Kaempferol, which is one of the general flavonoids, has recently been reported to suppress proliferation, induce cell cycle arrest and promote apoptosis in various human cancer cell lines. In the present study, the effect and mechanism of kaempferol on gastric cancer (GC) was examined. The results showed that kaempferol significantly inhibited the proliferation of MKN28 and SGC7901 cell lines. However, no significant inhibition in the GSE-1 normal gastric epithelial cell line in our experimental dose was detected. Additionally, significant apoptosis and G2/M phase cell cycle arrest were identified following the treatment of kaempferol. More importantly, we observed that kaempferol inhibited the growth of the tumor xenografts although no marked effects on liver, spleen or body weight were induced. The expression levels of G2/M cell cycle‑regulating factors, cyclin B1, Cdk1 and Cdc25C, were significantly reduced. In addition, kaempferol treatment markedly decreased the level of Bcl-2 concomitant with an increase in Bax expression, resulting in the upregulation of cleaved caspase-3 and -9, which promoted PARP cleavage. Kaempferol-treated cells also led to a decrease in p-Akt, p-ERK and COX-2 expression levels. The present study therefore provided evidence that kaempferol may be a therapeutic agent for GC.
In this paper, we apply discrete-event system techniques to model and analyze the execution of concurrent software. The problem of interest is deadlock avoidance in shared-memory multithreaded programs. We employ Petri nets to systematically model multithreaded programs with lock acquisition and release operations. We define a new class of Petri nets, called Gadara nets, that arises from this modeling process. We investigate a set of important properties of Gadara nets, such as liveness, reversibility, and linear separability. We propose efficient algorithms for the verification of liveness of Gadara nets, and report experimental results on their performance. We also present modeling examples of real-world programs. The results in this paper lay the foundations for the development of effective control synthesis algorithms for Gadara nets.
Gastric cancer (GC) is one of the most common malignant diseases worldwide. Although significant progress has been made in the early detection and treatment of GC over the past decades, the prognosis is still not satisfactory and the underlying mechanisms of carcinogenesis remain unknown. Long non-coding RNA MIAT has been established as a key player in the regulation of various biological and pathological processes including chronic lymphocytic leukemias, acute myocardial infarction and neuroendocrine prostate cancer. However, the function of MIAT in GC remains largely unknown. The expressions of lncRNA MIAT, miR-29a-3p and HDAC4 mRNA were analysed using quantitative real-time PCR (qRT-PCR). RNA interference approach was used to investigate the cellular functions of MIAT and miR-29a-3p. Cell Counting Kit-8 (CCK-8) assay and flow cytometry assay were performed to detect cell proliferation and apoptosis. Cell migration and invasion abilities were evaluated by Transwell assays. In the present study, we first confirmed the high expression level of MIAT in GC tissues and cell lines. In addition, knockdown of MIAT suppressed the proliferation, migration and invasion of GC cells in vitro. Furthermore, our results demonstrated that MIAT competitively binds to miR-29a-3p and consequently upregulates the expression of HDAC4, which is a downstream target of miR-29a-3p. In conclusion, the present study highlighted the involvement of the MIAT/miR-29a-3p/HDAC4 axis in the development of GC, which provided potential diagnostic and therapeutic targets for GC.
Cell lines are widely used as in vitro models of tumorigenesis. However, an increasing number of researchers have found that cell lines differ from their sourced tumour samples after long-term cell culture. The application of unsuitable cell lines in experiments will affect the experimental accuracy and the treatment of patients. Therefore, it is imperative to identify optimal cell lines for each cancer type. Here, we review the methods used to evaluate cell lines since 2005. Furthermore, gene expression, copy number and mutation profiles from The Cancer Genome Atlas and the Cancer Cell Line Encyclopedia are used to calculate similarity between tumours and cell lines. Then, the ideal cell lines to use for experiments for eight types of cancers are found by combining the results with Gene Ontology functional similarity. After verification, the optimal cell lines have the same genomic characteristics as their homologous tumour samples. The contaminated cell lines identified in previous research are also determined to be unsuitable in vitro cancer models here. Moreover, our study suggests that some of the commonly used cell lines are not suitable cancer models. In summary, we provide a reference for ideal cell lines to use in in vitro experiments and contribute to improving the accuracy of future cancer research. Furthermore, this research provides a foundation for identifying more effective treatment strategies.
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