Abstract
Gastric cancer (GC) is still an extremely severe health issue with high mortality due to the lacking of effective biomarkers. In this study, we aimed to investigate the alterations of salivary protein glycosylation related to GC and assess the possibility of salivary glycopatterns as potential biomarkers for the diagnosis of GC. Firstly, 94 patients with GC (n = 64) and atrophic gastritis (AG) (n = 30), as well as 30 age- and sex-matched healthy volunteers (HV) were enrolled in the test group to probe the difference of salivary glycopatterns using lectin microarrays, the results were validated by saliva microarrays and lectin blotting analysis. Then, the diagnostic model of GC (Model GC) and AG (Model AG) were constructed based on 15 candidate lectins which exhibited significant alterations of salivary glycopattern by logistic stepwise regression. Finally, two diagnostic models were assessed in the validation group including HV (n = 30) and patients with GC (n = 23) and AG (n = 24) and achieved high diagnostic power (Model GC (AUC: 0.89, sensitivity: 0.96 and specificity: 0.80), Model AG (AUC: 0.83, sensitivity: 0.92 and specificity: 0.72)). This study provides pivotal information to distinguish HV, AG and GC based on precise alterations in salivary glycopatterns, which have great potential to be biomarkers for diagnosis of GC.
Highlights
Gastric cancer (GC) is a kind of malignant tumor with high incidence and mortality especially in developing countries
This study provides pivotal information to distinguish healthy volunteers (HV), atrophic gastritis (AG) and GC based on precise alterations in salivary glycopatterns, which have great potential to be biomarkers for diagnosis of GC
The subjects assigned to scatterplots tended to cluster separately to form HV, AG, and GC pools with different colours and symbols in Figure 1D, which indicated that it was possible to distinguish among HV, AG, and GC based on precise alterations in salivary glycopatterns
Summary
Gastric cancer (GC) is a kind of malignant tumor with high incidence and mortality especially in developing countries. It affects approximately one million individuals per year worldwide [1]. Previous researchers have discovered several glycoprotein biomarkers (CA72-4, CA19-9, CEA, CA125) [4, 5]. All of the existing biomarkers are not enough sensitive or specific to characterize the early GC [6]. About 80% patients were diagnosed only at the advanced stages which makes high mortality [7]. Discovering effective biomarkers for accurately distinguishing early GC is an urgent assignment
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