Serum Protein Profiling of Patients at Risk to Develop Gastric Disease Based on a DSC Test

At present, the gold standard for gastric cancer (GC) confirmation relies mostly on histopathology, an invasive procedure. Noninvasive detection methods using serum for large-scale screening may be useful for the early diagnosis of GC. Helicobacter pylori (HP) infection and chronic atrophic gastritis are major GC risk factors. We recently developed a noninvasive test called the DSC test-based on the patient’s age, sex, their serum PGI and PGII, anti-HP immunoglobulin (IgG), and gastrin G17 levels-predicting GC risk as low (score 0, S0) or high (score 2, S2). The comparative investigation at the serum protein level of the two different patient groups detected by our DCS test (S0 and S2) may undoubtedly help to identify gastric disease-dependent proteins, resulting from bacterial infection or gastric mucosa inflammation, as well as get better insight into the molecular scenario associated with pre-cancerous conditions. We used an untargeted liquid chromatography– tandem mass spectrometry (LC-MS/MS)-based proteomic profiling approach, followed by univariate statistical analysis to compare the different DSC groups across two patient cohorts (exploratory and validation). Significantly differentially abundant proteins differing more than 1.5-fold between S0 and S2 groups were selected and validated, and their putative role(s) in gastritis and GC were discussed. In both the exploratory and the validation cohorts, four proteins (beta-2-microglobulin, EGF-containing fibulin-like extracellular matrix protein 1, complement factor D, and Cystatin-C) were more abundant, while two (sex hormone-binding globulin and pregnancy zone protein) were less abundant in the sera of S2 individuals (|fold change| ≥ 0.6, p < 0.05, t-test). The higher presence of beta-2-microglobulin (B2M) and the lower content of pregnancy zone protein (PZP) in S2 sera were validated by immunoblotting. Replacing age and sex in our DSC model with two specific candidate biomarkers can lead to a refined, albeit modest, improvement in classification accuracy. This study identified a proteomic signature that was differentially associated with the sera of patients with a different risk to develop advanced atrophy/GC according to the DSC test. Moving from a demographic model to a proteomic-driven model can better reflect the personalized biology of pathological processes associated with DSC.