Genetic and molecular biology of gastric cancer among Iranian patients: an update

The effect of miR-372-5p regulation on CDX1 and CDX2 in the gastric cancer cell line

OBJECTIVES

MicroRNA expression disruptions play an important function in the expansion of gastric cancer. Previous investigation has indicated that miR-372-5p doing as an oncogene in several malignancies. CDX1 and CDX2, as target genes of miR-372-5p, play the role of tumor suppressors and oncogenes in gastric cancer cells, respectively. The current investigation explored the effects of miR-372-5p regulation on CDX2 and CDX1 in AGS cell lines and studied their molecular mechanism.

METHODS

hsa-miR-372-5p miRCURY LNA miRNA Inhibitors and Mimic were transfected into AGS cell line. The cell viability and cell cycle calculation were defined by MTT assay and flow cytometry, respectively. The Expression levels of miR-372-5p, CDX1, CDX2 and transfection efficiency were measured using Real-time PCR. Statistical investigation p values <0.05 were considered to be meaningful.

RESULTS

miR-372-5p particularly was upregulated in control cells and also after transfection by mimic. While its expression was reduced by the inhibitor. Upregulation of miR-372-5p remarkably increased cell growth and led to accumulation in the G2/M phase, although the inhibitor decreased cell growth and accumulation in the S phase. Accordingly, upregulation of miR-372-5p increased CDX2 and decreased CDX1 expression. By inhibition of miR-372-5p, expression of CDX2 was decreased and expression of CDX1 was increased.

CONCLUSIONS

Up and down-regulation of miR-372-5P has a potential effect on the expression levels of its target genes, CDX1 and CDX22. Accordingly, the downregulation of miR-372-5p may be assumed as a possible therapeutic target in treating gastric cancer.

Optimizing prognostic factors of five-year survival in gastric cancer patients using feature selection techniques with machine learning algorithms: a comparative study

BACKGROUND

Gastric cancer is the most common malignant tumor worldwide and a leading cause of cancer deaths. This neoplasm has a poor prognosis and heterogeneous outcomes. Survivability prediction may help select the best treatment plan based on an individual's prognosis. Numerous clinical and pathological features are generally used in predicting gastric cancer survival, and their influence on the survival of this cancer has not been fully elucidated. Moreover, the five-year survivability prognosis performances of feature selection methods with machine learning (ML) classifiers for gastric cancer have not been fully benchmarked. Therefore, we adopted several well-known feature selection methods and ML classifiers together to determine the best-paired feature selection-classifier for this purpose.

METHODS

This was a retrospective study on a dataset of 974 patients diagnosed with gastric cancer in the Ayatollah Talleghani Hospital, Abadan, Iran. First, four feature selection algorithms, including Relief, Boruta, least absolute shrinkage and selection operator (LASSO), and minimum redundancy maximum relevance (mRMR) were used to select a set of relevant features that are very informative for five-year survival prediction in gastric cancer patients. Then, each feature set was fed to three classifiers: XG Boost (XGB), hist gradient boosting (HGB), and support vector machine (SVM) to develop predictive models. Finally, paired feature selection-classifier methods were evaluated to select the best-paired method using the area under the curve (AUC), accuracy, sensitivity, specificity, and f1-score metrics.

RESULTS

The LASSO feature selection algorithm combined with the XG Boost classifier achieved an accuracy of 89.10%, a specificity of 87.15%, a sensitivity of 89.42%, an AUC of 89.37%, and an f1-score of 90.8%. Tumor stage, history of other cancers, lymphatic invasion, tumor site, type of treatment, body weight, histological type, and addiction were identified as the most significant factors affecting gastric cancer survival.

CONCLUSIONS

This study proved the worth of the paired feature selection-classifier to identify the best path that could augment the five-year survival prediction in gastric cancer patients. Our results were better than those of previous studies, both in terms of the time required to form the models and the performance measurement criteria of the algorithms. These findings may be very promising and can, therefore, inform clinical decision-making and shed light on future studies.

The therapeutic potential value of Cancer-testis antigens in immunotherapy of gastric cancer

Gastric cancer (GC) is the fourth most common cause of mortality and the fifth for incidence, globally. Diagnosis, early prognosis, and therapy remains challenging for this condition, and new tumor-associated antigens are required for its detection and immunotherapy. Cancer-testis antigens (CTAs) are a subfamily of tumor-associated antigens (TAAs) that have been identified as potential biomarkers and targets for cancer immunotherapy. The CTAs-restricted expression pattern in tumor cells and their potential immunogenicity identify them as attractive target candidates in CTA-based diagnosis or prognosis or immunotherapy. To date, numerous studies have reported the dysregulation of CTAs in GC. Several clinical trials have been done to assess CTA-based immunotherapeutic potential in the treatment of GC patients. NY-ESO-1, MAGE, and KK-LC-1 have been used in GC clinical trials. We review recent studies that have investigated the potential of the CTAs in GC regarding the expression, function, aggressive phenotype, prognosis, and immunological responses as well as their possible clinical significance as immunotherapeutic targets with a focus on challenges and future interventions.

Design and Development of an Intelligent System for Predicting 5-Year Survival in Gastric Cancer

Background

Gastric cancer remains one of the leading causes of worldwide cancer-specific deaths. Accurately predicting the survival likelihood of gastric cancer patients can inform caregivers to boost patient prognostication and choose the best possible treatment path. This study intends to develop an intelligent system based on machine learning (ML) algorithms for predicting the 5-year survival status in gastric cancer patients.

Methods

A data set that includes the records of 974 gastric cancer patients retrospectively was used. First, the most important predictors were recognized using the Boruta feature selection algorithm. Five classifiers, including J48 decision tree (DT), support vector machine (SVM) with radial basic function (RBF) kernel, bootstrap aggregating (Bagging), hist gradient boosting (HGB), and adaptive boosting (AdaBoost), were trained for predicting gastric cancer survival. The performance of the used techniques was evaluated with specificity, sensitivity, likelihood ratio, and total accuracy. Finally, the system was developed according to the best model.

Results

The stage, position, and size of tumor were selected as the 3 top predictors for gastric cancer survival. Among the 6 selected ML algorithms, the HGB classifier with the mean accuracy, mean specificity, mean sensitivity, mean area under the curve, and mean F1-score of 88.37%, 86.24%, 89.72%, 88.11%, and 89.91%, respectively, gained the best performance.

Conclusions

The ML models can accurately predict the 5-year survival and potentially act as a customized recommender for decision-making in gastric cancer patients. The developed system in our study can improve the quality of treatment, patient safety, and survival rates; it may guide prescribing more personalized medicine.