DNA methylome and transcriptome landscapes of cancer-associated fibroblasts reveal a smoking-associated malignancy index.

Unlike the better-studied aberrant epigenome in the tumor, the clinicopathologic impact of DNA methylation in the tumor microenvironment (TME), especially the contribution from cancer-associated fibroblasts (CAFs), remains elusive. CAFs exhibit profound patient-to-patient tumorigenic heterogeneity. We asked whether such heterogeneity may be exploited to quantify the level of TME malignancy. We developed a robust and efficient methylome/transcriptome co-analytical system for CAFs and paired normal fibroblasts (NFs) from non-small-cell lung cancer patients. We found 14,781 CpG sites of CAF/NF differential methylation, of which 3,707 sites showed higher methylation changes in ever-smokers than in nonsmokers. Concomitant CAF/NF differential gene expression analysis pointed to a subset of 54 smoking-associated CpG sites with strong methylation-regulated gene expression. A methylation index that summarizes the ß values of these CpGs was built for NF/CAF discrimination (MIND) with high sensitivity and specificity. The potential of MIND in detecting premalignancy across individual patients was shown. MIND succeeded in predicting tumor recurrence in multiple lung cancer cohorts without reliance on patient survival data, suggesting that the malignancy level of TME may be effectively graded by this index. Precision TME grading may provide additional pathological information to guide cancer prognosis and open up more options in personalized medicine.

The M1/M2 spectrum and plasticity of malignant pleural effusion-macrophage in advanced lung cancer.

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-1ß and TGF-ß1, 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, ß-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.

Astaxanthin Counteracts Vascular Calcification In Vitro Through an Early Up-Regulation of SOD2 Based on a Transcriptomic Approach.

Vascular calcification (VC) is a critical contributor to the rising cardiovascular risk among at-risk populations such as those with diabetes or renal failure. The pathogenesis of VC involves an uprising of oxidative stress, for which antioxidants can be theoretically effective. However, astaxanthin, a potent antioxidant, has not been tested before for the purpose of managing VC. To answer this question, we tested the efficacy of astaxanthin against VC using the high phosphate (HP)-induced vascular smooth muscle cell (VSMC) calcification model. RNAs from treated groups underwent Affymetrix microarray screening, with intra-group consistency and inter-group differential expressions identified. Candidate hub genes were selected, followed by validation in experimental models and functional characterization. We showed that HP induced progressive calcification among treated VSMCs, while astaxanthin dose-responsively and time-dependently ameliorated calcification severities. Transcriptomic profiling revealed that 3491 genes exhibited significant early changes during VC progression, among which 26 potential hub genes were selected based on closeness ranking and biologic plausibility. SOD2 was validated in the VSMC model, shown to drive the deactivation of cellular senescence and enhance antioxidative defenses. Astaxanthin did not alter intracellular reactive oxygen species (ROS) levels without HP, but significantly lowered ROS production in HP-treated VSMCs. SOD2 knockdown prominently abolished the anti-calcification effect of astaxanthin on HP-treated VSMCs, lending support to our findings. In conclusion, we demonstrated for the first time that astaxanthin could be a potential candidate treatment for VC, through inducing the up-regulation of SOD2 early during calcification progression and potentially suppressing vascular senescence.

MicroRNA-125b in vascular diseases: An updated systematic review of pathogenetic implications and clinical applications.

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.

Risk Factors Associated With Altered Circulating Micro RNA -125b and Their Influences on Uremic Vascular Calcification Among Patients With End-Stage Renal Disease.

Background Micro RNA -125b (miR-125b) has been shown to regulate vascular calcification ( VC ), and serum miR-125b levels are a potential biomarker for estimating the risk of uremic VC status. However, it is unknown whether clinical features, including chronic kidney disease-mineral bone disorder molecules, affect serum miR-125b levels. Methods and Results Patients receiving chronic dialysis for ≥3 months were recruited from different institutes. Serum miR-125b and chronic kidney disease-mineral bone disorder effectors, including intact parathyroid hormone, 25- OH -D, fibroblast growth factor-23, osteoprotegerin, and fetuin-A, were quantified. We used multivariate regression analyses to identify factors associated with low serum miR-125b levels and an area under receiver operating characteristic curve curve to derive optimal cutoffs for factors exhibiting close associations. Further regression analyses evaluated the influence of miR-125b on VC risk. Among 223 patients receiving chronic dialysis (mean age, 67.3 years; mean years of dialysis, 5.2), 54 (24.2%) had high serum miR-125b levels. Osteoprotegerin ( P=0.013), fibroblast growth factor-23 ( P=0.006), and fetuin-A ( P=0.036) were linearly associated with serum miR-125b levels. High osteoprotegerin levels independently correlated with high serum miR-125 levels. Adding serum miR-125b levels and serum osteoprotegerin levels (≥400 pg/mL) into models estimating the risk of uremic VC increased the area under receiver operating characteristic curve values (for models without miR-125b/osteoprotegerin, with miR-125b, and both: 0.74, 0.79, and 0.81, respectively). Conclusions Serum osteoprotegerin levels ≥400 pg/mL and serum miR-125b levels synergistically increased the accuracy of estimating VC risk among patients receiving chronic dialysis. Taking miR-125b and osteoprotegerin levels into consideration when estimating VC risk may be recommended.

A brief survey on computational approaches to reveal drug and disease associations.

People worldwide are still threatened by various complex disease phenotypes, especially cancer which is usually caused by the accumulation of multi-factor-driven alterations. Although drugs achieve the therapeutic functions by targeting particular molecular, the therapies used nowadays against diseases are not effective enough due to the limitation of the knowledge about the drug-disease associations. The rapid increasing of the available experimental data and knowledge enable scientists to reveal drug-disease associations by the systematic integration and analysis. In this review, we show that several computational methods can help us to explain the underlying relationships between pharmacology and pathology. It is expected that newer computational methods will take advantage of heterogeneous and multi-dimensional data and increase the efficacy and safety of existing drugs for disease treatment.

Inferring drug-disease associations from integration of chemical, genomic and phenotype data using network propagation.

BACKGROUND: During 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: We 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.

Biomarker identification for prostate cancer and lymph node metastasis from microarray data and protein interaction network using gene prioritization method.

Finding a genetic disease-related gene is not a trivial task. Therefore, computational methods are needed to present clues to the biomedical community to explore genes that are more likely to be related to a specific disease as biomarker. We present biomarker identification problem using gene prioritization method called gene prioritization from microarray data based on shortest paths, extended with structural and biological properties and edge flux using voting scheme (GP-MIDAS-VXEF). The method is based on finding relevant interactions on protein interaction networks, then scoring the genes using shortest paths and topological analysis, integrating the results using a voting scheme and a biological boosting. We applied two experiments, one is prostate primary and normal samples and the other is prostate primary tumor with and without lymph nodes metastasis. We used 137 truly prostate cancer genes as benchmark. In the first experiment, GP-MIDAS-VXEF outperforms all the other state-of-the-art methods in the benchmark by retrieving the truest related genes from the candidate set in the top 50 scores found. We applied the same technique to infer the significant biomarkers in prostate cancer with lymph nodes metastasis which is not established well.

Pathway detection from protein interaction networks and gene expression data using color-coding methods and A∗ search algorithms.

With the large availability of protein interaction networks and microarray data supported, to identify the linear paths that have biological significance in search of a potential pathway is a challenge issue. We proposed a color-coding method based on the characteristics of biological network topology and applied heuristic search to speed up color-coding method. In the experiments, we tested our methods by applying to two datasets: yeast and human prostate cancer networks and gene expression data set. The comparisons of our method with other existing methods on known yeast MAPK pathways in terms of precision and recall show that we can find maximum number of the proteins and perform comparably well. On the other hand, our method is more efficient than previous ones and detects the paths of length 10 within 40 seconds using CPU Intel 1.73 GHz and 1 GB main memory running under windows operating system.

Identifying co-targets to fight drug resistance based on a random walk model.

BACKGROUND: Drug resistance has now posed more severe and emergent threats to human health and infectious disease treatment. However, wet-lab approaches alone to counter drug resistance have so far still achieved limited success due to less knowledge about the underlying mechanisms of drug resistance. Our approach apply a heuristic search algorithm in order to extract active network under drug treatment and use a random walk model to identify potential co-targets for effective antibacterial drugs. RESULTS: We use interactome network of Mycobacterium tuberculosis and gene expression data which are treated with two kinds of antibiotic, Isoniazid and Ethionamide as our test data. Our analysis shows that the active drug-treated networks are associated with the trigger of fatty acid metabolism and synthesis and nicotinamide adenine dinucleotide (NADH)-related processes and those results are consistent with the recent experimental findings. Efflux pumps processes appear to be the major mechanisms of resistance but SOS response is significantly up-regulation under Isoniazid treatment. We also successfully identify the potential co-targets with literature confirmed evidences which are related to the glycine-rich membrane, adenosine triphosphate energy and cell wall processes. CONCLUSIONS: With gene expression and interactome data supported, our study points out possible pathways leading to the emergence of drug resistance under drug treatment. We develop a computational workflow for giving new insights to bacterial drug resistance which can be gained by a systematic and global analysis of the bacterial regulation network. Our study also discovers the potential co-targets with good properties in biological and graph theory aspects to overcome the problem of drug resistance.

A network flow approach to predict drug targets from microarray data, disease genes and interactome network - case study on prostate cancer.

BACKGROUND: Systematic 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. METHODS: We 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. RESULTS: We 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. CONCLUSIONS: Our 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.

Identifying significant genetic regulatory networks in the prostate cancer from microarray data based on transcription factor analysis and conditional independency.

BACKGROUND: Prostate 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. RESULTS: To 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. CONCLUSIONS: We 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.