Significance of Serum Pepsinogens as a Biomarker for Gastric Cancer and Atrophic Gastritis Screening: A Systematic Review and Meta-Analysis

Background: Human pepsinogens are considered promising serological biomarkers for the screening of atrophic gastritis (AG) and gastric cancer (GC). However, there has been controversy in the literature with respect to the validity of serum pepsinogen (SPG) for the detection of GC and AG. Consequently, we conducted a systematic review and meta-analysis to assess the diagnostic accuracy of SPG in GC and AG detection. Methods: We searched PubMed, Embase, and the Chinese National Knowledge Infrastructure (CNKI) for correlative original studies published up to September 30, 2014. The summary sensitivity, specificity, positive diagnostic likelihood ratio (DLR+), negative diagnostic likelihood ratio (DLR-), area under the summary receiver operating characteristic curve (AUC) and diagnostic odds ratio (DOR) were used to evaluate SPG in GC and AG screening based on bivariate random effects models. The inter-study heterogeneity was evaluated by the I2 statistics and publication bias was assessed using Begg and Mazumdar’s test. Meta-regression and subgroup analyses were performed to explore study heterogeneity. Results: In total, 31 studies involving 1,520 GC patients and 2,265 AG patients were included in the meta-analysis. The summary sensitivity, specificity, DLR+, DLR-, AUC and DOR for GC screening using SPG were 0.69 (95% CI: 0.60–0.76), 0.73 (95% CI: 0.62–0.82), 2.57 (95% CI: 1.82–3.62), and 0.43 (95% CI: 0.34–0.54), 0.76 (95% CI: 0.72–0.80) and 6.01 (95% CI: 3.69–9.79), respectively. For AG screening, the summary sensitivity, specificity, DLR+, DLR-, AUC and DOR were 0.69 (95% CI: 0.55–0.80), 0.88 (95% CI: 0.77–0.94), 5.80 (95% CI: 3.06–10.99), and 0.35 (95% CI: 0.24–0.51), 0.85 (95% CI: 0.82–0.88) and 16.50 (95% CI: 8.18–33.28), respectively. In subgroup analysis, the use of combination of concentration of PGI and the ratio of PGI:PGII as measurement of SPG for GC screening yielded sensitivity of 0.70 (95% CI: 0.66–0.75), specificity of 0.79 (95% CI: 0.79–0.80), DOR of 6.92 (95% CI: 4.36–11.00), and AUC of 0.78 (95% CI: 0.72–0.81), while the use of concentration of PGI yielded sensitivity of 0.55 (95% CI: 0.51–0.60), specificity of 0.79 (95% CI: 0.76–0.82), DOR of 6.88 (95% CI: 2.30–20.60), and AUC of 0.77 (95% CI: 0.73–0.92). For AG screening, the use of ratio of PGI:PGII as measurement of SPG yielded sensitivity of 0.69 (95% CI: 0.52–0.83), specificity of 0.84 (95% CI: 0.68–0.93), DOR of 11.51 (95% CI: 6.14–21.56), and AUC of 0.83 (95% CI: 0.80–0.86), the use of combination of concentration of PGI and the ratio of PGI:PGII yield sensitivity of 0.79 (95% CI: 0.72–0.85), specificity of 0.89 (95% CI: 0.85–0.93), DOR of 24.64 (95% CI: 6.95–87.37), and AUC of 0.87 (95% CI: 0.81–0.92), concurrently, the use of concentration of PGI yield sensitivity of 0.46 (95% CI: 0.38–0.54), specificity of 0.93 (95% CI: 0.91–0.95), DOR of 19.86 (95% CI: 0.86–456.91), and AUC of 0.86 (95% CI: 0.52–1.00). Conclusion: SPG has great potential as a noninvasive, population-based screening tool in GC and AG screening. In addition, given the potential publication bias and high heterogeneity of the included studies, further high quality studies are required in the future.