Searching for New Biomarkers and the Use of Multivariate Analysis in Gastric Cancer Diagnostics

Aim: The first aim of this study was to search for new biomarkers to be used in gastric cancer diagnostics. The second aim was to verify the findings presented in literature on a sample of the local population and investigate the risk of gastric cancer in that population using a multivariant statistical analysis. Patients and Methods: We assessed a group of 36 patients with gastric cancer and 69 healthy individuals. We determined carcinoembryonic antigen, cancer antigen 19-9, cancer antigen 72-4, matrix nietalloproteinases (-1,-2,-7,-8 and-9), osteoprotegerin, osteopontin, prothrombin induced by vitamin K absence-11, pepsinogen 1, pepsinogen H, gastrin and Helicobacter pylori for each sample. Results: The multivariate stepwise logistic regression identified the following biomarkers as the best gastric cancer predictors: CEA, CA72-4, pepsinogen 1, Helicobacter pylori presence and MMP7. Conclusion: CEA and CA72-4 remain the best markers for gastric cancer diagnostics. We suggest a mathematical model for the assessment of risk of gastric cancer.