To explore the Radix Paeoniae Rubra-Flos Carthami herb pair's (RPR-FC) potential mechanism in treating ischemic stroke (IS) by network pharmacology and molecular docking technology.The Traditional Chinese Medicine Systems Pharmacology Database was used to screen the active components of the RPR-FC, and Cytoscape 3.8 software was used to construct a network map of its active components and targets of action. The GeneCards and OMIM databases were used to identify disease targets of IS, and the common targets were chosen as research targets and imported into the STRING database to construct a protein–protein interaction network map of these targets. R language software was used to analyze the enrichment of GO terms and KEGG pathways, and explore the mechanisms of these targets. Molecular docking technology was used to verify that the RPR-FC components had a good bonding activity with their potential targets.A total of 44 active components, which corresponded to 197 targets, were identified in the RPR-FC. There were 139 common targets between the herb pair and IS. GO functional enrichment analysis revealed 2253 biological process entries, 72 cellular components entries, and 183 molecular functions entries. KEGG pathway enrichment analysis was mainly related to the NF-kappa B signaling pathway, the TNF signaling pathway, apoptosis, the MAPK signaling pathway, the PI3K-Akt signaling pathway, the VEGF signaling pathway, etc. The molecular docking results showed the components that docked well with key targets were quercetin, luteolin, kaempferol, and baicalein.1 These targets are closely related to the NF-kappa B signaling pathway, the MAPK signaling pathway, the PI3K-Akt signaling pathway, the VEGF signaling pathway, and other signaling pathways. These pathways are involved in the recovery of nerve function, angiogenesis, and neuronal apoptosis and the regulation of inflammatory factors, which may have a therapeutic effect on IS.The active components  of the RPR-FC and their targets act on proteins such as MAPK1, AKT1, VEGFA, and CASP3, which are closely related to IS. Although choices for stroke treatment are still limited, the development of drug therapies and mechanical thrombolytic recanalization has led to improvements in IS patients’ recovery. However, due to the limitation of the thrombolytic time window, there is a great demand for developing drugs for acute ischemic stroke.Despite new developments in treatment strategies, stroke is still one of the main causes of death and disability worldwide. Recently, immunomodulatory therapy for IS has become a hot spot in neurology research. The immunomodulatory effects of traditional Chinese medicines on IS have been recently studied in basic and clinical trials. Studies have shown that Qi deficiency and blood stasis syndrome is common type of IS.,6 Therefore, strategies that promote blood circulation and remove blood stasis are common methods for treating IS. The proper and reasonable use of drugs that activate blood and remove stasis is important for promoting blood supply to the brain, saving the ischemic penumbra, protecting nerve cells, and regulating the immune response. Basic and clinical research on the efficacy of blood circulation promotion and blood stasis removal for IS treatment has been carried out. Radix Paeoniae Rubra-Flos Carthami herb pair's (RPR-FC) is commonly used for the treatment of IS. It is included in many classical traditional Chinese medicine prescriptions. The combination of the 2 herbs can enhance blood circulation and remove blood stasis. The specific research process is as Figure After IS occurs, the immunoregulatory response plays a huge role in regulating immune inflammation, oxidative stress, cell damage, apoptosis, and protecting nerve function.22.1We searched the TCMSP database for the keywords “Radix Paeoniae Rubra” and “Carthami Flos” and used bioavailability (F) ≥30% and drug-likeness (DL) ≥0.18 as criteria.F refers to the percentage of oral doses of a drug that reaches the blood circulatory system, and DL refers to the similarity of the components of a prescription to known drugs. Based on this search, the RPR-FC's effective components and the corresponding targets of the compound were identified from the TCMSP database.2.2http://www.uniprot.org/uniprot/) was used to determine the gene names and UniProt numbers of all the targets and convert them into target abbreviations. The active components of the RPR-FC and their corresponding targets were imported into Cytoscape 3.8 software to construct a “Component-Target” network diagram. In the network diagram, the nodes represent chemical components and targets. The edges represent interactions. The degrees indicate the number of edges connected to a node, and the degree values are positively correlated with function. The “Network Analyzer” was used to analyze and derive the degree values, betweenness, and other topology attributes.The UniProt database (2.3https://www.genecards.org) and OMIM database (http://omim.org/) using “ischemic stroke” as a keyword.,13 Duplicate targets were removed with UniProt. Using the Venny platform (http://bioinfogp.cnb.csic.es/Tools/Venny/), the targets of the active components of the RPR-FC were compared with the IS targets, and the targets of the RPR-FC for the treatment of IS were identified.Disease targets were identified from the GeneCards database (2.4https://string-db.org/). The species was limited to “human,” interactions were determined, and node1, node2, and combined score information were retained in the file. The above data were imported into Cytoscape 3.8 software, a PPI network diagram of the target was constructed, and the core network was screened.The common targets of the RPR-FC and IS were imported into the STRING database (2.5 Graphs and bubble graphs were generated from the data.R language (ClusterProfiler package) was used to carry out GO enrichment analysis and KEGG enrichment analysis at the biological process, molecular, and cell levels.2.6 In this study, the active components of the RPR-FC to effectively bind potential therapeutic targets were verified by molecular docking studies, and the docking results were analyzed to verify the reliability of this research.The 3D structures of the active components of the RPR-FC were downloaded from the TCMSP database, and the 3D structures of the candidate IS targets were downloaded from the PDB database. The targets’ structure was modified by removing ligands, water, and hydrogen by importing them into PyMOL 2.4 software. Using AutoDock 4.0 software, the candidate pharmacodynamic components and candidate targets were used for docking studies after preprocessing.33.1Using the TCMSP database for screening, 29 active components of Paeoniae Rubra, and 15 active components of Flos Carthami were identified . There were 2252 biological processentries, 183 molecular functions entries, and 72 cellular components entries identified by GO enrichment analysis. GO analysis revealed that the above targets play a biological role through the lipopolysaccharide reaction, oxidative stress response, etc. The proportion of cellular components terms in the intercellular space was large. Ubiquitin-like protein ligase binding (ubiquitin) was the most enriched molecular functions terms. The top 10 terms, according to corrected P values are displayed in the graphic in Figure R language (cluster profiler package) was used to analyze the enrichment of GO terms and KEGG pathways (P < .05), including fluid shear stress and atherosclerosis, the AGE-RAGE signaling pathway in diabetic compositions, the TNF signaling pathway, and the IL-17 signaling pathway were identified. The top 30 pathways are shown in the graphic in Figure  The TNF signaling pathway mainly regulates the inflammatory response after cerebral infarction. Clinical studies have shown that inflammatory cytokines are effective in the pharmacological regulation of cerebral infarction. Experimental evidence has shown that tumor necrosis is the main target for new stroke treatments. The MAPK signaling pathway regulates gene expression and basic cellular processes, such as cell proliferation, differentiation, migration, metabolism, and apoptosis, in eukaryotic cells. It is considered a therapeutic target for many diseases. To date, increasing evidence has shown that the MAPK signaling pathway is involved in the pathogenesis and development of IS. IL-17 regulates stem cells and the generation of cortical neural stem cells (NSCs) in adults after stroke. Knockout of IL-17 helps to regulate the PI3K-Akt pathway, promotes the proliferation of NSCs, and promotes nerve growth after IS. This study found that the RPR-FC has neuroprotective effects, promoting nerve repair, vascular remodeling, inhibition of neuronal apoptosis, and regulation of inflammatory factor expression after IS.A total of 160 KEGG pathways  was used to download the 3-dimensional structures of the proteins, PyMOL 2.4 software was used to optimize the protein receptors, and AutoDock 4.0 was used for component docking. In the “Component-Target-Pathway” network diagram, the 4 components with the highest degrees, namely, luteolin, quercetin, kaempferol, and baicalein, were selected as candidate therapeutic docking components. The targets with the highest degrees, namely, MAPK1, AKT1, VEGFA, and CASP3, were selected as candidate docking targets. Molecular docking analysis of these targets was performed, and the docking scores were calculated. A scoreless than the original ligand score indicated that the component has good docking activity with the target and verified the docking result of the chemical component. The relevant information of the molecular docking model is shown in Table The PDB database (4P < .05).Salvia miltiorrhiza can alleviate IS by promoting the survival of mesenchymal stem cells. In rats subjected to middle cerebral artery occlusion, treatment with S. miltiorrhiza can lead to recovery of the infarct area and positive changes in rat behavior. The RPR-FC is a common combination used for the treatment of cerebrovascular diseases and is a component in traditional Chinese medicine prescriptions such as Xuefu Zhuyu Decoction and Buyang Huanwu Decoction. Radix Paeoniae Rubra is sour and soft in nature. It has the functions of nourishing yin, promoting blood stasis, relieving pain, and cooling blood. Its main chemical components include paeoniflorin, paeoniflorin, paeonol, and S. miltiorrhiza. These components regulate NF-kappa B and other signaling pathways. For example, Paeoniae Radix Rubra can significantly reduce the volume of the cerebral infarct caused by ischemic injury and alleviate neuronal damage caused by transient cerebral ischemia by inhibiting glial hyperplasia and improving antioxidant activity. Radix Paeoniae Rubra is pungent, warm, and non-toxic and activates blood circulation, removed blood stasis, and relieves pain. Its main components are safflower yellow pigment, quercetin, kaempferol, and other components, which can regulate the NF-kappa B signaling pathway, the TNF signaling pathway, and other signal pathways to exert anti-inflammatory effects and prevent and treat cardiovascular and cerebrovascular diseases. For example, hydroxysafflor yellow A confers neuroprotection against focal cerebral ischemia by modulating the crosstalk between the JAK2/STAT3 and SOCS3 signaling pathways. The RPR-FC is a commonly used drug pair for the treatment of IS. The 2 herbs are able to enhance blood circulation and remove blood stasis. Both Radix Paeoniae Rubra and Flos Carthami extract have anti-inflammatory and neuroprotective effects against IS, but there is no literature on the mechanism of these 2 traditional Chinese medicines in the treatment of IS. Therefore, this study used network pharmacology combined with molecular docking analysis to study the mechanism of the RPR-FC in the treatment of IS to provide a scientific basis for the use of treatment strategies that promote blood circulation and remove blood stasis for immunoregulation in IS.Modern pharmacological studies have shown that the effects of some active components of Chinese herbal medicines against IS are related to anti-inflammatory, anti-oxidative stress, and nerve cell protection functions. For example, Chuanxiong can regulate IS-related targets, biological processes, and signaling pathways. Animal experiments have shown that Chuanxiong can improve the neurobehavioural scores of IS rats and has a protective effect on neurons  injury for 90 minutes and has a protective effect on mitochondria in the area of brain injury. Recent studies have shown that kaempferol may prevent ischemic brain injury and neuroinflammation by inhibiting the activation of STAT3 and NF-kappa B, thereby exerting a protective effect against ischemic brain injury. Baicalein is a widely distributed natural flavonoid that has many beneficial pharmacological effects, such as anti-inflammatory and antioxidant effects. Animal experiments have shown that baicalein can significantly reduce Nrf2 and AMPK levels, suggesting that baicalein may play a neuroprotective role by downregulating the expression of NF-kappa B and LOX-1 and inhibiting the AMPK/Nrf2 pathway.The active components of the RPR-FC that bind best to the key targets are quercetin, luteolin, kaempferol, and baicalein. Quercetin is a flavonoid compound extracted from food that plays an anti-atherosclerotic role, by exerting anti-inflammatory, antioxidant, and endothelial-dependent vasodilatory effects and lowering blood lipids.5At present, the RPR-FC is widely used for the treatment of IS, but pharmacological research reports are scarce. The active components of the drug pair such as quercetin, luteolin, kaempferol, and baicalein may act on MAPK1, TP53, TP53, TP53, and MAPK through IS-related pathways, including the MAPK signaling pathway, NF-kappa B signaling pathway, PI3K-Akt signaling pathway, and VEGF signaling pathway. MAPK8, AKT1, VEGFA, MMP9, EGF, and other targets exert anti-inflammatory, immunomodulatory, anti-thrombotic, and vascular endothelium protective effects to treat IS. In view of the limitations of network pharmacology, it is necessary to further experimentally verify the potential active components, targets, and related pathways of the RPR-FC in the future.Conceptualization: Dexi ZhaoFormal analysis: Xingyu ChenFunding acquisition: Dexi Zhao, Yue WangProject administration: Ruonan WangVisualization: Xingyu Chen, Ying MaWriting – original draft: Xingyu ChenWriting – review & editing: Xingyu Chen, Ying Ma


Even though this compound has been intensively used for decades, the structural basis of arabinogalactan protein precipitation by Yariv is not known. Multiple biophysical studies have been published, but none of them attempted to elucidate the three-dimensional structure of the Yariv-galactan complex. Here we use a series of molecular dynamics simulations of systems containing one or multiple molecules of ß-D-galactosyl Yariv reagent with or without oligo ß-D-(1→3)-galactan to predict the structure of the complex. According to our model of Yariv-galactan complexes, Yariv reagent forms stacked oligomers stabilized by π-π and CH/π interactions. These oligomers may contain irregularities. Galactan structures crosslink these Yariv oligomers. The results were compared with studies in literature.Arabinogalactan proteins are very abundant, heavily glycosylated plant cell wall proteins. They are intensively studied because of their crucial role in plant development as well as their function in plant defence. Research of these biomacromolecules is complicated by the lack of tools for their analysis and characterisation due to their extreme heterogeneity. One of the few available tools for detection, isolation, characterisation, and functional studies of arabinogalactan proteins is Yariv reagents. Yariv reagent is a synthetic aromatic glycoconjugate originally prepared as an antigen for immunization. Later, it was found that this compound can precipitate arabinogalactan proteins, namely, their  O-glycosylation -galactan backbone with β-D--galactan side chains, which are often further substituted by arabinose, rhamnose, fucose, or glucuronic acid  represent an extremely heterogeneous group of plant cell wall proteoglycans, which together with moderately glycosylated extensins and minimally glycosylated proline-rich proteins belong to the superfamily of hydroxyproline-rich glycoproteins HRGPs, . A genersylation . Commonlnic acid . Moreovenic acid . AGPs arnic acid .There are various tools to study these proteins including monoclonal antibodies, β-Yariv reagents, specific degradation of AGP sugar chains, chemical synthesis, and bioinformatics approach . While bD-glucosyl and β-D-galactosyl) were demonstrated to selectively bind to AGPs -galactan backbone, which has to be longer than five residues for the interaction to occur. Moreover, Yariv reagents  are synt to AGPs . Thus, t to AGPs . Only reD-galactosyl Yariv, further referred to as Yariv) and/or galactan oligosaccharides in explicitly modeled water. The protein part of AGP was not modeled because it has been shown that Yariv recognizes the carbohydrate part of AGPs .All simulations were done in Gromacs package version 2018 . GalactoSystems containing different molecular assemblies were solvated by TIP3P water molecules . Next, ivia Zenodo (DOI: 10.5281/zenodo.4767970).Simulations were analysed using in-house scripts in Python with MDTraj library . SimulatD-(1→3)-galactan molecules, we carried out simulations of, in total, 48 systems containing various numbers of Yariv reagent molecules (Y) and carbohydrate molecules. Carbohydrate molecules included trisaccharide -Gal-β-D-(1→3)-Gal), or hexasaccharide -Gal-β-D-(1→3)-β-D-Gal-β-D-(1→3)-Gal-β-D-(1→3)-β-D-Gal-β-D-(1→3)-Gal). They are further referred to as Y2 for a system with two molecules of Yariv reagent, YT for a system with Yariv reagent and trisaccharide, etc. Initial structures of the systems were assembled manually. In summary, simulated systems included Y2, YT, YH, Y4, Y4T, and Y4H, all in eight replicas.To elucidate the structural organisation of complexes of Yariv reagent with β-Our initial simulations (data not shown) showed that noncovalent interactions between a Yariv molecule and a carbohydrate and especially between two Yariv molecules are relatively strong. It would be necessary to run very long simulations to observe relevant structural transitions. Therefore, to map possible modes of interactions between Yariv and carbohydrates we carried out a series of short (100 ns) simulations starting from different initial structures. We believe such simulations are more representative than few long simulations due to long lifetimes of complexes. Initial coordinates were built manually to represent wide diversity in terms of initial distances and orientations of molecules.First, we were interested in the interactions between two Yariv molecules. The simulated systems of Yariv dimers contained 2073–2083 water molecules. This corresponds to a concentration of Yariv reagent equal to 53–54 mmol/L (approx 28 g/L). This is approximately 30 times higher than concentrations used in precipitation experiments of arabinogalactans, however, in simulations the interactions between Yariv reagent molecules are limited by periodic boundary conditions.Pi is the histogram count, k is Boltzmann constant, and T is the temperature in Kelvin. Finally, the value of the global minimum was subtracted. The difference between a free energy and the free-energy-like function used in this study is in the fact that the free energy depicts probability of finding a molecule at a certain point, whereas the free-energy-like function depicts the probability of finding any carbon atom at a certain point. The advantage of the free-energy-like function is in its higher resolution. The resulting free-energy-like functions are depicted in The first Yariv molecule of all snapshots was fitted onto the first snapshot to eliminate its translational and rotational motions. This was possible due to relatively high rigidity of the Yariv molecule (conjugated diazenyl groups). Coordinates of the second molecule were analysed in terms of free-energy-like function. 3D histograms of all carbon atoms of the second Yariv molecule were calculated with 1 Å × 1 Å × 1 Å bins. Next, these values were converted to free-energy-like functions as:π-π stacking via all four aromatic moieties, despite different initial monomer orientations. These interactions are relatively strong, but still reversible as indicated by replica seven. In this simulation replica, we observed that the second Yariv molecule  in a Yariv reagent molecule. Yariv-galactooligosaccharide complexes were stabilized by carbohydrate-aromatic CH/π interactions  were comparably strong as YT. Free-energy-like functions were mostly boomerang-shaped (five of eight simulations). This can be explained by the fact that the interconversion between complexes was much slower compared to YT and one assembly was predominant.via π-π stacking of their aromatic molecules. There were also CH/π interactions between the carbohydrate part of one Yariv molecule and an aromatic ring of another molecule. Furthermore, there were CH/π interactions in which aromatic rings were playing both roles—donors and acceptors.Assemblies with more than two molecules included Y4, Y4T, and Y4H. The complexes formed in these systems were visualized as structures after 100 ns –6. TheseComplexes of multiple Yariv molecules with a saccharide , 6 combiOne exception was the Y4H complex formed in the replica eight. In this case the oligosaccharide molecule docked into the groove formed by two peripheral and one central aromatic moieties of parallely stacked Yariv tetramer. This complex is stabilized by numerous hydrogen bonds. This assembly can be seen as an alternative model of Yariv-oligosaccharide interactions.π-π and CH/π interactions. Comparison of quantum chemical and molecular mechanical energies of sample π-π  and by the completeness of sampling. The studied complexes were dominated by mple π-π  or CH/π e π-π -galactans is depicted in These stacked oligomers are not perfect and contain irregularities. This is probably the reason Yariv reagent and its complexes resisted the application of conventional experimental methods. Some experimental methods of structure elucidation, such as crystallography, require strong periodicity of the studied system. Irregularities in oligomeric structures provide more accessible aromatic rings as platforms for interaction with carbohydrates.An assembly that cannot be ruled out as a model of Yariv-oligosaccharide interaction is the one formed in the eighth replica of Y4H simulation  and such oligo- or polysaccharide would bind to the Yariv reagent very weakly.π-π and CH/π interactions is in good agreement with the fact that Yariv assemblies are resistant to high ionic strength  (The fact that Yariv complexes are stabilized mostly by strength . These iophobic) .In conclusion, simulations of systems containing the Yariv reagent with model oligosaccharides provide predictions of main interaction types and structural arrangements in these complexes. We understand that our models are based on simplified systems and short time scales, nevertheless, we believe they can inspire other researchers studying the Yariv reagent to design new biophysical experiments or Yariv derivatives to complete our picture of the function of this useful reagent.


Tripteryguim wilfordii Hook (TWH) has significant anti-inflammatory and immunosuppressive properties and is widely used for treating autoimmune and inflammatory diseases. However, the multi-target mechanism of TWH on ankylosing spondylitis (AS) remains to be elucidated.Active components and their target proteins were screened from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Meanwhile, AS-related targets were obtained from the Genecards Database. After overlapping, the targets of TWH against AS were collected. Then protein–protein interaction (PPI) network and core targets analysis were conducted through STRING network platform and Cytoscape software. Moreover, molecular docking methods were utilized to confirm the high affinity between TWH and targets. Finally, DAVID online tool was used to perform gene ontology (GO) and Kyoto encyclopaedia of genes and genome (KEGG) pathway enrichment analysis of overlapping targets.The TCMSP Database results showed that there were11 active components of TWH against AS. PPI network and core targets analysis suggested that ESR1, VEGF, ICAM-1, and RELA were key targets against AS. Moreover, molecular docking methods confirmed the high affinity between bioactive molecular of TWH and their targets in AS. At last, enrichment analysis indicated that TWH participates in various biological processes, such as cell–cell adhesion, regulation of cell–matrix adhesion, acute inflammatory response, via TNF-α, NF-κB and so forth signalling pathways.in vivo/in vitro experiments.Verified by network pharmacology approach based on data mining and molecular docking methods, multi-target drug TWH may serve as a promising therapeutic candidate for AS but still needs further  Ankylosing spondylitis (AS) is a chronic inflammatory disease that inflammation and structural damage mainly occurs in the sacroiliac joint or spine. This inflammatory damage normally causes chronic back pain and morning stiffness in patients, resulting in restriction of spinal mobility . MoreoveThe treatment of AS has improved significantly over the last decades with non-steroidal anti-inflammatory drugs (NSAIDs), disease-modifying antirheumatic drugs (DMARDs), TNF-specific agents and other biologics. However, in pace with long-term NSAIDs therapy, the potential risks including cardiovascular, gastrointestinal and renal related risks increase . DMARDs Tripterygium wilfordii Hook (TWH), a woody vine of the genus Tripterygium, is a traditional Chinese medicine with anti-inflammatory, anti-rheumatic and immunomodulatory effects. Therefore, it is widely used in a variety of autoimmune diseases, including rheumatic diseases, Crohn’s disease, systemic lupus erythematosus, and Behcet’s disease. Triptolide, a principal ingredient in TWH, could reduce collagen formation, alkaline phosphatase activity and calcium deposition in vitro, therefore suggests a potential therapeutic agent for the treatment of AS [nt of AS . Additiont of AS . Howevernt of AS . Besidesnt of AS .Herein we applied network pharmacology to analyze the active ingredients, predict core targets and pathways of TWH for treatment of AS, then preliminarily verified these targets by using molecular docking. The study design and workflow are presented in http://tcmspw.com/tcmsp) [http://www.uniprot.org/uploadlists/).Active components of TWH were searched by Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform  . Oral bim/tcmsp) . The comm/tcmsp) . Then thhttps://www.genecards.org/) is a platform for providing all known human genes in genome, proteome, transcription, heredity and function [The Genecards Database (function . AS-relahttps://string-db.org/) to construct the protein–protein interaction (PPI) network. The protein type was set to Homo sapiens and the confidence was set to medium (0.400). The top 10 scores of target protein in network string interactions were output, which was ranked by the MCC method using Cytohubba plug-in.To identify the potential hub targets of TWH and the interactions between them, the selected targets were introduced into the STRING network platform (https://www.rcsb.org/). Then dehydrated and removed the ligand of the active centre by PyMOL software. The target protein was hydrogenated and converted to * pdbqt format by AutoDock software, and the drug small molecule rotation bond was set up and saved in * pdbqt format. The location of the active pocket was built depends on the original ligand’s position. A grid box size of 15 × 15 × 15 points with a spacing of 1.0 Å between grid points was set to cover almost the entire favourable protein-binding site. The X, Y and Z centres were adjusted on the original ligand of different receptors. Grid box centred at  Å, for the 4XIS (ESR1), grid box centred at  Å, for the 3BQN (ICAM1), grid box centred at  Å, for the 3QXY (RELA), and grid box centred at  Å, for the 4KZN (VEGF). Finally, AutoDock Vina was used to docking, and search for the optimal conformation. Finally, using Discovery Studio software, the detailed docking information between a drug molecule and target was analyzed.The structure of the drug small molecule (*Mol2 format) was download from the TCMSP Database. While the 3D structure of the target protein (*PDB format) was downloaded from PDB Database  for pathway enrichment analysis, the recognition type was set to official gene symbol, the list type was set to GeneList, the species was limited to Homo sapiens, then the KEGG pathway information with a significant difference was searched out. And the bubble plot of pathways was drawn by R software (R 3.6.0 of Windows). The most enriched pathway was download from DAVID Database, and the enriched genes were marked with red stars.By observing the distribution of the target in the pathway, the KEGG pathway could further explain the role of the targets in metabolism, signal transduction and other processes. The targets were imported into the DAVID Database  complex that plays a fundamental role in inflammatory and immune responses. Besides damage of sacroiliac joint or spine, patients with AS frequently have decreased pulmonary function negatively correlated with the NF-κB signalling pathway, oxidative indexes, and inflammatory factors . FurtherKEGG analysis suggested that the TNF signalling pathway was the highest enrichment pathway. Consistent with our results, celastrol, a bioactive ingredient of TWH, could reduce active oxygen free radicals, and partially decrease the secretion of TNF-α . Likewisin vitro and in mice model of rheumatoid arthritis [Although TWH has significant clinical efficacy, its toxic effects which are mainly associated with damaging the liver, spleen, kidneys, and blood circulatory system cannot be ignored . Thereforthritis . And a nrthritis .


The development of ‘age clocks’, machine learning models predicting age from biological data, has been a major milestone in the search for reliable markers of biological age and has since become an invaluable tool in aging research. However, beyond their unquestionable utility, current clocks offer little insight into the molecular biological processes driving aging, and their inner workings often remain non-transparent. Here we propose a new type of age clock, one that couples predictivity with interpretability of the underlying biology, achieved through the incorporation of prior knowledge into the model design. The clock, an artificial neural network constructed according to well-described biological pathways, allows the prediction of age from gene expression data of skin tissue with high accuracy, while at the same time capturing and revealing aging states of the pathways driving the prediction. The model recapitulates known associations of aging gene knockdowns in simulation experiments and demonstrates its utility in deciphering the main pathways by which accelerated aging conditions such as Hutchinson–Gilford progeria syndrome, as well as pro-longevity interventions like caloric restriction, exert their effects. While no other data type thus far allowed prediction accuracies quite on par with those achievable using DNA methylation data, features based on metabolite production or gene expression are arguably causally a step closer to the aging phenotype, thereby—at least conceptually—increasing the interpretability of the biomarker. Previously published age clocks based on these data types have not been capitalizing on this conceptual advantage however. On the contrary, interpretability has frequently been neglected as a property in these models so far, no matter the type of data used.In recent years the increasing availability of large-scale molecular biological data from high-throughput experiments, in parallel with technological advancements in machine learning and bioinformatics, have greatly accelerated the discovery of biomarkers and fueled the use of computational modeling to unravel complex biological phenomena. In aging research particularly, the discovery of the ‘epigenetic clock’—a machine learning model predicting individual age using genome-wide DNA methylation data—as a highly accurate and reliable biomarker of biological age, has understandably sparked immense interest in the research community. Since then, numerous age clocks have been developed and the concept expanded to further levels of biological data, using transcriptomic, proteomic, and metabolic features10. Normally, artificial neural networks feature densely connected layers of neural units, in which every neuron in a given layer is connected to every neuron of the next layer. As the information flow through the network is not linked to any particular processes and connections between neurons are essentially interchangeable, it is inherently hard to interpret, which is why deep learning models are frequently quoted as examples of ‘black box’ models. A defining feature of artificial neural networks however, is the flexibility they offer to implement architectures with unique properties. Omitting the fully connected design and restricting the connections between neurons as implemented for the proposed new age clock can be used to guide the flow of information within the network, thereby augmenting and controlling the way the model learns. Importantly, this allows for the embedding of prior information on biological processes, such as the pathway annotation of genes, directly into the model architecture and therefore ties the model’s learning process to known biological processes. Such a design thus enables the model to learn pathway-based representations of the molecular data, which—through the inspection of neuron activations in the pathway layers—allows the monitoring of pathway aging-states and delivers interpretability to the clock’s inner workings.We argue that increasing the interpretability of age clocks may unlock unprecedented utility of these machine learning models in aging research and help expand their use in applied research, e.g. in a human cell-culture-based screening setting, where finding suitable holistic cellular read-outs for the biological aging state is not an easy task and added interpretability could offer additional insight on potential mechanisms of action for given treatment approaches. The concept we propose to achieve this is based on a knowledge-primed artificial neural network, in which information on biological pathways in the form of gene-pathway annotations is incorporated into the architecture of the model. A similar approach has recently been shown to be effective in the modeling of yeast growth from transcriptomic datan = 887). Skin represents an extraordinarily well-suited tissue for studying aging, owing to its well-documented aging phenotype and the ease of sampling using non-invasive procedures. As it represents the body’s outermost layer, shielding other tissues from hazardous external influence, it also offers the unique possibility to study extrinsically accelerated aging, phenotypically well-documented in the form of photoaging11. The data used to construct the model was derived from the latest iteration of the ongoing Study of Health in Pomerania (SHIP), SHIP-TREND, a longitudinal cohort study generating a broad population-based picture of health and disease in northeastern Germany12. Owing to its unbiased observational design, the study is particularly well-suited to investigate the natural aging progression.In order to evaluate the utility of this approach for aging research, we constructed a pathway-based artificial neural network and trained it for age prediction based on a large transcriptomic dataset from epidermal skin samples . Based on our data, this suggests that there is a small trade-off between transparency and precision, albeit at a rate that might well be tolerable in practice.As a basis for model training, gene expression data were generated via RNA sequencing from epidermal skin samples collected from 887 subjects aged between 30 and 89 years in the SHIP-TREND cohort study . The data were randomly split into independent training and test sets (70/30), with the test set of 267 samples reserved for accuracy assessment and further in silico experiments, leaving 640 samples for model training. The 10 neural networks making up the final model were trained separately for 200 epochs each  after adjusting for chronological age and gender -mediated stress in model organisms26. Moving away from model organisms, a null-mutation of SERPINE1 is one of the few causal associations discovered so far, that links a single gene loss-of-function mutation directly with increased longevity in humans27. In line with the literature, simulated knockdown of the senescence-associated gene lead to a significant decrease in transcriptomic age predicted by the model. These simulations, while intended mainly as validation of the associations learned by the model, also highlight the utility of computational models for translational research, in this case, the ability to test the relevance of target genes identified in a systemic context or in other tissues to the biology of aging skin, which the model was trained on. An example of a gene association more specific to the skin however, is the knockdown of Krueppel-like Factor 4. KLF4 is, among others, a stemness factor and direct regulator of telomerase expression28, as well as importantly a regulator of keratinocyte senescence29. As such, KLF4 silencing alone has been shown to be sufficient to induce a senescent phenotype in human keratinocytes29. In line with these findings, the simulated knockdown resulted in an increased age prediction across subjects of all ages.Seeing that the performance of our clock compared reasonably well to ‘black box’ models and achieved transparency on the biological processes affected, we next set out to test how well the clock actually captured known aging mechanisms and associations through a series of in silico experiments. As discussed above, past research has not identified a single ‘master switch’ gene or pathway driving aging, nonetheless, several genes have been identified over the years, whose deregulation is associated with changes in lifespan in model organisms or the manifestation of aging phenotypes. To test if the model could recapitulate such associations, we performed virtual gene knockdowns of known aging target genes with a history of experimental data available from model organisms and human genome-wide association studies, to evaluate if the predictions accurately replicated the effects of the perturbation Fig. a. The kn30. The simulated overexpression of HK2 on the other hand was concordantly predicted by the model as a rejuvenating intervention  and negative impact genes (p = 2.4e−153). This indicates that the network architecture organically increases the impact of master regulators and genes which act as effectors in several different biological processes. This emergent property is very much desirable, as it reflects the underlying biology more closely than other machine learning models that tend to weight features purely based on predictivity or correlation to the modeled phenotype, rather than by the breadth of their biological impact. We subsequently expanded the pathway impact analysis to all genes covered by the model and found that using the single-gene knockdown data allowed reconstruction of the aging pathway landscape, with genes arranged by similarity in effect as well as capturing the structure of the diverse biological motifs and processes. The resulting map  investigate if the model recapitulates the overall effect of the signature and (ii) demonstrate the use of an interpretable machine learning model in deciphering the biological processes driving accelerated aging or rejuvenating conditions. For this, we searched the literature for gene expression data or published signatures of diverse aging-related conditions and simulated their impact on the predicted age of the test set Fig. a.Fig. 4A31. The condition is caused by mutations leading to incorrectly processed forms of lamin A that weaken the nucleus structure with diverse detrimental consequences. The overall effects of this are severe, and the average life expectancy for patients is only between 13 and 15 years32. Simulating the effect of the transcriptomic signature of HGPS33 likewise has a heavy impact on age estimation, with the clock putting out predictions beyond 1200 years after signature application . HGPS is a rare autosomal dominant genetic disorder that manifests very early in life, with symptoms that strikingly resemble those of natural aging particularly in regards to the skin, including wrinkle formation, the emergence of dyspigmentations (age spots), and a general thinning of the skin including a loss of subcutaneous fat, as well as alopeciaion Fig. a. Though11. Simulating the impact of the signature of chronically sun-exposed skin34 increases the predicted age by around 2.1 years on average  if left untreated37, one of the most common types of cancer in developed countries with predominantly fair-skinned populations38. Due to the direct link between photoaging and the emergence of AKs, and the direct progression path from AKs to SCCs, we decided to include signatures from these pre-cancerous and cancerous tissues into the analysis. Interestingly, both signatures39 induced substantial increases in predicted age across all samples, on average by 54 and 52 years, respectively . AKs, caused by the intraepidermal proliferation of atypical keratinocytes, are a frequently diagnosed skin condition in light-skinned individuals with a history of sun exposure42. This senescence-associated secretory phenotype (SASP) plays an important role in the recruiting of immune cells to the tissue, and as such has beneficial functions in wound healing and tissue regeneration43. In aging tissues however, the increasing accumulation of senescent cells impairs normal tissue function, and SASP has been proposed as one of the mechanisms that drive inflammation, the chronic low-grade inflammatory state of aging tissue45. As senescence is an important aspect of aging and also a common in vitro model of aging, we tested the signature of replicative exhaustion-induced senescence using the model. The simulations showed an increase in age of over 100 years on average aged skin75.The effects of the photoaging signature were similarly diverse, with the strongest impact also recorded on the ROS pathway Fig. f, here s37. The exact mechanisms determining which AKs progress meanwhile remain elusive. Analyzing the predicted pathway perturbations revealed a substantial correlation between pathway patterns induced by AK and SCC signatures , comprising another random sample of 4420 adults aged 20–79 years, started in 2008, again designed with regular follow-ups. The data used in this study consisting of 887 epidermal samples were collected during the first follow-up of the SHIP-TREND cohort, with subjects aged between 30 and 89 years . The study was approved by the ethics committee of the University Medicine Greifswald . All participants signed an informed consent form and all investigations were undertaken in accordance with the ethical principles outlined in the Declaration of Helsinki.SHIP was designed as a population-based study to assess the prevalence and incidence of common clinical diseases, subclinical disorders, and risk factors among the population of the Federal State of Mecklenburg/West Pomerania in Northeastern Germany81.The suction blister method applied in this study has been approved by the Ethics Commission of the University of Freiburg . Suction blisters of 7 mm diameter were taken from the volar forearms of all subjects as previously described16, tissue samples were suspended in the respective lysis buffers for DNA or RNA extraction and homogenized using an MM 301 bead mill (Retsch). DNA was then extracted using the QIAamp DNA Investigator Kit (Qiagen) according to the manufacturer’s instructions. RNA was extracted using the RNeasy Fibrous Tissue Mini Kit (Qiagen) according to the manufacturer’s instructions.As previously described82 for quality control, Trimmomatic 0.3683 for trimming, and Salmon 0.8.184 for read mapping against the GRCh38 build of the human transcriptome and read quantification in the form of transcripts per million (TPM).Transcriptome libraries were prepared using the TruSeq Library Prep Kit (Illumina) and sequencing performed at 1×50 bp on Illumina’s HiSeq system to a final sequencing depth of 100 million reads per sample. Sequencing data were processed using a custom pipeline including Fastqc 0.11.785 with a tensorflow86 backend and fully coded in R 3.6.187. In the following and for the purpose of this work, we will use the term “pathway” to denote any gene sets or knowledge-guided collections of genes involved in distinct biological processes. In order to embed this pathway information into the network, first a binary ‘gene × pathway’ filter matrix was constructed based on gene annotations to the Hallmark pathway collection13. This filter matrix was used to set the crucial gene-specific connections between input neurons and the neurons in the first pathway layer. The following hidden layers operated in a pathway-centric manner. Neurons assigned to the same pathway were densely connected to each other to allow the network maximum flexibility to process and learn pathway representations from the data, while no connections to neurons of other pathways were allowed, as this would break the chain of interpretability. Information of each pathway was then aggregated in a final neuron, serving a dual purpose as both a step to condense the pathway information in one neuron per pathway and as an auxiliary output of pathway neuron activations to update the network loss during training and for further analysis purpose during inference. Finally, this pathway output layer was connected to a common output neuron in the last layer, tasked with aggregating the information passed by the pathway neurons to a final age estimate. The number of neurons within the hidden layers was adjusted to the number of genes in each pathway and thus determined for every pathway individually as shown in Eq. . The number of hidden layers was set to 4, as testing with more layers showed no additional gains in accuracy justifying a further increase in complexity . Taken together, this setup resulted in a final network with 1,740,858 trainable parameters. In order to improve generalization ability and control overfitting of the model, dropout layers were inserted between the hidden layers, randomly dropping connections between the hidden layers in the training phase. Furthermore, global weight decay (regularization factor = 0.01) was implemented as another form of regularization, improving generalization ability of the model.This established a minimal size of 5 neurons per layer for each pathway, with additional neurons awarded with increasing pathway size to accommodate an increase in regulatory complexity. The neuron scaling factor 88 in all hidden layers, and was accordingly initialized using the He-initialization, a weight initialization scheme optimized for ‘relu’-like activation functions89.The model used ‘elu’  activation functionsalpha as shown in Eq.  of the main and auxiliary outputs of the network, joined together by a balancing hyperparameter n in Eq. :2\documeAlpha was set to 0.4 in the final model after testing different configurations .The advantages of this are two-fold: (i) It forces all parts of the network to be trained, ensuring that the all encoded information is utilized, and all pathway neurons are active. This is critical, as early testing showed that without the added auxiliary loss, the network would heavily rely on only one or few pathways, the selection of which varied greatly depending on initial weight configuration . This resulted in very poor reproducibility between network reboots and only a fraction of the available information being utilized. (ii) All pathway neurons now generate a positive continuous output, which is essentially an age estimate based on the information encoded in the pathway or ‘pathway age’. This has clear benefits for the interpretability of the neuron activations, whose scale and direction could otherwise vary greatly between network reboots and which stabilized significantly through the addition of the auxiliary loss . 90 and a learning rate of 0.001, with a mini-batch size of 16 samples for a total of 200 epochs. Table The training of the model was performed using stochastic gradient descent with AdamIn order to further improve both reproducibility and accuracy of the model, the final setup was designed as an ensemble of several individual networks. For this, 10 single networks were trained separately, and then joined to a common input layer and a shared main and auxiliary output. In the shared output layers, individual outputs by the 10 networks are averaged to generate the final model estimates. The ensemble setup proved successful in further stabilizing the intermediate neuron activations and thereby improving reproducibility .To assess any potential trade-off between transparency and model precision, we trained an ensemble of 10 fully connected neural networks with the same number of layers per network, a comparable number of parameters, trained for the same number of epochs on the same data with the same training/test split as used for our pathway-based model. Table 87 to test for an association between transcriptomic and visual age estimates, whilst adjusting for chronological age and gender , non-polarized and color-controlled frontal portrait images of the test subjects with their eyes closed, any hair  covered to reduce the impact of features unrelated to the skin, and any make-up removed beforehand. The images were then presented to a blinded panel of 31 experts that were asked to estimate the ages of the subjects based on these photographs. The individual age estimates were then averaged over the panel, which resulted in the final visual age estimates, which showed generally very good concordance with chronological ages with a median absolute error of 4.38 years. Linear models were then employed in Rn = 267) and comparing the model’s predictions with the unperturbed baseline predictions per sample. For the assessment of age impact, the changes in the main output neuron generating the overall age estimate were analyzed. For assessing the impact on the aging state of the biological pathways, the activity of the auxiliary output neurons was monitored instead, and the generated outputs of these neurons were similarly analyzed by comparing the ‘pathway age’ estimates with the unperturbed baseline estimates per sample.The perturbation of single genes was performed by up- or downregulating gene expression by a common log2 fold-change  in all samples of the test set  were mapped to their human homologs (genome build GRCh38) using the biomaRt R package93.n = 267) and comparing the predictions of the perturbed data with the unperturbed baseline predictions per sample, as with the single gene knockdowns. Significance of impact was determined using one-sample Wilcoxon rank-sum tests, testing for the difference in medians from an effect size of 0. When more than one comparison was performed, p-values were adjusted for multiple testing using the Holm–Bonferroni method94. Table Assessing the impact of more complex transcriptional signatures was performed by up- or downregulating each significantly differentially regulated gene (cutoff was an FDR < 0.05) in the signature by the exact effect size (determined by its log2 fold-change) recorded by the differential gene expression analysis. The analysis was again performed using all samples of the test set (87 further included the usage of the packages data.table95 and dplyr96 for data handling, as well as the packages ggplot297 and ggpubr98 for data visualization.Data analysis in RFurther information on research design is available in the Supplementary InformationReporting Summary


Glycosylation is a common posttranslational modification, and glycan biosynthesis is regulated by a set of glycogenes. The role of transcription factors (TFs) in regulating the glycogenes and related glycosylation pathways is largely unknown. In this work, we performed data mining of TF–glycogene relationships from the Cistrome Cancer database (DB), which integrates chromatin immunoprecipitation sequencing (ChIP-Seq) and RNA-Seq data to constitute regulatory relationships. In total, we observed 22,654 potentially significant TF–glycogene relationships, which include interactions involving 526 unique TFs and 341 glycogenes that span 29 the Cancer Genome Atlas (TCGA) cancer types. Here, TF–glycogene interactions appeared in clusters or so-called communities, suggesting that changes in single TF expression during both health and disease may affect multiple carbohydrate structures. Upon applying the Fisher’s exact test along with glycogene pathway classification, we identified TFs that may specifically regulate the biosynthesis of individual glycan types. Integration with Reactome DB knowledge provided an avenue to relate cell-signaling pathways to TFs and cellular glycosylation state. Whereas analysis results are presented for all 29 cancer types, specific focus is placed on human luminal and basal breast cancer disease progression. Overall, the article presents a computational approach to describe TF–glycogene relationships, the starting point for experimental system-wide validation. The glycan signatures of cells and tissue are controlled by the expression pattern of 300–350 glycosylating-related genes that are together termed glycogenes –2. TheseThere are currently several isolated studies of TF–glycogene interactions, but a systematic “systems-level analysis” is absent. Many of these previous studies are based on discrete glycogene promoter region analysis and reporter assays. These studies have established some notable TF–glycogene relationships, though they are limited to distinct cell types. Examples include the regulation of MGAT5 by ETS2 in NIH3T3 fibroblasts , controlIn the current article, we propose that more global and higher-throughput TF–glycogene relationships under biologically relevant conditions may be discovered using multiomics data mining. To this end, we sought to utilize multiomics experimental datasets and curated pathway DBs to relate cell-specific signaling processes to TFs, TFs to glycogenes, and glycogenes to glycosylation pathways . These cThe article follows a workflow shown in In the current article, we passed the TF–gene relationships established in Cistrome Cancer DB to identify TFs potentially interacting with 341 glycogenes  and PRDM1  may regulate the type 1 and 2 LacNAc pathways. CREB3L4 is known to primarily be expressed in the prostate and some breast cancer cell lines and has been linked to diverse roles involving chromatin organization in spermiogenesis, adipocyte regulation, and dysregulation in prostate cancer [e cancer –25. It he cancer . The ince cancer . CREB3L4MEF2C disproportionately regulates glycosaminoglycan synthesis pathways: MEF2C was found to regulate several genes in the chondroitin and dermatan sulfate synthesis pathways (p = 0.008). This TF plays roles in development, particularly in the development of neurons and hematopoietic cell differentiation towards myeloid lineages. It is known that MEF2C is directly impacted by TGF-β signaling, and thus increasing the metastatic potential of cancer [f cancer . MEF2C wMECP2 disproportionately regulates heparan sulfate chain elongation: The MECP2  was found to positively regulate heparan sulfate elongation. MECP2 regulates gene expression by binding to methylated promoters and then by recruiting chromatin remodeling proteins to condense DNA and repress gene expression [pression –30. MECPFewer TFs were found to be enriched to signaling pathways in basal breast cancer compared to luminal cancer . DespiteRUNX3 and fucosylation: The terminal fucosyltransferase FUT7  was found to be positively regulated by the RUNX3 TF . The RUNX family of TFs (including RUNX1–3), are involved in several developmental processes, including hematopoiesis, immune cell activation, and skeletal development. It was discovered that RUNX3 acts as a tumor suppressor gene in breast cancer. Upon cancer development, the RUNX3 promoter is hypermethylated, leading to reduced TF activity and loss of tumor suppression activity [activity . Our datRegulation of GalNAc-type O-linked glycans by SMAD2: SMAD2 was found to significantly affect core 1 and 2 O-linked glycan structures . SMAD proteins are activated by TGF-β signaling and bind to DNA to act as cofactors to recruit TFs. SMAD2 has been shown to act as a tumor metastasis suppressor in cell lines [ll lines –33. ThisThe number of enrichments above is high. In order to reduce the findings to a smaller set, we applied the Benjamini–Hochberg correction to our TF–glycopathway enrichments. While possibly reducing false positives, this may also reduce true positives. Nevertheless, after this correction, a total of 121 TF–glycopathway enrichments were found to be statistically significant across all cancer types (Padj = 0.00096), KLF5 (Padj = 0.0012), MECOM (Padj = 0.029), and TCF7L2 (Padj = 0.000087) were found to regulate several GalNAc transferases. FOXA1 is an important regulatory TF involved in the development of endoderm-derived organs. Upon pancreatic cancer development, FOXA1 expression is known to decrease, which drives the epithelial to mesenchymal transition [Filtering our TF–glycopathway enrichments illuminates the fact that pancreatic cancer shows a high degree of enrichment to the GalNAc-type O-glycan pathways, which is consistent with our prior experiments . FOXA1  data from the Gene Transcription Regulatory Database (GTRD) , which hSome caveats in our analysis are important to note. First, we only used selected values of RP and ρ to filter TF–glycogene relationships from the Cistrome Cancer DB. Further studies are needed in order to determine how the selected thresholds affect the discovered relationships. A full list of TF–glycogene relationships found Cistrome Cancer DB are provided in A majority of current studies in the Glycoscience field use experimental data and curations related to glycans only. Fewer investigations examine the links between the glycans, glycogenes and glycosylation pathways, and other nonglyco datasets. We set out to identify these relationships by mining publicly-available data. Using this, we describe putative regulatory relationships between TFs and glycogenes across 29 cancer types. Some TFs appear to regulate glycogenes in communities, indicating potential cross-talk across pathways in regulating glycosylation. The communities varied with cancer type, even in a single tissue, suggesting that these TF–glycogene interactions are dynamic in nature. Groups of TFs enriched to glycosylation pathways were also associated with signaling pathways. Thus, a connection between cell signaling, TF activity and glycosylation begins to emerge. Overall, the putative TF–glycosylation pathway enrichments found here represent the starting point for wet-lab and orthogonal dataset validation. Such studies could enhance our fundamental understanding of glycosylation pathway regulation, and lead to novel ways to control the glycogenes and glycan structures during health and disease.A list of 208 unique glycogenes involved in 20 different glycosylation pathways were used in this work (1) Glycolipid core: The enzymes in this group are involved in the biosynthesis of the glucosylceramide (GlcCer) and galactosylceramide  lipid core. Here, the GlcCer core is formed by the UDP-glucose:ceramide glucosyltransferase (UGCG), which transfers the first glucose. Following this, lactosylceramide is formed by the action of the β1-4GalT activity of B4GalT5 . The GalCer core is typically structurally small and is made by UDP-Gal:ceramide galactosyltransferase (UGT8). These structures can be further sulfated by GAL3ST1 or sialylated by ST3GAL5.2) P1-Pk blood group: The Pk, P1, and P antigens are synthesized on lactosylceramide glycolipid core. The activity of α1-4GalT (A4GALT) on this core results in the Pk antigen, followed by β1-3GalNAcT (B3GALNT1) to form the P antigen. The P1 antigen, on the other hand, is formed by the sequential action of β1-3GlcNAcT (B3GNT5), β1-4GalT (B4GALT1-6), and α1-4GalT (A4GALT) on the glycolipid core.3) Gangliosides: This pathway encompasses all glycogenes responsible for synthesizing a/b/c gangliosides. UGCG is included to consider the addition of glucose to ceramide. ST3GAL5 and ST8SIA enzymes are added to take the core ganglioside structures to the a, b, and c levels. B4GALTs and B4GALNT1 are included to account for ganglioside elongation. Decoration of the gangliosides with sialic acid occurs using ST6GALNAC3-6 and also ST8SIA1/3/5.4) Dolichol pathway: This results in the formation of the dolichol-linked 14-monosaccharide precursor oligosaccharide. This glycan is cotranslationally transferred en bloc onto Asn-X-Ser/Thr sites of the newly synthesized protein as it enters the endoplasmic reticulum. The enzymes involved is such synthesis include the ALG (asparagine-linked N-glycosylation) enzymes and additional proteins (part of OSTA and OSTB) involved in the transfer of the glycan to the nascent protein.5) Complex N-glycans: This pathway includes glycogenes responsible for processing the N-linked precursor structure emerging from the dolichol pathway into complex structures. Enzymes involved include mannosidases, glucosidases, some enzymes facilitating protein folding, and also enzymes that direct acid hydrolases to the lysosome.6) N-glycan branching: These glycogenes are responsible for the addition of GlcNAc to processed N-linked glycan structures. These include all the MGAT enzymes.7) GalNAc-type O-glycans: O-linked glycans are attached to serine (Ser) or threonine (Thr) on peptides, where GalNAc is the root carbohydrate. This is mediated by a family of about 20 Golgi-resident polypeptide N-acetylgalactosaminyltransferases . Core 1 structures result from the attachment of β1-3 linked galactose to the core GalNAc using C1GALT1 and the corresponding chaperone C1GALT1C1. Core 2 structures then form upon addition of β1-6-linked GlcNAc by GCNT1. Modifications of core 3 and core 4 glycans can occur during disease, and thus this classification includes core 3-forming B3GNT6 and core 4-forming GCNT3. Other O-glycan core types are rare in nature.8) Chondroitin sulfate and heparan sulfate initiation: Chondroitin and heparan sulfate glycosaminoglycans all have a common core carbohydrate sequence attaching them to the corresponding proteins. These are constructed by the activity of specific xylotransferases (XYLT1 and XYLT2), galactosyltransferses B4GALT7 and B3GALT6 that sequentially add two galactose residues to xylose, and the glucuronyltransferase B3GAT3 that adds glucuronic acid to the terminal galactose. Also involved in the formation of this core is FAM20B, a kinase that 2-O-phosphorylates xylose. At this point, the addition of GalNAc to GlcA by CSGALNACT1 and 2 results in the initiation of chondroitin sulfate chains. The attachment of GlcNAc by EXTL3 to the same GlcA results in heparan sulfates.9) Chondroitin sulfate and dermatan sulfate extension: Chondroitin sulfates and dermatan sulfates are extended via the addition of GalNAc-GlcA repeat units. This is catalyzed by CSGALNACT1, which is better suited for the initial GalNAc attachment, followed by CSGALNACT2, which is preferred for synthesizing disaccharide repeats. CHSY1, CHSY3, CHPF, and CHPF2 all exhibit dual β1-3GlcAT and β1-4GlcAT activity. Additional enzymes mediate sulfation. Epimerization of glucuronic acid to iduronic acid by DSE and DSEL results in the conversion of chondroitin sulfates to dermatan sulfates.10) Heparan sulfate extension: EXT1 and EXT2 both have GlcUA and GlcNAc transferase activities and are together responsible for HS chain polymerization. EXTL1–3 are additional enzymes with GlcNAc transferase activity that facilitate heparin sulfate biosynthesis. Additional enzymes that are critical for heparin sulfate function include the HS2/3/6ST sulfotransferases, the GlcA epimerase GLCE, and additional enzymes mediating N-sulfation .11) Hyaluronan synthesis: This pathway consists of the three hyaluronan synthases, HAS1–3.12) Glycophosphatidylinositol (GPI) anchor extension: This pathway includes glycogenes responsible for the synthesis of GPI-anchored proteins in the ER. This involves the synthesis of a glycan–lipid precursor that is en bloc transferred to proteins.13) O-Mannose: This is initiated by the addition of mannose to Ser/Thr using POMT1 or POMT2. β1-2 or β1-4 GlcNAc linkages can then be made using POMGNT1 or POMGNT2 to yield M1 or M3 O-linked mannose structures, respectively. MGAT5B can facilitate β1-4 GlcNAc linkage onto the M1 structure to yield the M2 core. Additional carbohydrates typically found on complex N-linked glycan antennae can then be attached. In particular, such extensions may be initiated by members of the B4GALT family or B3GALNT2. Specific variants are noted on α-dystroglycans.14) O-linked fucose: This pathway includes POFUT1, the enzyme responsible for the addition of fucose to Ser/Thr residues. MFNG, LFNG, and RFNG can attach β3GlcNAc to this fucose.15) Type 1 and 2 LacNAc: These enzymes help construct either Galβ1-3GlcNAc (type 1) or Galβ1-4GlcNAc (type 2) lactosamine chains on antennae of N-linked glycan, O-linked glycans, and glycolipids. Also included are GCNT1–3 that can facilitate formation of I-branches on N-glycans.16) Sialylation: This group encompasses all kinds of sialyltransferases: ST6GAL, ST3GAL, ST8SIA, and ST6GALNACs. Enrichments to this pathway capture overall increase in sialylation regardless of context.17) Fucosylation: these include α1-2  and α1-3  fucosyltransferases that can act on N-glycans, O-glycans and glycolipids.18) ABO blood group synthesis: these are enzymes involved in the biosynthesis of ABO antigens.19) LacDiNAc: glycogenes involved in the synthesis of LacDiNac structures.20) Sulfated glycan epitopes: this includes the enzymes attaching sulfate to different types of carbohydrates.Regulatory potential and gene correlation data were downloaded from the Cistrome Cancer DB in tab-delimited form (http://cistrome.org/CistromeCancer/CancerTarget/) . TF–geneCytoscape was used to visualize TF–glycogene regulatory relationships . To achip-value ≤ 0.05. These p-values were then adjusted using the Benjamini–Hochberg method to identify the strongest enrichments across all cancer types.A one-sided Fisher’s exact test was applied to determine if a particular TF disproportionately regulates one of the 20 glycosylation pathways described in p(FDR) < 0.1 were kept. A high p-value cutoff was chosen to allow users to gain a high-level perspective as to what potential pathways may be regulating enriched TFs. The connection between cell signaling pathways and TFs and that between the TFs and glycosylation pathways were visualized using alluvial plots generated using the R package ggalluvial. Only signaling pathways with <30 members are presented for brevity. A comprehensive listing of enriched signaling pathways is available in TFs enriched to glycosylation pathways were associated with putative regulatory pathways using the Reactome DB overrepresentation analysis API, which also uses Fisher’s exact test, to associate the TFs with signaling pathways . SignaliFile 1Comparison of wet-lab studies and entries in DBs as well as Alluvial plots for all cancer types.File 2Cistrome Cancer TF-to-glycogene subnetworks.File 3Supplementary tables.


TNF, DUSP1, ATF3, CXCR4, NR4A1, BHLHE40, CDKN1B, SOCS3, TNFAIP3, and MCL1 were the top 10 potential hub genes. The most important modules obtained in the PPI network were performed KEGG pathway analysis, which showed that these genes were mainly involved in the MAPK signaling pathway, the IL-17 signaling pathway, the TNF signaling pathway, osteoclast differentiation, and apoptosis. A transcription factor (TF) target network illustrated that FOXJ2 was a critical regulatory factor. Conclusions: This study identified the essential genes and pathways associated with exercise and monocytes. Among these, four essential genes  and the FoxO signaling pathway play vital roles in the immune function of monocytes. High-intensity exercise may improve the resistance of chronic inflammatory diseases by regulating the expression of these genes.Background: Monocytes are critical components, not only for innate immunity, but also for the activation of the adaptive immune system. Many studies in animals and humans have demonstrated that monocytes may be closely associated with chronic inflammatory diseases and be proved to be pivotal in the association between high-intensity exercise and anti-inflammation response. However, the underlying molecular mechanisms driving this are barely understood. The present study aimed to screen for potential hub genes and candidate signaling pathways associated with the effects of high-intensity exercise on human monocytes through bioinformatics analysis. Materials and Methods: The GSE51835 gene expression dataset was downloaded from the Gene Expression Omnibus database. The dataset consists of 12 monocyte samples from two groups of pre-exercise and post-exercise individuals. Identifying differentially expressed genes (DEGs) with R software, and functional annotation and pathway analyses were then performed with related web databases. Subsequently, a protein–protein interaction (PPI) network which discovers key functional protein and a transcription factors-DEGs network which predicts upstream regulators were constructed. Results: A total of 146 differentially expressed genes were identified, including 95 upregulated and 51 downregulated genes. Gene Ontology analysis indicated that in the biological process functional group, these DEGs were mainly involved in cellular response to hydrogen peroxide, response to unfolded protein, negative regulation of cell proliferation, cellular response to laminar fluid shear stress, and positive regulation of protein metabolic process. The top five enrichment pathways in a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were the FoxO signaling pathway, protein processing in the endoplasmic reticulum, influenza A, the ErbB signaling pathway, and the MAPK signaling pathway.  Monocytes are circulating blood leukocytes that are involved in the innate immune response to inflammation . MonocytPhysical exercise provokes an adaptive response and beneficial effects on health through modulation of the immune system . Many stDuring the research, the GSE51835 gene expression profile was obtained from the Gene Expression Omnibus (GEO) database . Then a https://www.ncbi.nlm.nih.gov/geo/, accessed on 17 Augest 2020.). GSE51835 was uploaded by Radom-Aizik S et al. [2, characteristics of the subjects are shown in In this study, the GSE51835 gene expression profile was obtained from the GEO database  [p < 0.05 and a |log fold change (FC)| > 0.5 were considered as significant DEGs [The classic Bayesian method in the limma R package   was applant DEGs . To conshttp://david.abcc.ncifcrf.gov/, accessed on 21 September 2020.) was applied to complete GO  [http://www.kegg.jp/, accessed on 22 September 2020.), pathway enrichment analysis of DEGs was performed using KOBAS [http://kobas.cbi.pku.edu.cn/, accessed on 22 September 2020.). A count ≥ 2 and a p < 0.05 were defined as the threshold.The Database for Annotation Visualization and Integrated Discovery (DAVID)  (http://r 2020.)  terms anr 2020.)   database (version 11.0), PPI network was created by Cytoscape software [To predict the interaction between proteins encoded by DEGs with the search tool for the retrieval of interacting genes/proteins  . The result was downloaded and analyzed using Cytoscape software. A PPI network was created after removing uncombined nodes. As shown in lues see .Furthermore, apply MCODE (version 1.6.1) in PPI network to search cluster modules. We observed that this module contains 21 nodes and 157 interaction pairs, as shown in CREM, FOXJ3, HSF1, and CHD1.We then sought to predict and analyze TF target gene relationship pairs using the iRegulon plug-in in Cytoscape software. As shown in TNF, DUSP1, ATF3, CXCR4, NR4A1, BHLHE40, CDKN1B, SOCS3, TNFAIP3, and MCL1) were identified and may play an essential role in the effects of exercise on monocytes. These DEGs were mainly enriched in cellular response to hydrogen peroxide and unfolded protein, negative regulation of cell proliferation, and positive regulation of protein metabolic process. In the KEGG analysis, these DEGs enriched in the FoxO signaling pathway, protein processing in endoplasmic reticulum, influenza A, the ErbB signaling pathway, and the MAPK signaling pathway. Moreover, the most significant module analysis indicated that the MAPK signaling pathway, IL-17 signaling pathway, TNF signaling pathway, osteoclast differentiation, and apoptosis were mainly involved. On the basis of our TF-DEGs regulating network, CHD1, TBPL2, BCL6, ATF3, MEF2C, and FOXJ2 were considered to be key regulators.Monocytes are an essential element of immune systems and play a fundamental role in diseases associated with chronic inflammation, such as atherosclerosis . Recent In this study, we found that TNF may be a key gene and significantly enriched in immunomodulatory functions. TNF is a member of the TNF/TNFR cytokine superfamily, which acquires major importance in the immune system’s maintenance and homeostasis, inflammatory response, and host defense. However, dysregulation of TNF leads to chronic inflammation by activating the nuclear factor-kappa B (NF-κB) signaling pathway, and is associated with several human inflammatory pathologies and diseases, such as cancer, atherosclerosis, and type-2 diabetes. Hence, TNF neutralization suppresses chronic inflammation and attenuates inflammatory pathology. It has been shown that TNF can induce tumor necrosis factor-induced protein 3 (TNFAIP3) expression. The protein encoded by TNFAIP3 gene is critical for limiting inflammation by terminating endotoxin- and TNF-induced NF-κB activation. Our study revealed that expression of TNF was downregulated, whereas TNFAIP3 upregulated its expression after high-intensity exercise, which can inhibit the activation of the NF-κB signaling pathway and enhances the physiological functions of monocytes.DUSP1 is a member of the dual-specificity phosphatase family . DUSP1 hCXCR4 is a chemokine receptor that plays a crucial role in the trafficking and homeostasis of immune cells such as T lymphocytes . FurtherNR4A1 was observed to be significantly upregulated after exercise. The proteins encoded by NR4A1 belong to an orphan member of the nuclear steroid/thyroid receptor superfamily . Its recFOXJ2 was involved in regulating genes related to immunity, such as DUSP1, TNF, and SOCS3. The protein encoded by FOXJ2 participates in several cellular physiological processes, such as cell cycle, cell proliferation, and apoptosis [In our TF-DEGs regulatory network, poptosis ,40. Addipoptosis . Furtherpoptosis . This stpoptosis ,44. OverFOXO1, FOXO3a, FOXO4, and FOXO6. The FoxO signaling pathway is involved in the regulation of cell cycle, apoptosis and metabolism [In the present study, we found that the most significantly enriched pathway was the FoxO signaling pathway. The proteins encoded by FoxO genes are a family of transcription factors . In mammtabolism . Moreovetabolism ,48,49. Ttabolism , systemitabolism , and athtabolism . Our stuStudies on gender differences in the effects of high-intensity exercise on monocytes gene expression are strikingly scarce. However, it has been reported that monocyte counts and proportion of non-classical monocytes are different amongst men and women under physiological conditions . These dRadom-Aizik et al.’s study showed that high-intensity exercise significantly altered the expression of genes and microRNAs that were likely to regulate monocytes’ participation in anti-inflammatory and anti-atherogenic pathway and thus promote vascular health . In thisIn this study, we identified several essential genes and signaling pathways involved in anti-inflammation response. Although the gene expression associated with the immune function of monocytes was altered immediately after exercise, this can be a fitness to a reparative response due to the tissue damage induced by exercise and metabolic changes.However, there are some limitations in the present study. Firstly, the sample size for gene expression profiling analysis was small, which might lead to false positive results. Secondly, we cannot fulfil a follow-up downstream research, which is urgently needed to verify our findings.TNF, DUSP1, CXCR4, and NR4A1, and the FoxO signaling pathway, play vital roles in the immune function of monocytes. These findings indicate that high-intensity exercise may enhance resistance to diseases with chronic inflammation by regulating the expression of these genes. However, further experiment of molecular biology studies is needed to confirm the findings of this study.In this study, we identified 95 upregulated and 51 downregulated genes associated with exercise and monocytes. Four essential genes,


To explore the mechanism of action of Citri Reticulatae Pericarpium-Pinelliae Rhizoma (CRP-PR) in treating gastric cancer (GC) by using pharmacology network.  Based on oral bioavailability and drug-likeness, the main active components of CRP-PR were screened using the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). DisGeNET Database was used to establish target databases for GC. Cytoscape software was used to construct a visual interactive network diagram of “Active Component-Target” and screen out the key targets. The STRING database was used to construct a protein interaction network. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed on the key targets. Additionally, TCGA and HPA databases were used for key target verification.  Thirty-seven active components of CRP-PR were screened. The results of network analysis showed that the main components include 8-octadecenoic acid, stigmasterol, ferulic acid, and naringenin of the CRP-PR herb pair. The key targets of the PPI network mainly involved GAPDH, MAPK3, JUN, STAT3, GSK3B, SIRT1, ERBB2, and SMAD2. GO enrichment analysis involves 540 biological processes, 118 cellular components, and 171 molecular functions. CRP-PR components were predicted to exert their therapeutic effect on the tumor signaling pathway, PI3K-Akt signaling pathway, MAPK signaling pathway, and estrogen signaling pathway. The validation of the key genes in the TCGA and HPA database showed that most of the key target verification results were consistent with this article.  CRP-PR can treat GC by mediating PI3K-Akt signal pathway, MAPK signal pathway, and other biological processes such as tumor cell proliferation, apoptosis, and vascular regeneration, which embodies the synergistic effect of multi-components, multi-targets, and multi-channels, and provides the theoretical basis and research ideas for further study of CRP-PR in treating GC. 8-octadecenoic acid, stigmasterol, ferulic acid, and naringenin may be the material basis for the treatment of GC. Gastric cancer (GC), a primary epithelial-derived malignancy of the stomach, is the third most common cause of cancer death, second only to lung cancer and liver cancer . GC is aIt has been verified that traditional Chinese medicine (TCM) has the characteristics of “multiple components, multiple targets, and multiple pathways” in the treatment of various diseases . Citri RFor example, CRP-PR herb pair can treat coronary microcirculation disorders by protecting the function of coronary microcirculation endothelial cells and affecting the inflammatory response . It can G1 phase of tumor K562 cells and reduced the number of cells entering the DNA synthesis phase (S phase), leading to prolonged cell proliferation cycle, thereby inhibiting cell proliferation [β-sitosterol and tangerine peel, which have anti-inflammatory, anti-cancer, and antioxidant effects. Both of them can inhibit the superoxide anion produced in the xanthine oxidase/hypoxanthine system, and their ability to scavenge free radicals increases with the increase of the drug concentration within a certain concentration range. Both β-sitosterol and tangerines are good antioxidants, and they can scavenge free radicals and increase reactive oxygen species in cells, thereby acting on Akt and regulating cell proliferation and apoptosis [PR is pungent in flavor and warm in nature, having the effect of drying dampness and dissolving phlegm, reducing adverse qi, stopping vomit, eliminating mass and resolving hard lump, detumescence, and relieving pain. The study has shown the PR can inhibit the invasion of cancer cells , Mao 1414 has coferation . CRP is feration , 17. Nobferation . The maipoptosis –21.As an emerging discipline that integrates bioinformatics, computer technology, pharmacology, and many other disciplines, network pharmacology can reveal the regulatory effects of various drugs on the body from the system level and played a vital role in promoting the modernization of TCM . The devNetwork pharmacology elaborates the mechanism of multi-component, multi-target, and multi-pathway in treating diseases by herbs from micro to macro, which is consistent with the overall concept of traditional Chinese medicine . CRP-PR http://lsp.nwu.edu.cn/tcmsp.php). In order to better screen the effective active compounds in the pair, the candidate active components of CRP-PR were obtained based on the criteria of drug-likeness (DL) [All active components of CRP-PR herb pair were retrieved from the Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP)  (http://ess (DL)  of ≥0.05ess (DL)  of ≥30%.ess (DL) .https://pubchem.ncbi.nlm.nih.gov/) and then imported into the Swiss Target Prediction website (http://www.swisstargetprediction.ch/) to predict all potential targets of CRP-PR. In this study, the probability TOP50 of each SMILES structure prediction target was selected as the criterion. The keywords “gastric cancer”, “gastric carcinoma”, “stomach cancer”, and “stomach carcinoma” were searched in DisGeNET (https://www.disgenet.org/home/) database to obtain GC-related targets.The screened active components were transformed into the SMILES structure by the PubChem database ,” “close centrality (CC),” and “local edge connectivity (LAC)” of network nodes were calculated based on CytoNCA plug-in to extract these candidate node network relations; the key targets of CRP-PR herb pair for treating GC were screened by selecting the above three nodes whose eigenvalues were greater than their corresponding median.https://string-db.org/cgi/input.pl), and the PPI network was constructed.The key targets of CRP-PR for GC treatment were imported into the STRING database (https://david.ncifcrf.gov/), and the species was defined as Homo sapiens for GO and KEGG enrichment analysis. GO enrichment analysis consists of three parts: biological process (BP), cellular component (CC), and molecular function (MF). P < 0.05 and error detection rate (FDR) < 0.05 were used for GO enrichment analysis; P < 0.01 was used for KEGG enrichment analysis.The targets of diseases treated by herbs were imported into the David database  to verify its expression in TCGA-STAD. And then the protein expression and distribution of 8 key genes were investigated in HPA database (https://www.proteinatlas.org/).The 8 key genes were input into the online tool of GEPIA  was calculated, and a total of 634 GC targets with a score gad >0.053551136 were selected.The components and corresponding targets were imported into Cytoscape software to obtain the network diagram of “Active Component-Target,” shown in The intersection network included 4698 nodes and 146394 edges. The median degree value was 48.5, so the nodes with “Degree ≥97 ” were selected as the candidate targets after topology analysis. The key targets were further screened with “BC ≥ 8.84E-04, CC ≥ 0.430975, and LAC ≥ 4.026471”. Finally, a total of 303 key targets of CRP-PR for the treatment of GC were obtained, as shown in The PPI network diagram was constructed with Cytoscape software. Node size and color were set to reflect the degree of freedom of the node (degree), and the thickness of the edge represented the combined score . GAPDH, R language, where the bubble size represents the number of enriched genes and the bubble color difference represents the significant magnitude of target gene enrichment, as shown in The biological function enrichment analysis of GO and KEGG was performed on 303 targets involved in the treatment of GC by David software. The results showed that the predicted target genes of CRP-PR against active components were mainly enriched in 540 biological processes, 118 cellular components, and 171 molecular functions, biological processes involving mainly the positive regulation of transcription from RNA polymerase II promoter and negative regulation of transcription from RNA polymerase II promoter transcription. Cell components mainly include the nucleus, cytoplasm, and cytosol. Molecular functions mainly include protein binding, DNA binding, and ATP binding. Through KEGG enrichment analysis, 103 significantly enriched pathways were identified, which were mainly involved in the tumor signaling pathway, PI3K-Akt signaling pathway, cell cycle, and MAPK signaling pathway. The results of GO and KEGG analysis were ranked as TOP20 from high to low in count value. TOP20 were plotted into bubble charts using The bubble size represents the number of enriched genes, and the bubble color difference represents the significant magnitude of target gene enrichment.The GC-related pathways and target information in the first 20 KEGG enrichment were input into Cytoscape, and the “Disease Target-Pathway” network diagram was drawn as shown in GEPIA database was used to view GAPDH, MAPK3, JUN, STAT3, GSK3B, SIRT1, ERBB2, and SMAD2 in the STAD samples in the TCGA database. The results showed that GAPDH, GSK3B, and ERBB2 were highly expressed in GC tissues . The HPAP=0.0042). Especially for patients with stage III GC, the percentage of reduction in the risk of recurrence and metastasis increased to 34.7% (P=0.0072) [GC can be classified into such categories as “epigastric pain,” “accumulation,” and “abdominal mass” in TCM. The masses are usually formed due to the retention of turbid phlegm, blood stasis, and heat toxin in the stomach for a long time. The CRP-PR are the core composition and classic pair of Chinese medicinal compounds for the treatment of GC, which have been widely used in clinical practice. CRP-PR herb pair are the main ingredients of the prescription for Jianpi Yangwei decoction (JPYW). Our team has done many experiments in vivo and in vitro to verify the therapeutic effect of JPYW prescription on GC in the early stage –36. The =0.0072) . Tang [2=0.0072)  used MTT=0.0072) .−1 was injected into severe combined immunodeficiency (SCID) mice. It was observed that 8-octadecenoic acid had a significant inhibitory effect on the growth of GC transplanted tumors [μg/mL–44.8 μg/mL was inhibited to varying degrees. The higher the dose of phytosterol, the more obvious the inhibitory effect [μmol/L naringenin can significantly reduce the adhesion, invasion, and migration ability of GC cells (P < 0.05), while 100 μmol/L naringenin can only reduce the migration ability of GC cells (P < 0.05), but had no obvious effect on adhesion and invasion ability [The CRP-PR herb pair played a one-to-many role in regulating and controlling GC in the study. Analysis of the “Active Components-Targets” revealed that 8-octadecenoic acid, stigmasterol, ferulic acid, and naringenin might be the main components of the CRP-PR herb pair for the treatment of GC. 8-octadecenoic acid with a mass fraction ≥0.2 g kgd tumors . Stigmasy effect . Ferulicy effect . Naringey effect , which hy effect . 200 andβ1 signaling pathway. The TGF-β1/SMAD pathway is involved in many biological processes [GAPDH, MAPK3, JUN, STAT3, GSK3B, SIRT1, ERBB2, and SMAD2, which are at the key of the PPI network, are important targets of CRP-PR herb pair for the treatment of GC. The validation of the key genes in the TCGA and HPA database showed that GAPDH, GSK3B, and ERBB2 were highly expressed in GC tissues. GAPDH and MAPK3 protein genes were medium expressed in normal gastric tissues and were highly expressed in GC tissues. The JUN, STAT3, and SIRT1 protein genes were medium expressed in normal gastric tissues and lowly expressed in GC tissues. The GSK3B and ERBB2 protein genes were not detected in normal gastric tissues, but lowly expressed in GC tissues. The SMAD2 protein gene was lowly expressed in normal gastric tissues, but not detected in GC tissues. Most of the above verification results were consistent with this article. It has been demonstrated that extracellular GAPDH  or its N-terminal domain can inhibit the growth of GC cells , and therocesses  such as rocesses  has reveα36 (ER−α36)-mediated rapid estrogen signaling pathway plays an important role in the occurrence and development of GC. Estrogen can stimulate the growth of GC cells through ER−α36 signaling pathway. ER−α36 is highly expressed in GC tissues, and the expression of ER−α36 in intestinal GC is higher than that in diffuse GC [The KEGG pathway enrichment analysis excluded pathways unrelated to GC and revealed that the pathogenesis of gastric cancer was related to tumor signaling pathway, PI3K-Akt signaling pathway, cell cycle, MAPK signaling pathway, cancer transcription imbalance, and estrogen signaling pathway. In tumor cells, PI3K-Akt is one of the main signaling pathways that regulate proliferation, invasion, and migration . PI3K/Akffuse GC .The action mechanism of CRP-PR herb pair in the treatment of GC was studied by network pharmacology method of active component screening, network construction, and pathway analysis in this study. Then, we validated the key genes in TCGA and HPA database; most of the above verification results were consistent with this article. By comparing the research results with known experimental results, it was concluded that CRP-PR herb pair treat diseases by mediating the biological processes such as tumor signaling pathway, PI3K-Akt signaling pathway, MAPK, and others to regulate the proliferation, apoptosis, metastasis, and vascular regeneration of GC cells, reflecting the synergistic effect of Chinese medicine from multiple components, multiple targets, and multiple pathways, which will lay a solid foundation for further elucidation of its action mechanism. The deficiency is due to the prevalence of COVID-19, experimental conditions, time, and other constraints; we are unable to carry out relevant experimental verification in a short time. We will definitely conduct in-depth experimental research on the key targets and pathways of this article when conditions permit at a later period.


Esophageal carcinoma (EsC) is a member of the cancer group that occurs in the esophagus; globally, it is known as one of the fatal malignancies. In this study, we used gene expression analysis to identify molecular biomarkers to propose therapeutic targets for the development of novel drugs. We consider EsC associated four different microarray datasets from the gene expression omnibus database. Statistical analysis is performed using R language and identified a total of 1083 differentially expressed genes (DEGs) in which 380 are overexpressed and 703 are underexpressed. The functional study is performed with the identified DEGs to screen significant Gene Ontology (GO) terms and associated pathways using the Database for Annotation, Visualization, and Integrated Discovery repository (DAVID). The analysis revealed that the overexpressed DEGs are principally connected with the protein export, axon guidance pathway, and the downexpressed DEGs are principally connected with the L13a-mediated translational silencing of ceruloplasmin expression, formation of a pool of free 40S subunits pathway. The STRING database used to collect protein-protein interaction (PPI) network information and visualize it with the Cytoscape software. We found 10 hub genes from the PPI network considering three methods in which the interleukin 6 (IL6) gene is the top in all methods. From the PPI, we found that identified clusters are associated with the complex I biogenesis, ubiquitination and proteasome degradation, signaling by interleukins, and Notch-HLH transcription pathway. The identified biomarkers and pathways may play an important role in the future for developing drugs for the EsC. Esophageal carcinoma (EsC) is a member of the cancer group that occurs in the esophagus; globally, it is known as one of the fatal malignancies. In the year of 2018, EsC ranked as the ninth most common type of cancer with 572,000 new cases  and the sixth most common form of cancer in mortality with 509,000 deaths , 5. Obeshttps://www.ncbi.nlm.nih.gov/geo/) [Many studies have been conducted on esophageal cancer to explore genetic biomarkers –15. But ov/geo/) . GSE9375ov/geo/) . GSE1438P value < 0.05 are applied [Limma stands for linear models for microarray data, and most of the functionality of limma has been developed for microarray data. Using limma for microarray data processing is simple, and its result is mostly accurate. We used the limma package of the R language to convert the raw files of our selected four datasets . The dat applied , 20.http://david.abcc.ncifcrf.gov/) is fruitful to gather all outcomes [P value < 0.05 is maintained for identifying the final outcomes.Gene Ontology (GO) analysis provides wide biological exploration outcomes for a single gene or gene set. In recent years, GO analysis is a crucial part of system biology-related studies. In another corner, pathway enrichment analysis assists in explore mechanistically insight between gene sets produced from the wide genome-scale analysis . In thisoutcomes . Statisthttps://string-db.org/) repository is used to explore internal interactions between DEGs [k − core = 2, and maximum depth = 100 is counted as a minimum criterion. The functional pathway analysis in the cluster is performed by using the REACTOME database.The Search Tool for the Retrieval of Interacting Genes/Proteins  and analysis . The topWe applied functional analysis using the DAVID database to achieve further knowledge into the function of identified DEGs. The functional analysis reveals significant enriched GO terms and pathways of identified DEGs. The GO analysis explores that the overexpressed DEGs are mainly associated with protein ubiquitination, and regulation of cell cycle for biological process (BP); endoplasmic reticulum membrane and nucleoplasm for cellular component (CC); and protein binding, DNA binding for molecular function (MF) . On anotWe used four different databases to achieve the associated pathways more clearly. The pathway analysis revealed that the overexpressed DEGs are principally connected with the protein export, axon guidance, and RHO GTPases Activate Formins pathway ; the dowUsing the STRING database, we generated the PPI network and visualized with Cytoscape software. Constructed PPI network has 646 nodes and 2055 connections, including 172 upregulated DEGs and 474 downregulated DEGs . Using CCluster analysis is conducted using the MCODE method. In this analysis, 11 clusters are identified where the number of nodes is greater than 5. We identified four significant clusters from the constructed PPI network. The most significant cluster is enriched with MCODE score 17.5 and node density 33; 2nd significant cluster has MCODE score 12 and node density 12; 3rd significant cluster has MCODE score 9.238 and node density 22; the 4th significant cluster has MCODE score 5 and node density 9. Pathway enrichment analysis explored that clusters are significantly enriched with the complex I biogenesis, mitochondrial translation termination, ubiquitination and proteasome degradation, signaling by interleukins, and Notch-HLH transcription pathway . ClusterGlobally EsC is considered one of the most deadly diseases for its fast development and base presage. Around 80% of EsC cases are recorded from less developed regions in the world . In 2012In this study, PPI network is constructed by using identified DEGs. From the PPI network, we found 10 hub genes  using three combined methods. Interleukin 6 (IL6) gene is a member of the Interleukin family, and it takes part in cell growth operation. IL6 can act as both a proinflammatory cytokine and an anti-inflammatory myokine, and it is associated with many types of cancer development . A studyHDRC affected women to embrace a high risk of having breast cancer . HDRC paIn the PPI analysis section, we applied the MCODE method to identify clusters. Significant four clusters are identified, and pathway analysis is performed. Pathway analysis showed that the clusters are principally enriched with complex I biogenesis, mitochondrial translation termination, mitochondrial translation initiation, and interferon-alpha/beta signaling pathway. Mitochondrial biogenesis develops breast cancer tumors in the epithelial cell lines .The authors believe the outcomes of this study will make an impact on the biomarker identification of EsC. But more studies are required to prove the statement. Lack of tools and established laboratory, we could not verify our outcomes which is the limitation of this study. For future goals, we will use the outputs to explore microRNA biomarkers for EsC, which will give us deeper knowledge regarding EsC development.