Molecular Mechanism of Macrophage Activation by Red Ginseng Acidic Polysaccharide from Korean Red Ginseng

Red ginseng acidic polysaccharide (RGAP), isolated from Korean red ginseng, displays immunostimulatory and antitumor activities. Even though numerous studies have been reported, the mechanism as to how RGAP is able to stimulate the immune response is not clear. In this study, we aimed to explore the mechanism of molecular activation of RGAP in macrophages. RGAP treatment strongly induced NO production in RAW264.7 cells without altering morphological changes, although the activity was not strong compared to LPS-induced dendritic-like morphology in RAW264.7 cells. RGAP-induced NO production was accompanied with enhanced mRNA levels of iNOS and increases in nuclear transcription factors such as NF-κB, AP-1, STAT-1, ATF-2, and CREB. According to pharmacological evaluation with specific enzyme inhibitors, Western blot analysis of intracellular signaling proteins and inhibitory pattern using blocking antibodies, ERK, and JNK were found to be the most important signaling enzymes compared to LPS signaling cascade. Further, TLR2 seems to be a target surface receptor of RGAP. Lastly, macrophages isolated from RGS2 knockout mice or wortmannin exposure strongly upregulated RGAP-treated NO production. Therefore, our results suggest that RGAP can activate macrophage function through activation of transcription factors such as NF-κB and AP-1 and their upstream signaling enzymes such as ERK and JNK

Anti-inflammatory activity of AP-SF, a ginsenoside-enriched fraction, from Korean ginseng

Background: Korean ginseng is an ethnopharmacologically valuable herbal plant with various biological properties including anticancer, antiatherosclerosis, antidiabetic, and anti-inflammatory activities. Since there is currently no drug or therapeutic remedy derived from Korean ginseng, we developed a ginsenoside-enriched fraction (AP-SF) for prevention of various inflammatory symptoms. Methods: The anti-inflammatory efficacy of AP-SF was tested under in vitro inflammatory conditions including nitric oxide (NO) production and inflammatory gene expression. The molecular events of inflammatory responses were explored by immunoblot analysis. Results: AP-SF led to a significant suppression of NO production compared with a conventional Korean ginseng saponin fraction, induced by both lipopolysaccharide and zymosan A. Interestingly, AP-SF strongly downregulated the mRNA levels of genes for inducible NO synthase, tumor necrosis factor-α, and cyclooxygenase) without affecting cell viability. In agreement with these observations, AP-SF blocked the nuclear translocation of c-Jun at 2 h and also reduced phosphorylation of p38, c-Jun N-terminal kinase, and TAK-1, all of which are important for c-Jun translocation. Conclusion: Our results suggest that AP-SF inhibits activation of c-Jun-dependent inflammatory events. Thus, AP-SF may be useful as a novel anti-inflammatory remedy

Prediction System for Prostate Cancer Recurrence Using Machine Learning

Prostate cancer is the fourth most common cancer affecting South Korean males, and the biochemical recurrence (BCR) of prostate cancer occurs in approximately 25% of patients five years after radical prostatectomy. The ability to predict BCR would help clinicians and patients to make better treatment decisions. Therefore, in this study, we have proposed a web-based clinical decision support system that predicts the BCR of prostate cancer in Korean patients. The data were obtained from the Korean Prostate Cancer Registry (KPCR) database, which contained information about 7394 patients with prostate cancer who were treated at one of the six major medical institutions in South Korea between May 2001 and December 2014. We tested 13 prediction models and selected the gradient boosting classifier because it demonstrated excellent prediction performance. Using this model, we were able to create a web application and once clinical data from patients were entered, the three- and five-year post-surgery BCR predictions could be extracted. We developed a clinical decision support system to provide a prostate cancer BCR predictive function to facilitate postoperative follow-up and clinical management. This system will help clinicians develop a strategic approach for prostate cancer treatment by predicting the likelihood of prostate cancer recurrence

Transglutaminase 2 as an independent prognostic marker for survival of patients with non-adenocarcinoma subtype of non-small cell lung cancer

Abstract Background Expression of transglutaminase 2 (TGase 2) is related to invasion and resistance to chemotherapeutic agents in several cancer cells. However, there has been only limited clinical validation of TGase 2 as an independent prognostic marker in cancer. Methods The significance of TGase 2 expression as an invasive/migratory factor was addressed by in vitro assays employing down-regulation of TGase 2. TGase 2 expression as a prognostic indicator was assessed in 429 Korean patients with early-stage non-small cell lung cancer (NSCLC) by immunohistochemical staining. Results TGase 2 expression increased the invasive and migratory properties of NSCLC cells in vitro, which might be related to the induction of MMP-9. In the analysis of the immunohistochemical staining, TGase 2 expression in tumors was significantly correlated with recurrence in NSCLC (p = 0.005) or in the non-adenocarcinoma subtype (p = 0.031). Additionally, a multivariate analysis also showed a significant correlation between strong TGase 2 expression and shorter disease-free survival (DFS) in NSCLC (p = 0.029 and HR = 1.554) and in the non-adenocarcinoma subtype (p = 0.030 and HR = 2.184). However, the correlation in the adenocarcinoma subtype was not significant. Conclusions TGase 2 expression was significantly correlated with recurrence and shorter DFS in NSCLC, especially in the non-adenocarcinoma subtype including squamous cell carcinoma.</p

A Deep Belief Network and Dempster-Shafer-Based Multiclassifier for the Pathology Stage of Prostate Cancer

Object. Pathologic prediction of prostate cancer can be made by predicting the patient’s prostate metastasis prior to surgery based on biopsy information. Because biopsy variables associated with pathology have uncertainty regarding individual patient differences, a method for classification according to these variables is needed. Method. We propose a deep belief network and Dempster-Shafer- (DBN-DS-) based multiclassifier for the pathologic prediction of prostate cancer. The DBN-DS learns prostate-specific antigen (PSA), Gleason score, and clinical T stage variable information using three DBNs. Uncertainty regarding the predicted output was removed from the DBN and combined with information from DS to make a correct decision. Result. The new method was validated on pathology data from 6342 patients with prostate cancer. The pathology stages consisted of organ-confined disease (OCD; 3892 patients) and non-organ-confined disease (NOCD; 2453 patients). The results showed that the accuracy of the proposed DBN-DS was 81.27%, which is higher than the 64.14% of the Partin table. Conclusion. The proposed DBN-DS is more effective than other methods in predicting pathology stage. The performance is high because of the linear combination using the results of pathology-related features. The proposed method may be effective in decision support for prostate cancer treatment