Breast cancer is one of the most common malignancies. However, the molecular mechanisms underlying its pathogenesis remain to be elucidated. The present study aimed to identify the potential prognostic marker genes associated with the progression of breast cancer. Weighted gene coexpression network analysis was used to construct free-scale gene coexpression networks, evaluate the associations between the gene sets and clinical features, and identify candidate biomarkers. The gene expression profiles of GSE48213 were selected from the Gene Expression Omnibus database. RNA-seq data and clinical information on breast cancer from The Cancer Genome Atlas were used for validation. Four modules were identified from the gene coexpression network, one of which was found to be significantly associated with patient survival time. The expression status of 28 genes formed the black module (basal); 18 genes, dark red module (claudin-low); nine genes, brown module (luminal), and seven genes, midnight blue module (nonmalignant). These modules were clustered into two groups according to significant difference in survival time between the groups. Therefore, based on betweenness centrality, we identified TXN and ANXA2 in the nonmalignant module, TPM4 and LOXL2 in the luminal module, TPRN and ADCY6 in the claudin-low module, and TUBA1C and CMIP in the basal module as the genes with the highest betweenness, suggesting that they play a central role in information transfer in the network. In the present study, eight candidate biomarkers were identified for further basic and advanced understanding of the molecular pathogenesis of breast cancer by using co-expression network analysis.

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Nonalcoholic fatty liver disease (NAFLD) is a very common liver disease, and its incidence has significantly increased worldwide. The liver X receptor α (LXRα) is a multifunctional nuclear receptor that controls lipid homeostasis. Inhibition of LXRα transactivation may be beneficial for NAFLD and hyperlipidemia treatment. Ursolic acid (UA) is a plant triterpenoid with many beneficial effects; however, the mechanism of its action on LXRα remains elusive. We evaluated the effects of UA on T0901317 (T090)-induced LXRα activation and steatosis. UA significantly decreased the LXR response element and sterol regulatory element-binding protein-1c (SREBP-1c) gene promoter activities, mRNA, protein expression of LXRα target genes, and hepatic cellular lipid content in a T090-induced mouse model. A molecular docking study indicated that UA bound competitively with T090 at the LXRα ligand binding domain. UA stimulated AMP-activated protein kinase (AMPK) phosphorylation in hepatic cells and increased corepressor, small heterodimer partner-interacting leucine zipper protein (SMILE) but decreased coactivator, steroid receptor coactivator-1 (SRC-1) recruitment to the SREBP-1c promoter region. In contrast, UA induced SRC-1 binding but decreased SMILE binding to reverse cholesterol transport-related gene promoters in intestinal cells, increasing lipid excretion from intestinal cells. Additionally, UA reduced valproate-induced LXRα mediated and rifampin-induced pregnane X receptor mediated lipogenesis, offering potential treatments for drug-induced hepatic steatosis. Thus, UA displays liver specificity and can be selectively repressed while RCT stimulation by LXRα is preserved and enhanced. This is a novel therapeutic option to treat NAFLD and may be helpful in developing LXR agonists to prevent atherosclerosis.

BackgroundSeveral dynamic models of a gene regulatory network of the light-induced floral transition process in Arabidopsis have been developed to capture the behavior of gene transcription and infer predictions based on experimental observations. It has been proven that the models can make accurate and novel predictions, which generate testable hypotheses.Two major issues were addressed in this study. First, construction of dynamic models for gene regulatory networks requires the use of mathematic modeling that comprises equations of a large number of parameters. Second, the binding mechanism of the transcription factor with DNA is another factor that requires detailed modeling. The first issue was tackled by adopting an optimization algorithm, and the second was addressed by comparing the performance of three alternative modeling approaches, namely the S-system, the Michaelis-Menten model and the Mass-action model. The efficiencies of parameter estimation and modeling performance were calculated based on least square error (O(p)), mean relative error (MRE) and Akaike Information Criterion (AIC).ResultsWe compared three models to describe gene regulation of the flowering transition process in Arabidopsis. The Mass-action model is the simplest and has the least parameters. It is therefore less computation-intensive with the smallest AIC value. The disadvantage, however, is that it assumes the system is simply a second order reaction which is not the case in our study. The Michaelis-Menten model also assumes the system is homogeneous and ignores the intracellular protein transport process. The S-system model has the best performance and it does describe the diffusion effects. A disadvantage of the S-system is that it involves the most parameters. The largest AIC value also implies an over-fitting may occur in parameter estimation.ConclusionsThree dynamic models were adopted to describe the dynamics of the gene regulatory network of the flowering transition process in Arabidopsis. Based on MRE, the least square error and global sensitivity analysis, the S-system has the best performance. However, the fact that it has the highest AIC suggests an over-fitting may occur in parameter estimation. The result of this study may need to be applied carefully when modeling complex gene regulatory networks.

To characterize the association between epilepsy, use of antiepileptic drugs (AEDs), and the risk of hyperlipidemia, we conducted a nationwide population-based cohort study with data obtained from the National Health Insurance Research Database of Taiwan. The effects of AEDs on lipogenic gene expression were also examined in vitro. We identified 3617 cases involving patients, whose epilepsy was newly diagnosed between 2000 and 2011, and selected a comparison cohort comprising 14,468 patients without epilepsy. The Cox proportional hazards model was used to evaluate the association between epilepsy, AED use, and hyperlipidemia. The incidence rate of hyperlipidemia was higher in the epilepsy cohort than in the comparison cohort, with an adjusted hazard ratio (aHR) of 1.21 [95% confidence interval (CI): 1.06-1.38] after adjusting for comorbidities and medications. Epilepsy patients not taking AEDs had a higher risk of hyperlipidemia (aHR 1.65; 95% CI 1.35-2.03). Among AEDs, only valproate treatment showed a higher risk of hyperlipidemia (aHR 1.53; 95% CI 1.01-2.33), although the dose-dependent effect did not reach statistical significance. In vitro studies with two hepatic cell lines showed that valproate may exert its effects by activating the liver X receptor alpha (LXRα) signaling pathway, inducing the expression of lipogenesis-related genes and increasing cellular lipid contents. In silico calculations concluded that valproate can bind stably with the ligand-binding domain of LXRα. Thus, valproate-induced hepatic lipogenic gene expression may occur through LXRα activation. Predicting the 'off-target' effects of valproate may prove valuable in developing antiepileptic agents with fewer adverse reactions. Monitoring blood lipid levels throughout the course of treatment is recommended.

P-glycoprotein (P-gp) effluxes lots of chemotherapeutic agents and leads to multidrug resistance (MDR) in cancer treatments. The development of P-gp inhibitors from natural products provide a potential strategy for the beneficial clinical outcomes. This study aimed to evaluate the effects of the natural flavonoid taxifolin, luteolin, (−)-gallocatechin, and (−)-catechin on human P-gp activity. The kinetic interactions and underlying mechanisms of taxifolin-mediated transporter inhibition were further investigated. The transporter inhibition ability was evaluated in human P-gp stable expression cells (ABCB1/Flp-InTM-293) by calcein-AM uptake assays. The kinetics study for P-gp inhibition was evaluated by doxorubicin and rhodamine123 efflux assays. The MDR reversal ability of taxifolin were performed by SRB assays to detect the cell viability in sensitive cancer cell line (HeLaS3), and resistant cancer cell line (KB-vin). Cell cycle analysis and ABCB1 real-time RT-PCR were used for mechanical exploration. The results demonstrated that taxifolin decreased ABCB1 expression in a concentration-dependent manner. The function of P-gp was inhibited by taxifolin through uncompetitive inhibition of rhodamine 123 and doxorubicin efflux. The combination of taxifolin significantly resensitized MDR cancer cells to chemotherapeutic agents. These results suggested that taxifolin may be considered as a potential P-gp modulator for synergistic treatment of MDR cancers.

Multidrug resistance (MDR) is a complicated ever-changing problem in cancer treatment, and P-glycoprotein (P-gp), a drug efflux pump, is regarded as the major cause. In the way of developing P-gp inhibitors, natural products such as phenolic acids have gotten a lot of attention recently. The aim of the present study was to investigate the modulating effects and mechanisms of caffeic acid on human P-gp, as well as the attenuating ability on cancer MDR. Calcein-AM, rhodamine123, and doxorubicin were used to analyze the interaction between caffeic acid and P-gp, and the ATPase activity of P-gp was evaluated as well. Resistance reversing effects were revealed by SRB and cell cycle assay. The results indicated that caffeic acid uncompetitively inhibited rhodamine123 efflux and competitively inhibited doxorubicin efflux. In terms of P-gp ATPase activity, caffeic acid exhibited stimulation in both basal and verapamil-stimulated activity. The combination of chemo drugs and caffeic acid resulted in decreased IC50 in ABCB1/Flp-InTM-293 and KB/VIN, indicating that the resistance was reversed. Results of molecular docking suggested that caffeic acid bound to P-gp through GLU74 and TRY117 residues. The present study demonstrated that caffeic acid is a promising candidate for P-gp inhibition and cancer MDR attenuation.

Background: Urothelial cancer (UC) includes carcinomas of the bladder, ureters, and renal pelvis. New treatments and biomarkers of UC emerged in this decade. To identify the key information in a vast amount of literature can be challenging. In this study, we use text mining to explore UC publications to identify important information that may lead to new research directions. Method: We used topic modeling to analyze the titles and abstracts of 29,883 articles of UC from Pubmed, Web of Science, and Embase in Mar 2020. We applied latent Dirichlet allocation modeling to extract 15 topics and conducted trend analysis. Gene ontology term enrichment analysis and Kyoto encyclopedia of genes and genomes pathway analysis were performed to identify UC related pathways. Results: There was a growing trend regarding UC treatment especially immune checkpoint therapy but not the staging of UC. The risk factors of UC carried in different countries such as cigarette smoking in the United State and aristolochic acid in Taiwan and China. GMCSF, IL-5, Syndecan-1, ErbB receptor, integrin, c-Met, and TRAIL signaling pathways are the most relevant biological pathway associated with UC. Conclusions: The risk factors of UC may be dependent on the countries and GMCSF, IL-5, Syndecan-1, ErbB receptor, integrin, c-Met, and TRAIL signaling pathways are the most relevant biological pathway associated with UC. These findings may provide further UC research directions.