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Startseite Expert Systems with Applications Characterizing Basal-like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining..
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Journal Pre-proof Characterizing Basal-like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach Amir Zadeh Associate Professor , Qamar Alsabi , Jaime E. Ramirez-Vick Professor , Nasim Nosoudi Assistant Professor PII: DOI: Reference: S0957-4174(20)30078-6 https://doi.org/10.1016/j.eswa.2020.113253 ESWA 113253 To appear in: Expert Systems With Applications Received date: Revised date: Accepted date: 7 August 2019 24 November 2019 29 January 2020 Please cite this article as: Amir Zadeh Associate Professor , Qamar Alsabi , Jaime E. Ramirez-Vick Professor , Nasim Nosoudi Assistant Professor , Characterizing Basal-like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach, Expert Systems With Applications (2020), doi: https://doi.org/10.1016/j.eswa.2020.113253 This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd. Highlights Data mining algorithms are explored to classify BLBC Basal-like breast cancers Several ensembles of classifiers and features are proposed and evaluated Several genes are identified in association with BLBC breast cancer Identification of gene signatures can lead to new pathways to develop BLBC treatments Predictive models gained prediction accuracy from a feature selection ensemble Characterizing Basal-like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach Amir Zadeh Associate Professor, Department of Information Systems and Supp; ly Chain Management Raj Soin College of Business Wright State University Rike Hall 251, 3640 Colonel Glenn Hwy, Dayton, OH 45435 USA Voice: +1-937-775-2895 Email: firstname.lastname@example.com Web: https://people.wright.edu/amir.zadeh Qamar Alsabi Department of Biomedical Engineering College of Engineering Wright State University Dayton OH 45435, USA Russ Engineering Center 207, 3640 Colonel Glenn Hwy, Dayton, OH 45435 USA Voice: +1-937-775-5044 Email: email@example.com Jaime E. Ramirez-Vick Professor, Department of Biomedical Engineering and Human Factor College of Engineering Wright State University Russ Engineering Center 207, 3640 Colonel Glenn Hwy, Dayton, OH 45435 USA Voice: +1-937-775-5044 Email: email@example.com Nasim Nosoudi Assistant Professor, Department of Biomedical Engineering College of Information Technology and Engineering Marshall University Weisberg Family Applied Engineering Complex, Huntington, WV 25755-2586 USA Voice: +1-304-696-3207 Email: firstname.lastname@example.com Characterizing Basal-like Triple Negative Breast Cancer using Gene Expression Analysis: A Data Mining Approach Abstract Triple-negative breast cancer (TNBC) is characterized by the absence of expression of the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 (HER2). Therefore, TNBC is unresponsive to targeted hormonal therapies, which limits treatment options to nonselective chemotherapeutic agents. Basal-like breast cancers (BLBCs) represent a subset of about 70% of TNBCs, more frequently affecting younger patients, being more prevalent in African-American women and significantly more aggressive than tumors of other molecular subtypes, with high rates of proliferation and extremely poor clinical outcomes. Proper classification of BLBCs using current pathological tools has been a major challenge. Although TNBCs have many BLBC characteristics, the relationship between clinically defined TNBC and the gene expression profile of BLBC is not fully examined. The purpose of this study is to assemble publicly-available TNBC gene expression datasets generated by Affymetrix gene chips and define a set of genes, or gene signature, that can classify TNBC samples between BLBC and Non-BLBC subtypes. We compiled over 3,500 breast cancer gene expression profiles from several individual publicly available datasets and extracted Affymetrix gene expression data for 580 TNBC cases. Several popular data mining methods along with dimensionality reduction and feature selection techniques were applied to the resultant dataset to build predictive models to understand molecular characteristics and mechanisms associated with BLBCs and to classify them more accurately according to important features extracted through microarray data analysis of BLBC and NonBLBC cases. Our result can lead to proper identification and diagnosis of BLBCs, which can potentially direct clinical implications by dictating the most effective therapy. Keywords: gene expression, basal-like breast cancer, triple-negative breast cancer, data mining 1. Introduction The triple-negative breast cancer (TNBC) subtype is defined as one which has the absence of the progesterone receptor (PR), estrogen receptor (ER) and human epidermal growth factor receptor (HER2). TNBC constitutes approximately 20%-25% of all breast cancer cases with poor prognosis (Bauer et al., 2007), tending to be more aggressive than other breast cancer types. Due to the lack of ER, PR, and HER2, treatment options, such as hormone therapies, anti-HER2 targeted therapies, endocrine therapy, and trastuzumab, are not effective, leaving a very limited set of alternatives. The TNBC subtype achieves 19% clinical-complete-response for chemotherapy (Choi et al., 2012). This type of breast cancer is more common among African-American women than among women of other ethnicities. For instance, African-American premenopausal women represent 39% of TNBC cases, while only 16% in Caucasian women (Carey et al., 2006). Nearly two decades ago, a microarray analysis technology was used to propose five subtypes of breast cancer based on the gene expression and molecular feature. Although TNBC has many basal-like breast cancer characteristics, the relationship between clinically defined triple-negative breast cancer and the gene expression profile of basal-like breast cancer (BLBC) is not fully examined. This type of cancer is oftentimes defined as ER negative, PR negative, cytokeratin 5/6 positive and/or HER2 positive (Boyle, 2012). According to immunohistochemical and molecular profiling studies, only a subset of TNBC expresses the combination of basal cytokeratin markers (CK5/6, CK14, CK 17, and EGFR). Another study reported that 6 of 31 (19.4%) triple-negative breast tumors were classified as non-basal-like cancer, while 15 out of 207 (6.3%) non-triple-negative tumors have basal cytokeratin markers (Tan et al., 2008). Moreover, 69.7% of TNBC classified as BLBC group (Rody et al., 2011). However, there is still no accepted definition for basal-like breast cancer. Previous studies have used microarraybased expression profiling to define BLBCs, whereas other studies have used panels of immunohistochemical surrogates which proposed a definition of BLBC as: (1) absence of ER, PR, and HER2 expression 'triple-negative'; (2) one or more expression of high-molecularweight/Basal cytokeratin (CK5/6,CK14, and CK17); (3) absence of ER and HER2 in conjunction with CK5/6 and or EGFR); (4) absence of ER, PR, and HER2 expression in conjunction with CK5/6 and/or EGFR (Cheang et al., 2008). Yet, the therapeutic implications of molecular heterogeneity and the prognostic significance within BLBC cancer remain to be established. Further studies are needed for understanding TNBC, BLBC, and Non-BLBC to get better prognostic markers and new therapy. Recent technological advances allow for high throughput profiling of biological systems at the molecular level in a cost-efficient manner. The relatively low cost of data generation is leading us to the "Big Data Era". Today big data can be created out of small data and the combination of datasets from various sources is a major aspect of "big data". The availability of such large datasets provides unprecedented opportunities for data mining, deep learning, and integrative analysis over various layers of data which set the goal to link all the molecular information and translate it back into meaningful information in precision medicine, systems biology, molecular physiology or pathophysiology. Translational modeling is not new to cancer research. Predictive modeling has been applied in clinical domains and into a wide variety of problems in breast cancer such as diagnosis (Akay, 2009; Devarriya et al., 2020; Onan, 2015; Ting et al., 2019), survivability (Delen et al., 2005; J. Kim & Shin, 2013; Pendharkar et al., 1999; Thongkam et al., 2009), prognosis (A. H. Chen & Yang, 2012; Gevaert et al., 2006; H. Lu et al., 2019), susceptibility (Ayer et al., 2010; Listgarten et al., 2004; Sutton & Patkar, 2009), and recurrence (W. Kim et al., 2012; Park et al., 2014). However, the extent to which microarray data can improve the diagnosis of BLBC cancer has not been fully examined. The purpose of this study is to assemble publicly available TNBC gene expression datasets generated on Affymetrix gene chips and define a set of genes, or gene signature, that can classify those belonging to BLBC and Non-BLBC subtypes under TNBC type. Several popular data mining, along with variable reduction and feature selection, techniques are applied to the resultant datasets to build predictive models to understand molecular characteristics and mechanisms associated with BLBCs and classify them more accurately according to important features extracted from Microarray data analysis of BLBC and Non-BLBC cases. Our result can lead to proper identification and diagnosis of BLBCs, which can potentially direct clinical implications by dictating the most effective therapy. 2. Research Methodology It is widely known among data scientists that big data is composed of not only a large volume of data but also from several different sources, in various formats, from which greater insights can be gleaned. Therefore, a considerable amount of time and effort needs to be devoted on data management. Moreover, recent development in high performance analytical methods has improved our ability to extract meaningful insights from high dimensional data, which can be investigated using statistical analysis. In this section, we discuss how these important tasks are accomplished. We describe the data and research methodology used in this study in the following subsections. Our research methodology consists of four major phases: data acquisition, data integration, data preprocessing, and predictive modeling. The analytical methodology is depicted in Figure 1. DB2 DBn-1 DBn Data Integration Combined TNBC DB Data Preprocessing Cleaning Dimension Reduction Variable Selection Decision Tree LARS/LASSO Principal Component Analysis Data Preprocessing Feature Selections Chi-Square Data Acquisition DB1 Predictive Models Tabulated Relative Variable Importance Predictive Modeling Tabulated Model Performance Results (Accuracy, Misclassification, Specificity, Sensitivity) (Variable/Gene Profiling, Gene Markers) Figure 1. Research Methodology. 2.1. Data Acquisition and Integration Microarray data generation is a very expensive process; therefore, collecting large data microarray is challenging and requires a substantial amount of resources. A pool of several datasets from different laboratories was used to perform analysis on a large sample size for this study (Rody et al., 2011). We integrated multiple public datasets that were built according to the most widely microarray platform (Affymetrix U133A and U133 Plus 2.0 chips) and included only cases that were defined as triple negative based on the mRNA expression of ER, PgR, and HER2 as previously described (Sizemore et al., 2014). We compiled a total of 3,488 publicly available breast cancer gene expression profiles from 28 individual datasets and extracted Affymetrix gene expression data for 579 TNBC cases. 2.2. Data Preprocessing Data preprocessing is a critical step in data mining, which involves data cleaning, variable reduction and feature selection. It involves cleaning the data from missing, out of range, or invalid values. It also allows for a better understanding of what the data represents, to explore variable statistics and distributions, to perform appropriate transformations, and to reduce the data, among others. Although data preprocessing is time consuming, it is an important step in ensuring the accuracy of the results. Data preprocessing takes up to 80% of the overall time of data mining. With microarray data being high dimensional, characterized by many variables and few observations, it requires feature selection and dimension reduction techniques to remove genes that do not provide significant incremental information. In this study, we observed five missing genes in some of the expression datasets, so we excluded those genes from the analysis. Moreover, we applied various feature selection methods, such as chi-square, decision tree, LARS/LASSO, principle component analysis (PCA) and ensemble (multi-method) algorithms, to identify key variables (genes) that could explain the differences in the observations and could be used to simplify the analysis and prediction of BLBCs. It is recognized that different feature selection techniques may result in different sets of biomarkers, that is, different groups of genes highly correlated to a given condition; however, together, these results can be used to identify driving pathways in basal-like breast cancer. 2.3. Predictive Models In this study, we used five different feature selection methods (i.e., Chi-square, tree, LARS, LASSO and ensemble), along with eight predictive models (i.e., Logistic Regression, Decision tree, Random Forest, Support Vector Machine, Neural Networks, LARS, Gradient Boosting, and Bayesian Classifier) in an empirical investigation to understand, characterize and predict BLBCs. The data was divided into 70% for training and 30% for validation. We performed supervised analysis to define a set of gene markers that distinguished molecular subsets within TNBCs. The 574 cases were divided into 394 for discovery and 185 for validation. The initial step was to build stratified datasets for this analysis. The second step involved applying various popular data mining techniques, including Decision trees, Regression analysis, Random Forest, Neural Network, Least Angle Regression, Bayesian Classifier, Gradient Boosting, and Support Vector Machine, to classify BLBC and non-BLBC cases and to identify structures in the molecular data of the targeted disease. Of all the cases evaluated, 394 were used for training and 185 for validation. More than 22,000 genes expression data were correlated with survival using multivariate analysis, including pathological and routine clinical data. Those metagenes included the basal-like phenotype, apocrine/androgen and claudin-low molecular subtypes, or reflected various non-neoplastic cell populations, including blood, stroma, immune cells, adipocytes, inflammation and angiogenesis within the cancer. In summary, 40 different predictive models were built to identify gene signatures and determine which genes contribute most to BLBC. 2.3.1. Artificial Neural Network Artificial Neural Networks (ANNs) are known as "massively parallel processors, which tend to preserve experimental knowledge and enable their further use" (Hájek, 2011). This model is created based on learning the processes of the neurological function of the brain and the cognitive system (Paliwal & Kumar, 2009). ANNs can provide a large complex of non-linear functions and predict new observations. The advantage of the parallel computing environment of ANNs allows users to improve the predictive power of the algorithm. Applying neural networks enables users to build models with significantly more lift, by allowing more runs to incrementally enhance its predictive power. Additional features of this model include smart defaults for most neural network parameters, automatic selection of a validation data group, and automatic standardization of input data and targeted variables. 2.3.2. Logistic Regression Logistic Regression is a statistical method used to analyze a dataset and to classify cases into the most likely category based on one or more independent variables. In this study, linear regression could not be applied since the response variable is discrete. Therefore, the regression will predict the odds of its occurrence into two categories instead of predicting the estimation point. Some of the advantages of using Logistic Regression include simplicity, interpretability and efficiency. 2.3.3. Decision Tree Decision tree is a classification algorithm which starts with a single node, which then branches into possible class predictions. The size of the decision tree and classification accuracy are used to determine the quality of the predictive model. Decision tree uses mathematical algorithms to search and identify a useful variable and its corresponding threshold, which branches the input data into two or more subgroups. The process is repeated at each node until the tree is fully constructed. The split search algorithm uses corresponding threshold to maximize the homogeneity of the outcome subgroups. The most common mathematical algorithm used to split the observations into multiple classes are entropy-based information gain, Gini index, and Chi-square test. Finally, the complexity of the tree is optimized via pruning between training and validation sets. Advantages of Decision trees include ease of deployment, interpretation and visualization. 2.3.4. Random Forest Random Forest is a collection of multiple Decision trees. It is a supervised learning algorithm that draws random samples from the training dataset to grow the trees of the forest to the largest extent possible. Trees are trained in parallel and no pruning is carried out to reduce the size of the trees. In order to categorize new data, it is first inputted to each of the trees to generate classifications or votes based on the selected variables. The Random Forest then chooses the final classification outcome based on the votes scored among all the trees. Some of the advantages of using Random Forest includes high accuracy, ability to handle large volumes of multidimensional data, and effective imputation of missing data among others (Zolbanin et al., 2015). 2.3.5. Support Vector Machine Support Vector Machine (SVM) is a class of machine learning algorithms that enables users to fit a discriminant function of features such as polynomial and sigmoid nonlinear kernels to separate one class from another. The nonlinear kernel transforms the input data to a high dimensional space such that the data space become separable using two parallel separating hyperplanes. The distance between the parallel hyperplanes is maximized to the extent in which an optimized classification model can be realized. An SVM model can also be used for outlier detection and regression. Some of the SVM's advantages include effective handling of unbalanced data and less complexity compared to other classifiers such as ANNs(Delen, 2009). Previous research has reported that SVM can provide diagnosis ability with high accuracy in cancer prediction (Huang et al., 2008; Zheng et al., 2014). 2.3.6. Least Angle Regression Least Angle Regression (LARS) belongs to a family of generalized linear models designed to handle high-dimensional data. The algorithm uses a forward stepwise selection method to identify the optimal variable set; however, instead of adding variables at each step based on some prespecified criteria such as adjusted R2 or Akaike, it selects the variable that is most correlated with the target variable and then increases the estimated parameters in the least-squares direction until another variable has as much correlation with the target as the current one has. This selection process is repeated until none remain to be chosen. Some major advantages of LARS method are its abilities to handle high dimensional multicollinear data and identify the best set of variables. 2.3.7. Bayesian Classifier Bayesian Classifiers are a family of simple probabilistic models that rely on Bayes' theorem to make class predictions given some data. In Bayesian machine learning, the input variables are assumed to be independent from each other. To classify a new observation, it simply estimates the probability that the given data point falls in a certain class and at the end, chooses the classification that has the highest probability. Some of the advantages of using a Bayesian Classifier model includes handling continuous and discrete data, making probabilistic predictions, and requiring less training data. 2.3.8. Gradient Boosting Gradient Boosting is a supervised machine learning algorithm for classification and regression applications. It is an ensemble of many prediction models using decision trees. Unlike Random Forest that uses random samples to build independent trees in parallel, Gradient Boosting builds trees one at a time in a sequential manner such that each tree is dependent on the residuals of the previous one. Gradient Boosting first draws a random sample (with replacement) from the original data, trains a decision tree, and tests its performance on the entire data. Then, the next random sample is drawn from the original dataset, which includes data points that were misclassified with the previous tree, and used that to build the second tree, and so on. This process is repeated until the error function does not change. While Gradient Boosting provides a very high predictive accuracy, it is less interpretable and prone to overfitting due to its greater flexibility in fitting data. 2.3.9. Measures for Performance Evaluation There are three common performance measures used in binary classification models. The first is accuracy, which determines the overall classification performance of the model. The second is sensitivity, which measures the proportion of actual positives that are correctly identified. The third is specificity, which measures the proportion of actual negatives that are correctly classified. These performance metrics can be obtained mathematically by the following expressions: 𝐴𝑐𝑐𝑢𝑟𝑎𝑐𝑦 = 𝑇𝑃 + 𝑇𝑁 𝑇𝑃 + 𝑇𝑁 + 𝐹𝑃 + 𝐹𝑁 𝑆𝑒𝑛𝑠𝑖𝑡𝑖𝑣𝑖𝑡𝑦 = 𝑇𝑃 𝑇𝑃 + 𝐹𝑁 𝑆𝑝𝑒𝑐𝑖𝑓𝑖𝑐𝑖𝑡𝑦 = 𝑇𝑁 𝑇𝑁 + 𝐹𝑃 TP, TN, FP, and FN represent True Positive, True Negative, False Positive, and False Negative, respectively. Either misclassification or accuracy rate can be used to evaluate the overall performance of prediction models. 3. Results A summary of the model's performances on 22,000 gens of 579 TNBC cases used in this study is shown in Table 1 The table includes the misclassification, accuracy, sensitivity, and specificity rates for each classifier. According to the result, the neural network model shows the highest average accuracy compared to other methods. The Gradient Boosting and Logistics Regression models have very close values of accuracy, while the Decision tree has the lowest average accuracy. Table 1. Models' performance. Feature Selection method: Chi-Square Misclassification Accuracy Sensitivity Method 0.0455 0.9545 0.9922 Decision Tree 0.0511 0.9489 0.9766 LARS 0.0455 0.9545 0.9688 Neural Network 0.0398 0.9602 0.9688 Logistics Regression 0.0230 0.9770 0.9921 SVM 0.0341 0.9659 0.9922 Gradient Boosting 0.0341 0.9659 0.9922 Random Forrest 0.0520 0.9480 0.9680 Bayesian Classifier Feature Selection method: Decision Tree Misclassification Accuracy Sensitivity Method 0.0517 0.9483 0.9921 Decision Tree 0.0345 0.9655 0.9764 LARS 0.0556 0.9444 0.9398 Neural Network 0.0287 0.9713 0.9843 Logistics Regression 0.0230 0.9770 0.9921 SVM 0.0230 0.9770 0.9843 Gradient Boosting 0.0402 0.9598 0.9921 Random Forrest 0.0575 0.9425 0.9528 Bayesian Classifier Feature Selection method: LARS/LASSO Misclassification Accuracy Sensitivity Method 0.0625 0.9375 0.9922 Decision Tree 0.0511 0.9489 0.9766 LARS 0.0118 0.9882 0.9918 Neural Network 0.0345 0.9655 0.9764 Logistics Regression 0.0455 0.9545 0.9844 SVM 0.0172 0.9828 0.9921 Gradient Boosting 0.0455 0.9545 0.9844 Random Forrest 0.0625 0.9375 0.9531 Bayesian Classifier Feature Selection method: Principal Component Analysis Misclassification Accuracy Sensitivity Method 0.1207 0.8793 0.9606 Decision Tree Specificity 0.8542 0.8750 0.9167 0.9375 0.9362 0.8958 0.8958 0.8958 Specificity 0.8298 0.9362 0.9574 0.9362 0.9362 0.9574 0.8723 0.9149 Specificity 0.7917 0.8750 0.9792 0.9362 0.8750 0.9574 0.8750 0.8958 Specificity 0.6596 0.0460 LARS 0.0345 Neural Network 0.0402 Logistics Regression 0.0517 SVM 0.0805 Gradient Boosting 0.0707 Random Forrest 0.1034 Bayesian Classifier Feature Selection method: Multi-method Misclassification Method 0.0862 Decision Tree 0.0230 LARS 0.0172 Neural Network 0.0287 Logistics Regression 0.0287 SVM 0.0172 Gradient Boosting 0.0230 Random Forrest 0.0230 Bayesian Classifier 0.9540 0.9764 0.8936 0.9655 0.9764 0.9362 0.9598 0.9764 0.9149 0.9483 0.9843 0.8511 0.9195 0.9685 0.7872 0.9293 0.9781 0.7872 0.8966 0.9370 0.7872 Accuracy Sensitivity Specificity 0.9138 0.9606 0.7872 0.9770 0.9921 0.9362 0.9828 0.9843 0.9787 0.9713 0.9843 0.9362 0.9713 0.9843 0.9362 0.9828 0.9921 0.9574 0.9770 0.9921 0.9362 0.9770 0.9843 0.9574 The results indicate that most predictive models gained prediction accuracy from a multi-method feature selection approach, compared to each individual approach. We found no evidence that a certain feature selection method is particularly well suited for use in combination with a specific predictive model. However, Decision tree, Gradient Boosting, Random Forest, and Bayesian Classifier did not gain much prediction accuracy from one principal component compared to another. The feature selection analysis revealed over 500 genes, which appear to be associated with BLBC. Table 2 summarizes the top 40 genes from pathways, which are associated with BLBC. These genes are sorted according to the number of times they were selected by the feature selection algorithms as input for predictive models. Table 2. Top 40 Genes Associated to BLBC. Gene Gene Description _205044_at Gamma-aminobutyric acid (GABA), A receptor, pi(GABRP) _220425_x_at Rhophilin associated tail protein 1B (ROPN1B) Count 14 14 _204855_at _213260_at _205157_s_at _209800_at _202037_s_at _206560_s_at _209387_s_at _219768_at _209504_s_at _211682_x_at _212236_x_at _60474_at _201820_at _208998_at _209126_x_at _210473_s_at _202036_s_at _202504_at _204751_x_at _213680_at _217901_at _218868_at _200832_s_at _201485_s_at _202342_s_at _203058_s_at _204268_at _205265_s_at _207397_s_at _208063_s_at _209351_at _209791_at _209842_at _210074_at _212147_at _214598_at _219301_s_at _219795_at Serpin peptidase inhibitor, clade B (ovalbumin), member 5 (SERPINB5) Forkhead box C1 (FOXC1) Keratin 17, type I (KRT17) keratin 16, type I (KRT16) Secreted frizzled-related protein 1 (SFRP1) Melanoma inhibitory activity (MIA) Transmembrane 4 L six family member 1 (TM4SF1) V-set domain containing T cell activation inhibitor 1 (VTCN1) Pleckstrin homology domain containing, family B (evectins) member 1 (PLEKHB1) UDP glucuronosyltransferase 2 family, polypeptide B28 (UGT2B28) Junction plakoglobin (JUP) Fermitin family member 1 (FERMT1) Keratin 5, type II (KER5) Uncoupling protein 2 (mitochondrial, proton carrier) (UCP2) Keratin 6B, type II (KRT6B) Adhesion G protein-coupled receptor A3 (ADGRA3) Secreted frizzled-related protein 1 (SFRP1) Tripartite motif-containing 29 (TRIM 29) Desmocollin-2 (DSC2) Keratin 6B, type II (KRT6B) Desmoglein 2 (DSG2) ARP3 actin-related protein 3 homolog B (yeast) (ACTR3B) Stearoyl-CoA desaturase (delta-9-desaturase) (SCD) Reticulocalbin 2, EF-hand calcium binding domain (RCN2) Tripartite motif containing 2 (TRIM 2) 3'-phosphoadenosine 5'-phosphosulfate synthase 2 (PAPSS2) S100 calcium binding protein A2 (S100A2) SPEG complex locus (SPEG) Homeobox D13 (HOXD13) Calpain 9 (CAPN9) Keratin 14, type I (KRT14) Peptidyl arginine deiminase, type II (PADI2) SRY (sex determining region Y)-box 10 (SOX10) Cathepsin V (CTSV) Nonsense mediated mRNA decay factor (SMGS) Claudin 8 (CLDN8) Contactin associated protein-like 2 (CNTNAP2) Solute carrier family 6 (amino acid transporter), member 14 (SLC6A14) 11 11 10 10 9 9 9 8 7 7 7 7 6 6 6 6 5 5 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 Figure 2 The neural network architecture Neural Network (NNs) is considered to be one of the powerful methods to analyze the data with high accuracy. In this study, one of the NN models identified 12 important genes that have correlation with BLBC. Figure 2 shows a link graph of relationships between those genes and BLBC class. The small blue circles on the far left of the link graph represent all genes, which have correlation with BLBC. The target binary variable placed on the far right of the link graph; which is in this case represents class=1 (the BLBC subtype). H1, H2, and H3 are the hidden layers in our optimal neural network architecture. The color and the width of the lines indicate the magnitude of the link weight; the thinner, blue lines represent a smaller weight, and the thicker red line indicates a link weight with larger magnitude. According to Figure 2, chromosome 19 open reading frame 73(C19orf73), phospholipase A2, group IB (pancreas)(PLA2G1B), and GDP-mannose 4,6dehydratase(GMDS) have pink and thicker lines, which means that the magnitude of the weight of the connection is significant. These genes linked to hidden "layer 3" (H3); H3 linked to class=1 (BLBC) by red, thicker line, which shows a strong connection. These genes include transglutaminase 2 (TGM2), discs large membrane-associated guanylate kinase scaffold protein 5 (DLG5), cytochrome b5 reductase 1 (CYB5R1), desmocollin-2 (DSC2), transmembrane protein 5 (TMEM5), GDP-mannose 4,6-dehydratase (GMDS), gamma-aminobutyric acid type A receptor pi subunit (GABRP), phospholipase A2 group 1B (PLA2G1B), junction plakoglobin (JUP), chromosome 19 open reading frame 73 (C19orf73), rhophilin associated tail protein 1B (ROPN1B), and nuclear factor I/X (NFIX). These 12 genes are known to be involved in various aspects of TNBC's pathogenesis. For instance, TGM2 is involved in TNBC epithelial-to-mesenchymal transition (EMT), which promotes their migratory and invasive properties, and controls their chemoresistance and immune escape (W. He et al., 2015). In addition, TGM2 expression is frequently upregulated during inflammation and wounding. Emerging evidence indicates that TGM2 expression is aberrantly upregulated in multiple cancer cell types, particularly those selected for resistance to chemotherapy and radiation therapy and those isolated from a metastatic site (Agnihotri et al., 2013). Loss of DLG5 promotes TNBC cell proliferation by inhibiting the Hippo signaling pathway, increasing nuclear Yes-associated protein (YAP) expression, and inducing EMT (Jie Liu et al., 2017). DLG5 plays important roles in epithelial cell polarity maintenance, precursor cell division, cell proliferation, cell migration and invasion, and transmission of extracellular signals to the membrane and cytoskeleton. Failure in establishment and maintenance of epithelial cell polarity contributes to tumorigenesis. Loss of expression and function of cell polarity proteins is directly related to epithelial cell polarity maintenance (Jie Liu et al., 2014). Another gene whose expression correlates with EMT is CYB5R1, which is a widely expressed oxidoreductase involved in oxidative stress reactions and drug metabolism. Although its specific role in cancer progression is still not clear, its transcriptional level expression strongly correlates with EMT in colorectal cancer (Woischke et al., 2016). The DSC2 protein is a major component of desmosomes which provides strength and stability to tissues. This protein has been shown to be highly expressed in TNBCs, being able to significantly predict patient survival, and suggesting their role in the aggressiveness seen in these tumors (Hill et al., 2015). TMEM5 is a type II transmembrane protein, thought to be a glycosyltransferase involved in the glycosylation of dystroglycan, which is part of a complex that links the extracellular matrix to the cytoskeleton. Aberrant glycosylation leads to the disruption of this link thus favoring migration and invasiveness seen in many tumors (Palmieri et al., 2017). TMEM5 is significantly over-expressed in BRCA1-mutated breast cancer cells (Privat et al., 2018), with these types of mutations occurring in TNBC more frequently than in the general population (Peshkin et al., 2010), (H. Chen et al., 2018). GMDS is involved in the process of cellular fucosylation of glycoproteins, which is involved in the functional regulation of adhesion molecules and growth factor receptors, with high levels of fucusylation being reported in various types of cancer (Miyoshi et al., 2008). This has been associated with TNBC and EMT, making GMDS a potential player in this process (Listinsky et al., 2011). Expression of GABRP is shown to be associated with the BLBC/TN subtype, and herein, we reveal its expression also correlates with metastases to the brain and poorer patient outcome (Sizemore et al., 2014). PLA2G1B are esterases that preferentially cleave glycerophospholipids into biologically active fatty acids and lysophospholipids, and are differentially expressed in breast cancer (Yamashita et al., 1994). These active lipids have biological functions relevant to cancer progression and each can be further metabolized into additional functional biomolecules (Scott et al., 2010). These active lipids modulate cellular differentiation, proliferation, apoptosis and senescence, whose dysregulation can result in the uncontrolled growth and metastasis seen in tumors. JUP is a cell adhesion protein and was recently reported as a determinant of circulating tumor cells types, single or clustered. This protein could be functioning as a double-edge sword, since loss of its expression leads to increased motility of epithelial cells, thereby promoting EMT and further metastasis. However, studies also show that JUP can function as an oncogene, with high expression of JUP resulting in clustered tumor cells in circulation with high metastatic potential in breast cancer and shortened patient survival. In addition, JUP may be a potential prognostic biomarker that can be exploited to develop as a therapeutic target for breast cancer (L. Lu et al., 2015). Although C19orf73 is a hypothetical protein that has not been characterized, a search of the GEO Profiles database (Barrett & Edgar, 2006) revealed that it is overexpressed in TNBC (GEO accession GDS4069) (Yang et al., 2011). Ropporin is a sperm-specific protein and is associated with sperm motility. Its expression was also found in motile cilia helping them to move in one direction in a synchronized pattern. Ropporin (ROPN1 and ROPN1B) was identified as differentially-expressed in several gene lists commonly associated with bad prognosis in our breast cancer investigation (Mehta, 2010). The Nuclear Factor I (NFI) family of site-specific DNA binding proteins functions in adenoviral DNA replication and in the regulation of transcription of a large variety of cellular and viral genes. This family is comprised of four genes in vertebrates (NFIA, NFIB, NFIC and NFIX), whose encoded proteins interact with DNA as homo- or hetero-dimers. They bind to the palindromic sequence TTGGC(N5)GCCAA with high affinity, resulting in transcriptional activation or repression, depending on the cellular context and regulatory region . Binding sites for these factors have been identified in promoter, enhancer and silencer regions of a plethora of genes expressed in almost every organ and tissue (Becker-Santos et al., 2017). It's been found that expression is increased in TNBC across the datasets (Han et al., 2008) in a study but NFIX needs further studies. The decision tree model represents the ranking of the gene expressions from the most important to least important. As shown in Figure 3, GABRP is the gene with the highest importance, reflecting 72.73% of TNBC are BLBC (176 out of 185) in the validation group. Table 3 shows the gene ranking by importance using the decision tree model. Figure 3. Decision tree model. Table 3. Gene importance using the decision tree model. Gene GABRP JUP ROPN1B DSC2 TMEM5 Importance 1 0.5097 0.4033 0.3393 0.2383 Description Gamma-aminobutyric acid A receptor pi subunit Junction plakoglobin Rhophilin associated tail protein 1B Desmocollin 2 Transmembrane protein 5 The random forest model targeted 10 genes. Table 4 represents the number of splitting rules for each gene. As the gene splits more, the importance of the gene increases. Table 4. Random Forrest with variable selection enabled. Variable Name _202504_at _205157_s_at _202342_s_at _204855_at _206560_s_at _214404_x_at _219615_s_at _202431_s_at _205044_at _209504_s_at Gene Name Tripartite motif-containing 29 (TRIM 29). Keratin 17, type I (KRT17). Tripartite motif containing 2 (TRIM 2). Serpin peptidase inhibitor, clade B (ovalbumin), member 5 (SERPINB5). Melanoma inhibitory activity (MIA). SAM pointed domain containing ETS transcription factor (SPDEF). Potassium channel, two pore domain subfamily K, member 5 (KCNK5). v-myc avian myelocytomatosis viral oncogene homolog (MYC). gamma-aminobutyric acid (GABA) A receptor, pi(GABRP). Pleckstrin homology domain containing, family B (evectins) member 1 (PLEKHB1). Number of splitting rules 15.0 12.0 9.0 9.0 9.0 8.0 8.0 7.0 7.0 6.0 In human normal tissue, TRIM29 is selectively expressed in squamous cell lineages in the skin, tongue, and esophagus; myoepithelial cells in the mammary gland; and basal cells in the prostate gland. Regarding the expression of TRIM29 in cancer tissues, TRIM29 has been reported to be overexpressed in several cancers, including esophageal, breast, lung, bladder, colorectal, and pancreatic cancers (Hatakeyama, 2016). Figure 4. Least angle regression model. Figure 4 represents the least angle regression model, which estimates the correlation between the gene expression levels and BLBC. The blue bars represent a positive correlation between overexpression of the gene with BLBC, so the highest gene expression represents the highest chance to be related to BLBC. The red bars represent a negative correlation between genes that are under expressed in BLBC when compared with non-BLBC. According to Figure 4, UDP glucuronosyltransferase 2 family, polypeptide B2 (UGT2B28) and cytoplasmic linker associated protein 1 (LASP1) have a negative correlation with BLBC. Bayesian classifier is another method used to analyze the data. This model shows the genes most relevant to BLBC. The result of this model is represented in Table 5, where the most important 12 genes are presented. Table 5. Gene Ranking (importance) Using Bayesian Classifier. Variable _220425_x_at _205044_at _212236_x_at _204751_x_at _204875_s_at _202263_at _222339_x_at _201042_at _220151_at _201681_s_at _206311_s_at _204808_s_at Name of the gene ROPN1B GABRP JUP DSC2 GMDS CYB5R1 NFIX TGM2 C19orf73 DLG5 PLA2G1B TMEM5 Order 1.0 2.0 3.0 4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 Score -149.8388379843322 -152.06028382414326 -178.30128029698253 -197.10605719691765 -250.02823043246084 -252.2041273457195 -255.19927200917277 -256.2482325776195 -257.503065161863 -260.21558211552104 -261.0894822749076 -261.13372976549573 Gradient Boosting is the model with the highest accuracy performance in this study (97.15%). This model shows the 12 most important genes, which have a high correlation with BLBC, as shown in Table 6. According to the results, ROPN1B is the most important gene expression, while GMDS in the lowest important among those 12 genes. Table 6. Gene importance via Gradient Boosting. NAME NAME OF THE GENE LABEL NRULES RATIO IMPORTANCE VIMPORTANCE _220425_x_at _205044_at _212236_x_at _204751_x_at ROPN1B GABRP JUP DSC2 220425_x_at 205044_at 212236_x_at 204751_x_at 24 13 20 7 1 0.976866 0.6846 0.403207 1 0.843971 0.570677 0.437559 1 0.863958 0.833592 1.085197 _201042_at _201681_s_at _202263_at _204808_s_at _220151_at _222339_x_at _206311_s_at _204875_s_at TGM2 DLG5 CYB5R1 TMEM5 C19orf73 NFIX PLA2G1B GMDS 201042_at 201681_s_at 202263_at 204808_s_at 220151_at 222339_x_at 206311_s_at 204875_s_at 3 2 2 2 1 2 1 0 0.093932 0.093776 0.084822 0.062419 0.061109 0.059511 0.032846 0 0 0 0 0 0.060705 0 0 0 0 0 0 0 0.993387 0 0 4. Discussion Based on these results, ROPN1B and GABRP are by far the most important genes where ROPN1B was the first important gene in both Gradient Boosting and Bayesian classifiers with an average of 96.01% accuracy, and the third important gene in Logistic Regression and Decision tree with an average of 95.87% accuracy. GABRP also is a strong relevant gene to BLBC, it is the first important gene in both Logistic Regression and Decision tree with an average of 95.87% accuracy, and the second in Gradient Boosting and Bayesian models with an average 96.01% accuracy. However, the Gradient Boosting model shows the highest accuracy of 0.971591, compared to other methods, it can provide more accurate results, compared to the other models. According to a study, ROPN1B was significantly up-regulated in breast cancer patients who relapsed compared to patients who did not. Moreover, ROPN1B was not expressed in 54 out of 57 of the patients who did not relapse. In addition, ROPN1B was up-regulated in a sub-group of ER-negative BC with a high incidence of relapse (Mehta, 2010). Therefore, it is crucial to investigate the ROPN1B role in the basal group of breast cancers since little is known about this protein. According to another study, GABRP expression was reported to correlate with BLBC. Moreover, GABRP gene not only correlates with BLBC, but also correlated with metastatic dissemination to the brain, showing poorer prognosis (Sizemore et al., 2014). They reported that silencing GABRP in BLBC cells decreased migration, BLBC-associated cytokeratins and ERK1/2 activation. Hence, knocking down GABRP expression may be a new approach for BLBC treatment (Wali et al., 2019). In addition, JUP might be a function of a double-edged sword molecule. Decrease in JUP expression causes an increase of motility of epithelial cells. Therefore, epithelial-mesenchymal transition is prompted and further cancer metastasis. Other studies show that JUP is an oncogene function. High JUP expression causes in clustered tumor cells with high metastatic potential in BC and reduces the probability of patient survival (L. Lu et al., 2015). Thus, JUP might not be the best gene for diagnosis or therapy. TMG2 is a proinflammatory protein associated with the resistance of drugs and the metastatic phenotype in BC. TGM2 was reported as an important link in interleukin (IL)-6 mediated cancer cell aggressiveness, it is also an important mediator of distant metastasis (Oh et al., 2011). Suppressing TGM2 appears to increase the chemosensitivity of cancer cells that were treated with drugs and could be a therapeutic approach (Wenxing He et al., 2015). DLG5 is another gene that correlated with BLBC whose loss of expression resulted in Hippo pathway inhibition through the induction of Scribble mislocalization and down regulating its expression. Also, loss of DLG5 leads to increasing Yes-associated protein (YAP) nuclear localization. This finding is consistent with previous studies that loss of DLG5 expression promotes breast cancer malignancy, hence, a more effective tumor therapy can be achieved by over-expression of DLG5 (Jie Liu et al., 2017). This study along with previous studies show that DLG5 acts as a tumor suppressor in breast cancer (Jie Liu et al., 2019), and its expression is up‐ regulated in BLBC according to our study. In addition, TMEM5 gene is significantly up-regulated in BRCA1, it is expressed about three times more in BRCA1 mutated cell line (SL) compared to BRCA1 wild-type cell line (BS) (Han et. al., 2008). In addition, MASPIN (also known as SERPINB5) gene expression could correlate with the basal-like subtype rather than myoepithelial in TNBC (Umekita et al., 2011). MASPIN may play a substantial role in regulating processes that are associated with the progression and metastatic cascade of TNBC and could present an exclusive and specific target for the diagnosis and therapeutic intervention of TNBC. FOXC1 is considered to be a cancer-association gene in BLBC, where it was identified as a BLBC biomarker by its overexpression (Ray et al., 2010). FOXC1 is a desirable avenue for further research as a possible therapeutic target in cancer treatment using nanomedicine. Recent studies have shown that SFRP1 is overexpressed in TNBC. Also, they found that proapoptotic and migratory pathways are regulated in a different way in SFRP1 knockdowns (Bernemann et al., 2014a). An study showed that SFRP1 strongly correlates with the triple negative breast cancer subtype and that SFRP1 might be used as a marker classifying patients to positively respond to neoadjuvant chemotherapy (Bernemann et al., 2014b). Melanoma inhibitory activity (MIA) is known as a small secreted protein expressed in cartilage, a recent study reported that it is overexpressed in breast cancer (Bosserhoff et al., 1999). A high level of TM4SF1 was shown in TNBC tissues, suggesting that TM4SF1 might be a biomarker of TBNC (Xing et al., 2017). Another study shows that UGT2B28 expression in a breast cancer cell line, suggests its role in androgen and estrogen metabolism (Farrar et al., 2006). There is currently no study showing a correction between UGT2B28 and the TNBC subtype. According to related studies, UCP1, 2 and 3 act as inductors for autophagy and mitochondrial dysfunction in breast cancer cells, which cause a significant reduction in tumor growth (Sanchez-Alvarez et al., 2013). However, as with UGT2B28, there are no published results available to support UPC2's correlation with either TNBC or BLBC. On the other hand, a study of a microarray and RT-qPCR results show that TRIM29 being overexpressed in basal tumors, also showing a high correlation to other basal-like breast tumor biomarkers, such as FOXC1, CK5/6, and CK17. Furthermore, TRIM29 may work as a factor of a transcriptional regulatory involved in carcinogenesis (Jin Liu & Bernard, 2010). Using TRIM29 needs more study since in breast cancer cells, TRIM29 suppresses invasiveness by downregulating the expression of TWIST1, whereas TRIM29 promotes cell invasion by regulating MMP-9 in lung cancer (Hatakeyama, 2016). Finally, HOXD13 is related with positive LNM and tumor size in breast cancer. According to Zhong et al. (2015), low levels of HOXD13 correlate with poorer survival in breast cancer patients. Recent studies have also suggested that calpain-9 may correlate with breast cancer, and low expression of calpain-9 correlates with poor clinical outcomes for patients after endocrine therapy. Therefore, calpain-9 may be important to provide prognostic information (Davis et al., 2014). Also, PADI2 expression correlates with breast cancer, where PADI2 expression contributes to migration of abnormal in breast cancer tumor cells (Wang et al., 2016). 5. Conclusion Triple-negative breast cancer (TNBC) constitutes approximately 20%-25% of all breast cancer cases with poor prognoses, with Basal-like breast cancer (BLBC) being a subtype representing 72.8% of TNBC. Classifying BLBC subtypes is of paramount importance for proper diagnosis, with direct clinical implications by dictating the most effective course of treatment. Although prior research has shown that profiling breast cancers using gene expression data has been useful in investigating and defining prognosis and therapy, little attention has been paid to the molecular characteristics of the basal-like group of breast cancers. Most (if not all) microarray studies of BLBC have been based on small sample size and conducted in isolation from one another in most cases, which may limit the generalizability of their results. To illustrate the significance of data integration in microarray gene profiling of basal-like breast cancers, this study combined over 24,000 genes of 579 TNBC patients from several TNBC gene expression datasets to identify several important gene signatures in BLBC subtype. A series of different predictive models were built to analyze the data with acceptable accuracy rates. In order to address the dimensionality issue present in the large gene expression data, which would otherwise negatively affect the models' performance due to overfitting, several feature selection algorithms were applied to the combined microarray data in order to identify useful genes for building predictive models for basal-like group of breast cancers. Our results show the usefulness of data integration approach in finer understanding of gene expressions among basal-like breast cancers. In addition, a combination of data mining and feature selection techniques allow new genes related to basal-like breast cancers to be identified from many different data sources that may have been otherwise difficult to ascertain. In particular, our results showed that the most important genes that correlate with BLBC are ROPN1B and GABRP, SERPINB5, FOXC1, KRT16 and KRT17. Our analysis provided new insights into the pathways in the basal-like group of breast cancers which need to be further investigated in order to develop BLBC specific treatments. In summary, the primary focus of different therapeutic approaches for cancer treatment is cancer cells apoptosis. Nanomedicine may be the treatment of choice for all the different types of breast cancer due to their excellent efficacy in penetration, specific retention and killing of tumor cells. However, the success of nanomedicine is associated to the specific markers and signatures of the cancer cells which can be achieved by analyzing gene datasets. We should not forget that an mRNA does not necessarily translate to protein expression and, hence, histology of human cancerous tissue is required in order to confirm that. Author Contributions Dr. Zadeh, and the master student, Qamar Alsabi, performed data analyses. Drs. Nosoudi, Zadeh, and Ramirez-vick worked together to interpret the result. Everybody helped in writing and preparing the manuscript. Dr. Nosoudi initiated the research and participated in study design and analyses. Drs. Zadeh and Ramirez-vick edited the final version of this manuscript. CONFLICT OF INTEREST We declare that the authors have no competing interests as defined by Expert Systems with Applications journal, or other interests that might be perceived to influence the results and/or discussion reported in this paper. 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Expert Systems with Applications, 120, 103-115. Umekita, Y., Ohi, Y., Souda, M., Rai, Y., Sagara, Y., Sagara, Y., Tamada, S., & Tanimoto, A. (2011). Maspin expression is frequent and correlates with basal markers in triple-negative breast cancer. Diagnostic pathology, 6(1), 36. Wali, V. B., Patwardhan, G. A., Pelekanou, V., Karn, T., Cao, J., Ocana, A., Yan, Q., Nelson, B., Hatzis, C., & Pusztai, L. (2019). Identification and Validation of a Novel Biologics Target in Triple Negative Breast Cancer. Scientific reports, 9(1), 1-10. Wang, H., Xu, B., Zhang, X., Zheng, Y., Zhao, Y., & Chang, X. (2016). PADI2 gene confers susceptibility to breast cancer and plays tumorigenic role via ACSL4, BINC3 and CA9 signaling. Cancer cell international, 16(1), 61. Woischke, C., Blaj, C., Schmidt, E. M., Lamprecht, S., Engel, J., Hermeking, H., Kirchner, T., & Horst, D. (2016). CYB5R1 links epithelial-mesenchymal transition and poor prognosis in colorectal cancer. Oncotarget, 7(21), 31350-31360. doi:10.18632/oncotarget.8912 Xing, P., Dong, H., Liu, Q., Zhao, T., Yao, F., Xu, Y., Chen, B., Zheng, X., Wu, Y., & Jin, F. (2017). Upregulation of transmembrane 4 L6 family member 1 predicts poor prognosis in invasive breast cancer. Medicine, 96(52), e9476-e9476. Yamashita, S., Ogawa, M., Sakamoto, K., Abe, T., Arakawa, H., & Yamashita, J. (1994). Elevation of serum group II phospholipase A2 levels in patients with advanced cancer. Clin Chim Acta, 228(2), 91-99. doi:10.1016/0009-8981(94)90280-1 Yang, L., Wu, X., Wang, Y., Zhang, K., Wu, J., Yuan, Y. C., Deng, X., Chen, L., Kim, C. C., Lau, S., Somlo, G., & Yen, Y. (2011). FZD7 has a critical role in cell proliferation in triple negative breast cancer. Oncogene, 30(43), 4437-4446. doi:10.1038/onc.2011.145 Zheng, B., Yoon, S. W., & Lam, S. S. (2014). Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms. Expert Systems with Applications, 41(4), 1476-1482. Zhong, Z.-B., Shan, M., Qian, C., Liu, T., Shi, Q.-Y., Wang, J., Liu, Y., Liu, Y., Huang, Y.-X., & Pang, D. (2015). Prognostic significance of HOXD13 expression in human breast cancer. International journal of clinical and experimental pathology, 8(9), 11407. Zolbanin, H. M., Delen, D., & Zadeh, A. H. (2015). Predicting overall survivability in comorbidity of cancers: A data mining approach. Decision Support Systems, 74, 150-161.


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November 11, 2016
Gua Sha course. Learning about relieving headaches and chronic neck pain. Visually, this appears painful, but it actually does not hurt. It is very effective and I can't wait to share my experience. Stay tuned for more photos and info.
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When your business starts growing, typically the range and reach of your business grow as well. It's common now that businesses have employees working remotely, national and international clients, and other stakeholders and vendors working around the globe. Globalization is easier now than ever, with advancements in technology making it simple to create a broader reach in all sectors of their organization.
One key concern that comes up in this idea of expanding business, however, is something we used to take for granted when paper was king: access to documents. No longer do you have to print multiple copies of an important document, make painstaking changes numerous times, or maintain old records in giant filing cabinets. Mobile technology and cloud storage means our documents are available at the touch of a finger on a screen.
Document Management makes this trend even more effective. With numerous benefits ranging from access control to data extraction, Document Management elevates your ability to manage digital documentation.
Collaboration is a key component of a global business, and Document Management's tools for collaboration and tracking ensure you and your employees, customers, and vendors can easily collaborate on whatever documents are needed. This creates a more productive workflow, and it allows for savings in money, space, and time.
Let's take a look at an example. Imagine that you are working on a new contract headed up by two of your employees. Your client, who lives in another state, wants to take a hands-on approach to developing the document, and you also need to involve the vendors who you will be collaborating with. Document Mangement simplifies the process of editing the necessary documents, but it also keeps the documents safe along the way. You can even use the service to convert any paper documents into digital formats to add to the files. The entire process is seamless and each party has the ability to access and work on the documents at the time that works best for them.
No matter what your goals for going global might be, Document Management can help. Contact us today to get started!


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Some promotions could also be combined; others should not eligible to be mixed with different gives. For details, please see the Terms & Conditions associated with these promotions.
As a precaution, you need to always check together with your doctor if in case you have any considerations about beginning a keto weight loss program. You ought to particularly be wary when you're at the moment taking medicines for a pre-current condition as further monitoring may be wanted. Be careful when breastfeeding as it's possible you'll need to extend carb intake. These are among the lesser common problems that I am e-mailed about on a semi-consistent basis. Many of these problems also relate to hydration and micronutrients, so just be sure you are ingesting loads of water and replenishing electrolytes.
i have been on the low carb weight loss program for a few week now (eating about 40g a day) but i'm stuck at 5 lbs. i dont seem to be loosing anymore! any suggestions? i'm a vegetarian and excersise about four-5 days a week. And i love yogurt but it's so excessive in carbs are you able to recommend any replacements much like yogurt? thanks! Moving forward, he and his team will continue to investigate the reams of data collected during the yearlong examine, and they hope to partner with scientists across Stanford to uncover keys to individual weight loss.
What the last sentence means is that medical doctors ought to monitor the ldl cholesterol and triglyceride levels of their patients on low-carb diets and modify the dosage of the medicine prescribed to manage blood sugar ranges. Unauthorized use or duplication of this content with out permission from this web site's author is prohibited. Photos & excerpts with a hyperlink can be used, however not whole recipes or posts. Full and clear credit have to be given to with particular direction to the unique content. Reminder: This does not occur for everybody. Some people can continue a low-carb eating regimen efficiently without the beforehand famous conditions.
Have protein snacks obtainable. I hold Cheddar cheese, cooked chicken cut into chunk size items, laborious boiled eggs, nuts (not that many of those though) celery with sugar free peanut butter. in my fridge able to seize. Keep going you will get there. Okay, that is NOT to be confused with coconut oil. Coconut butter is the actual flesh of a coconut, pureed into a variety Healthy Cooking, very similar to peanuts are pureed into a butter. It's often referred to and labeled as coconut manna" so when you're buying, look for either title. A fast option for dinner that would be simple to hold into your next snack. Grab a number of ounces after a workout to assist with repair and restoration.
Interventions Health educators delivered the conduct modification intervention to HLF (n = 305) and HLC (n = 304) contributors through 22 weight loss plan-particular small group periods administered over 12 months. The classes centered on methods to attain the bottom fats or carbohydrate consumption that might be maintained lengthy-time period and emphasised weight loss plan high quality. For what it's value, I eat this way on a regular basis. Most days my carb consumption is probably 20 to 60 grams, some days rising to one hundred.
Most diets prohibit and deprive your physique of energy, and when following the low carb lifestyle, your objective is the alternative. You need to gas your physique with meals to keep your metabolism revved up in order that your physique can work for you. Any time you are hungry, your metabolism is already slowing down in try to conserve energy Pregnancy Foods until you correctly gasoline it again. When you starve it, your body works in opposition to you and in flip you are feeling drained, cranky and will likely have problem concentrating and shedding weight. Keep your metabolism revved up by fueling with actual foods each time you're hungry.
If carbs have been severely restricted, your body is also compelled to provide ketone our bodies to produce energy to elements of the body that can't use fat as an vitality supply. This particularly includes the brain and red blood cells. At this level a person is in a state of ketosis and is noticeable characterised by smelly breath (acetone like smell equivalent to nail varnish) and side-results comparable to fatigue and nausea. Many aspect-results of very low carbohydrate weight-reduction plan embody: nausea, dizziness, lethargy, dehydration, unhealthy breath, constipation, lack of appetite, 'brain fog', irritable and grouchy.


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Let me just state that this is not my normal kind of blog post that is focused on research and facts. This energy report is based on the energies in the collective conscious and my translations of how they will effect cluster b personality disordered individuals. The energies of the stars effect us all differently based on our constitution. If you prefer my fact based blog posts, just simply click on a different link to your right or at the bottom of this post.
The Virgo super full moon is full of energies that will cause havoc for cluster b personality disordered individuals. On all full moons cluster b personality disordered individuals become more erratic and predictably unpredictable. Due to this being a super moon the energies will be stronger and the reactions more dramatic. Drama and cluster b personality disorders go hand in hand.
There is a focus to looking within, being honest about who we are, and who we want to be on this Virgo full moon. This will lead cluster b personality disorders to avoidance with projecting and finding fault with the people in their lives, especially family members. If they do take stock of who they are, it will not be an accurate assessment. They will see themselves as the victim and take this out on the people around them(more than normal).
As a collective we are moving into the divine feminine energy and with this shift cluster b personality disorders will feel more out of control. Feminine energy is about releasing and receiving. Cluster b personalities like to control and dominate rather than relax and see where their efforts divinely take them. Thus, this shift is something they are very resistant to.
In the age of Aquarius, there will be a reoccurring theme of letting go of control and letting the divine lead us to our destiny. With this lack of control feeling being more dominate than normal, cluster b personalities will be grasping for control where ever they can find it. Do not take it personally if they try to dominate you in an effort to feel in control of their own life. However, you do not have to accept this behavior. Make a point to let them know that moving forward this behavior will not be acceptable.
Take care of yourself and your own growth this Virgo super full moon. The veil to the other realms is thin. Relax and download the information that will help you achieve your higher purpose. Your intuition is strong, allow this to lead you to where you need to be.


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Steff advises clients in all areas of property. Acting for landlords and tenants in lease negotiations relating to a wide range of properties. For high street and private banks in secured lending transactions, and for clients buying and selling commercial and residential property, with a particular focus on residential development. She is particularly interested in supporting sustainable housing.
How do you make a difference for your clients?
I like to give my clients flexibility and put them at ease by working with them wherever it's convenient for them. That could be remotely, in a meeting room at our office, at a coffee shop, a client's office, and sometimes in a portakabin! I also try to be always available, and responsive to their needs. I think many of them really appreciate the efforts I'll go to, to meet them in person.
Tell us about your achievements?
I've worked with top law firms in London, including Lawrence Graham, Clegg Manuel and Fladgate. Most recently I was with Cook & Co in Bristol. But I think all my top achievements fall under one heading: building up my own practice with Carbon. It's been a great step for me. Now I can play to my strengths, enjoy greater autonomy, and use the flexible working environment to my professional and clients' advantage. For me, this outshines anything I'd achieved before that. And being part of such a supportive and flexible team is great - I know the achievements will keep piling up.


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Wow. Well that sucked. What a disaster of a day in Foxboro. The Bruins got blasted by the Canadiens today in the NHL's outdoor Winter Classic at Gillette Stadium, but what else did you expect?
The Bruins were without David Krejci who is on the IR with an upper body injury and Brad Marchand served the first game of his 3 game suspension for an undisciplined low hit. So instead the Bruins had to dress 2 AHL regulars (Seth Griffith and Alex Khokhlachev) in their lineup for the NHL's "Super Bowl." But that may have not mattered either. The Bruins were completely outplayed and outworked and it was a troubling sight to see. It seemed like they had absolutely no urgency or desire at any point. The final result is what they deserved, a 5-1 Habs win in an abomination of a game.
Here are BostonPucks.com's 3 thoughts:
1. Did the Bruins forget that this game was for first place in the Atlantic?
I know this was a just a regular season game, 1 of 82, but this game is the NHL's biggest event every year. Regardless of who is missing from the lineup theres no excuse Boston can make for their effort. They should have had the fire and motivation to come out with a ton more energy than they showed. The Bruins showed some push back at times, but they did not do nearly enough. And even when they seemed to improve at moments in the 2nd and 3rd periods the damage was already done.
I thought Bruins coach Claude Julien should have shortened his bench earlier than he did. Griffith and Koko just did not look like they were up to the challenge of the increased intensity of the Winter Classic. The Bruins just could not keep up with the faster Canadiens team and it was ugly.
2. I'm Not Sure What Rinaldo Is Doing In The Lineup
Rinaldo has one goal on the year. Granted he has not been suspended yet, so our "more goals than games suspensions" prediction is still looking good. But I am not sure what he is bringing to the lineup other than hitting everything in sight. He has not shown the speed that he showed in the preseason. Maybe because all the AHLers aren't around anymore.
Is he only in the lineup because the Bruins gave up a 3rd round pick for him? He is supposed to be able to kill penalties, but I do not think I have seen him out on the penalty kill in a while. I think the lineup would be better served with Tyler Randell in the lineup instead of Rinaldo. What Rinaldo did at the end of the game to Torey Mitchell (sucker punch) was bush league and unnecessary.
Back before Marc Bergevin took over as Canadiens' GM the Bruins would push them around and intimidate them. The Canadiens are no longer intimidated and it has not worked for the Bruins for the past 3 seasons. They need players that are able to skate with Montreal, which Rinaldo should be able to do, but he is too worried about beating them up.
3. It Would Have Been 10-1 Without Tuukka Rask
How often can you say a goalie gave up 5 goals in a game and looked great? Today was one of those rare occasions.
Tuukka Rask was one of the few in black n' gold who showed up today. And even though he only had 25 saves, it seemed like every one was huge because of the prime chances the Bruins were giving up. Multiple times he robbed the Habs in tight and even more times he was left high and dry while Canadiens players stood around the crease without fear or consequences. When asked after the game about the way the team played Rask summed it up perfectly stating, "I'd say embarrassed more than disappointed. Yeah, embarrassing loss." He's never afraid to tell it like it is, but today was definitely not on him.
- A crushing blow came in the second period when the Bruins were on the power play. Jimmy Hayes poked in a loose puck that would have cut the lead to 2-1 and really changed the momentum of the game. Instead, the ref lost sight of the puck during the scramble and blew his whistle too quick before the puck went in. It was clearly not covered and Boston was robbed of a goal.
- The Joe Morrow and Kevan Miller defensive pairing was a -3 for the day. Joe Morrow has skills that can benefit this Bruins team. I like how he has the ability to skate the puck out of the danger areas and he has started to jump in the play offensively, but he cannot be -3.
- On a positive Matt Beleskey scored the lone Bruins goal. That gives him 4 goals and 5 points in the past 5 games. Here's a clip of Beleskey's tip in:
- Thumbs down to the NHL and NBC for their musician selections. I would have liked to see them at least include some local musicians. Instead we got Simple Plan. You could have told me Simple Plan's bus went off a bridge in a fiery blaze a decade ago and I wouldn't have known if you were lying or not. This is NHL hockey, not pairs figure skating, and some music from a real rock band with a little edge would have been nice.
- Ryan Spooner had the best chance of the day and Canadiens goalie Mike Condon broke his heart with a fantastic highlight reel save. The play summed up the Bruins game perfectly. Had Spooner been ready to fire it would have been in the net in the blink of an eye. But he hesistated, wasn't urgent, and allowed Condon to get back to the near post to make the save. Here's a clip of the save:
It doesn't get any easier on the schedule either. Next game is against the Washington Capitals. They come into Boston Tuesday night for a 7pm puck drop.


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Further evidence of the public sector's commitment to sustainability is the 4 Star Green Star SA Office Design v1.1 certification that the KZN Department of Education: Ugu District Office has received from the Green Building Council South Africa (GBCSA).
Delta Property Fund, a specialist black-managed and substantially black-owned REIT with a significant sovereign underpin, today reported flat interim results for the six months ended 31 August 2017.
Public Works Minister Nkosinathi Nhleko on Monday called on the construction industry to embrace innovative technologies that have superior energy efficiency performance compared to conventional materials.
The National Home Builders Registration Council (NHBRC) has appointed Mr Mziwonke Dlabantu as Chief Executive Officer with effect from 01 October 2017.
Delta Property Fund, a specialist black-managed and substantially black-owned REIT with a significant sovereign underpin, today reported sound results for the financial year ended 28 February 2017, declaring a dividend of 97.24 cents per share, up 7,1% on the prior financial year and in line with the Group's growth forecast.
Key players in the engineering industry are taking legal action against the Department of Public Works (DPW) and the Engineering Council of South Africa (ECSA), alleging that the appointment of the new ECSA council is unlawful.
The South African Institute of Black Property Practitioners supports the call at the recent State of the Nation Address (SONA) for land reform, and expediting the transformation of the property sector.
A draft Property Practitioners Bill will be published by the Department of Human Settlements for public comment with a purpose of establishing a more inclusive, representative sector, towards radical economic transformation.
Department of Public Works starts renegotiating its leases with PIC as part of the government's drive to effect R25bn in savings over the medium term.
eProperty News is a leading online commercial property marketplace serving the Southern African Investment, Office, Retail and Industrial property and allied sectors.


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Published on
Don't use a dull blog post image ever again; learn how to brand free images with your original design and make your blog images truly stand out.
This is a guide to brand your images with PicMonkey.
See the full tutorial as well as using other tools to rock your images in this post at Traffic Generation Café: http://www.trafficgenerationcafe.com/create-free-images-for-blog-posts
This presentation was created by Darren DeMatas of IntertwineMarketing.com from Traffic Generation Café.


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Drink or Drive
A campaign to raise the awareness of anti-drink-driving. The medium to communicate the message is translated on the Reversi board game, as the disk pieces can only be black or white, it reminds the target audiences that they should too, only choose one between drinking or driving. A board game with customised disks is made to be sent as direct mailers.


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by Andrew Fiebert
We're big fans of Vanguard, but admittedly, investing in Vanguard funds is a bit more complicated than using a Robo Advisor. In this article, we break down what we think of Vanguard's eight best funds while balancing both performance and cost.
Before we jump in, it's important to mention why we are focusing so heavily on fees here. Due to their exponential nature, fees of just 1% can cause you to lose up to 25% of your earnings. That's pretty horrendous and often what turns investors on to Vanguard in the first place.
I also highly suggest you check the fees on your accounts via the free Personal Capital fee analyzer. It runs simulations and pinpoints all of the overly fee-hungry funds across your accounts – retirement or otherwise.
If you're looking for a deeper dive into our logic as well as some colorful commentary than check out the podcast episode we did on this:
First, let's quickly discuss what an Index Fund (ETF) and a Mutual Fund are. Who better to ask then Vanguard themselves?
An ETF is a collection (or "basket") of tens, hundreds, or sometimes thousands of stocks or bonds in a single fund. If you've ever owned a mutual fund—particularly an index fund—then owning an ETF will feel familiar because it has the same built-in diversification and low costs.
Source: Vanguard
A Mutual Fund is very similar to an ETF with one crucial difference:
You can set up automatic investments and withdrawals into and out of mutual funds based on your preferences.
Source: Vanguard on ETF vs. Mutual Fund
In other words, if you want to automate your investing, then you use a Mutual Fund. If you want cheaper fees over time and don't mind making contributions every month, then you should choose an ETF. I use ETFs because I don't mind making investments manually, and fees are the worst.
We often get asked how much you need to invest in Vanguard. If you're investing in an ETF, then all you need is $1. If you're investing in a Vanguard Mutual Fund, then the minimum initial investment is between $1,000 and $3,000.
Total Stock Market (ETF) – VTI
NYSEARCA:VTI | Vanguard | MorningStar | Fee: 0.04% | 5 Year Avg: 14.24%
This ETF is Vanguard's flagship fund and in our opinion, their best. It's a blend of Large, Mid, and Small cap companies in the US. It's the lowest fee we've ever seen on a fund. That's because the fund tracks a few smaller indexes allowing it to be largely automated.
Often when people mention that they invested in Vanguard, they are referring to this fund. Since 92%+ of fund managers can't even beat this, I'd be very skeptical if anyone suggested they can perform better after-fees than this fund. Even Warren Buffet agrees. Of course, it would be shrewd to invest in more than just VTI. It's impressive, but it's not all things.
MUTF:VFIFX | Vanguard | MorningStar | Fee: 0.15% | 5 Year Avg: 10.28%
This fund is a lifecycle fund, so it starts with most of the money invested in stocks and slowly tapers into bonds over time. The point is you take on risk now while you're young and slowly reduce risk as you reach retirement age, so big market swings don't wipe out your retirement money.
While this fund isn't their best regarding the fee, it covers a much-needed gap in most people's portfolio. As you know, we're big fans of buy and hold, and this fund fits in there perfectly.
The number in the fund name, like "2050", corresponds to your "typical" retirement date – usually, that's when you're 59 1/2. We often find ourselves picking funds with dates well past typical retirement age, so we get something a bit more growth-focused early on.
500 Index Fund (Admiral Class) – VFIAX
MUTF:VFIAX | Vanguard | MorningStar | Fee: 0.04% | 5 Year Avg: 14.48%
This fund was the industry's first for individual investors. Invest in 500 of the biggest, baddest companies based in the US. By definition, this fund contains the best Large Cap companies, and since it focuses on the biggest companies in the US, it's the closest to tracking the US economy.
REIT Index Fund (Admiral Shares) – VGSLX
MUTF:VGSLX | Vanguard | MorningStar | Fee: 0.12% | 5 Year Avg: 9.97%
Why own a property and rent it when your money gets stuck in the home, and there is so much work to be done? Instead, invest in a REIT and take rental profit and liquidity. This index fund is not just a REIT but a fund of many REITs, so you're heavily diversified in the rental game.
Note: You won't find much yield here, which is a bit of a drag considering real estate is a solid income play. As a replacement for the income portion of your portfolio, we recommend Fundrise.
FundriseDiversify into income-producing real estate without the dramatics of actual tenants. Currently, their return is more than 2x that of the Vanguard REIT Index Fund.
Compared to VGSLX, Fundrise sticks to mid-size deals overlooked by large funds and as a result, provides a markedly higher return. You can also opt to concentrate on income or appreciation focused funds.
Growth Index Fund (Admiral Shares) – VIGAX
MUTF:VIGAX | Vanguard | MorningStar | Fee: 0.05% | 5 Year Avg: 16.17%
With the Growth Index, Vanguard picks high-growth companies that will knock it out of the park for you. It's a bit riskier, but the returns are solid.
Even though the focus is on high growth companies, the fund follows a buy and hold approach where once they locate a stable company, they stay invested in them for a while.
The air is crisp in Admiral.
Total Bond Market (ETF) – BND
NYSEARCA:BND | Vanguard | MorningStar | Fee: 0.05% | 5 Year Avg: 2.44%
Any well-balanced portfolio has bonds in it. They're much less sexy than stocks but are also much less risky. When you're young, 10% of your portfolio should be in something similar to BND, and as you get older, you'll increase that percentage significantly. All the bonds that are in this fund are investment grade, and you should aim to hold this fund in the medium to long term based on its contents.
In preparation for market corrections or as we see them, investment opportunities, we tend to hold more bonds. Since long-term bond funds, rose ~20% in price in 2008, we see this as a win-win.
Since bonds tend to do better when the stock market is doing poorly, we want our Opportunity Funds to be full of them.
We recommend keeping your Opportunity Fund in a cash account with best in class interest rates:
Strategic Equity Fund (Investor Shares) – VSEQX
MUTF:VSEQX | Vanguard | MorningStar | Fee: 0.18% | 5 Year Avg: 14.45%
Like the Growth Index fund but smaller companies, potentially higher growth and largely selected by a computer. The fee is the highest here because proportionately the most amount of work goes into running this fund. 0.29% isn't a big fee by a long shot, but I do think it's important to note.
Also, again, this one's the riskiest of the bunch. Of your Vanguard investments, we wouldn't recommend making this one more than 10% of the total amount you invest.
Total International Stock Index Fund (Investor Shares) – VGTSX
MUTF:VGTSX | Vanguard | MorningStar | Fee: 0.17% | 5 Year Avg: 5.80%
Similar in approach to our #1 choice, VTI, only this fund focuses only on companies outside the US. The fund covers both developed and emerging markets.
It's pretty volatile, so we keep it as a small portion of our portfolio to help offset our heavy US exposure.
Vanguard Fund Tracking and Monitoring
Manage your cash and optimize your investments in one place. With Personal Capital, you can analyze your 401k to diversify your holdings better and reduce fees. I had no idea I was paying over 1% of my assets in fees every year but their help I was able to get it down below 0.3%.
Once you have all of your accounts linked, you can also leverage their Retirement Planner to plot out exactly what your retirement would look like. Using a Monte Carlo simulation, they determine how likely it is that you'll reach the level of income in retirement that you're hoping for.
Vanguard Select Funds
Vanguard created a shortlist of their funds called the Vanguard Select Funds. One interesting thing about the list is how they determine what funds get on it:
So, basically, they're hand-picked using voodoo. I will say that a lot of their most expensive funds (where they can make the most money) are on that list like the Windsor II who's fee is 0.34%.
It's worth mentioning that most of the funds on our list are on their list with the exception that we excluded the high-cost funds. There are a billion studies that show there is no correlation between a high cost and a high return. That's why we focus on "shooting for the average" on the show, easily the best bang for your buck given the risk.
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Those examples above are excellent bedding top easy interior decor design project, however, most of them possess zero ribbon since the outer lining of the human entire body looks bland. Possibly you would like a dresser for your bed which will not seem dull. If you're, you do not will need to worry anymore due to the fact purple quilted bedspread is the response you're searching for! This dresser is an equally incredible addition for your own bed as it comprises excellent carvings in front of its body and it definitely appears stunning, in addition to tasteful. In the event you would like to get this dresser, you want to cover 67146 648. Wow, this sort of economical yet lovely bed dressers to have!
If you are searching for a bedding top easy interior decor design project, the first factor you need to feel about is: what kind of bed do I desire? It's possible to start by creating your own schema for the dark purple bedspread, or you may even envision it from your head. In the event you adore looking at a bed style and design magazine, you might have often heard of Monochrome lots of. When it comes to a tiny bed, this is going to perform the magic, as one pops will probably cause you to get competent to target it doesn't look too bloated. You may take 1 example from monochromatic Swedish home design, in that it plays very well with character. The advised palette of colors is brown and white, in which it compliments one another. This shade will loosen up your own room, making it more spacious and more classy. By the window, you may add a few plants and spacious nonetheless simple curtains, permitting the sun lights to come as a result of. Vintage theme cannot go wrong afterall.
It's to determine the design of the bedding top easy interior decor design project. The design of this bed takes an important role prior to purchasing a luxurious modern day bed set. You need to find out the plan of the bed therefore it is able to stay there. Try to imagine the look of one's bed so you will all know type s of furniture. Then, you're able to evaluate your lifestyle. The lavender coverlet set will conform to your lifestyle that it combines your personality and style.
The next solution for the children are the modern coverlets. Bed Bath and Beyond makes a terrific choice if you wish your children possess a easy but still interesting bed. You may possibly have heard about the name and it is ordinary since the company's been in existence for lengthy before selling several goods for the bed, bathroom, as well as else. Even the bed products, including for the children, are wonderful yet so simple and elegant. You can try the 6-drawer ambigu vest created by Da Vinci, the convertible stroller, or cheetah-patterned bed as bedding top easy interior decor design project for the kids kids!
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