at an advanced stage. This study aimed to evaluate pepsinogens (PGIs) and gastrin-17 (G-17) levels and to determine the diagnostic performances for gastric cancer and chronic atrophic gastritis among Mongolian individuals. We enrolled a total of 120 subjects, including gastric cancer (40), atrophic gastritis (40), and healthy control (40), matched by age (+/- 2) and sex. Pepsinogen I (PGI), Pepsinogen II (PGII), G-17, and H. pylori IgG levels were measured using GastroPanel ELISA kit (Biohit, Helsinki, Finland). Also, PGI to PGII ratio (PGR) was calculated. For atrophic gastritis, when the optimal cut-off value of PGI was <= 75.07 ng/ml, the sensitivity and specificity were 75% and 50%, respectively; when the optimal cut-off value of PGR was <= 6.25, sensitivity and specificity were 85% and 44.7%, respectively. For gastric cancer, when the optimal cut-off value of PGI was <= 35.25 ng/ml, the sensitivity and specificity were 47.2% and 86.8%, respectively; when the optimal cut-off value of PGR was <= 5.27, sensitivity and specificity were 75% and 60.5%, respectively. Combinations of biomarkers with risk factors could improve diagnostic accuracy (AUC for atrophic gastritis 74.8, 95% CI 64.0-85.7, p<0.001; AUC for gastric cancer 75.5, 95% CI 64.2-86.8, p<0.001). PGI, PGR biomarkers combined with the risk of age, family history of gastric cancer, and previous gastric disease could not be an alternative test for upper endoscopy but might be a supportive method which is identifying individuals at medium- and high risk of gastric cancer and precancerous lesions who may need upper endoscopy.

Automated summary

This study evaluated the levels of pepsinogens and gastrin-17 in Mongolian individuals and determined their diagnostic performance for gastric cancer and chronic atrophic gastritis. The study found that combinations of biomarkers with risk factors could improve diagnostic accuracy.