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Aim: The 20(S)-ginsenoside Rh2 (Rh2) is being developed as a new antitumor drug. However, to date, little is known about the kinetics of its deglycosylation metabolite (protopanoxadiol) (PPD) following Rh2 administration. The aim of this work was to 1) simultaneously characterise the pharmacokinetics of Rh2 and PPD following intravenous and oral Rh2 administration, 2) develop and validate a mechanism-based pharmacokinetic model to describe the deglycosylation kinetics and 3) predict the percentage of Rh2 entering the systemic circulation in PPD form.Methods: Plasma samples were collected from rats after the I.V. or P.O. administration of Rh2. The plasma Rh2 and PPD concentrations were determined using HPLC-MS. The transformation from Rh2 to PPD, its absorption, and elimination were integrated into the mechanism based pharmacokinetic model to describe the pharmacokinetics of Rh2 and PPD simultaneously at 10 mg/kg. The concentration data collected following a 20 mg/kg dose of Rh2 was used for model validation.Results: Following Rh2 administration, PPD exhibited high exposure and atypical double peaks. The model described the abnormal kinetics well and was further validated using external data. A total of 11% of the administered Rh2 was predicted to be transformed into PPD and enter the systemic circulation after I.V. administration, and a total of 20% of Rh2 was predicted to be absorbed into the systemic circulation in PPD form after P.O. administration of Rh2.Conclusion: The developed model provides a useful tool to quantitatively study the deglycosylation kinetics of Rh2 and thus, provides a valuable resource for future pharmacokinetic studies of glycosides with similar deglycosylation metabolism.

Purpose: This study aims to reveal the relationship between RNA N6-methyladenosine (m6A) regulators and tumor immune microenvironment (TME) in breast cancer, and to establish a risk model for predicting the occurrence and development of tumors.Patients and methods: In the present study, we respectively downloaded the transcriptome dataset of breast cancer from Gene Expression Omnibus (GEO) database and The Cancer Genome Atlas (TCGA) database to analyze the mutation characteristics of m6A regulators and their expression profile in different clinicopathological groups. Then we used the weighted correlation network analysis (WGCNA), the least absolute shrinkage and selection operator (LASSO), and cox regression to construct a risk prediction model based on m6A-associated hub genes. In addition, Immune infiltration analysis and gene set enrichment analysis (GSEA) was used to evaluate the immune cell context and the enriched gene sets among the subgroups.Results: Compared with adjacent normal tissue, differentially expressed 24 m6A regulators were identified in breast cancer. According to the expression features of m6A regulators above, we established two subgroups of breast cancer, which were also surprisingly distinguished by the feature of the immune microenvironment. The Model based on modification patterns of m6A regulators could predict the patient’s T stage and evaluate their prognosis. Besides, the low m6aRiskscore group presents an immune-activated phenotype as well as a lower tumor mutation load, and its 5-years survival rate was 90.5%, while that of the high m6ariskscore group was only 74.1%. Finally, the cohort confirmed that age (p < 0.001) and m6aRiskscore (p < 0.001) are both risk factors for breast cancer in the multivariate regression.Conclusion: The m6A regulators play an important role in the regulation of breast tumor immune microenvironment and is helpful to provide guidance for clinical immunotherapy.

Background: The prolongation or shortening of heart rate-corrected QT (QTc) predisposes patients to fatal ventricular arrhythmias and sudden cardiac death (SCD), but the association of dynamic change of QTc interval with mortality in the general population remains unclear.Methods: A total of 11,798 middle-aged subjects from the prospective, population-based cohort were included in this analysis. The QTc interval corrected for heart rate was measured on two occasions around 3 years apart in the Atherosclerosis Risk in Communities (ARIC) study. The ΔQTc interval was calculated by evaluating a change in QTc interval from visit 1 to visit 2.Results: After a median follow-up of 19.5 years, the association between the dynamic change of QTc interval and endpoints of death was U-shaped. The multivariate-adjusted hazard ratios (HRs) comparing subjects above the 95th percentile of Framingham–corrected ΔQTc (ΔQTcF) (≥32 ms) with subjects in the middle quintile (0–8 ms) were 2.69 (95% CI, 1.68–4.30) for SCD, 2.51 (1.68–3.74) for coronary heart disease death, 2.10 (1.50–2.94) for cardiovascular death, and 1.30 (1.11–1.55) for death from any cause. The corresponding HRs comparing subjects with a ΔQTcF below the fifth percentile (

A growing amount of evidence has indicated immune genes perform a crucial position in the development and progression of breast cancer microenvironment. The purpose of our study was to identify immunogenic prognostic marker and explore potential regulatory mechanisms for breast cancer. We identified the genes related to ImmuneScore using ESTIMATE algorithm and WGCNA analysis, and we identified the differentially expressed gene (DEGs). Then, Glia maturation factor γ (GMFG) was determined as a predictive factor by intersecting immune-related genes with DEGs and survival analysis. We found the expression of GMFG was lower in breast cancer tissues compared with normal breast tissues, which was further verified by immunohistochemical (IHC). Moreover, the decreased expression of GMFG was significantly related to the poor prognosis. Besides, the expression of GMFG was related to the age, ER status, PR status, HER2 status and tumor size, which further suggested that the expression of GMFG was correlated with the subtype and the growth of tumor. The univariate and multivariate Cox regression analyses revealed that age, stage, the expression level of GMFG and radiotherapy were independent factors for predicting the prognosis of breast cancer patients. Subsequently, a prognostic model to predict the 3-year, 5-year and 10-year overall survival rate was developed based on the above four variables, and visualized as a nomogram. The values of area under the curve of the nomogram at 3-year, 5-year and 10-year were 0.897, 0.873 and 0.922, respectively, which was higher than stage in prognostic accuracy. In addition, we also found that GMFG expression level was correlated with sensitivity of some breast cancer chemotherapy drugs. Furthermore, the results of GSEA indicated immune-related pathways were mainly enriched in GMFG-high-expression group. CIBERSORT analysis for the proportion of tumor-infiltrating immune cells (TIICs) suggested that expression of GMFG was positively association with multiple kinds T-cell in BC. Among them, CD8+ T cells had the strongest correlation with GMFG expression, which revealed that GMFG might has an antitumor effect by increasing the infiltration of CD8+ T cells in breast cancer. Accordingly, GMFG has the potential to become a novel immune biomarker for the diagnosis and treatment of breast cancer.

ObjectivesRecently, an increasing number of studies have revealed that N6-methyladenosine (m6A) functions as a significant post-transcriptional modification which plays a critical role in the occurrence and progression of enriched tumors by regulating coding and non-coding RNA biogenesis. However, the biological function of m6A in breast cancer remains largely unclear.Materials and MethodsIn this study, we used a series of bioinformatic databases and tools to jointly analyze the expression of m6A methylation transferases (METTL3, METTL14, WTAP, RBM15, RBM15B and ZC3H13) and investigate the prognostic value of METTL14 and ZC3H13 in breast cancer. Besides, we analyzed the downstream carcinogenic molecular mechanisms related to METTL14 and ZC3H13 and their relationship with immune infiltration in breast tumor tissues.ResultsThe results showed that METTL14 and ZC3H13 were the down-regulated m6A methylation transferases in breast cancer. Survival outcome analysis suggested that abnormally low expression of METTL14 and ZC3H13 could predict unfavorable prognosis in four breast cancer subtypes. Moreover, their down-regulation was associated with ER-, PR- and triple-negative breast cancer patients, as well as tumor progression (increased Scarff, Bloom and Richardson grade status and Nottingham Prognostic Index classification). Co-expression analysis revealed that METTL14 and ZC3H13 had a strong positive correlation with APC, an antagonist of the Wnt signaling pathway, indicating they might cooperate in regulating proliferation, invasion, and metastasis of tumor cells. METTL14, ZC3H13, and APC expression levels had significant positive correlation with infiltrating levels of CD4+ T cells, CD8+ T cells, neutrophils, macrophages, and dendritic cells, and negative correlation with Treg cells in breast cancer.ConclusionsThis study demonstrated that down-regulation of METTL14 and ZC3H13 which act as two tumor suppressor genes was found in breast cancer and predicted poor prognosis. Their abnormal expression promoted breast cancer invasion by affecting pathways related to tumor progression and mediating immunosuppression.

ObjectiveMany primary tumors have insufficient supply of molecular oxygen, called hypoxia. Hypoxia is one of the leading characteristics of solid tumors resulting in a higher risk of local failure and distant metastasis. It is quite necessary to investigate the hypoxia associated molecular hallmarks in breast cancer.Materials and MethodsAccording to the published studies, we selected 13 hypoxia related gene expression signature to define the hypoxia status of breast cancer using ConsensusClusterPlus package based on the data from The Cancer Genome Atlas (TCGA). Subsequently, we characterized the infiltration of 24 immune cell types under different hypoxic conditions. Furthermore, the differentially expressed hypoxia associated microRNAs, mRNAs and related signaling pathways were analyzed and depicted. On this basis, a series of prognostic markers related to hypoxia were identified and ceRNA co-expression networks were constructed.ResultsTwo subgroups (cluster1 and cluster2) were identified and the 13 hypoxia related gene signature were all up-regulated in cluster1. Thus, we defined the cluster1 as “hypoxic subgroup” compared with cluster2. The infiltration of CD8+ T cell and CD4+ T cell were lower in cluster1 while the nTreg cell and iTreg cell were higher, indicating that there was immunosuppressive status in cluster1. We observed widespread hypoxia-associated dysregulation of microRNAs and mRNAs. Next, a risk signature for predicting prognosis of breast cancer patients was established based on 12 dysregulated hypoxia associated prognostic genes. Two microRNAs, hsa-miR-210-3p and hsa-miR-190b, with the most significant absolute logFC value were related to unfavorable and better prognosis, respectively. Several long non-coding RNAs were predicted to be microRNA targets and positively correlated with two selected mRNAs, CPEB2 and BCL11A. Predictions based on the LINC00899/PSMG3-AS1/PAXIP1-AS1- hsa-miR-210-3p-CPEB2 and SNHG16- hsa-miR-190b-BCL11A ceRNA regulation networks indicated that the two genes might act as tumor suppressor and oncogene, respectively.ConclusionHypoxia plays an important role in the initiation and progression of breast cancer. Our research provides potential mechanisms into molecular-level understanding of tumor hypoxia.