Landscape of Druggable Molecular Pathways Downstream of Genomic CDH1/Cadherin-1 Alterations in Gastric Cancer

Identifying natural products for gastric cancer treatment through pharmacophore creation, 3D QSAR, virtual screening, and molecular dynamics studies

BACKGROUND

Gastric cancer (GC) is known as the fourth leading cause of cancer-related death and the fifth major cancer in the world, and this is a serious threat to general health all over the world. The lack of early detection markers results in a belated diagnosis, i.e. the final stages, which could be associated with the ineffectiveness of the treatment strategies, and naturally, it leads to poor prognosis. Even though a variety of treatments have been developed, there is a trend of studying traditional medicinal plants, due to the worrying side effect of drugs available in the market.

METHODS

In this study, pharmacophore generation and 3D-QSAR model were created using 50 compounds with anti-gastric cancer activity (with IC50 had been reported in the previous studies).

RESULTS

Based on three of the best pharmacophoric hypotheses, virtual screening was performed to discover the top anti-gastric cancer compounds from a database of 183,885 compounds. The selected compounds were used for molecular docking with three protein receptors 7BKG, 4F5B, and 4ZT1 to investigate the intermolecular interactions between these ligands and receptors. Finally, 21 lead compounds with the highest amount of docking score ranging from - 13.366 to -6.404 kcal/mol were selected, and then the ADME/Tox properties of these compounds were calculated. All these compounds have a fitness score above 1.8, a molecular weight of less than 500 g/mol, hydrogen bond donors up to 3, hydrogen bond acceptors up to 8.50, and logP of 1.013 to 4.174. Finally, molecular dynamic simulations for top-scoring ligand-receptor complexes were investigated.

CONCLUSION

These selected lead compounds have the most anti-gastric cancer effects among the 183,885 compounds in the database. Therefore, lead compounds might be considered for gastric cancer therapy in future studies.

Pioneering use of genetic analysis for CDH1 to identify candidates for prophylactic total gastrectomy to prevent hereditary diffuse gastric cancer

Worldwide, gastric cancer results in significant morbidity and mortality. Ten per cent of patients with gastric cancer have a strong family history of the disease. CDH1 (E-cadherin) has been identified as a key gene whose mutation leads to hereditary diffuse gastric cancer. We overviewed 33 articles with prophylactic total gastrectomy and assessed the outcomes and benefits. Families with mutations in CDH1 may benefit from early prophylactic total gastrectomy. Dr Mark Duncan has applied his experience as a high-volume gastric cancer surgeon to treat not only individual patients, but several generations of patients within a family. This use of prophylactic total gastrectomy is well tolerated by patients and prevents the future development of gastric cancer.

Integrative analysis of DNA methylation and gene expression through machine learning identifies stomach cancer diagnostic and prognostic biomarkers

DNA methylation is an early event in tumorigenesis. Here, by integrative analysis of DNA methylation and gene expression and utilizing machine learning approaches, we introduced potential diagnostic and prognostic methylation signatures for stomach cancer. Differentially-methylated positions (DMPs) and differentially-expressed genes (DEGs) were identified using The Cancer Genome Atlas (TCGA) stomach adenocarcinoma (STAD) data. A total of 256 DMPs consisting of 140 and 116 hyper- and hypomethylated positions were identified between 443 tumour and 27 nontumour STAD samples. Gene expression analysis revealed a total of 2821 DEGs with 1247 upregulated and 1574 downregulated genes. By analysing the impact of cis and trans regulation of methylation on gene expression, a dominant negative correlation between methylation and expression was observed, while for trans regulation, in hypermethylated and hypomethylated genes, there was mainly a negative and positive correlation with gene expression, respectively. To find diagnostic biomarkers, we used 28 hypermethylated probes locating in the promoter of 27 downregulated genes. By implementing a feature selection approach, eight probes were selected and then used to build a support vector machine diagnostic model, which had an area under the curve of 0.99 and 0.97 in the training and validation (GSE30601 with 203 tumour and 94 nontumour samples) cohorts, respectively. Using 412 TCGA-STAD samples with both methylation and clinical data, we also identified four prognostic probes by implementing univariate and multivariate Cox regression analysis. In summary, our study introduced potential diagnostic and prognostic biomarkers for STAD, which demands further validation.