Biomarkers for predicting diabetes in gastric cancer patients with machine learning methods based on proteomic data

Abstract

Gastric cancer is a type of cancer that occurs when cells in the stomach tissue grow and multiply abnormally. Gastric cancer usually starts in the inner layer of the stomach wall and can spread to other layers over time. This type of cancer is most common in people over the age of 50, but it can also occur in younger people. Symptoms of gastric cancer include indigestion and stomach pain, nausea and vomiting, loss of appetite and weight loss, bloody stools, fatigue and weakness. Although the exact cause of stomach cancer is not known, several risk factors have been identified. These risk factors include infection with the bacterium Helicobacter pylori, a family history of stomach cancer, consumption of excessively salty foods, smoking, heavy alcohol use and some genetic factors. Diabetes, on the other hand, is a hormonal disorder that regulates the body's blood sugar levels. Normally, an organ called the pancreas controls blood sugar by producing a hormone called insulin. Insulin helps glucose (sugar) enter the cells so that they can make energy. In diabetes, this regulation is disrupted, which can lead to high blood sugar and various health problems. The relationship between stomach cancer and diabetes is not yet fully understood. In this study, machine learning models (Stochastic Gradient Boosting, Bagged Classification and Regression Trees) based on proteomic data were used to predict the diabetes risk of 40 gastric cancer patients, 21 with DM and 19 with non-DM. Performance metrics for the optimal model (Stochastic Gradient Boosting) the accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value and F1-score values are 0.86, 0.83, 0.67, 1.00, 1.00, 0.80, 0.80, respectively. According to the variable importance values obtained as a result of the model, Mucin-13 protein has a positive predictive value in predicting the diabetes risk of gastric cancer patients in the clinic.