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Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. This study aims to develop predictive models using different machine learning (ML) classifiers based on both demographic and clinical variables to predict metastasis status of patients with GC. The data applied in this study including 733 of GC patients, divided into a train and test groups at a ratio of 8:2, diagnosed at Taleghani tertiary hospital. In order to predict metastasis in GC, ML-based algorithms, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Decision Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, F1 score, precision, sensitivity, specificity, area under the curve (AUC) of receiver operating characteristic (ROC) curve and precision-recall AUC (PR-AUC) were obtained. 262 (36%) experienced metastasis among 733 patients with GC. Although all models have optimal performance, the indices of SVM model seems to be more appropiate (training set: AUC: 0.94, Sensitivity: 0.94; testing set: AUC: 0.85, Sensitivity: 0.92). Then, NN has the higher AUC among ML approaches (training set: AUC: 0.98; testing set: AUC: 0.86). The RF of ML-based models, which determine size of tumor and age as two essential variables, is considered as the third efficient model, because of higher specificity and AUC (84% and 87%). Based on the demographic and clinical characteristics, ML approaches can predict the metastasis status in GC patients. According to AUC, sensitivity and specificity in both SVM and NN can be regarded as better algorithms among 6 applied ML-based methods.

Background Gastric cancer (GC), with a 5-year survival rate of less than 40%, is known as the fourth principal reason of cancer-related mortality over the world. The study aimed to perform machine learning (ML) methods in GC patients. Methods The data applied in this study including 733 of GC patients diagnosed at Taleghani hospital. In order to predict metastasis in GC, machine learning approaches, including Naive Bayes (NB), Random Forest (RF), Support Vector Machine (SVM), Neural Network (NN), Regression Tree (RT) and Logistic Regression (LR), with 5-fold cross validation were performed. To assess the model performance, precision, sensitivity, specificity and AUC of Receiver operating characteristic (ROC) curve were obtained. Results 262 (36%) experienced metastasis among 733 patients with GC. The RF of ML-based models, with size of tomur and age as two essential variables, is considered as efficient model, because of higher specificity and AUC (84% and 87%). Also, the sensitivity in SVM model seems to be better (93%). Conclusion According to AUC, sensitivity and specificity, both RF and SVM can be regarded as better ML-based algorithms among six applied ML-based methods.

Long noncoding RNAs (lncRNAs), as well-known modulator of the epigenetic processes, have been shown to contribute to normal cellular physiological and pathological conditions such as cancer. Through the interaction with epigenetic regulators, an aberrant regulation of gene expression can be resulted due to their dysregulation, which in turn, can be involved in tumorigenesis. In the present study, we reviewed the lncRNAs’ function and mechanisms that contributed to aberrant epigenetic regulation, which is directly related to gastrointestinal cancer (GI) development and progression. Findings indicated that epigenetic alterations may involve in tumorigenesis and are valuable biomarkers in case of diagnosing, assessing of risk factors, and predicting of GI cancers. This review summarized the accumulated evidence for biological and clinical application to use lncRNAs in GI cancers, including colorectal, gastric, oral, liver, pancreatic and oesophageal cancer.

Harris family of distributions models lifetime of a series system when the number of components is a positive random variable. In this paper, we reveal several stochastic comparisons in the Harris family with different tilt parameters and different baseline distributions with respect to the usual stochastic, shifted stochastic, proportional stochastic and shifted proportional stochastic orderings. Such comparisons are particularly useful in lifetime optimization of reliability systems. We shall also present two bounds for a Harris family survival function conditioned on its tilt parameter which are useful when aging properties are considered. Our results generalize several previous findings in this connection.