(1) Background: Gastric cancer (GC) is the fourth leading cause of cancer death worldwide. Silver nanoparticles (Ag-NPs) have been increasingly used in the diagnosis and treatment of cancer due to their physicochemical properties. This study investigated the role of a kind of biosynthetic silver nanoparticle (b-Ag) in the development of GC, the enhancement of 5-fluorouracil (5F), and its mechanism. (2) Methods: X-ray, transmission electron microscopy (TEM), and UV absorbance were used to detect the characterizations of AgNPs. CCK8, Colony formation and a Transwell assay were performed to confirm the malignant behaviors of GC. DCFH-DA and DHE were used to detect intracellular reactive oxygen species (ROS). Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to detect the mRNA expression of apoptosis-related genes. (3) Results: Compared with the chemosynthetic silver nanoparticles (c-Ag), b-Ag had a stronger cytokilling effect, and it had a better inhibition on the malignant phenotype of GC when combined with 5F. The b-Ag increased the expression of Bax and P53 while decreasing the expression of Bcl2. It also promoted the generation of intracellular ROS. (4) Conclusions: By promoting cell apoptosis and increasing intracellular ROS, b-Ag inhibited the development of GC and enhanced the inhibition of 5F on GC.

Gastric cancer (GC) is among the most lethal tumors worldwide. Long noncoding RNAs (lncRNAs) are reported to be critical during the occurrence and progression of malignancies. The HOXC cluster antisense RNA 1 (HOXC-AS1) has been suggested to participate in the genesis and development of GC. Therefore, we examined GC cells and tissues for the expression of HOXC-AS1 and correlated the expression levels with the disease specific survival of the patients, finding that HOXC-AS1 was overexpressed and probably had a tendency of leading to a poor prognosis. The Cell Counting Kit-8 assay and colony formation assay were then performed under knockdown of HOXC-AS1, revealing that cell proliferation of GC was distinctly decreased. Afterwards, miR-99a-3p was predicted to bind with HOXC-AS1 by DIANA tools. We carried out dual-luciferase reporter gene assays to identify the interaction between them. After knockdown of HOXC-AS1, miR-99a-3p was clearly overexpressed in GC cells. In addition, matrix metalloproteinase 8 (MMP8) was shown to be combined with miR-99a-3p using TargetScan. Similar experiments, along with western blot, were conducted to validate the correlation between miR-99a-3p and MMP8. Finally, rescue experiments for CCK-8 were completed, disclosing that HOXC-AS1 promoted cell progression of GC through sponging miR-99a-3p followed by subsequent upregulation of MMP8.

Multidrug resistance is a primary factor in the poor response to chemotherapy and subsequent death in gastric cancer patients. However, the molecular mechanisms involved remain unclear. In this study, the high expression of special AT‐rich sequence binding protein 1 (SATB1) in gastric cancer was found to be associated with reduced sensitivity to various chemotherapy drugs. Our results demonstrate that SATB1 can promote chemotherapy resistance in gastric cancer in vitro and in vivo. SATB1 exerts its effect by enhancing the activity of multiple ATP‐binding cassette (ABC) transporters (P‐glycoprotein, multidrug resistance‐associated protein, and breast cancer resistance protein) in gastric cancer cell lines. We also found that SATB1 affects ABC transporters by altering the subcellular localization of the ABC transporter rather than its expression. Subsequently, we confirmed that Ezrin binds to various ABC transporters and affects their subcellular localization. In addition, we found that SATB1 can also bind to the Ezrin promoter and regulate its expression. In the present study, we elucidate the mechanism of SATB1‐mediated multidrug resistance in gastric cancer, providing a basis for SATB1 as a potential target for reversal of resistance.

Previous studies on forecasting Value-at-Risk mostly focus on using the information directly obtained from the market itself. In this paper, we propose a new method for predicting Value-at-Risk by incorporating both internal and external information using artificial neural network. We show that artificial neural network can provide us a flexible framework for including external information, and these additional factors can help to improve the accuracy of Value-at-Risk prediction.