The results suggest that serum miR-125b levels are associated with VC severity and serve as a novel predictive marker for the risk of uremia-associated calcification progression.

BackgroundSystematic approach for drug discovery is an emerging discipline in systems biology research area. It aims at integrating interaction data and experimental data to elucidate diseases and also raises new issues in drug discovery for cancer treatment. However, drug target discovery is still at a trial-and-error experimental stage and it is a challenging task to develop a prediction model that can systematically detect possible drug targets to deal with complex diseases.MethodsWe integrate gene expression, disease genes and interaction networks to identify the effective drug targets which have a strong influence on disease genes using network flow approach. In the experiments, we adopt the microarray dataset containing 62 prostate cancer samples and 41 normal samples, 108 known prostate cancer genes and 322 approved drug targets treated in human extracted from DrugBank database to be candidate proteins as our test data. Using our method, we prioritize the candidate proteins and validate them to the known prostate cancer drug targets.ResultsWe successfully identify potential drug targets which are strongly related to the well known drugs for prostate cancer treatment and also discover more potential drug targets which raise the attention to biologists at present. We denote that it is hard to discover drug targets based only on differential expression changes due to the fact that those genes used to be drug targets may not always have significant expression changes. Comparing to previous methods that depend on the network topology attributes, they turn out that the genes having potential as drug targets are weakly correlated to critical points in a network. In comparison with previous methods, our results have highest mean average precision and also rank the position of the truly drug targets higher. It thereby verifies the effectiveness of our method.ConclusionsOur method does not know the real ideal routes in the disease network but it tries to find the feasible flow to give a strong influence to the disease genes through possible paths. We successfully formulate the identification of drug target prediction as a maximum flow problem on biological networks and discover potential drug targets in an accurate manner.

Vascular calcification (VC) is highly prevalent in patients with advanced age, or those with chronic kidney disease and diabetes, accounting for substantial global cardiovascular burden. The pathophysiology of VC involves active mineral deposition by transdifferentiated vascular smooth muscle cells exhibiting osteoblast-like behavior, building upon cores with or without apoptotic bodies. Oxidative stress drives the progression of the cellular phenotypic switch and calcium deposition in the vascular wall. In this review, we discuss potential compounds that shield these cells from the detrimental influences of reactive oxygen species as promising treatment options for VC. A comprehensive summary of the current literature regarding antioxidants for VC is important, as no effective therapy is currently available for this disease. We systematically searched through the existing literature to identify original articles investigating traditional antioxidants and novel compounds with antioxidant properties with regard to their effectiveness against VC in experimental or clinical settings. We uncovered 36 compounds with antioxidant properties against VC pathology, involving mechanisms such as suppression of NADPH oxidase, BMP-2, and Wnt/β-catenin; anti-inflammation; and activation of Nrf2 pathways. Only two compounds have been tested clinically. These findings suggest that a considerable opportunity exists to harness these antioxidants for therapeutic use for VC. In order to achieve this goal, more translational studies are needed.

BackgroundProstate cancer is a world wide leading cancer and it is characterized by its aggressive metastasis. According to the clinical heterogeneity, prostate cancer displays different stages and grades related to the aggressive metastasis disease. Although numerous studies used microarray analysis and traditional clustering method to identify the individual genes during the disease processes, the important gene regulations remain unclear. We present a computational method for inferring genetic regulatory networks from micorarray data automatically with transcription factor analysis and conditional independence testing to explore the potential significant gene regulatory networks that are correlated with cancer, tumor grade and stage in the prostate cancer.ResultsTo deal with missing values in microarray data, we used a K-nearest-neighbors (KNN) algorithm to determine the precise expression values. We applied web services technology to wrap the bioinformatics toolkits and databases to automatically extract the promoter regions of DNA sequences and predicted the transcription factors that regulate the gene expressions. We adopt the microarray datasets consists of 62 primary tumors, 41 normal prostate tissues from Stanford Microarray Database (SMD) as a target dataset to evaluate our method. The predicted results showed that the possible biomarker genes related to cancer and denoted the androgen functions and processes may be in the development of the prostate cancer and promote the cell death in cell cycle. Our predicted results showed that sub-networks of genes SREBF1, STAT6 and PBX1 are strongly related to a high extent while ETS transcription factors ELK1, JUN and EGR2 are related to a low extent. Gene SLC22A3 may explain clinically the differentiation associated with the high grade cancer compared with low grade cancer. Enhancer of Zeste Homolg 2 (EZH2) regulated by RUNX1 and STAT3 is correlated to the pathological stage.ConclusionsWe provide a computational framework to reconstruct the genetic regulatory network from the microarray data using biological knowledge and constraint-based inferences. Our method is helpful in verifying possible interaction relations in gene regulatory networks and filtering out incorrect relations inferred by imperfect methods. We predicted not only individual gene related to cancer but also discovered significant gene regulation networks. Our method is also validated in several enriched published papers and databases and the significant gene regulatory networks perform critical biological functions and processes including cell adhesion molecules, androgen and estrogen metabolism, smooth muscle contraction, and GO-annotated processes. Those significant gene regulations and the critical concept of tumor progression are useful to understand cancer biology and disease treatment.

Epigenetic changes, particularly non‐coding RNAs, have been implicated extensively in the pathogenesis of vascular diseases. Specific miRNAs are involved in the differentiation, phenotypic switch, proliferation, apoptosis, cytokine production and matrix deposition of endothelial cells and/or vascular smooth muscle cells. MicroRNA‐125b has been studied in depth for its role in carcinogenesis with a double‐edged role; that is, it can act as an oncogene in some cancer types and as a tumour suppressor gene in others. However, cumulative evidence from the use of advanced miRNA profiling techniques and bioinformatics analysis suggests that miR‐125b can be a potential mediator and useful marker of vascular diseases. Currently, the exact role of miR‐125b in vascular diseases is not known. In this systematic review, we intend to provide an updated compilation of all the recent findings of miR‐125b in vascular diseases, using a systematic approach of retrieving data from all available reports followed by data summarization. MiR‐125b serves as a pathogenic player in multiple vascular pathologies involving endothelia and vascular smooth muscle cells and also serves as a diagnostic marker for vascular diseases. We further provide a computational biologic presentation of the complex network of miR‐125b and its target genes within the scope of vascular diseases.

BackgroundDuring the last few years, the knowledge of drug, disease phenotype and protein has been rapidly accumulated and more and more scientists have been drawn the attention to inferring drug-disease associations by computational method. Development of an integrated approach for systematic discovering drug-disease associations by those informational data is an important issue.MethodsWe combine three different networks of drug, genomic and disease phenotype and assign the weights to the edges from available experimental data and knowledge. Given a specific disease, we use our network propagation approach to infer the drug-disease associations.ResultsWe apply prostate cancer and colorectal cancer as our test data. We use the manually curated drug-disease associations from comparative toxicogenomics database to be our benchmark. The ranked results show that our proposed method obtains higher specificity and sensitivity and clearly outperforms previous methods. Our result also show that our method with off-targets information gets higher performance than that with only primary drug targets in both test data.ConclusionsWe clearly demonstrate the feasibility and benefits of using network-based analyses of chemical, genomic and phenotype data to reveal drug-disease associations. The potential associations inferred by our method provide new perspectives for toxicogenomics and drug reposition evaluation.

Vascular calcification (VC) describes the pathophysiological phenotype of calcium apatite deposition within the vascular wall, leading to vascular stiffening and the loss of compliance. VC is never benign; the presence and severity of VC correlate closely with the risk of myocardial events and cardiovascular mortality in multiple at-risk populations such as patients with diabetes and chronic kidney disease. Mitochondrial dysfunction involving each of vascular wall constituents (endothelia and vascular smooth muscle cells (VSMCs)) aggravates various vascular pathologies, including atherosclerosis and VC. However, few studies address the pathogenic role of mitochondrial dysfunction during the course of VC, and mitochondrial reactive oxygen species (ROS) seem to lie in the pathophysiologic epicenter. Superoxide dismutase 2 (SOD2), through its preferential localization to the mitochondria, stands at the forefront against mitochondrial ROS in VSMCs and thus potentially modifies the probability of VC initiation or progression. In this review, we will provide a literature-based summary regarding the relationship between SOD2 and VC in the context of VSMCs. Apart from the conventional wisdom of attenuating mitochondrial ROS, SOD2 has been found to affect mitophagy and the formation of the autophagosome, suppress JAK/STAT as well as PI3K/Akt signaling, and retard vascular senescence, all of which underlie the beneficial influences on VC exerted by SOD2. More importantly, we outline the therapeutic potential of a novel SOD2-targeted strategy for the treatment of VC, including an ever-expanding list of pharmaceuticals and natural compounds. It is expected that VSMC SOD2 will become an important druggable target for treating VC in the future.

Background Malignant pleural effusion (MPE)-macrophage (Mφ) of lung cancer patients within unique M1/M2 spectrum showed plasticity in M1–M2 transition. The M1/M2 features of MPE-Mφ and their significance to patient outcomes need to be clarified; furthermore, whether M1-repolarization could benefit treatment remains unclear. Methods Total 147 stage-IV lung adenocarcinoma patients undergoing MPE drainage were enrolled for profiling and validation of their M1/M2 spectrum. In addition, the MPE-Mφ signature on overall patient survival was analyzed. The impact of the M1-polarization strategy of patient-derived MPE-Mφ on anti-cancer activity was examined. Results We found that MPE-Mφ expressed both traditional M1 (HLA-DRA) and M2 (CD163) markers and showed a wide range of M1/M2 spectrum. Most of the MPE-Mφ displayed diverse PD-L1 expression patterns, while the low PD-L1 expression group was correlated with higher levels of IL-10. Among these markers, we identified a novel two-gene MPE-Mφ signature, IL-1β and TGF-β1, representing the M1/M2 tendency, which showed a strong predictive power in patient outcomes in our MPE-Mφ patient cohort (N = 60, p = 0.013) and The Cancer Genome Atlas Lung Adenocarcinoma dataset (N = 478, p < 0.0001). Significantly, β-glucan worked synergistically with IFN-γ to reverse the risk signature by repolarizing the MPE-Mφ toward the M1 pattern, enhancing anti-cancer activity. Conclusions We identified MPE-Mφ on the M1/M2 spectrum and plasticity and described a two-gene M1/M2 signature that could predict the outcome of late-stage lung cancer patients. In addition, we found that “re-education” of these MPE-Mφ toward anti-cancer M1 macrophages using clinically applicable strategies may overcome tumor immune escape and benefit anti-cancer therapies.