Investigators often interpret genome-wide data by analyzing the expression levels of genes within pathways. While this within-pathway analysis is routine, the products of any one pathway can affect the activity of other pathways. Past efforts to identify relationships between biological processes have evaluated overlap in knowledge bases or evaluated changes that occur after specific treatments. Individual experiments can highlight condition-specific pathway-pathway relationships; however, constructing a complete network of such relationships across many conditions requires analyzing results from many studies.functionally co-occurring. We demonstrate PathCORE-T by analyzing an existing eADAGE model of a microbial compendium and building and analyzing NMF features from the TCGA dataset of 33 cancer types. The PathCORE-T framework includes a demonstration web interface, with source code, that users can launch to (1) visualize the network and (2) review the expression levels of associated genes in the original data. PathCORE-T creates and displays the network of globally co-occurring pathways based on features observed in a machine learning analysis of gene expression data.We developed PathCORE-T framework by implementing existing methods to identify pathway-pathway transcriptional relationships evident across a broad data compendium. PathCORE-T is applied to the output of feature construction algorithms; it identifies pairs of pathways observed in features more than expected by chance as The PathCORE-T framework identifies transcriptionally co-occurring pathways from the results of unsupervised analysis of gene expression data and visualizes the relationships between pathways as a network. PathCORE-T recapitulated previously described pathway-pathway relationships and suggested experimentally testable additional hypotheses that remain to be explored.The online version of this article (10.1186/s13040-018-0175-7) contains supplementary material, which is available to authorized users. The number of publicly available genome-wide datasets is growing rapidly . High-thpathway co-occurrence relationships in transcriptomic data) software, which implements existing methods that jointly consider features and gene sets to map pathways with shared transcriptional responses.Feature extraction methods are a class of unsupervised algorithms that can reveal unannotated biological processes from genomic data . Each fePathCORE-T offers a data-driven approach for identifying and visualizing transcriptional pathway-pathway relationships. In this case, relationships are drawn based on the sets of pathways, annotated in a resource of gene sets, occurring within constructed features. Because PathCORE-T starts from a feature extraction model, the number of samples in the compendium used for model generation and the fraction of samples needed to observe a specific biological or technical pattern is expected to vary by feature extraction method. Pathways must be perturbed in a sufficient fraction of experiments in the data compendium to be captured by any such method. To avoid discovering relationships between pathways that share many genes—which could more easily be discovered by directly comparing pathway membership—we implement an optional pre-processing step that corrects for genes shared between gene sets, which Donato et al. refer to as pathway crosstalk . Donato P. aeruginosa) gene expression data (doi:10.5281/zenodo.583694) [https://pathcore-demo.herokuapp.com/PAO1). To show its use outside of the microbial space, we also demonstrate PathCORE-T analysis of Pathway Interaction Database (PID)-annotated [We apply PathCORE-T to a microbial and a cancer expression dataset, each analyzed using different feature extraction methods, to demonstrate its broad applicability. For the microbial analysis, we created a network of KEGG pathways from recently described ensemble Analysis using Denoising Autoencoders for Gene Expression (eADAGE) models trained on a compendium of Pseudomonas aeruginosa (.583694) . We provnnotated  non-negannotated , 13 extrnnotated .https://pathcore-demo.herokuapp.com/) links to the documentation and source code for our implementation and example usage of PathCORE-T. Methods implemented in PathCORE-T are written in Python and pip-installable (PyPI package name: PathCORE-T). Examples of how to use these methods are provided in the PathCORE-T analysis repository . In addition to scripts that reproduce the eADAGE and NMF analyses described in this paper, the PathCORE-T-analysis repository includes a Jupyter notebook (https://goo.gl/VuzN12) with step-by-step descriptions for the complete PathCORE-T framework.In addition to visualizing the results of these two applications, the PathCORE-T web interface . We recommend that those interested in using the PathCORE-T software consult the documentation and scripts in PathCORE-T-analysis. Each of the functions in PathCORE-T that we describe here can be used independently; however, we expect most users to employ the complete approach for interpreting pathways shared in extracted features , and examples of how to use the methods in PathCORE-T are provided in the PathCORE-T-analysis repository , this is the loadings matrix [W, where the NMF approximation of the input dataset A is A ~ WH [https://goo.gl/VuzN12) that demonstrates how a weight matrix can be constructed by applying ICA to the P. aeruginosa gene compendium.s matrix ; in indes matrix ; in ADAGs matrix , 7; in Ns A ~ WH . In addiGene signature rule(s). To construct a pathway co-occurrence network, the weight matrix must be processed into gene signatures by applying threshold(s) to the gene weights in each feature—we refer to these as gene signature rules. Subsequent pathway overrepresentation will be determined by the set of genes that makes up a feature’s gene signature. These are often the weights at the extremes of the distribution. How gene weights are distributed will depend on the user’s selected feature construction algorithm; because of this, a user must specify criterion for including a gene in a gene signature. PathCORE-T permits rules for a single gene signature or both a positive and a negative gene signature. The use of 2 signatures may be appropriate when the feature construction algorithm produces positive and negative weights, the extremes of which both characterize a feature . Because a feature can have more than one gene signature, we maintain a distinction between a feature and a feature’s gene signature(s).A list of pathway definitions, where each pathway is defined by a set of genes . We provide the files for the P. aeruginosa KEGG pathway definitions and the Nature-NCI PID pathway definitions in the PathCORE-T analysis repository .PathCORE-T requires the following inputs:In practice, users can obtain a weight matrix from many different methods. For the purposes of this paper, we demonstrate generality by constructing weight matrices via eADAGE and NMF.k = 300 features was an appropriate size for the current P. aeruginosa compendium. The authors also compared eADAGE to two other commonly used feature construction approaches, PCA and ICA [k = 300 features from the compendium of genome-scale P. aeruginosa data. Because PathCORE-T supports the aggregation of co-occurrence networks created from different models on the same input data, we use all 10 of these models in the PathCORE-T analysis of eADAGE models (doi:10.5281/zenodo.583172).eADAGE is an unsupervised feature construction algorithm developed by Tan et al.  that use and ICA . Tan et n x k, where n genes in the compendium are assigned positive or negative gene weights, according to a standard normal distribution, for each of the k features. Tan et al. determined that the gene sets contributing the highest positive or highest negative weights (+/− 2.5 standard deviations) to a feature described gene expression patterns across the compendium, and thus referred to the gene sets as signatures. Because a feature’s positive and negative gene signatures did not necessarily correspond to the same biological processes or functions, Tan et al. analyzed each of these sets separately [Tan et al. refers to the features constructed by eADAGE as nodes. They are represented as a weight matrix of size parately . Tan et A ~ WH [A is the input expression dataset of size n x s (n genes by s samples), NMF aims to find the optimal reconstruction of A by WH such that W clusters on samples (size n x k) and H clusters on genes (size k x s). In order to match the number of features constructed in each eADAGE model by Tan et al., we set k, the desired number of features, to be 300 and used W as the input weight matrix for the PathCORE-T software. We found that the gene weight distribution of an NMF feature is right-skewed and (as the name suggests) non-negative  where each additional component explained less than 0.5% of the variance. We found that using a very small number of constructed features resulted in a substantial loss of power: PathCORE-T analysis with a single k = 24 model yielded no significant edges after permutation test. However, PathCORE-T can be applied over multiple models as long as the feature construction method produces different solutions depending on random seed initialization. We performed 10 factorizations to generate an aggregate of 10 k = 24-feature NMF models and found that the resulting co-occurrence network was denser (364 edges) than our k = 300 factor network (119 edges). 65 edges were found in both networks. These shared edges had higher weights, on average, in both networks compared to edges unique to each network (https://goo.gl/vnDVNA).As an additional analysis, we determined the number of components , is considered significantly enriched in a given gene signature. This cutoff value should be selected to most aid user interpretation of the model. The next step of PathCORE-T is to convert pathway-node relationships into pathway-pathway relationships. For this, we apply a subsequent permutation test over pathway-pathway edge weights that accounts for the frequency at which pathways are observed as associated with features. This permutation produces a p-value for each edge. Two pathways co-occur, or share an edge in the pathway co-occurrence network, if they are both overrepresented in a gene signature. The weight of each edge in the pathway-pathway graph corresponds to number of times such a pathway pair is present over all gene signatures in a model  edge weight against the null. The p-value for each edge e is calculated by summing the number of times a random network contained e at a weight greater than or equal to its observed edge weight and dividing this value by N. Following Benjamini—Hochberg FDR correction by the number of edges in the observed network, pathway-pathway relationships with adjusted p-values above alpha (user-settable default: 0.05) are removed from the network of co-occurring pathways , we can divide each observed edge weight by its expected weight (dividing by 1 if the expected edge weight is 0 based on the N permutations) to get the edge’s odds ratio. Edges in the final network are weighted by their odds ratios.Because the expected weight of every edge can be determined from the Pathways can co-occur because of shared genes Fig. , b, d. TThere was no existing open source implementation of this algorithm, so we implemented Donato et al.’s maximum impact estimation as a Python package . This software is separate from PathCORE-T because we expect that it may be useful in its own right for other analytical workflows, such as differential expression analysis. The procedure is written using NumPy functions and data structures, which allows for efficient implementation of array and matrix operations in Python .In PathCORE-T, we used this software to resolve overlapping genes before pathway overrepresentation analysis. Overlap correction is applied to each feature of the model independently. This most closely matches the setting evaluated by the original authors of the method. Work on methods that resolves overlap by using information shared across features may provide opportunities for future enhancements but was deemed to be out of the scope of a software contribution.With this step, the pathway co-occurrence network identifies relationships that are not driven simply by the same genes being annotated to multiple pathways. Without this correction step, it is difficult to determine whether a co-occurrence relationship can be attributed to the features extracted from expression data or gene overlap in the two pathway annotations. We incorporate this correction into the PathCORE-T workflow by default; however, users interested in using PathCORE-T to find connections between overlapping gene sets can choose to disable the correction step.P. aeruginosa edges file is available for download on the demo application: http://pathcore-demo.herokuapp.com/quickview. While we chose to represent pathway-pathway relationships as a network, users can use this file output to visualize the identified relationships as an adjacency matrix or in any other format they choose.The PathCORE-T analysis workflow outputs a list of pathway-pathway relationships, or edges in a network visualization, as a text file. An example of the KEGG As an optional step, users can set up a Flask application for each PathCORE-T network. Metadata gathered from the analysis are saved to TSV files, and we use a script to populate collections in a MongoDB database with this information. The co-occurrence network is rendered using the D3.js force-directed graph layout . Users cWe created a web interface for deeper examination of relationships present in the pathway co-occurrence network. The details we included in an edge-specific page (1) highlight up to twenty genes—annotated to either of the two pathways in the edge—contained in features that also contain this edge, after controlling for the total number of features that contain each gene, and (2) display the expression levels of these genes in each of the fifteen samples where they were most and least expressed. The quantity of information  we choose to include in an edge page is intentionally limited so that users can review it in a reasonable amount of time.To implement the functionality in (1), we computed an odds ratio for every gene annotated to one or both pathways in the edge. The odds ratio measures how often we observe a feature enriched for both the given gene and the edge of interest relative to how often we would expect to see this occurrence. We calculate the proportion of observed cases and divide by the expected proportion--equivalent to the frequency of the edge appearing in the model’s features.k be the number of features from which the PathCORE-T network was built. kG is the number of features that contain gene G (i.e. G is in kG features’ gene signatures), kE the number of features that contain edge E (i.e. the two pathways connected by E are overrepresented in kE features), and kG & E the number of features that contain both gene G and edge E. The odds ratio is computed as follows:Let An odds ratio above 1 suggests that the gene is more likely to appear in features enriched for this pair of pathways. In the web interface, we sort the genes by their odds ratio to highlight genes most observed with the co-occurrence relationship.The information specified in (2) requires an “expression score” for every sample. A sample expression score is calculated using the genes we selected in goal (1): it is the average of the normalized gene expression values weighted by the normalized gene odds ratio. Selection of the most and least expressed samples is based on these scores. We use two heatmaps to show the (maximum of twenty) genes’ expression values in each of the fifteen most and least expressed samples Fig. .For each sample in an edge page, a user can examine how the expression values of the edge’s twenty genes in that sample compare to those recorded for all other samples in the dataset that are from the same experiment Fig. . Genes tUnsupervised methods can identify previously undiscovered patterns in large collections of data. PathCORE-T overlays curated knowledge after feature construction to help researchers interpret constructed features in the context of existing knowledgebases. Specifically, PathCORE-T aims to clarify how expert-annotated gene sets work together from a gene expression perspective. PathCORE-T starts from an unsupervised feature construction model. Before applying the software, users should evaluate models to make sure that they capture biological features in their dataset. Model evaluation can be performed in numerous ways depending on the setting and potential assessments include consistency across biological replicates, reconstruction error given a fixed dimensionality, and independent validation experiments. Tan et al. described several ways that models could be evaluated . Datasethttps://github.com/greenelab/PathCORE-T-analysis). We also implemented an optional step, which corrects for overlapping genes between pathway definitions, described by Donato et al. [We implemented the methods contained in the PathCORE-T software in Python. The implementations of the primary steps are pip-installable (PyPI package name: PathCORE-T), and examples of how to use the methods in PathCORE-T are provided in the PathCORE-T-analysis repository  [P. aeruginosa transcriptional data though such a relationship has not yet been described. Structural analysis of the iron-responsive regulator Fur found that it also productively binds zinc in E. coli and Bacillus subtilis providing a mechanism by which these pathways may be linked [The eADAGE co-occurrence network that resulted from our exploratory analysis contained a number of pathway-pathway relationships that have been previously characterized by other means Fig. . Three g enzymes , 36 and  enzymes . Furthere system ) were ide linked , 42.Fig.The network constructed using the PathCORE-T framework had 203 edges between 89 pathways. For comparison, we constructed a KEGG pathway-pathway network where edges were drawn between pathways with significant gene overlap (FDR-corrected hypergeometric test < 0.05). The overlap-based network had 406 edges between 158 pathways. Only 35 of the edges in the PathCORE-T network were between pathways that shared genes, with an average Jaccard Index of only 0.035. The network constructed using PathCORE-T (with overlap-correction applied by default) captured pathway co-occurrences not driven by shared genes between pathways.PathCORE-T is not specific to a certain dataset, organism, or feature construction method. We constructed a 300-feature NMF model of TCGA pan-cancer gene expression data, which is comprised of 33 different cancer-types from various organ sites and applied the PathCORE-T software to those features. We chose NMF because it has been used in previous studies to identify biologically relevant patterns in transcriptomic data  and by mPathCORE-T detected modules of co-occurring pathways that were consistent with our current understanding of cancer-related interactions Fig. . BecauseS1P3 is a known regulator of angiogenesis [Two modules in the network related to angiogenesis, or the formation of new blood vessels Fig. , d. Tumoogenesis , and hasogenesis . Moreoveogenesis . Taken togenesis .Finally, PathCORE-T revealed a large, densely connected module of immune related pathways Fig. . We founP. aeruginosa KEGG pathways case study, we constructed a network only from PID pathways with significant gene overlap. The network constructed using PathCORE-T and NMF features had 119 edges between 57 pathways. The overlap-based network was much denser: it had 3826 edges between 196 pathways. This suggested a high degree of overlap between PID pathways. For the PathCORE-T NMF co-occurrence network, 96 of the edges were between pathways that shared genes. However, the average Jaccard Index for these pathway pairs remained low, at 0.058.Just as we did for the eADAGE-based Unsupervised analyses of genome-scale datasets that summarize key patterns in the data have the potential to improve our understanding of how a biological system operates via complex interactions between molecular processes. Feature construction algorithms capture coordinated changes in the expression of many genes as features. The genes that contribute most to each feature co-vary. However, interpreting the features generated by unsupervised approaches remains challenging. PathCORE-T creates a network of globally co-occurring pathways based on features created from the analysis of a compendium of gene expression data. Networks modeling the relationships between curated processes in a biological system offer a means for developing new hypotheses about which pathways influence each other and when. Our framework provides a data-driven characterization of the biological system at the pathway-level by identifying pairs of pathways that are overrepresented across many features.PathCORE-T connects the features extracted from data to curated resources. It is important to note that PathCORE-T will only be able to identify pathways that occur in features of the underlying model, which means these pathways must be transcriptionally perturbed in at least some subset of the compendium. Models should be evaluated before analysis with PathCORE-T. The network resulting from PathCORE-T can help to identify global pathway-pathway relationships—a baseline network—that complements existing work to identify interactions between pathways in the context of a specific disease. The specific niche that PathCORE-T framework aims to fill is in revealing to researchers which gene sets are most closely related to each other in machine learning-based models of gene expression, which genes play a role in this co-occurrence, and which conditions drive this relationship.Project name: PathCORE-TProject home page:https://pathcore-demo.herokuapp.comArchived version:https://github.com/greenelab/PathCORE-T-analysis/releases/tag/v1.2.0 (links to download .zip and .tar.gz files are provided here)Operating system: Platform independentProgramming language: PythonOther requirements: Python 3 or higherLicense: BSD 3-clauseAdditional file 1:Figure S1. The weight distributions of genes to constructed features for NMF models of various sizes. (EPS 494 kb)Additional file 2:PathCORE-T network constructed from a Pseudomonas aeruginosa compendium using 10 eADAGE models analyzed with KEGG pathway annotations. (TSV 16 kb)Additional file 3:PathCORE-T network constructed from TCGA data using NMF models analyzed with PID pathway annotations.  (TSV 5 kb)


Wendan granule (WDG) for the treatment of Alzheimer's disease, network pharmacology method integrating absorption, distribution, metabolism, and excretion (ADME) screening, target fishing, network constructing, pathway analysing, and correlated diseases prediction was applied. To explore the mechanisms of the volatiles of Twelve small molecular compounds of WDG were selected as the objects from 74 volatiles with the relative abundances above 2 %, and their ADME parameters were collected from Traditional Chinese Medicine Systems Pharmacology platform (TCMSP), and the corresponding targets, genes, pathways, and diseases were predicted according to the data provided by TCMSP, DrugBank, Uniport, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID). Then the related pathways and correlation analysis were explored by the Kyoto Encyclopedia and Genomes (KEGG) database. Finally, the networks of compound target, target pathway, and pathway disease of WDG were constructed by Cytoscape software. Twelve compounds interacted with 49 targets, of which top three targets were gamma-aminobutyric acid receptor subunit alpha-1 (GABRA1), prostaglandin G/H synthase 2 (PGHS-2), and sodium-dependent noradrenaline transporter. Interestingly, these targets were highly associated with depression, insomnia, and Alzheimer's disease that mainly corresponded to mental and emotional illnesses. The integrated network pharmacology method provides precise probe to illuminate the molecular mechanisms of the main volatiles of WDG for relieving senile dementia related syndromes, which will also facilitate the application of traditional Chinese medicine as an alternative or supplementary to conventional treatments of AD, as well as follow-up studies such as upgrading the quality standard of clinically applied herbal medicine and novel drug development. With the worldwide reduction in birth rates and prolonged life span expectancies, the Alzheimer's disease together with other dementias was considered to be one of the 10 leading causes of disability among people with the age above 60 globally . Variouscretase,  inhibitiase, and  inhibitirylation  that onlTraditional Chinese medicine (TCM) prescriptions play an important role in the treatment of various serious diseases, especially those the western medicine find it difficult to tackle, because the therapeutic effects of TCM are based on synergistic and holistic theory. Unfortunately, the uncertainty of potential active compounds, explicit targets, and underlying pharmacological mechanisms impeded the modernization of TCM. Network pharmacology , an emerWendan granule (WDG) is a hospital preparation of traditional Chinese medicine prescription for the treatment of AD. It is a modern dosage form produced based on a modified prescription of Wendan decoction, which is an ancient and classical prescription with the function of “Hua-Tan (化痰)”. TCM believes that Tan can stay in various parts of the body, including brain, and produce all sorts of diseases. On the other hand, just like the aggregated β-amyloid in brains of AD patients, Tan itself is also a pathological product of various diseases. Therefore, the pharmacological efficacies of WDG are predicted to be reducing the production and promoting the clearance of β-amyloid in brains of AD patients by multiple mechanisms. Our previous study has identified 74 volatiles by employing HS-SPME-GC-MS [TCMSP (http://lsp.nwu.edu.cn/index.php) is a systems pharmacology platform and database of Chinese herbal medicines . DrugBanChemBioOffice Ultra 12.0  is applied for candidate compounds structures constructing. Cytoscape 3.5.1 is open source software for visualizing complex networks and integrating them with all sorts of types of attribute data . ⩾30%, Caco-2 permeability>0, BBB  ⩾0.2. Based on the ADME parameters in TCMSP and RA values obtained from previous work [Our previous study has identified 74 volatiles from WDG by employing HS-SPME-GC-MS. In order to acquire the potential main volatile compounds from the granule, four screening criteria were defined as follows: OB  barrier ) ⩾0.3, a ⩾3 were chosen as candidate targets and others were eliminated.To identify the corresponding targets of main compounds of WDG, several approaches were implemented. First of all, TCMSP and DrugBank database were applied to find out the potential targets. Then, “drug-target” network will be constructed by Cytoscape 3.5.1 software. The candidate targets were mainly screened by degree that represents the number of edges adjacent to a node [GeneMANIA was used for predicting the function of genes and gene sets. A relationship network of genes was given after input of a set of gene list and species selected as “homo sapiens.”The targets were input to the DAVID for further investigation such as Gene Ontology (GO), pathways. Construct the “target-pathway” network. The pathways that are equal to or above degree 3 were analysed for candidate pathways identification.The KEGG gave you information about related disease when candidate pathways entered and then constructed the “pathway-disease” network and preliminarily speculate the pharmacological mechanisms of WDG with all the information above.⩾30%, Caco-2 permeability>0, BBB⩾0.3, and RA⩾0.2. A total of 12 compounds including γ-Asarone, trans-ligustilide, and senkyunolide A, which showed poor OB but have high abundance, were selected as the candidate compounds for more accurate investigation  production, and Aβ degradation[β-adrenergic receptors that mediate catecholamine-induced activation of adenylate cyclase through the sensitization of the G protein [Thirty candidate targets were chosen for further investigation by using DAVID (gradation. Furthergradation. Interesgradation. ADRB1 a protein . ADRA2A  protein .For the purpose of systematically deciphering the multiple underlying mechanisms of volatiles of WDG, all of the pathways interacting with candidate targets were extracted from KEGG pathways database using DAVID, and then the “target-pathway” network was constructed . Twenty-In TCM prescriptions were prescribed for fighting against diseases from ancient China till now based on the theories of traditional Chinese medicine. Multicomponents, multitargets, and various pharmacological actions within herbs were the superiority, but also the key issues of the modernization studies of traditional Chinese medicine. Here, we presented efficient tactics that contribute to understanding the pharmacological mechanism of TCM prescription by “network pharmacology” in molecular level to reveal the interaction between small molecule compounds with protein targets, pathways, and diseases. The “network pharmacology” was first proposed by Andrew L. Hopkins . DiffereTwelve main compounds with favourable ADME were selected from seventy-four volatiles of WDG identified using HS-SPME-GC-MS. Then online databases, TCMSP and DrugBank, were applied to obtain potential interaction targets with the pathways, followed by GO enrichment clustering analysis. The results showed some compounds and targets identified in this research that might be conductive to the therapy and prevention of Alzheimer's disease had already been reported previously, which highlighted the credibility of the ADME evaluation system and target predicting databases. One target was hit by multiple ingredients, which coincided with the holistic view and synergistic action theory of TCM. Prediction of potential targets might be the first step in drug discovery within the wide application of computational target fishing technology. As shown in Previous studies had identified that WDG can improve the learning and memory ability by repairing the damage of cholinergic system, inhibiting the cytokine levels of transition-activated microglia and reducing the level of transient phosphorylation Tau protein –47.Based on the series of results from the database, platform, and software given, we could preliminarily predict that the mechanism of WDG involved a large network which contained drugs, targets, pathways, and diseases. The main targets were GABRA1, PTGS2, SLC6A2, CHRM1, ADRA1B, CHRM2, CHRM3, ADRA1A, ADRB1, ADRB2, PTGS1, and SLC6A3 that conducted with neuroactive ligand-receptor interaction pathways, signaling pathways, and metabolism pathways related to analgesic, anticonvulsant, and repair reduction of cholinergic neurons and eliminate the inflammatory response to reduce the secretion of neurotoxic inflammatory cytokine to cure diseases related to AD and insomnia.This study chose a novel strategy for the systematic understanding of the molecular mechanisms of the main volatiles of WDG relieving senile dementia related syndromes, which could facilitate the application of TCM in modern medicine and prescription/dosage form optimization . Further


Chronic myelogenous leukemia (CML) is a myeloproliferative neoplasm that arises from the acquisition of constitutively active BCR‐ABL tyrosine kinase in hematopoietic stem cells. The persistence of bone marrow leukemia stem cells (LSCs) is the main cause of TKI resistance and CML relapse. Therefore, finding a key target or pathway to selectively target LSCs is of great significance for the thorough treatment of CML.In this study, we aimed to identify key microRNAs, microRNA targets and pathways for the treatment of CML LSCs by integrating analyses of three microarray data profiles. We identified 51 differentially expressed microRNAs through integrated analysis of GSE90773 and performed functional gene predictions for microRNAs. Then, GSE11889 and GSE11675 were integrated to obtain differentially expressed genes (DEGs), and the overlapping DEGs were used as models to identify predictive functional genes. Finally, we identified 116 predictive functional genes. Clustering and significant enrichment analysis of 116 genes was based on function and signaling pathways. Subsequently, a protein interaction network was constructed, and module analysis and topology analysis were performed on the network.A total of 11 key candidate targets and 33 corresponding microRNAs were identified. The key pathways were mainly concentrated on the PI3K/AKT, Ras, JAK/STAT, FoxO and Notch signaling pathways. We also found that LSCs negatively regulated endogenous and exogenous apoptotic pathways to escape from apoptosis.We identified key candidate targets and pathways for CML LSCs through bioinformatics methods, which improves our understanding of the molecular mechanisms of CML LSCs. These candidate genes and pathways may be therapeutic targets for CML LSCs. Subsequently, to increase the accuracy of the prediction target, gene expression on two gene chips was compared based on the gene microarray data, common differentially expressed genes (DEGs) were screened, DEGs and predicted targets were intersected, and the microRNA targets were screened for the next study. The three gene chips were from National Center of Biotechnology Information (NCBI) gene expression omnibus (GEO) database (https://www.ncbi.nlm.nih.gov/geo). The three original microarray datasets included the expression profiles GSE90773 , Gene Ontology (http://www.geneontology.org/), Kegg Pathway (http://www.genome.jp/kegg), WikiPathways (https://www.wikipathways.org/), Reactome ( https://reactome.org/); these analyses were employed to assist with Go analysis and KEGG analysis. Strings (https://string-db.org) were used to construct the DEG protein interaction network. CytoNCA, an analysis plug‐in for Cytoscape 3.5.1, was used to topologically analyze the DEGs; MCODE, an analysis tool for Cytoscape 3.5.1, was employed to perform module analysis of the protein network. Via protein network construction, topology analysis and module analysis integration were employed to identify hub genes in CML. It is expected that in this study, the biomarkers and pathways identified in CML may reveal the potential molecular mechanisms of LSC canceration in CML. The identified new key candidate targets provide new ideas and methods for the treatment of CML.To further elucidate the molecular mechanisms of LSC carcinogenesis in CML and to screen CML biomarkers and key candidate targets, we screened differentially expressed microRNAs using microarray data and predicted the target of microRNAs through the miRTarBase database and TargetScanHuman database (22.1http://www.ncbi.nlm.nih.gov/geo/) is an international public repository for high‐throughput microarray and next‐generation sequence functional genomic data sets submitted by the research community. GEO2R is an R‐based web application which can analyze GEO data  platform, including seven CML patient marrow LSCs and five normal bone marrow HSCs . GSE11675 data is based on the GPL8300 ([HG_U95Av2] Affymetrix Human Genome U95 Version 2 Array) platform, including three CML patient marrow LSCs and three normal bone marrow HSCs (Submission date 4 Jun 2008). We selected these two microarray datasets for comprehensive analysis; both groups were based on gene expression profiles of HSC in the bone marrow. We used the GEO2R online analysis tool identify statistically significant DEGs between CML patient marrow LSCs and normal bone marrow HSCs. Statistically significant DEGs were defined as http://bioinfogp.cnb.csic.es/tools/venny/index2.0.2.html) is a free online website. To enhance the accuracy of the research results, we screened out common DEGs of two high‐throughput microarray databases using Venny 2.0.2.Venny 2.0.2 (2.3http://mirtarbase.mbc.nctu.edu.tw/index.html)   and Cytoscape software to build target protein networks and analyze the network  were analyzed by analyzing the GSE11889 microarray data expression profile  was performed on 116 target genes, with p < .05. We constructed an enrichment pathway network  to build an interactive network of 116 targets. The restrictive condition is “human species”, the network is constructed and the “TSV” format file of the gene interaction relationship is downloaded, and then the file is imported into Cytoscape software for module analysis and topology analysis. We have obtained a protein network with 340 nodes and 1,839 edges . The enrichment analysis results revealed that the 11 targets were enriched in the hematopoietic cell lineage, pathways in cancer, and microRNAs in cancer pathways. In addition, they were significantly enriched in the PI3K‐Akt signaling pathway, TNF signaling pathway, Ras signaling pathway and cAMP signaling pathway. These pathways are closely related to cell growth, metabolism, differentiation, proliferation, canceration, and apoptosis ”, “closeness(CC)”, “eigenvector(EC)”, “local average connectivity‐based method(LC)”, “network(NC)”, “subgraph (SC)”, and “information (IC)”. Standards were topologically analyzed and subsequently identified as 30 nodes, which were defined as major nodes. Among the 30 nodes Table , 11 majoWe employed Cytoscape to construct microRNA‐Target network based on the relationship between differentially expressed microRNAs and 11 major nodes Figure . The net4Chronic myelogenous leukemia is a HSC‐driven hematological malignancy that is characterized by HSC translocation, leading to the expression of the active tyrosine kinase BCR/ABL. TKIs are very effective at inducing remission, but they fail to achieve therapeutic effects in the targeted treatment of LSCs. Leukemia stem cells maintain minimal residual tumors and can cause CML recurrence after discontinuing TKI treatment .LC3, p62/SQSTM1 and mTOR inhibition, suggesting that autophagy can be a novel measure for CML therapy ‐45α down‐regulation and therefore can be used as a downregulator of GADD45α oncomiR, activating JNK1/2 and P38 signal transduction (Yang et al., KIT, MCL1, FOS, BRCA1, FLT3, CD44, AKT2, CHEK1, BCL6, and BCL2L1, and CREB1. KIT is a type III receptor tyrosine kinase that promotes cell survival and cells by activating downstream signaling pathways, proliferation and inhibition of apoptosis. KIT is a target for the treatment of CML (Kaitsiotou et al., MCL1 is an antiapoptotic protein, and decreasing MCL1 expression enhances IM‐induced apoptosis in CML cells (Shao et al., MCL1 was significantly enriched in antiapoptosis pathways, especially in the resistance to exogenous withering upon the death signal, indicating that MCL1 may be closely related to CML HSC resistance and apoptotic escape and may be used as a candidate target for the treatment of CML resistance. Genetic deletion of FOS inhibited tumor growth in a BCR‐ABL fusion protein kinase‐induced CML mouse model, and therefore, inhibition of high FOS expression reduced the intrinsic resistance of CML to TKI therapy (Kesarwani et al., BRCA1 is down‐regulated in CML patients, promoting abnormal mitosis and aneuploidy as well as altered DNA damage responses; studies have shown that BCR‐ABL strongly downregulates the BRCA1 protein levels (Wolanin et al., FLT3 in CML cells attenuates imatinib‐induced apoptosis, and FLT3 is associated with disease progression and prognosis (Kim et al., CD44, and down‐regulation of CD44 lead to K562 cell G0/G1 arrest, whereby proliferation of the cell cycle is reduced. Therefore, CD44 blockade may be beneficial for the treatment of CML (Chang et al., AKT2 is a functional target of miR‐2278. Up‐regulation of miR‐2278 expression leads to inhibition of the proliferation of resistant leukemia cells and induction of apoptosis. AKT2 inhibits leukemia cell proliferation and induces apoptosis upon experimental verification (Kaymaz et al., BCL6 proto‐oncogene is a key effector of FoxO in self‐renewing signaling in CML‐initiating cells and inhibits Arf and p53 in CML cells. The BCL6 proto‐oncogene is required for colony formation and leukemic initiation. Studies suggest that pharmacological inhibition of BCL6 may be possible, representing a new strategy to eradicate leukemia initiation cells in CML (Hurtz et al., BCL2L1 is the gene for the primary antiapoptotic survivin protein. The study of BCL2L1 expression by molecular changes strongly supports its involvement in ABT‐263/PP242‐induced CML‐BC progenitor cells. Apoptosis may also be a novel therapeutic target in CML (Lucas et al., BCL6 and MCL1 inhibitors may lead to the complete eradication of CML stem cells (Madapura et al., Through identification, we screened 11 CML HSC‐treated biomarkers and candidate targets, including There are some limitations in our research. Bioinformatics analysis based on chip samples only stays at the level of prebasic data analysis and prediction. The results are often determined by the original samples, the complexity of the original samples and the amount of samples can play an important role in interfering the results. Nonetheless, our research is based on real‐world sample data, and the results provide initial hypothesis for in vivo and in vivo target validation studies, which can effectively reduce the shortcomings of previous studies with low innovation and high clinical loss.5In this study, we used multiple microarray datasets and performed bioinformatics analysis. During the analysis, a total of 51 microRNAs and 116 target genes for differentially expressed microRNAs were identified. Through the construction of protein‐protein interaction networks, module analysis and topology analysis of networks were performed. Finally, we obtained a total of 33 differentially expressed microRNAs and 11 key targets. The key targets were mainly enriched in the PI3K/AKT, Ras, JAK/STAT, FoxO and Notch signaling pathways and were resistant to endogenous and exogenous apoptosis. These findings can significantly improve our understanding of the molecular pathogenesis of LSCs. These key candidate genes and pathways may encompass the thorough treatment of CML strategies.There are no conflicts of interest to declare.Conceptualization, C.G.S., H.Y.L., L.J.L.; Methodology, C.G.S., H.Y.L., L.J.L., J.Z., C.L., C.Z., J.Y., C.D.G., G.X.L.; Software, H.Y.L., L.J.L., J.Z., C.L., C.Z.; Validation, C.L., C.Z., J.Y., C.D.G., G.X.L.; Visualization, H.Y.L., L.J.L., J.Z., C.L.; Roles/Writing—original draft, H.Y.L., L.J.L.; Writing—review & editing, C.G.S., H.Y.L., L.J.L., J. Z. Click here for additional data file. Click here for additional data file. Click here for additional data file.


OBJECTIVES/SPECIFIC AIMS: To understand the role of PGF2a and to characterize a novel cyclooxyrgenase (COX)-independent prostaglandin synthesis pathway in epithelial ovarian cancer. METHODS/STUDY POPULATION: We used high grade epithelial ovarian cancer cell line (OVCAR3) as a model to study our pathway. Our main mode of PGF2a detection is through mass spectrometry. RESULTS/ANTICIPATED RESULTS: Our current results suggest the OVCAR3 cells may synthesize PGF2a independently of COX enzymes. We anticipate this novel pathway may be dependent on the TGFb pathway. DISCUSSION/SIGNIFICANCE OF IMPACT: Understanding the role and synthesis pathway of PGF2a may allow us to uncover a novel therapeutic pathway for high grade ovarian cancer.


Biological pathways play important roles in the development of complex diseases, such as cancers, which are multifactorial complex diseases that are usually caused by multiple disorders gene mutations or pathway. It has become one of the most important issues to analyze pathways combining multiple types of high-throughput data, such as genomics and proteomics, to understand the mechanisms of complex diseases. In this paper, we propose a method for constructing the pathway network of gene phenotype and find out disease pathogenesis pathways through the analysis of the constructed network. The specific process of constructing the network includes, firstly, similarity calculation between genes expressing data combined with phenotypic mutual information and GO ontology information, secondly, calculating the correlation between pathways based on the similarity between differential genes and constructing the pathway network, and, finally, mining critical pathways to identify diseases. Experimental results on Breast Cancer Dataset using this method show that our method is better. In addition, testing on an alternative dataset proved that the key pathways we found were more accurate and reliable as biological markers of disease. These results show that our proposed method is effective. The growth in knowledge of large-scale transcriptomic and proteomic technologies has enabled the identification of risk factors of complex diseases, personalized medicine, and so forth. Many algorithms  have been developed to process these data for acquiring important biological biomarkers. However, these methods still have some limitations and challenges. First, transcriptomic data analysis is the inherent complexity of multiple biological processes. Second, the data from different platforms also lead to noise. Although some methods were used to reduce the deviation, it is difficult to obtain robust result of these data. To circumvent these limitations, some computational methods project gene expression data into a molecular signaling network, but errors generated by variations in experiment also affect the accuracy to distinct different samples.Biological networks are powerful resources for the discovery of genes and genetic modules that drive disease. Fundamental to network analysis is the concept that genes underlying the same phenotype tend to interact; this principle can be used to combine and to amplify signals from individual genes , a bioloThere are some popular pathway analysis tools, such as GSEA, SPIA, DAVID, and Pathologist. They provide different methods to explain the function of the pathway analysis.GSEA   processeSPIA   combinesDAVID   information and gene expression profile data into the pathway, and then the candidate genes associated with disease phenotypes were screened using mutual information calculations. Secondly, we integrated gene's GO information into pathway to calculate the correlation between the pathways and then construct the pathway network. Finally, the critical pathways are identified in the network. The experimental results of this method with breast cancer data show that our method can not only find the high risk of gene and signaling pathways, but also find an association between the risk pathways.Gene expression data often aim to identify genes that are differentially regulated across different classes of samples, for example, finding the genes affected by a treatment, or finding marker genes that discriminate diseased from healthy subjects. Using gene expression profiles obtained from a number of genes for several samples or experimental conditions, we can obtain a gene set that shows a differential expression pattern across different samples. However, a differential expression gene set does not guarantee the existence of a real interaction between the corresponding proteins. Instead, it only suggests that there may be an interaction between the proteins. To accurately describe the change in gene interactions for several samples or experimental conditions, here we screen genes with PPI network.In the PPI network, if the two nodes (gene) of the edge are both in the set of differentially expressed genes, then the two genes were reserved; otherwise they were removed. So we get the candidate gene set (CG set). The biological pathway is a complete metabolic pathway which contains all genes that constitute a set. The gene-gene interaction of pathway is different in different tissues or samples. These differences may be caused by genes interactions of the pathway or neighbor pathway. To find the related biological pathway of the disease, we analyze the expression of each gene set in different sample, calculating the activity score of the biological pathway (active fraction) according to ; main pr(1) We get the PPI data from HPRD (human protein reference database) and gene expression data of the breast cancer from NCBI GEO ).(2) We compute differential expression genes in samples and filter through PPI network to get the candidate gene set (CGS).(3) The biological signaling pathways are obtained from database MSigDB c2  which includes 1329 sets of biological metabolism and signaling pathways. The candidate genes are projected into the pathway, calculating the active score of each pathway. p genes whose expression values can form a matrix of p rows. Each biological path corresponds to an activity vector, and the dimension of the vector is the number of samples; that is, in each sample j, an activity score can be calculated. The calculation formula is as follows:(4) In the GEO data, we divide the samples into disease and control parts and normalize gene expression profiles of the samples and then calculate to get the differentially expressed genes. We map differential gene to the PPI network to obtain the candidate gene set (CGS). The activity scores are calculated according to the expression value of candidate gene set (CGS) on each sample. Gene expression value of gene in biological pathway for every sample constitutes an expression value matrix; each column represents a sample and each row represents a gene. A biological pathway includesatj represents the activity score of the t biological pathway in the j sample, and eij represents the gene expression value of the i gene in the t biological pathway of the sample j, and p is the number of genes in the biological pathway. After that, we get an activity vector of biological pathways; vector   represents the activity score of the t biological pathway in m samples.Next, a phenotypic vector is constructed based on the phenotypic labels of the samples, and then the mutual information between the activity vectors and the phenotypic vectors of the samples is computed by combining the activity vectors of each biological pathway.Mutual information (MI) is a commonly used method of information measurement in information theory. In , the ratc1, c2, c3,…cm], the phenotypic vector is a zero-one vector, and if the sample is tumor, the corresponding value is one, otherwise zero.Constructing a phenotype vector based on the phenotype of the sample, . c is used to represent the phenotypic vectors of m samples, c = . So the correlation between biological pathways and disease phenotype S(i) can be represented by mutual information MI(a′(i), c) between a′(i) vector and c  vector. a′ is a discretized form of a. The formula is as follows:Using a is discretized into ⌊log2(sum  of  smaples) + 1⌋ equally spaced bins to obtain a′, respectively, p is the joint probability density function of a′  and c, and p(x)  and  p(y)  are the marginal probability density function of a′ and c.The activity score The interaction of the pathway can be represented as a network which was constructed as follows.G comprise a set V of pathways and a set E denote the weighted pathway-pathway interaction network with E ∈ V∗V. Here, we use similarity to define weight of the network. V = {p1, p2 … pn}, n = |V| is the number of pathways. Matrix A represents the weighted n × n adjacency matrix of G, where wij denotes the weight of the edge connecting pathway pi to pj and wij is calculated by the similarity of pi and pj. Supposing two pathways p1and p2, p1, p2 is a set of genes. p1 = {g11, g12 … gn1}, and  p2 = {g21, g22 … gm2}.The similarity of p1 and p2 can be calculated as weight of network. The similarity between the two pathways is the ratio of the sum of the similarities of all similar genes to the sum of the two pathway's elements. The formula is shown in Let M = |pi| is the number of genes on pi, and N = |pj| is the number of genes on pj.By this formula, the similarity values of each pair of candidate pathways can be calculated, and the pathway network can be constructed to identify the critical pathway.sim) between genes, including molecular function similarity (MF), biological processes similarity (BP), and Cellular Component (CC) similarity. Since a pathway contains multiple genes, we calculate the similarity of genes on the pathway as the similarity between the pathways to obtain the pathway similarity matrix simpi,pj.We use GOSemSim package of R language  to calcuBased on the biological pathway information and the similarity information of genes in the pathway, we constructed the biological pathway interaction network. In the network, each node represents a biological pathway, and if the two pathways contain differentially expressed genes, then they are connected. And the similarity of the differential genes on the two pathways is used as the similarity of the two pathways. MI, and the transfer matrix is obtained by the pathway similarity matrix . According to the random walk GeneRank algorithm, starting from the initial node, the final stable pathway node sequence is obtained by iterative calculation of the transfer matrix. This approach considers both the pathway and phenotype information and the semantic similarity between pathways, thus avoiding the node as isolated individuals and ignoring the important nodes that are highly correlated with other nodes. Therefore, this method can find potential pathway nodes.After constructing the biological pathway network, we hope to find biological pathways related to cancer phenotype. We use the random walk algorithm combined with phenotypic information to find the key nodes in the pathway network. The GeneRank method is a sorting algorithm proposed by Morrison et al. . In our P = {p1,p2, …, pN} represent n nodes in the pathway network. According to whether there is a similarity relationship between the two pathway nodes, the adjacency matrix w can be obtained by a similar matrix simpi,pj according to a threshold θ, as shown in We let set P according to the mutual information (MI) of pathway and phenotype. The initial row rank of P is obtained, which is denoted as x, ex = .Sort the pathway set  W is a symmetric matrix, wi,j = W = W = wj,i. According to graph theory, the degree of the i node is equal to the sum of the elements of row i of matrix w and is expressed in  degi, as shown in According to the definition,d is the damping coefficient, and the closer the value of d is to 0, the greater the impact of the mutual information is on the node sorting; on the contrary, if d is closer to 1, node sorting is more affected by the similarity. In our algorithm, we initialized d to 0.85.The algorithm steps are as follows:Input: initialization vector ex = ; adjacency matrix w; parameter d.ε is the error value; max is the maximum iterations.Output: r;Data Preprocessing:Iteration:Stop Conditionresn][;Return https://www.ncbi.nlm.nih.gov/geo/), including GSE33447 [The Breast Cancer Dataset was downloaded from GEO (Gene Expression Omnibus) website  [http://software.broadinstitute.org/gsea/msigdb) [The gene expression profiles of 369 cases of breast cancer and 73 cases of normal breast tissues were obtained, and the differentially expressed genes were analyzed, PPI network was obtained from the Human Protein Reference Database (rd.org/) . The pat/msigdb) . The dat/msigdb) , KEGG [1/msigdb) , and Rea/msigdb)  databaseTo identify the significance of the given pathway, first, we computed differential expression genes of the sample datasets  and then dealt with the PPI data. PPI network was mapped into the differential expression gene; we obtained candidate gene set (CGS). Secondly, the candidate genes are mapped into the pathways, calculating the active score of each pathway, and then the mutual information is computed by combining the activity vectors of each biological pathway. Finally, we calculate the similarity of the pathway to obtain pathway network, and the random walk algorithm combined with phenotypic information was used to find significance node of the pathway network.To provide a more comprehensive understanding of the proposed method, we discuss the method from the following aspects separately.U—the number of genes in the human genome; N—the number of differential genes; M—the number of genes in the pathway; x—the number of differentially expressed genes in this pathway. According to the above description, the rank of each pathway is the degree of relevance between the given pathway and the corresponding disease. The rank is calculated by algorithm see in . In thisThe top 1 pathway is KEGG_FATTY_ACID_METABOLISM; the pathway is supported by –22, and One significant pathway identified by our method was P53_SIGNALING_PATH-WAY –31. CurrAnother significant pathway was KEGG_PPAR_SIGNALING_PATHWAY. In , the autThe other pathway was PID_SHP2_PATHWAY; in , the resMoreover, we obtained top 15 pathways; their p-values are all less than 0.05. In order to test the effectiveness of this method, we compare with the most used DAVID software. The identified top 15 pathways are compared with DAVID method and our method to identify the risk pathway of breast cancer as shown in DAVID is a widely used and approved method. According to the pathway set we have identified  and took 80 genes in the selected pathways as features, and SVM  is emploWe applied the feature gene set to the independent gene expression datasets . This set of data is not related to the data previously used, and they are independent of each other. In order to evaluate our proposed method objectively, we used the same number of biological pathways or gene markers to compare the results. The biological pathway markers obtained by our method have a good discrimination in the dataset, higher than 0.6, close to 0.7, and the results are shown in https://stringdb-static.org/); the sources of other data are the same. The dataset consists of 177 samples obtained from comparing 87 tumor samples with 90 normal samples, which is shown in Secondly, in order to test the robustness of our method, we apply the method in this article to four gastric cancer gene expression datasets. The dataset was also downloaded from GEO, which includes GSE63089 , GSE5680We used our method to test on the gastric cancer dataset, and we selected the same number of biological pathways and gene markers. Compared with the T-test, our method has also achieved a higher or comparable value. We used GSE63089 and GSE56807 as the training set, with GSE33335 and GSE19826 as the test set. A 10-fold cross-validation was used to train and test SVM. The above experiment was repeated 50 times; the average value is taken as the final result. Our method also has good discrimination in this dataset, higher than 0.6 and better than T-test; the results on gastric cancer data are shown in Complex diseases, especially cancer, are extremely harmful to human health. Therefore, the identification of cancer markers is the key of the study. Pathway analysis combined with multiple types of high-throughput data reflects the biological processes more clearly. Therefore, pathway-based complex disease analysis method has become a hot research topic. Unlike previously available pathway analysis methods, we have considered not only the genes interaction of the pathway, but also the interaction between the pathways.In this paper, we proposed a new approach to consider the correlation between biological pathways and establish a biological pathway interaction network. Then, the GeneRank algorithm based on random walk and the mutual information of phenotype were used to select cancer related biological pathways. Finally, we use the support vector machine and feature selection method to apply to cancer datasets. The results show that our method achieves better results than T-test method. In addition, the validation in the independent dataset and the functional analysis of the biological pathway indicate that the pathway we identified as a biological marker of disease is more accurate and reliable. We will employ more datasets to assess the validity of our approach in future research.


Tripterygium wilfordii Hook (TWH) is a traditional Chinese medicine commonly used in the treatment of rheumatoid arthritis (RA); the unique therapeutic effect has been confirmed by a large number of research papers. TWH has many compounds that lead to its active compounds. However, the potential targets and pharmacological and molecular mechanism of its action treatment of rheumatic diseases are not entirely clear. Therefore, the network pharmacology approach is needed to further study and explore its treatment mechanism. We have successfully set up 10 networks, including four major networks and other networks. Four major networks include rheumatoid arthritis disease network, compound-compound target network of TWH, TWH compound target-rheumatoid arthritis disease network, and TWH-rheumatoid arthritis disease-mechanism network. Other networks consist of RA disease and TWH related targets clusters, biological processes, and pathways network. Our study successfully predicted, explained, and confirmed the TWH of RA disease molecular synergy and found the potential of RA related targets, cluster, biological process, and pathways. This study not only provides prompts to the researcher who explores pharmacological and biological molecular mechanism of TWH applying to RA disease, but also proves a feasible method for discovering potential activated compounds from Chinese herbs.Traditional Chinese medicine has specific effect on some chronic diseases in clinic, especially in rheumatic diseases. Rheumatoid arthritis (RA) is a long-term autoimmune disorder with a global incidence of 1 percent and most common in middle-aged women, characterized by warm, pain, swollen, and stiffness of peripheral joints; it may result in progressive joint damage, deformity, dysfunction, and increased mortality .Currently, the commonly used treatment ways in RA are surgery, medicine, and psychotherapy. Drugs used to treat RA are classified into nonsteroidal compounds (NSAIDS), disease-modifying antirheumatic drugs (DMAKDS), and glucocorticoids .These medications may prevent or reduce joint damage and help to maintain regular structure and function of the joint. However, these drugs can only control the disease progress of RA and usually associated with many adverse effects such as methotrexate pteridine, DMARDs drugs; its side effects include nausea, vomiting, and digestive tract reaction, oral inflammation, skin rashes, hair loss, bone marrow suppression, and liver toxicity; occasionally occurring can lead to death of pulmonary interstitial lesions, and glucocorticoids drugs are associated with osteoporosis and infection due to their effects on hormones . NSAIDS Tripterygium wilfordii Hook (TWH) was recorded in the compendium of materia medica, written by Li Shizhen, one of the most important traditional Chinese medicine classics and is a famous medicine for the treatment of RA. According to the theory of Chinese medicine, this medicine can resist rheumatism, activate blood circulation, and reduce swelling and pain. It is commonly used in the treatment of RA and ankylosing spondylitis and other diseases. Its antirheumatic effect is inferior to other steroid drugs and is superior to other antirheumatic Chinese and western medicines. It can replace most of the steroids for treatment, reducing its dependence and dosage with no rebound phenomenon after drug withdrawal [thdrawal , 9. HoweDue to the multichemical components, multipharmacological effects and multiaction targets of TCM in the treatment of diseases, the traditional research methods are difficult to entirely explain the mechanism of action . Howeverhttp://lsp.nwu.edu.cn/index.php), and literature review. In total, the information of 144 TWH compounds was collected [All the composite compounds of TWH were obtained from the following database: TCM database@Taiwan (http://tcm.cmu.edu.tw/), The Traditional Chinese Medicine Systems Pharmacology Database  models simulated in silico systems . We usedOral bioavailability (OB): the OB value is calculated on silicon system using the OBioavail 1.1 model to calculate the relationship between the compound and the P4503A4 enzyme and the transporter (p-glycoprotein). The OB value reflects the ability of compounds to enter the human circulatory system . We seleCaco-2 permeability: absorption of the drug in the intestine is closely related to its permeability in the intestinal epithelial cells. The human intestinal cell line Caco-2 is often used as an effective in vitro model to study the passive diffusion of drugs through the intestinal epithelium . We screDrug-Like value: the DL value is used for the ability of a prospective compound to become a drug by calculating the similarity between a potential compound and a known drug. Drug-like compounds are not drugs, but they have the potential to become drugs. Such compounds are called drug-like molecules or drug analog molecules . We usedDrug half-life refers to the concentration of the drug in the blood or the amount of drug in the body reduced to one-half, for the important properties of the drug; you can calculate the dosing interval, determine the dose, and calculate the volume of drug accumulation . The simBy screening TWH-combined OB, Caco-2, DL, and HL indicators, 30 potentially drug-producing compounds were finally obtained Figures . The dethttp://lsp.nwu.edu.cn/tcmsp.php), which is a unique systematic pharmacology platform of Chinese herbal medicines that captures the relationships between drugs and targets [All the active ingredients were input into Traditional Chinese Medicine Systems Pharmacology Database ( targets . Since thttps://www.pharmgkb.org/); and (4) Genetic Association Database (GADA)(https://geneticassociationdb.nih.gov/) [We collected the therapeutic target data for the treatment of arthritis from four resources: (1) Therapeutic Target Database (TDD) (https://db.idrblab.org/ttd/); (2) Online Mendelian Inheritance in Man(OMIM) (http://omim.org/); (3)PHARMGKB (ih.gov/) . In thesWe used the Bisogenet3.0 plug-in in Cytoscape3.6 to look for the Protein–protein interaction (PPI) data. Bisogenet is a very powerful bioinformatics plug-in that directly accesses 6 open biological databases on protein-protein interactions . When weThe following networks were constructed: (1) rheumatoid arthritis disease network; (2) compound-compound target network of TWH; (3) TWH-rheumatoid arthritis network; (4) TWH- rheumatoid arthritis disease-mechanism network.All the networks can be generated via utilizing the network visualization software Cytoscape   were chosen for further analysis.We applied the Database for Annotation, Visualization and Integrated Discovery (DAVID) to carry out GO enrichment analysis and pathway enrichment analysis of proteins in PPI network . P valueThis network is composed of 173 nodes (150 compound target nodes and 23 compound nodes) and 518 edges. In the compounds-targets network we can see that some compounds can act on multiple physiological targets . Some compounds only act on a few targets; for example, Triptofordin B1 compound only regulates ESR1. At the same time, some targets can be regulated by multiple compounds, which may play important roles in the treatment of diseases. For instance, ESR1, AR, F2, and PTGS2 are regulated by over 60% of potentially active compounds. Some targets are only regulated by one compound, such as ADH1B, ADRA1D, and AHR targets. The network details are provided in The nodes in the disease PPI network represent the interrelationships during the development of the RA. We constructed a total of 250 nodes and 633 edges in the PPI network of RA. The area size of the node is positively related to the degree of the node, and degree indicates the number of relationships between the node and other nodes in the network. The areas of nodes such as PIK3R1, IKBKG, FN1, LCK, and LYN are larger and can be easily found in the PPI network; the corresponding degree values of each node are 37, 30, 29, 28, 27, and 26. These targets may play a key role in the development of RA disease. The network details are provided in  P value < 0.001. Analyzing another cluster by the same way, we get the same related biological processes, molecular functions, and cell components. The details are described in By using the MCODE plug-in cluster analysis of the PPI network of RA disease, seven clusters were obtained . These pathways can regulate complex biological and metabolic processes. Interfering with certain targets of these pathways may be a potential strategy for the future treatment of RA diseases.α inhibitor, B-cell depletion and blocking antibodies, IL-6 inhibition, and IL-1 inhibition can affect RA diseases by influencing these pathways.RA is an autoimmune disease characterized by chronic inflammation of the synovial membrane. Its main pathways include PI3K-Akt signaling pathway, IL-17 signaling pathway, TNF signaling pathway, MAPK signaling pathway, and T cell receptor signaling pathway, NF-kappaB signaling pathway, Fc epsilon RI signaling pathway, Neurotrophin signaling pathway, Ras signaling pathway, RIG-I-like receptor signaling pathway, FoxO signaling pathway, and HIF-1 signaling pathway. These pathways are closely related to the balance between anti-inflammatory factors and inflammatory factors, inflammatory cell migration, infiltration, cartilage extracellular matrix degradation and cell proliferation, differentiation, and apoptosis. Clinically used immunological preparations such as TNF-TWH's C-D Target Merge Network is obtained by merging the disease PPI network with a drug target network. The C-D Target Merge Network directly and concisely expresses the relationship between multitargets of multicompounds, as well as presenting the relationship between multicompound targets and disease targets in one figure . The netIn order to better analyze the mechanism of TWH's action on RA disease, we have deleted the drug compounds and indirect targets in the TWH-RA network to obtain the TWH-RA's C-D target intersection network. The C-D target intersection network consists of 118 nodes and 667 edges. We performed clustering and pathway analysis on this network to obtain the main mechanisms of TWH-treated RA disease. The network details are provided in We also only deleted the drug compounds that did not directly relate the target to obtain the C-D target intersection network for later analysis. The network details are provided in The cluster analysis method is the same as the PPI network clustering method, and the following thirteen different categories are obtained  and enriThe other enrichment analysis is the same as the above analysis method. We have obtained biological processes, molecular functions, and cell components for different clusters of targets. The details of the enrichment analysis process are provided in Cluster analysis of TWH's role in the major genes of RA disease can be understood that RA treatment of RA can inhibit angiogenesis, regulate innate immune response, and adapt to immune response and inhibit migration and invasion of fibroblast-like synoviocytes and other ways. Now research shows that Tripterygium can also treat RA through the above-mentioned effects.α and IL-6 [The effect of TWH on RA was not inferior to methotrexate, and its adverse reaction was slightly lower than methotrexate . The latand IL-6 .β-stimulated RSC-364 cells (rat synoviocyte line) and inhibit cartilage breakage. In the collagen-induced severe combined immunodeficient mouse model of RA, triptolide can reduce the activation of TNF-α and MMP-9 to inhibit the expression of JNK signaling pathway and specifically inhibit the migration of RA FLS cells and improve joint inflammation and cartilage breakout in RA mice [The migration and invasion of fibroblast-like synoviocytes (FLS) to cartilage and bone cells are critical processes for the destruction of RA cartilage. TWH can downregulate the expression levels of IL-32, MMP-1, and MMP-9 in IL-1 RA mice .+/CD8+ was significantly higher in RA patients compared with normal controls, and the levels of B-cells and plasma lipid peroxides (LPO) were also significantly higher than those in normal subjects. After one month of treatment with TWH, the patient's clinical symptoms were significantly relieved. The ratio of CD4+/CD8+, B cell, and plasma LPO levels were significantly lower than before treatment. CD4+ is a helper/inducing cell (Th/Ti) that has the function of helping T lymphocytes to transform into effector cells, B cells to generate antibodies and macrophage activation, and plays a role in assisting and inducing cellular and humoral immunity; CD8+ cells are T-inhibitory/Killer cells (Ts/CTL) having the effect of inhibiting T cell activation, inhibiting B cell production of antibodies and cytotoxicity, and inhibiting cellular and humoral immunity [In the TWH test on proliferation of splenic lymphocytes, the triptolide group showed dose-dependent inhibition of Con A-induced proliferation of T lymphocytes and inhibited LPS-induced proliferation of B lymphocytes. This suggests that the effect of TWH on RA treatment is related to inhibition of T lymphocyte proliferation activity . The ratimmunity . This shDrug compounds associated with RA genes were placed in the DAVID for pathway analysis and Cytoscape was used to establish compound target–pathway network . There aThe PI3K-Akt signaling pathway in RA is involved in the promotion of aggressive immune-cell and synovial cell proliferation and survival, neovascularization, apoptosis, and altered innate immunity. It can affect the increase of chemotaxis of immune cells such as neutrophils, macrophages and eosinophils, degranulation of mast cells, and activation, maturation, and survival of activated T cells and B cells. At the same time, this pathway can also regulate some cytokines such as VEGF and FGF factors to induce apoptosis and survival of synovial cells, chondrocytes, and immune cells , 33.α/IFN-γ-induced apoptosis and regulate a series of anti-inflammatory genes such as IL-10. NOD2 can also be regulated to adapt to the immune system [κB, TRAF6, and IKK [The NOD2 signaling pathway mainly regulates the innate immune system and plays an important role in eliminating infection and avoiding excessive tissue damage. NOD2 can attenuate TNF-e system . It is p and IKK .The IL-17 signaling pathway and TNF signaling pathway are considered classical immune signaling pathways, which can activate the intracellular molecular signal of RA pathogenesis. The route of delivery leads to the activation of endoplasmic reticulum cells and recruitment of congenital and adaptive immune cells to synovial cells, resulting in inflammation of synovial cells, increased angiogenesis, and decreased lymphangiogenesis. For these cytokine inhibitors have been used clinically or in clinical trials –38.The MAPK family includes members such as tyrosine phosphoprotein kinase (p38), c-Jun N-terminal kinase (JNK), and extracellular-signal regulated protein kinase (ERK 1/2). In synovial tissue of RA, p38 is highly expressed and activated. The commonly used p38 MAPK inhibitor SB203580 reduces the production of proinflammatory cytokines in monocytes/macrophages, neutrophils, and T lymphocytes. JNK plays a pivotal role in the induction of cytokines and matrix metalloproteinase (MMP) gene expression in synovial cells. The synovial cells mainly include two subtypes of JNK1 and JNK2. Insufficiency of NK2 is only evident in preclinical models of arthritis, but JNK1 deficiency can reduce synovial inflammation and joint destruction. JNK1 also facilitates osteoclast differentiation because the progenitor cells of osteoclasts deficient in JNK1 are immature, unable to undergo bone resorption and differentiate into osteoclasts. The role of ERK in RANKL-mediated osteoclast differentiation is also crucial. The destruction of the osteoblast lineage ERK1 and ERK2 results. Osteoclasts are produced by the nuclear stimulating factor receptor (RANKL), which in turn causes osteoclasts to decrease , 40.The network can see the relationship between the compound and the target, and the target interacts with the gene. However, due to the complex intracellular relationships between molecules, this network analysis is too complex. For example, MOL000528 compounds directly act on 3 RA-related genes and 31 indirectly on RA genes. These 31 genes act on 82 RA-related genes, and all targets also act on 8 major cell signaling pathways.In order to better and concisely represent the mechanism of action of TWH compounds in the treatment of RA, we deleted indirect target and indirect acting genes, resulting in target interactions and pathway maps between TWH compounds . The netJUN is an important node of the JNK signaling pathway, which is widely involved in the regulation of cell proliferation, differentiation, apoptosis, metabolism, movement, and DNA damage repair , 42. In In RA, nitric oxide (NO) is involved in inflammation, angiogenesis, and tissue destruction. Nitric oxide synthase regulates nitric oxide synthesis in the body . Our stuCaspase-3 (CASP-3) is known to cause apoptosis in cells. The expression of caspase-3 mRNA in PBLs was significantly higher in MT-treated RA patients than in patients without MT . Our stu+ macrophages in the synovial fluid of RA patients increases. Inhibition of the PI3-kinase/Akt-1 or STAT-3 pathway significantly reduced the percentage of CD14+ cells within the synovial fluid and led to a decrease in Mcl-1 and induction of synovial macrophage apoptosis. Transfection of RA synovial macrophages with Mcl-1 siRNA resulted in apoptotic cell death [Mcl-1 is a Bcl-2 family of antiapoptotic molecules that are essential for the survival of T and B lymphocytes and macrophages and also is essential for the survival of synovial fibroblasts in RA . Our stull death .2+ levels and phosphorylation, resulting in its translocation from the cytoplasm and nucleus to the perinuclear membrane vesicles. PLA2G4A is regulated by MOL005828 compound, which is enriched in MAPK signaling pathway and VEGF signaling pathway. Oral pyrrole administration achieves antiarthritis activity by inhibiting cPLA2α activity, resulting in decreased eicosanoid levels and inhibition of MMP and COX-2 mRNA expression. These results support the potential therapeutic role of alpha in the treatment of RA by the alpha 2 inhibitor of cPLA [The phospholipase A2 group IVA gene (PLA2G4A) encodes a member of the cytoplasmic phospholipase A2 group IV family. The enzyme catalyzes the hydrolysis of membrane phospholipids to release arachidonic acid, which is then metabolized to eicosanoids. Eicosanoids including prostaglandins and leukotrienes are lipid-based cytokines that regulate hemodynamics, inflammatory responses, and other intracellular pathways. The hydrolysis reaction also produces lysophospholipids that are converted into platelet activating factors. The enzyme is activated by increasing intracellular Ca of cPLA .The MMP1, MMP9, MAPK14, TNF, and PIK3CG proteins in the previous discussion can affect the inflammatory response system, affecting processes such as apoptosis, cell cycle, and immune cell migration. Our study showed that MMP1 can be directly affected by MOL000422 compound and the gene was enriched in the pathway of IL-17 signaling pathway. The MMP9 was regulated MOL005828 and the gene was enriched in the two pathways of IL-17 signaling pathway and TNF signaling pathway. MAPK14 is regulated by four compounds  and accumulated to seven pathways in the enrichment analysis of IL-17 signaling pathway, MAPK signaling pathway, and Neurotrophin signaling pathway, NOD-like receptor signaling pathway, T cell receptor signaling pathway, TNF signaling pathway, and EGF signaling pathway. TNF by MOL000422 and MOL003187 is regulated by two compounds and accumulated to six pathways in the enrichment analysis of IL-17 signaling pathway, MAPK signaling pathway, NF-kappa B signaling pathway, NOD-like receptor signaling pathway, T cell receptor signaling pathway, and TNF signaling pathway. PIK3CG is regulated by MOL000358 and MOL000422 compounds and this gene is enriched in PI3K-Akt signaling pathway.We found that different compounds in TWH play different roles in the treatment of RA. MOL000422 is the compound that TWH has the most target for RA. It acts on 13 pathways through 8 targets . MOL003187 compounds act on 12 RA-related pathways via the 8 targets . MOL003217 compound directly act on the NOS3 and MMK14 target, and these two targets act on 9 pathways. MOL005828 compound directly acts on JUN, MMP9, and PLA2G4A3 targets, and these three targets act on 8 pathways. MOL003199 compound acts on 7 RA-related pathways via the MAPK14 targets. MOL000358 compound acts on 9 RA-related pathways via the 5 targets . Some compounds act together on the same target and act on the same pathway, such as MOL000296, MOL004449, and MOL003182 and simultaneously act on F1F-1, PI3K-AKT, and VEGF signaling pathways through NOS3 target.Our study found that some compounds in TWH can directly act on relevant targets to exert anti-inflammatory effects, such as luteolin, quercetin, and naringenin, etc., and some compounds indirectly exert anti-inflammatory effects through their targets, such as Triptonolide, Tryptophenolide, and Zhebeiresinol, etc. In this study, by collecting TWH compounds and their properties, screening active compounds, collecting targets, and disease-related targets, and constructing a variety of pharmacological networks we predict, explore, and clarify the molecular cooperation mechanism of TWH in the treatment of RA. We have also successfully identified targets related to RA, clusters of targets, biological processes of targets, molecular functions, and cell components and related pathways. The construction of the pathway of RA disease protein-protein interactions reveals the complex underlying molecular interaction mechanisms of RA. Finally, the C-D-Target network and the pathway of C-D-Target network were constructed to analyze, confirm, and reveal the potential pharmacological and molecular mechanisms of TWH in the treatment of RA.