To explore the metabolic differences of follicular thyroid carcinoma (FTC) by metabonomics, to find potential biomarkers for the diagnosis of FTC, and to explore the pathogenesis and diagnosis and treatment strategies of FTC.The metabonomics of 15 patients with FTC and 15 patients with follicular thyroid nodules(FTN) treated in Henan Cancer Hospital were analyzed by liquid chromatography-mass spectrometry (LC-MS).The analysis showed that the metabolite profiles of FTC tissues could be well distinguished from those of control tissues, and 6 kinds of lipids were identified respectively, including lysophosphatidic acid(LysoPA) ,LysoPA[20:4/0:0)]; phosphatidic acid(PA) ; lysophosphatidylcholine(LPC) ,LPC(17:0),LPC. There are 2 kinds of amino acids, including L-glutamate,L-glutamine.There are 3 other metabolites, including retinol,flavin adenine dinucleotide,androsterone glucuronide.Lipid metabolites are the main metabolites in these metabolites.The metabolic pathways related to FTC were analyzed by KEGG and HMDB, and 9 metabolic pathways were found, including 4 amino acid related metabolic pathways, 1 lipid metabolic pathways and 4 other related pathways.There are significant differences in many metabonomic characteristics between FTC and FTN, suggesting that these metabolites can be used as potential biomarkers. Further study found that LysoPA and its analogues can be used as biomarkers in the early diagnosis of FTC.It may be related to the abnormal metabolism of phospholipase D (PLD), the key enzyme of LysoPA synthesis caused by RAS pathway. At the same time, it was found that the metabolic pathway of amino acids and lipids was the main metabolic pathway of FTC. The abnormality of LysoPA may be the cause of follicular tumor carcinogenesis caused by lipid metabolic pathway. Follicular thyroid tumor mainly include FTC, atypical follicular thyroid adenoma and follicular thyroid adenoma(FTA). FTC is one of the highly differentiated malignant tumors, and its incidence is second only to papillary thyroid carcinoma (PTC). The main reason for its occurrence is the abnormal differentiation of thyroid follicular epithelial cells . In 2017Metabolomics is a subject of qualitative and quantitative analysis of low molecular weight metabolites in an organism or cell to monitor the changes of chemical products in living cells. It has significant advantages in the early screening of tumor markers . The mosThe subjects were from the patients treated in Henan Cancer Hospital. All the patients in the experimental group were confirmed to be FTC by operation and pathological examination.The experimental group will meet the following items: a). It was confirmed by pathology as FTC;b). No history of other cancers; c). Age ≥ 18 years old; d). No history of blood transfusion; e). No history of taking immunosuppressive drugs. The control group was matched with case frequency according to age and sex. The study was carried out with the informed consent of the subjects.Take 50mg solid sample or 100 μl liquid sample into 1.5ml centrifuge tube, add 400 μ l extract (acetonitrile: methanol = 1:1), after vortex mixing for 30s, extract 30min  by low temperature ultrasonic extraction, place the sample at-20°C, 30 min, 4°C, 13000g centrifugal 15min, remove the supernatant, dry with nitrogen, re-dissolve 120 μ l complex solution (acetonitrile: water = 1:1), and extract 5min , 4°C, 13000g centrifugal 5min, the supernatant was removed to the injection vial with internal intubation for analysis.All the sample metabolites of the same volume were mixed into quality control samples (QC). In the process of instrumental analysis, one QC sample was inserted into every 10 samples to investigate the repeatability of the whole analysis process.The instrument platform of this LC-MS analysis is AB SCIEX’s UPLC-TripleTOF system of ultra high performance liquid chromatography tandem time of flight mass spectrometry. Chromatographic conditions: 10ul samples were separated by BEH C18 column  and then detected by mass spectrometry. Mobile phase A: water (containing 0.1% formic acid), mobile phase B: acetonitrile/isopropanol (1pm 1) (containing 0.1% formic acid). Separation gradient: 0-3 min, mobile phase A from linear 95% to 80%, mobile phase B from linear 5% to 20% min, mobile phase A from linear 80% to 5%, mobile phase B linear from 20% to 95% min, mobile phase A linear to 5%, mobile phase B linear to 95%. 13.0-13.1 min, the linearity of mobile phase An increases from 5% to 95%, the linearity of mobile phase B decreases from 95% to 5%, the linearity of mobile phase A maintains 95%, and the linearity of mobile phase B maintains 5%. The flow rate is 0.40 mL/min and the column temperature is 40 °C. Mass spectrometry conditions: the sample mass spectrometry signal was collected in positive and negative ion scanning mode, and the mass scanning range (m/z) was 50-1000. Ion spray voltage, positive ion voltage 5000V, negative ion voltage 4000V, de-cluster voltage 80V, fog 50psi, auxiliary heater 50psi, air curtain gas 30psi, ion source heating temperature 500°C, 20-60V cycle collision energy.http://www.hmdb.ca/) and Metlin (https://metlin.scripps.edu/) database. Metabolite identifications were accepted if they could be established on a basis of at least one unique metabolite identified with a high confidence (FDR < 1%). Metabolite abundances were calculated using intensity of all precursors. For each case, normalized abundance by SEQUEST searches for the metabolite. The metabonomics data of a specimen was determined by the metabolite with the largest normalized abundance.After the completion of the computer, the LC-MS raw data are imported into the metabonomics processing software Progenesis QI  for baseline filtering, peak identification, integration, retention time correction and peak alignment, and finally a data matrix of retention time, mass-to-charge ratio and peak intensity is obtained. The data matrix uses the 80% rule to remove the missing values, that is, to retain at least one group of samples with non-zero values of more than 80%. Then fill the vacancy value . In order to reduce the error caused by sample preparation and instrument instability, the response intensity of the essential spectrum peak of the sample is normalized by the sum normalization method, and the normalized data matrix is obtained.At the same time, the variables with relative standard deviation (RSD) > 30% of QC samples are deleted and logarithmized by log10 to get the final data matrix for follow-up analyses. At the same time, the metabolite information was obtained by matching the mass spectrometry information of MS and MSMS with the metabolic public database HMDB (https://cloud.majorbio.com) for data analysis. The R software package ropls (Version1.6.2) carries out orthogonal least square discriminant analysis (OPLS-DA), and uses 7 cycles of interactive verification to evaluate the stability of the model. In addition, student’s t test and multiple of difference analysis were performed. The selection of significant differential metabolites was based on the variable weight value (VIP) obtained by OPLS-DA model and the p value of student’s t test. The metabolites with VIP > 1 and p <0.05 (student’s t test) were significant differential metabolites. A total of 11 differential metabolites were screened.The preprocessed data is uploaded to Meiji biological cloud platform (https://www.kegg.jp/kegg/pathway.html) to obtain the pathways involved by differential metabolites. KEGG enrichment analysis: the pathway enrichment analysis was carried out by Python software package scipy.stats, and the biological pathway most related to the experimental treatment was obtained by Fisher accurate test. HMDB compound classification: the differential identified metabolites were classified by HMDB compound classification database. IPath metabolic pathway analysis: iPath3.0 (http://pathways.embl.de) was used to visually analyze the metabolic pathways involved in metabolic sets to view the metabolic pathway information of the whole biological system.Notes for KEGG access: the metabolic pathways were annotated by KEGG database  scores based on the OPLS-DA model indicate the potential metabolites as biomarkers  and human metabolome database(HMDB), and the KEGG metabolic pathways could be divided into six categories (Thyroid cancer(TC) is one of the malignant tumors with the fastest increasing incidence in the world in recent years . With thThe main methods of metabolic research include nuclear magnetic resonance (NMR) spectroscopy and mass spectrometry (MS), gas chromatography (GC) and liquid chromatography (LC) and other separation techniques. Nuclear magnetic resonance technology has the advantages of non-selective and non-destructive analysis of samples, low sample consumption, simple sample pretreatment, non-invasive and other advantages, and can be used for the analysis of various body fluids . HoweverThe development of thyroid cancer requires the existence of specific conditions under which cell metabolism is reprogrammed to meet its bioenergy and biosynthesis needs and to allow cells to proliferate uncontrolled. Therefore, the low molecular weight metabolites of cancer tissues are significantly different from those of other tissues. Metabonomics technology can accurately detect the differences between these metabolites and find the relative relationship between these metabolites and physiological and pathological changes to diagnose diseases and monitor health. As an important metabolite, lipids participate in the self-assembly of phospholipids to form biofilms and participate in cell differentiation and signal transduction as second messengers. Previous studies  have fouGuo  studied Interestingly, we found that a variety of lysophosphatidic acid (LysoPA) levels are generally increased in FTC, and LysoPA is produced under physiological and pathophysiological conditions of cells and extracellular fluid. It mediates a variety of cellular responses and activities, including cell proliferation, migration, invasion, cytokine production, reactive oxygen species (ROS) production and cancer cell progression . The latLysoPA has two main biosynthetic pathways. First, phosphatidic acid (PA) is formed by the decomposition of phospholipids by phospholipase D (PLD), and then converted by phospholipase A1 or A2 (PLA1 and PLA2) to LysoPA. Second, lysophospholipids are produced by the corresponding phospholipids through PLA1 or PLA2. They are then cut into LysoPA by the action of lysophospholipase D ) . RelatedThe characteristics of low molecular weight metabolites in tumor tissues are significantly different from those in other tissues. These metabolite characteristics can be expressed in two ways:1. The concentration of low molecular weight metabolites is constantly changing at each stage of tumor tissue progression, and these differential metabolites can be detected by metabonomics to diagnose diseases. 2. There are also many changes in metabolic pathways involved in each stage of tumor tissue progression. After annotating KEGG and HMDB and analyzing their pathways and enrichment, we found that important metabolic pathways are related to FTC. The changes of amino acids in FTC mainly include L-glutamate and L-glutamine. The KEGG pathways involved in L-glutamate and L-glutamine include D-glutamine and D-glutamate metabolism, alanine, aspartic acid and glutamate metabolism, arginine biosynthesis, glutathione metabolism and so on. Recently, Gu  studied In short, there are significant differences in a variety of metabonomic characteristics between FTC and FTN, suggesting that these metabolites can be used as potential biomarkers. At the same time, our study found that LysoPA has a very strong diagnostic ability for FTC, which may be related to the abnormal metabolism of phospholipase D (PLD), the key enzyme of LysoPA synthesis caused by RAS pathway. In addition, LysoPA can be detected in a variety of human body fluids, which has the potential to be used in clinic, but it still needs a large number of experiments to confirm. We also found that Amino acid metabolic pathway and lipid-related metabolic pathway may be the key pathways of follicular tumor carcinogenesis, which need to be further studied to explore its potential mechanism and its role in the development of FTC.This study provides new insights into the diagnosis of FTC by studying the differences of related metabolites and abnormal metabolic pathways of FTC, and explores the potential biomarkers of FTC, which has great potential in the diagnosis and treatment of FTC.The original contributions presented in the study are included in the article/Our study was approved by the Institutional Ethics Committee of the Henan Cancer Hospital. All participants signed informed consent to medical research before the initial treatment, and all experiments were performed in accordance with the relevant guidelines and regulations.JF and JQ: conceptualization and original draft; JF and YY: data curation and methodology; JQ, JF and YY: manuscript review and editing; JF, DW and JQ: software; SL and RL: investigation; YW and GL: project administration; JF, RL and SL: supervision, validation, and visualization. All authors contributed to the article and approved the submitted version.


However, the comprehensive analysis of ceRNA-mediated pathway crosstalk is lacking. Here, we constructed the landscape of the ceRNA-mediated pathway–pathway crosstalk of eight major cardiovascular diseases (CVDs) based on sequencing data from ∼2,800 samples. Some common features shared by numerous CVDs were uncovered. A fraction of the pathway–pathway crosstalk was conserved in multiple CVDs and a core pathway–pathway crosstalk network was identified, suggesting the similarity of pathway–pathway crosstalk among CVDs. Experimental evidence also demonstrated that the pathway crosstalk was functioned in CVDs. We split all hub pathways of each pathway–pathway crosstalk network into three categories, namely, common hubs, differential hubs, and specific hubs, which could highlight the common or specific biological mechanisms. Importantly, after a comparison analysis of the hub pathways of networks, ∼480 hub pathway-induced common modules were identified to exert functions in CVDs broadly. Moreover, we performed a random walk algorithm on the hub pathway-induced sub-network and identified 23 potentially novel CVD-related pathways. In summary, our study revealed the potential molecular regulatory mechanisms of ceRNA crosstalk in pathway–pathway crosstalk levels and provided a novel routine to investigate the pathway–pathway crosstalk in cardiology. All CVD pathway–pathway crosstalks are provided in http://www.licpathway.net/cepathway/index.html.Pathway analysis is considered as an important strategy to reveal the underlying mechanisms of diseases. Pathways that are involved in crosstalk can regulate each other and co-regulate downstream biological processes. Furthermore, some genes in the pathways can function with other genes  With the increasing scale of high-throughput expression data, pathway analysis has become an important routine for understanding the underlying biological mechanisms of multiple diseases. Recent studies have demonstrated that pathways are involved in a lot of the processes of cardiovascular diseases (CVDs), such as the AMPK signaling pathway , TGF-betvia the miRNA-induced silencing complex in a post-transcriptional level (via functioning as an endogenous miR-223 sponge to inhibit miR-223 activity in heart failure (Micro RNAs (miRNAs) are an abundant type of small, non-coding RNAs (∼22 nt long), which can repress the expression of messenger RNAs (mRNAs) al level . Dysregual level . Moreoveal level . For insal level . CircRNA failure . Our pre failure . PathwayCardiovascular diseases have become the leading cause of morbidity and mortality worldwide. Although various types of CVDs showed different symptoms, some common mechanisms had also been demonstrated. For example, both myocardial infarction and cardiac hypertrophy could be regulated by the TGF-beta signaling pathway , and theHere, we systematically investigated the global ceRNA-mediated pathway–pathway crosstalk in eight major CVDs by integrating the analysis of ∼2,800 CVD samples and ∼1,200 pathways. First of all, the topological analysis was performed for all pathway–pathway crosstalk networks and some conserved features were identified across CVDs. A common pathway–pathway core network was also highlighted from our research. Next, we split the hubs in each disease network into three types based on the neighbor similarity. Importantly, some common modules were identified by integrating the analysis of common, differential hubs and known CVD-related pathways. Moreover, the algorithm of random walk with restart was performed in the pathway–pathway crosstalk network to optimize novel CVD-related pathways. Summarily, all these results revealed the crucial roles of the ceRNA-mediated pathway–pathway crosstalk in multiple CVDs and proposed a novel molecular mechanism to exert functions in cardiology.https://www.ncbi.nlm.nih.gov/geo/), which was a public functional genomics data repository supporting MIAME-compliant data submissions. Specifically, we investigated eight major CVDs in this study, including coronary artery disease (CAD), hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), heart failure (HF), myocardial infarction (MI), pulmonary hypertension (PAH), and congenital heart disease (CHD) from 21 gene expression datasets. All these profiles were used to identify ceRNA pairs based on our previous pipeline , which was a collection of annotated gene sets for use with GSEA software. We only reserved the pathways from four popular databases, including KEGG, BIOCARTA, REACTOME, and PID. In total, 1,273 pathways were collected for further research.All the pathways encompassing curated gene sets were downloaded from the C2 group of MsigDB V5.2 (http://www.cuilab.cn/hmdd), miR2Disease (http://www.mir2disease.org/), and miREnvironment (http://cmbi.bjmu.edu.cn/miren), and mapped these miRNAs to each of the eight CVDs performed in our study manually. As a result, 702 miRNA-CVD pairs were collected totally after combining various databases.Several bioinformatics databases have provided a comprehensive resource of miRNA-disease relationships. We downloaded CVD-associated miRNAs from HMDD . The nodes of networks represented pathways and edges represented the significant crosstalk between the pathways.After identifying all pathway–pathway crosstalk pairs, the ceRNA-mediated pathway–pathway crosstalk networks of each CVD-related profile were constructed and all the networks were visualized by Cytoscape , a fast program for locating and visualizing overlapping, to identify the cliques as conserved modules. Moreover, we only reserved the modules that contained more than five pathways. Similarly, the specific pathway–pathway crosstalk modules were identified by mapping specific hub pathways into the sub-networks of each disease.To identify the common CVD-associated pathway–pathway crosstalk modules, firstly, we mapped all known CVD-related pathways to each disease pathway–pathway crosstalk network and all the sub-networks were merged. Next, we extracted the common hub pathway and differential hub pathway-associated component of the merged sub-network. Finally, we performed the CFinder . Our study conformed to the regulations of the Ethics Committee of Harbin Medical University. Transverse aortic constriction (TAC) models were used to construct cardiac hypertrophy models based on our previous protocol . For myo2 atmosphere. Angiotensin-II  100 nmol/L was treated to induce cardiomyocyte hypertrophy in vitro for 24 h. Small interfere RNA (siRNA) and X-treme GENE siRNA  were mixed, which were transferred and co-cultured with cells to knockout target genes. The PI3K siRNA sequence was Forward: GGGACCCACUAUCUGAAAUTT, Reverse: AUUUCAGAUAGUGGGUCCCTT. The TNF-α siRNA sequence was Forward: GACAACCAACUAGUGGUGCTT, Reverse: GCACCACUAGUUGGUUGUCTT. The negative control sequence was Forward: UUCUCCGAACGUGUCACGUTT, Reverse: ACGUGACACGUUCGGAGAATT.HL-1 cell lines were cultured in the recommended DMEM medium, containing 10% fetal bovine serum (HiClone), 100 units/ml penicillin, and 100 mg/ml streptomycin (Beyotime). The cells were grown at 37°C and a 5% COTotal protein was extracted from fresh tissues and cells based on the previous protocol . BrieflyTotal RNA from heart tissues and cells was extracted with Trizol (Invitrogen) according to the manufacturer’s protocol. cDNA was synthesized with reverse transcriptase . The relative gene expression was normalized to the Gapdh level in each sample. The primer sequences were synthesized by Sangon Biotech (Shanghai) Co., Ltd.p < 0.01. After merging all significant pathway–pathway pairs of each profile into a network, 21 pathway–pathway crosstalk networks were constructed. All these networks can be viewed in To systematically investigate the potential roles of the pathway–pathway crosstalk in CVDs, we performed two steps to construct the ceRNA crosstalk-mediated pathway–pathway crosstalk network in 21 sets of CVD-related expression profiles, including coronary artery disease (CAD), hypertrophic cardiomyopathy (HCM), dilated cardiomyopathy (DCM), ischemic cardiomyopathy (ICM), heart failure (HF), myocardial infarction (MI), pulmonary hypertension (PAH), and congenital heart disease (CHD). All the ceRNA pairs of each expression profile identified from our previous study were used for counting the number of ceRNA crosstalk between all candidate pathway–pathway pairs. For a candidate pathway–pathway pair, a 1,000 times permutation test was performed to yield the statistical significance. We only reserved the pathway–pathway pairs that met the threshold of To investigate the global properties of pathway–pathway crosstalk in CVDs, the topological features of crosstalk networks across CVDs were analyzed. First of all, degree distributions of all pathway–pathway crosstalk networks were examined. We found that all these networks followed a power law distribution , that waMoreover, to investigate the differences of connectivity among different origin pathways, we performed the topological feature analysis to the four groups of pathways belonging to four pathway databases. By integrating the analysis results of degree , betweenp value < 1e-30). Interestingly, the pathology outcome of dilated cardiomyopathy was heart failure, so we inferred that a lot of pathway–pathway crosstalk should be conserved between DCM and HF, and the results supported our presumption. Furthermore, there existed some similar pathology mechanisms between ischemic cardiomyopathy and myocardial infarction, and in our result, a high Simpson index score  was measured between the network of ICM (GSE60993) and the network of MI (GSE 1869). Moreover, the networks from the same disease shared more pathway–pathway pairs. For instance, the network of MI (GSE48060) and the network of MI (GSE34198) were both myocardial infarction-related networks, which got a high Simpson index in our results. Taken together, the pathway–pathway crosstalk networks revealed high similarities of biological processes among CVDs in the pathway level.Although some common features were identified in multi-CVDs by integrating the analysis of the networks, in the global view to compare all these CVD related pathway–pathway crosstalk networks, we found that ∼22% pathway–pathway crosstalk only occurred in one network. In addition, ∼14% pathway–pathway crosstalk were conserved in more than ten pathway–pathway crosstalk networks , which iTo assess whether there was a common core sub-network to maintain the regulatory mechanisms of pathway–pathway crosstalk across multiple CVDs, we extracted pathway–pathway crosstalk that occurred in more than 15 networks and these pathway pairs formed a connected component , which min vivo and in vitro cardiac hypertrophy models to investigate the crosstalk between MAPK and MTOR . In addition, the network of MI also presented a different degree distribution compared with the network of ICM . These significant differences of topological features in crosstalk networks revealed that pathway–pathway crosstalk strongly impacted the mechanisms of CVDs and played important roles in cardiology.Previous studies found that pathway–pathway crosstalk could participate in the regulation of various diseases. Next, we attempted to investigate the mechanisms of pathway–pathway crosstalk in CVDs by performing a topological analysis to pathway–pathway crosstalk networks. Firstly, 21 pathway–pathway crosstalk networks were merged into eight disease-related pathway–pathway crosstalk networks according the same disease names. Then, we compared degree cumulative distributions across CVDs and the results showed that most networks were characterized by nodes with highly variable degrees, from pathways with few links to pathways with hundreds of connections . For insAccording to the prior knowledge, hub nodes played crucial roles in biological networks, thus, we identified hub pathways from each CVD pathway–pathway crosstalk network. Interestingly, we found that most hub pathways showed a high connectivity in multiple CVD networks . In orde). A multi-level analysis of these modules revealed that most pathways were regulated by disease-related genes and miRNAs. For example, a common module encompassed 21 pathway–pathway crosstalk among seven hub pathways . We mapped all the known CVD-related pathways into the hub-induced sub-network as the seed nodes. After 5,000 times shuffling, 23 hub pathways were identified as the potentially novel CVD-related pathways , suggesterfusion . Moreoveerfusion . In addierfusion .http://www.licpathway.net/cepathway/index.html (p values, disease numbers, dataset numbers, and pathway sources information (To facilitate the use of ceRNA-mediated pathway–pathway crosstalk of CVDs, we also developed a friendly web source using PHP at dex.html . Users cormation , top. Usormation , bottom.via construction and analysis of pathway–pathway crosstalk networks. As a result, we found some conserved features across CVDs. For instance, all the degree distributions of pathway–pathway crosstalk networks were scale free and followed a power law distribution. The pathways with a high connectivity in networks showed a high crosstalk activity and contained more genes. Additionally, after the comparison of pathway–pathway interactions of each pathway–pathway crosstalk network, results showed that a fraction of pathway–pathway crosstalk was conserved in multiple CVDs and a core pathway–pathway crosstalk network was identified, indicating that there was a stable framework-encompassed pathway–pathway crosstalk, which might play a basic role in the mechanisms of cardiology. Hub nodes were more essential than other nodes in biological networks, thus we split all the hub pathways of each pathway–pathway crosstalk network into three categories: common hubs, differential hubs, and specific hubs, which could reveal the common or specific biological mechanisms in-depth analysis of networks. Furthermore, we mapped all common hub pathways, differential hub pathways, and known CVD-related pathways into a merged global disease pathway–pathway crosstalk network and extracted the sub-network among them. Importantly, after performing the CFinder algorithm on the sub-network, ∼480 common modules were identified, indicating that these hubs linked closely in the network and might exert their regulatory functions in high coordination. Moreover, we performed a random walk in the pathway–pathway crosstalk network and identified a subset of pathways that were highly related to CVD.In this study, we systematically analyzed the landscape of ceRNA-mediated pathway–pathway crosstalk across eight major CVDs Pathway crosstalk had been proposed for some years, which were demonstrated to play crucial roles in biological regulatory mechanisms of multiple diseases . PreviouHere, we proposed a novel method to identify pathway–pathway crosstalk by measuring the statistical significance of CVD-related ceRNA crosstalk between pathways. Compared with previous biological network-based methods, our method focused on the ceRNA crosstalk and identified more significant pathway–pathway crosstalk that encompassed more ceRNA pairs. Furthermore, from this study, we identified some pathway drivers in CVDs, such as MAPK and mTOR signal pathways. Biological experiments also demonstrated that ceRNA crosstalk between pathways had the effects on the pathway crosstalk and pathological processes.Pathway crosstalks are considered as key regulatory mechanisms in multiple diseases, from cancer to CVDs . A systeHowever, there were also some limitations in our study. For instance, we used the pathway data from four unique pathway databases that curated in MSigDB. However, only ∼7,000 genes were annotated in these pathways; with the development of biology, we considered that a scale with more pathway annotations could provide a more stable pathway–pathway crosstalk result. Secondly, in the field of CVDs, there was no established database like TCGA to investigate molecular interactions till now. We downloaded the CVD-related gene expression data from the GEO database, but these data came from multiple platforms and different samples, which might produce false-positives, although we also found some common features in these data. In addition, we used a popular strategy to identify ceRNA crosstalk, if a more accurate method can be proposed to identify ceRNA crosstalk, we believe that our results will be more stable. Finally, in the current version of cePCD, we only focused on displaying the ceRNA-mediated pathway crosstalk and ignored the functional annotation or enrichment for the ceRNAs that were involved in pathway crosstalk. We will update our database to embed more analysis tools in the next version, such as gene set enrichment analysis, gene ontology analysis, and pathway analysis.In conclusion, we performed a global view to investigate the ceRNA-mediated pathway–pathway crosstalk in eight major CVDs. All results suggested that ceRNA-mediated pathway–pathway crosstalk participated in CVD-related biological progresses broadly. Importantly, we identified a lot of common modules, which were formed by hub pathway nodes in the networks and considered as crucial regulatory structures. Furthermore, some pathways were identified as novel CVD-related pathways by a random walk algorithm. In total, our results revealed the potential molecular regulatory mechanisms of ceRNA crosstalk in pathway–pathway crosstalk levels and provided a novel routine to investigate the pathway–pathway crosstalk of cardiology.


Sjögren's syndrome (SS) is a chronic autoimmune disease characterized by progressive oral and ocular dryness that correlates poorly with autoimmune damage to the glands. CheReCunJin (CRCJ) formula is a prescription formulated according to the Chinese medicine theory for SS treatment.  This study aimed to explore the underlying mechanisms of CRCJ against SS.  The databases, including Traditional Chinese Medicine System Pharmacology, Encyclopedia of Traditional Chinese Medicine, Bioinformatics Analysis Tool for the molecular mechanism of Traditional Chinese Medicine, and Traditional Chinese Medicine Integrated Databases, obtained the active ingredients and predicted targets of CRCJ. Then, DrugBank, Therapeutic Target Database, Genecards, Comparative Toxicogenomics Database, and DisGeNET disease databases were used to screen the predicted targets of SS. Intersected targets of CRCJ and SS were visualized by using Venn diagrams. The overlapping targets were uploaded to the protein–protein interaction network analysis search tool. Cytoscape 3.8.2 software constructed a “compound-targets-disease” network. Gene Ontology and the Kyoto Encyclopedia of Genes and Genomes analyses characterized potential targets' biological functions and pathways. AutoDock Vina 1.1.2 software was used to research and verify chemical effective drug components and critical targets.  From the database, we identified 878 active components and 2578 targets of CRCJ, and 827 SS-related targets. 246 SS-related genes in CRCJ were identified by intersection analysis, and then ten hub genes were identified as crucial potential targets from PPI, including ALB, IL-6, TNF, INS, AKT1, IL1B, VEGFA, TP53, JUN, and TLR4. The process of CRCJ action against SS was mainly involved in human cytomegalovirus infection and Th17 cell differentiation, as well as the toll-like receptor signaling and p53 signaling pathways. Molecular docking showed that the bioactive compounds of CRCJ had a good binding affinity with hub targets.  The results showed that CRCJ could activate multiple pathways and treat SS through multiple compounds and targets. This study lays a foundation for better elucidation of the molecular mechanism of CRCJ in the treatment of SS, and also provides basic guidance for future research on Chinese herbal compounds. Sjögren's syndrome (SS) is a global systemic autoimmune disease with a prevalence rate second only to rheumatoid arthritis , 2. The Network pharmacology is a new strategy based on system biology, bioinformatics, and multidisciplines, which can systematically and comprehensively observe the intervention of drugs on disease networks –20. It iThe chemical compounds in the CRCJ formula were obtained from the Traditional Chinese Medicine System Pharmacology (TCMSP), Traditional Chinese Medicine Integrated Database (TCMID), Encyclopedia of Traditional Chinese Medicine (ETCM), and Bioinformatics Analysis Tool for Molecular mechANism of Traditional Chinese Medicine (BATMAN-TCM) databases. Subsequently, a herbs-compounds network was constructed using Cytoscape 3.8.2 software. Potential targets were selected according to four drug-target databases: the TCMSP , ETCM 2, BATMAN-Prediction of possible chemical compounds for CRCJ was performed by searching TCMSP, ETCM, BATMAN-TCM, and TCMID databases, using Chinese herbs as keywords. When the same drug possesses various components in multiple databases, we would take the union to gather complete information about the chemical's contents. The parameters for the selection of the active ingredients were set as follows: oral absorption availability (OB) ≥ 30% and drug-likeness (DL) ≥ 0.18 . The actBased on the selected active ingredients, component-related protein targets were searched and predicted from the TCMSP, ETCM, and BATMAN-TCM databases, and the finding gene targets were standardized using the UniProt KB database .We constructed reference targets related to SS from the extensive databases DrugBank, TTD, CTD, Genecards, and DisGeNET. Similarly, a reference protein set was established by merging all the disease databases –35. FinaThe intersection of the CRCJ-related targets and SS-related targets was selected to construct the protein–protein interactions (PPI) network , 37. Onlp ≤ 0.05) [http://www.graphpad.com) [http://sangerbox.com/) for data visualization and statistical analysis.To understand the center of the functional genes and the signaling pathways, the ImageGP was used to carry out functional enrichment to explore GO terms, including “biological processes” (BP), “molecular functions” (MF), “cellular component” (CC), and the KEGG pathway ( ≤ 0.05) . Finallypad.com)  and Sanghttps://www.rcsb.org/). PyMOL 2.3.0 software was employed to identify the active binding sites and visualize software-generated protein structure diagrams. Based on the affinity between the active compound and the target, heatmaps were plotted using Graph Pad Prism 7.We utilized the AutoDock Vina 1.1.2 software to simulate molecular docking and examine the potential molecular interactions between compounds of the CRCJ formula and hub genes. Firstly, based on the node degree in the PPI, we identified hub genes with nodes larger than 120 degrees. Proteins with three-dimensional structures in the protein database (PDB) and AlphaFold were selected as targets for molecular docking. Then, the compounds to be docked were chosen based on the ordering of compound degree values. According to the target information of the CRCJ, TCMSP, ID, and PubChem CID were matched with the corresponding candidate target components . The strThe CRCJ formula is composed of 12 types of Chinese herbs. To reveal the potential treatment mechanisms of the CRCJ in SS, we integrated the ingredient information from the TCMID, ETCM, TCMSP, and BATMAN databases. 642, 353, 100, and 279 active ingredients were obtained from the four databases, respectively . After tTo ensure the potential target of the CRCJ in SS, we uploaded 878 compounds to the TCMSP, BATMAN-TCM, and ETCM databases. Finally, 1558, 856, and 306 proteins were found in the BATMAN-TCM, ETCM databases, and TCMSP database, respectively. Each UniProt ID for the targets was converted to the corresponding gene name. As a result, a total of 2578 genes were obtained after removing the repetition  of the 246 proteins identified 19 statistically significant signaling pathways. The volcanic maps were constructed based on the differential p-value analysis  to alleviate the inflammatory response caused by streptococcus suis [κB signal can cause chronic inflammation and ultimately lead to SS [Quercetin is a natural flavonoid, and research indicates that it exhibits antioxidant, anti-inflammatory, immunoregulation, and many antiallergic bioactive compound activities , 60. Numcus suis . Evidencad to SS . Moreovead to SS . Quercetad to SS . Overallα. The elevated levels are linked to ESSDAI, ESR, and CRP. Simultaneously, SS patients with ANA, anti-SSA antibodies, and anti-SSB antibodies positive have higher TNF-α levels [α in the salivary glands of SS patients increases when compared with the normal population [α may promote the development and progression of SS by inducing the oral mucosal epithelium and ocular mucosal epithelial cell apoptosis [α, which in turn affects the target control of glandular cells. IL-6 is a B cell growth and differentiation factor and can promote immunoglobulin synthesis.This is consistent with the pathological mechanism of autoimmune diseases, especially SS, and shows that CRCJ has a potential therapeutic effect on SS. We can identify ten interacting target genes by analyzing the PPI network according to the degree value and show that the CRCJ components act on multiple target genes and synergistic interactions among different genes. The top targets obtained according to the rank value were ALB, IL-6, TNF, INS, AKT1, IL1B, VEGFA, TP53, JUN, and TLR4. The results indicate that they may be the key targets of the CRCJ therapy for SS. The results showed that the active components of CRCJ acted on multiple target genes and synergistic interactions among individual genes. Among TNF, IL-6, AKT1, and VEGFA are associated with the development of SS. TNF is mainly produced by innate immune cells and is a critical regulatory cytokine of autoimmunity . One stuα levels . These spulation . Moreovepoptosis , 71. Theα release, and the production of TNF-α is closely associated with SS [IL-6 plays a role in inflammation and B cell maturation. Sun et al. measured the IL-6 levels in serum and salivary glands of 21 pSS postmenopausal patients and 21 healthy postmenopausal women based on enzyme-linked immunosorbent assay . In addi with SS . We specα, IL-1β and other inflammatory cytokines, which induce the body to produce more inflammatory factors. One study showed that the inflammatory cytokines in small salivary glands of pSS patients are mainly IL17. Moreover, Th17-centric cytokines IL-17, IL-6, IL-23, and IL-12 are significantly increased in the plasma of SS patients [KEGG pathway enrichment analysis results showed that the top ten signaling pathways of CRCJ for SS treatment are as follows: human cytomegalovirus infection, toll-like receptor signaling pathway, pertussis, allograft rejection, Th17 cell differentiation, p53 signaling pathway, chronic myeloid leukemia, glioma, graft vs host disease, and the Ras signaling pathway. Toll-like receptor (TLR) could play an essential role in the pathogenesis of autoimmune disorders. Recent studies have found TLR1, TLR2, and TLR4 mRNA levels are upregulated in the epithelial cells of salivary glands of pSS patients compared with the healthy control group . TLR7 anpatients . Thus, oThis study demonstrates the synergistic effect of the active compounds on CRCJ. The regulation of the immune and anti-infection and inflammation may contribute to SS treatment. Taken together, the results indicate that CRCJ may promote a reduction in the SS pathological immune network and provide new strategies for immunomodulatory therapies for SS.A total of 878 active compounds and 2578 predicted targets in CRCJ and 827 SS-related targets were identified. 246 key targets of CRCJ for SS treatment were obtained including ALB, IL-6, TNF, INS, AKT1, IL1B, VEGFA, TP53, JUN, and TLR4. GO and KEGG enrichment analyses suggested that these key targets are mainly involved in multiple pathways, including the human cytomegalovirus infection, toll-like receptor signaling, Th17 cell differentiation, p53 signaling, pertussis, allograft rejection, and Ras signaling. Besides, the molecular docking study also showed that the main active CRCJ components have a good binding affinity with key protein targets, which provided an important basis for further investigation. This study combines network pharmacology and molecular docking, underlying a foundation for further research on the mechanism of CRCJ.


Background: Nonalcoholic fatty liver disease (NAFLD) is the most common metabolic disease and is intertwined with cardiovascular disorders and diabetes. Chaihu Shugan powder (CSP) is a traditional Chinese medicine with a significant therapeutic effect on metabolic diseases, such as NAFLD. However, its pharmacological mechanisms remain to be elucidated.Methods: The main compounds of CSP were measured using LC-MS/MS. A network pharmacology study was conducted on CSP. Its potential active ingredients were selected according to oral bioavailability, drug similarity indices, and phytochemical analysis. After obtaining the intersected genes between drug targets and disease-related targets, the component-disease-target network and protein-protein interaction analysis were visualized in Cytoscape. GO and KEGG enrichment analyses were performed using the Metascape database. Six-week-old male C57BL/6 mice fed a high-fat high-fructose diet for 16 weeks plus chronic immobilization stress for 2 weeks, an in vivo model, were administered CSP or saline intragastrically. Liver histology, triglyceride and cholesterol levels, ELISA, and RT-PCR were used to assess hepatic inflammation and steatosis. Immunohistochemistry and western blotting were performed to assess protein levels.Results: A total of 130 potential target genes in CSP that act on NAFLD were identified through network pharmacology assays, including tumor necrosis factor (TNF), interleukin-6 (IL6), interleukin-1β (IL-1β), and peroxisome proliferator-activated receptor γ (PPARG). KEGG enrichment analysis showed that the main pathways were involved in inflammatory pathways, such as the TNF and NF-κB signaling pathways, and metabolism-related pathways, such as the MAPK, HIF-1, FoxO, and AMPK signaling pathways. The results in vivo showed that CSP ameliorated liver inflammation and inhibited hepatic fatty acid synthesis in the hepatocyte steatosis model. More specifically, CSP therapy significantly inhibited the expression of tumor necrosis factor α (TNFα), accompanied by a decrease in TNF receptor 1 (TNFR1) and the ligand availability of TNFR1.Conclusion: Through the combination of network pharmacology and in vivo validation, this study elucidated the therapeutic effect of CSP on NAFLD, decreasing liver inflammation and inhibiting hepatic fatty acid synthesis. More specifically, the anti-inflammatory action of CSP was at least partially mediated by inhibiting the TNFα/TNFR1 signaling pathway. Nonalcoholic fatty liver disease (NAFLD) encompasses a wide spectrum of diseases, ranging from simple steatosis and nonalcoholic steatohepatitis (NASH) to liver cirrhosis, which can progress to liver cancer, liver failure, and even death . A recenRadix Bupleuri (Chaihu in Chinese), Paeoniae Radix Alba (BaiShao in Chinese), Chuanxiong Rhizoma (Chuan Xiong in Chinese), Aurantii Fructus (ZhiQiao in Chinese), Citrus Reticulata (ChenPi in Chinese), Cyperi Rhizoma (XiangFu in Chinese) and licorice (GanCao in Chinese). It has been widely used in the treatment of depression and digestive diseases in China for four centuries (Chaihu Shugan powder (CSP), also called Chaihu Shugan San (CSS), is a traditional Chinese medicine (TCM) formula containing extracts of enturies . Quercetenturies . Our groenturies . Howeverin vivo experiments were used to unravel the underlying mechanisms.Meanwhile, there is evidence that inflammation plays a crucial role in NAFLD progression . Indeed,https://old.tcmsp-e.com/tcmsp.php, updated in 2020). The TCMSP is a specialized systems pharmacology database platform of Chinese traditional herbal medications, which contains the relationships among medications, targets, and diseases. Oral bioavailability (OB) is an important pharmacokinetic parameter for oral TCM, as it indicates the quantity of orally administered unchanged drugs that reach the systemic circulation. The Drug-likeness (DL) index refers to the structural similarity of compounds with known drugs, and it is widely utilized to assess the suitability of a component to be used as medication. Herein, the screening criteria in this research were an OB value of ≥30% and a DL value of ≥0.18.The active compounds of CSP were collected from the Traditional Chinese Medicine Systems Pharmacology database . The Uniport database  was used to convert protein names to their corresponding gene names.The potential targets of active compounds in CSP were retrieved from the TCMSP database and Swiss Target Prediction database , Online Mendelian Inheritance in Man database , and Therapeutic Target Database . “Non-alcoholic fatty liver disease” was used as the keyword.NAFLD-related human genes were identified by searching the Gene Cards database . The PPI network focused on finding hubs in which highly connected proteins were considered to play a crucial role between related ingredients and their probable targets. Afterward, all data were imported into Cytoscape 3.9.1 software to obtain PPI complex diagrams in a representable form to characterize the association between CSP and NAFLD intersection genes. The color and size of nodes represent the degree of the targets.The protein-protein interaction (PPI) network was obtained from the STRING database , with p < 0.01, min overlap value 3 and min enrichment value 1.5.To further characterize the molecular mechanism of CSP in NAFLD, Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment were performed by the Metascape database , Paeoniae Radix Alba , Chuanxiong Rhizoma , Aurantii Fructus , Citrus Reticulata , Cyperi Rhizoma  and licorice  was 6:6:4.5:4.5:4.5:4.5:1.5. To prepare the aqueous extract of CSP, all crude CSP herbs were decocted with an eightfold volume of distilled water twice, the first time for 1.5 h and the second time for 2 h. Then, the decoction was collected after filtration. Finally, the decoction was concentrated at 1.892 g/ml under reduced pressure using a rotary evaporator , and stored at 4°C.The CSP crude herbs were obtained pharmacy from Guang Dong Provincial Hospital of Chinese Medicine. All of these herbal medicines conformed to the requirements of the Chinese Pharmacopoeia 2020 edition and are in strict accordance with national execution standards. The dosage proportions of A Q-Exactive mass spectrometer  and an A Waters, ACQUITY UPLC HSS T3  column were used for LC separation. Gradient elution: acetonitrile/water (0.1% formic acid); flow rate of 0.2 ml/min. The column oven temperature was 45°C. The volume of the injection was 1 μL. Mass spectrometry analysis was performed using an electrospray ionization (ESI) source in positive and negative ion scanning mode. The parameters of the ESI source were as follows: sheath gas flow rate  was 35; aux gas (Arb) was 10; pray voltage was 3500 V; heated capillary temperature, 320°C; the analysis was carried out using a scan from 70 to 1,050 m/z. Scanning mode: full-scan mass spectra were set as follows: resolution, 70000; isolation window 70–1050 m/z, and data-dependent MS2 (dd-MS2) was set as follows: the resolution was 17500; normalized collision energy (NCE) was set at 20, 40, 60.Six-week-old male C57BL/6 mice were purchased from the Animal Research Laboratory of Guangdong Province . The present study was conducted after approval by the Research Institute of the Animal Protection and Use Committee of Guangdong Provincial Hospital of Chinese Medicine [SCXK(Yue) 2021047]. After adaptive feeding, mice were randomly assigned to five groups (n = 6-7 per group): the control group (Control), the model group (Model), the low-dose of CSP intervention group (CSPL), the medium-dose of CSP intervention group (CSPM) and the high-dose of CSP intervention group (CSPH). The experimental protocols are as follows. Mice were fed a high-fat high-fructose diet  for 16 weeks plus chronic immobilization stress  to construct a NAFLD model. The mice in the control group were fed a normal diet only . The daily dose of CSP decoction for human adults is 1.05 g/kg/d, and the equivalent dose in mice is 9.46 g/kg/d. The equivalent dose was selected as the medium dose; the high dose was doubling of the medium dose, while the low dose was half the medium dose.Mice were fasted for 6 h, and fasting blood-glucose (FBG) levels were examined by using a glucometer . Blood lipid profiles, including total cholesterol (TC), triglycerides (TG), high-density lipoproteins (HDL-C) and low-density lipoproteins (LDL-C), and TC and TG contents in the liver were all measured using commercial kits . The serum levels of alanine aminotransferase (ALT) and aspartate aminotransferase (AST) were measured by AST and ALT kits . The concentration of TNFα in the liver was examined by ELISA . All of the operations were performed strictly in accordance with the manufacturer’s protocols.(SREBP-1)  and peroxisome proliferator activated receptor (PPARγ)  expression and localization in the liver sections of mice were investigated using immunohistochemical staining. After dewaxing with xylene and rehydration, the liver sections were stained using the EliVision™ plus immunohistochemical staining kits  following the kit’s instruction. The staining was visualized by using a microscope .Fresh liver samples were collected from the mice and fixed with 4% paraformaldehyde overnight and embedded in paraffin. Small sections from the liver (4 μm thick) were dewaxed with xylene, rehydrated in a graded ethanol series and stained with hematoxylin and eosin (H&E). The frozen liver samples were sliced (8 μm thick) and stained with Oil Red O (ORO) staining solution . Nuclear factor κB (NF-κB) , sterol regulatory element-binding protein-1−ΔΔCt method. The primer pairs used in this study were synthesized by Shanghai General Biotech Co., Ltd., The primer information is listed in Total mRNA was isolated from fresh liver samples using RNAiso Plus  and reverse-transcribed into cDNA using an Evo M-MLV RT kit  according to the manufacturer’s protocol. SYBR Green Master Mix (Rox Plus)  was applied to quantify PCR amplification. Relative mRNA expression levels were normalized to the β-actin expression level. Real-time PCR results were calculated using the 2Total protein samples were isolated from tissue samples using RIPA lysis buffer  containing both PMSF and phosphatase inhibitors for western blot analyses. A BCA Protein Assay Kit  was used to determine the protein concentration. The protein samples were separated on the 10% or 12% SDS-PAGE gels, and then transferred to a polyvinylidene fluoride  membranes. After blocking with 5% skim milk, the PVDF membranes were incubated overnight at 4°C with the following primary antibodies: anti-TNFR1 , anti-phospho-NF-κB p65(Ser536) , anti-IL1β , IL6 , iNOS , anti-TNFα , anti-SREBP-1 , anti-PPARγ  and β-actin . After that, the PVDF membranes were incubated with the corresponding secondary antibodies. An ECL kit  was used to visualize the chemiluminescence signals. Protein expression levels were quantified with ImageJ software. The housekeeping protein β-actin was utilized as loading control.p value < 0.05 was considered significant.All data in this study are expressed as the mean ± s.d. One-way analysis of variance (ANOVA) followed by LSD or Dunnett’s T3 post-hoc test was applied for the results using the SPSS 25.0 statistical software package. Data for graphing were processed with GraphPad Prism 8.4.3 software. A The phytochemical composition of CSP was evaluated using LC-MS/MS. The total positive and negative ion chromatograms of CSP aqueous extracts, such as quercetin, kaempferol, naringenin, isorhamnetin, and nobiletin, are shown in A total of 139 active compounds in CSP were selected from the TCMSP database according to our search criteria . After tTNF, IL6, IL1B, AKT1, PPARG, JUN, TP53, VEGFA, MMP9, and CASP3.The 130 overlapping targets were uploaded to the STRING database to obtain information on predicted interactions. Then, the STRING data were imported into Cytoscape to devise a PPI network . There wGO and pathway enrichment analyses of 130 overlapping targets were conducted using the Metascape Database . The GO . As illustrated in , the mice of the model group also exhibited a prominent fatty liver phenotype featuring a pale and enlarged liver, higher liver-to-body weight ratio, increased fat vacuoles shown in H&E staining, and increased red lipid droplets shown in Oil red O staining. The lipid profiles in the liver and in the serum and liver enzyme levels were all elevated in the model group , interleukin-1β (IL-1β), and inducible nitric oxide synthase (iNOS). Compared with the control group in liver tissue, TNFα, IL-1β, IL-6, and iNOS in the liver of the model group were all significantly increased at the mRNA level . In contSREBP-1 and PPARγ, both key transcriptional regulators in hepatic lipogenesis, were subjected to immunochemical staining of the liver in this study. Compared with the control group, a marked increase in the nuclear expression of SREBP-1 and PPARγ in the liver of the model mice and CSP treatment could reverse this tendency . The proThe TNF signaling pathway is one of the most well-studied inflammatory signaling pathways. In addition, the results of the KEGG pathway enrichment analysis indicated that the overlapping targets of CSP in NAFLD were significantly enriched in the TNF signaling pathway. To determine whether CSP treatment ameliorated liver inflammation by regulating the TNF signaling pathway. The liver TNFα contents were examined using an ELSA kit, and the results showed that TNFα secretion in the liver of the model group was markedly increased compared with that in the control group and decreased after CSP treatment . TNF sigTNF, IL6, IL1B, AKT1, PPARG, JUN, and TP53 have shown a more important position and have close interactions between targets. A previous study found that quercetin could markedly exude and increase the degree of inflammation in the lamina propria of the nasal cavity, in parallel with reducing the secretion of TNFα and IL6  and TNF-receptor-2 (TNFR2)  . TNFR1 i (TNFR2) . Indeed,and iNOS . It is wand iNOS . TNFα trand iNOS . Meanwhiand iNOS . Intriguin vivo experiments, this research revealed that CSP may have the capacity to effectively alleviate hepatic steatosis in the progression of NAFLD and inhibit the inflammatory response through the TNFα/TNFR1 signaling pathway, which also paves the way for the specific molecular mechanism of CSP in NAFLD. Additionally, the network pharmacology framework explained in this study has the potential to provide fresh insight into the treatment of NAFLD with TCM.In conclusion, this study provided valuable novel insights to advance our comprehension of the mechanism underlying the action of CSP on NAFLD. Using network pharmacology and


Background: A new viral outbreak caused by monkeypox has appeared after COVID-19. As of yet, no specific drug has been found for its treatment. Shengma-Gegen decoction (SMGGD), a pathogen-eliminating and detoxifying agent composed of four kinds of Chinese herbs, has been demonstrated to be effective against several viruses in China, suggesting that it may be effective in treating monkeypox, however, the precise role and mechanisms are still unknown. Methods: Network pharmacology was used to investigate the monkeypox-specific SMGGD targets. These targets were analyzed via String for protein-to-protein interaction (PPI), followed by identification of hub genes with Cytoscape software. Function enrichment analysis of the hub targets was performed. The interactions between hub targets and corresponding ligands were validated via molecular docking. Results: Through screening and analysis, a total of 94 active components and 8 hub targets were identified in the TCM-bioactive compound-hub gene network. Molecular docking results showed that the active components of SMGGD have strong binding affinity for their corresponding targets. According to functional analysis, these hub genes are mainly involved in the TNF, AGE-RAGE, IL-17, and MAPK pathways, which are linked to the host inflammatory response to infection and viral replication. Therefore, SMGGD might suppress the replication of monkeypox virus through the MAPK signaling pathway while also reducing inflammatory damage caused by viral infection. Conclusion: SMGGD may have positive therapeutic effects on monkeypox by reducing inflammatory damage and limiting virus replication. Monkeypox virus, first isolated from a monkey in 1958, belongs to the Orthopoxvirus, a kind of double-stranded DNA virus ,3,4,5. ICimicifugae Rhizoma), Gegen (Radix Puerariae), Baishao (Paeoniae Radix Alba), and Gancao , was originally used against measles in children for many hundreds of years in China. SMGGD has been proved against different viruses including measles virus . Monkeypox-related targets were obtained under the following criteria: p value < 0.05, |log2FC| > 0.5. The Volcano Plot was produced by Xiantao Xueshu web tool  in which R software (3.6.3) and ggplot2 (3.3.3) package were employed [https://www.genecards.org, accessed on 10 September 2022) using the keywords monkeypox, monkeypox virus [https://www.drugbank.com, accessed on 10 September 2022) was used to obtain more related genes [GSE36854 was downloaded from the GEO database , and a TCM-compound-target network was constructed.https://string-db.org/, accessed on 4 October 2022). The species was set as “Homo sapiens”, all PPI relationships were obtained, and the confidence score was ≥0.4 [A total of 27 monkeypox-specific SMGGD target genes were imported into the String database  in which R software (3.6.3) and R-language packages, including “ggplot2 (3.3.3)” and “ClusterProfiler (3.14.3)”, were applied [To systematically understand the mechanisms of SMGGD in the treatment of monkeypox, GO and KEGG analysis of hub targets was performed. The top 20 terms and terms with a https://www.rcsb.org/, accessed on 4 October 2022) and saved in PDB format. The obtained 3D structures (PDB format) were further processed by removing water molecules (command of “remove solvent”) and the ligand (command of “remove organic”) using PyMOL sofware (version 2.2.0).Preparation of receptors: The 3D structure of 8 hub targets (receptors) were obtained from the RCSBPDB database  [Preparation of ligands: 2D structure files (SDF format) of active compounds were downloaded from the PubChem website (er 2022) . Then, tMolecular docking: Hydrogen and Gasteiger charges were added to the above receptors and ligands using ADT software before they were saved in PDBQT format. The AutoGrid Tool of the ADT software was used to set the docking frame parameters including setting the grid box containing the entire receptor. The parameter was set as the Lamarckian genetic algorithm (LGA) to generate 10 docking results for each ligand with the corresponding receptor. All of the docking results were visualized via PyMOL software.Semi-flexible docking was carried out using AutoDock Tools (ADT) software (version 1.5.6) to verify the binding ability of bioactive components to key targets ,29. The A total of 19 compounds from Shengma, 7 compounds from Gegen, 15 compounds from Baisao, and 94 compounds from Gancao were found via the TCMSP platform. A total of 127 active components were selected for further analysis after removal of the duplicates. The corresponding targets of the SMGGD were obtained via the TCMSP database as well. The corresponding targets of Shengma, Gengen, Baisao, and Gancao filtered out by TCMSP were 47, 134, 77, and 218 respectively. After removing the repeated targets, a total of 271 potential targets were obtained.p value < 0.05, |log2FC| > 0.5, a total of 418 upregulated genes and 315 downregulated genes were identified  were obtained by performing microarray analysis on the human HeLa cells infected with monkeypox virus and uninfected cells. In this study, the DEGs were obtained via GEO2R online tool analysis. According to the criteria, which was entified a. More mAfter merging and deleting the duplicate values from all the above sources, a total of 769 monkeypox related genes were obtained b. By comNext, the TCM-compounds-common targets network was constructed using Cytoscape software. The network consists of 125 nodes  and 254 edges, in which the circle node represents the active compound, the V-shaped node represents TCM, and the rectangle node represents the target . 7 key aThe 27 monkeypox-specific SMGGD target genes were introduced into the String database online service platform to construct a PPI network consisting of 26 nodes and 141 interaction edges a. All thPTGS2 was the highest node and had the degree value of 93. IL6, VEGFA, FOS, and ICAM1 had a value of 3, respectively. The rest of the hub genes only had one degree value. Next, the TCM-bioactive compound-hub gene network, which had 106 nodes  and 206 edges, was constructed c. The deGO analysis of the above 8 hub genes was performed. The top BP, CC, and MF terms are shown in bubble charts a,c,e. ThThe top 5 enrichment analysis results of BP terms were as follows: response to lipopolysaccharide (GO:0032496), response to molecule of bacterial origin (GO:0002237), positive regulation of vascular endothelial growth factor production (GO:0010575), positive regulation of acute inflammatory response (GO:0002675), regulation of cell-cell adhesion (GO:0022407). The top 5 CC terms were as follows: platelet alpha granule lumen (GO:0031093), platelet alpha granule (GO:0031091), endoplasmic reticulum lumen (GO:0005788), membrane raft (GO:0045121), membrane microdomain (GO:0098857). The top 5 MF terms were as follows: growth factor receptor binding (GO:0070851), receptor ligand activity (GO:0048018), cytokine activity (GO:0005125), cytokine receptor binding (GO:0005126), growth factor activity (GO:0008083).p value were rheumatoid arthritis (hsa05323), the TNF signaling pathway (hsa04668), the IL-17 signaling pathway (hsa04657), the AGE-RAGE signaling pathway in diabetic complications (hsa04933), fluid shear stress and atherosclerosis (hsa05418).To clarify the role of the hub genes in signal transduction, KEGG enrichment analysis was carried out. The top KEGG analysis is shown in Next, the interactions between the key targets and related compounds were validated via molecular docking. Puerarin (MOL012297), daidzein (MOL000390), quercetin (MOL000098), and kaempferol (MOL000422) could target multiple proteins in the TCM-bioactive compound-hub gene network. Especially, quercetin targeted all of the hub genes. Puerarin could target VEGFA, FOS, and PTGS2. Daidzein could target VEGFA, FOS, PTGS2, IL6, and ICAM1. Kaempferol could target PTGS2 and ICAM1. The rest of the compounds targeted PTGS2, except that paeoniflorin (MOL001924) targeted IL6. Puerarin, daidzein, quercetin, and kaempferol therefore were chosen as ligands a, and thAll the molecular docking results are listed in An unexpected outbreak of monkeypox has emerged. There is no specific medication established for its treatment so far. It is urgent to find safe and effective drugs to treat monkeypox. TCM has accumulated abundant clinical experiences and effective formulas on the prevention and treatment of epidemic diseases. It has been proved to cure viral diseases by inhibiting virus replication. Previous clinical experiences of TCM on treating SARS and SARS-CoV-2 hint that TCM intervention might have positive efficacy against monkeypox. Moreover, the Chinese government has released the Diagnosis and Treatment Guideline of Monkeypox in which TCM is suggested to treat this disease. This study aimed to discover the efficacy of SMGGD for treating monkeypox using a network pharmacology approach. After identifying the common targets between monkeypox-related genes and targets of bioactive compounds from SMGGD, 8 hub genes were obtained via a PPI network followed by Cytoscape software analysis. GO and KEGG enrichment analysis showed the hub targets were associated with acute inflammatory response and cytokine activity and virus infection . SMGGD mRadix Puerariae [Cimicifugae Rhizoma [Cimicifugae Rhizoma, it is one of the main compounds in Paeoniae Radix Alba [Glycyrrhiza uralensis, and kaempferol presents in Glycyrrhiza uralensis and Paeoniae Radix Alba. Wang et al. discovered puerarin exhibited antiviral activity against influenza virus by inhibiting the NA activity of the virus and blocking the nuclear output of key proteins [A total of 94 active components and 8 hub targets were identified in the TCM-bioactive compound-hub gene network. Among these active components, puerarin, daidzein, and daidzin are the main compounds in uerariae , isoferu Rhizoma , paeonifdix Alba , quercetproteins . Puerariproteins . He et aproteins . Daidzeiproteins . Zhang eproteins . Daidzinproteins . Isoferuproteins ,43. Yu eproteins . Quercetproteins ,47,48,49proteins . Kaempfeproteins ,51,52. Tproteins ,54,55,56proteins . The preproteins . The bioA total of 8 hub targets, including IL1B, EGF, IL6, FOS, VEGFA, CCL2, ICAM1, and PTGS2 were identified in our study. According to the TCM-bioactive compound-hub gene network, it was likely that SMGGD exhibited a therapeutic effect on monkeypox by mainly targeting the 8 proteins. Among the targets, PTGS2, which could be targeted by 93 compounds from SMGGD, ranked the highest. PTGS2 is one of the key enzymes in prostaglandin biosynthesis, related to inflammation and mitosis. One study showed the inhibition of HIV-1, HCV, and HSV by serpin antithrombin III (ATIII) may be the result of downstream synthesis of eicosanoids (including prostaglandin) mediated by PTGS2 . FurtherThe GO term enrichment of hub targets revealed that SMGGD treating monkeypox mainly involved positive regulation of vascular endothelial growth factor production, positive regulation of acute inflammatory response, regulation of cell-cell adhesion, growth factor receptor binding, receptor ligand activity, cytokine activity, cytokine receptor binding, and growth factor activity. The KEGG pathway enrichment analysis of these hub proteins showed they were involved in the TNF signaling pathway, the IL-17 signaling pathway, the AGE-RAGE signaling pathway in diabetic complications, Kaposi sarcoma-associated herpesvirus infection, human cytomegalovirus infection, influenza A, EGFR tyrosine kinase inhibitor resistance, the MAPK signaling pathway, the NF-kappa B signaling pathway, the Toll-like receptor signaling pathway, the C-type lectin receptor signaling pathway, and Th17 cell differentiation . The RAGThe molecular docking results showed that most of the hub targets and the corresponding ligands had a strong binding affinity, except that the docking scores of daidzein and FOS was more than −5 kcal/mol . All of the docking results are shown in Taken together, the present study explored the interaction between bioactive compounds of SMGGD and monkeypox-specific SMGGD targets using a network pharmacological approach and bioinformatic analysis, which suggests that SMGGD may have beneficial therapeutic effects on monkeypox by reducing inflammatory damage caused by AGE-RAGE, TNF, and IL-17 signaling and restricting virus replication via the MAPK pathway.


The gut microbiota, peripheral inflammatory cytokines, and glia-mediated neuroinflammation in substantia nigra were evaluated. A total of 99 target genes were intersected between targets of TPG and deferentially expressed genes in PD. The PPI network analysis indicated the proinflammatory cytokine as essential targets. GO and KEGG analyses indicated that inflammatory response and its related signaling pathways were closely associated with TPG-mediated PD treatment. In vivo studies revealed that class Negativicutes and order Selenomonadales decreased, whereas class Mollicutes, order Enterobacteriales, and Mycoplasmatales increased in fecal samples of PD rats via 16S rRNA sequence analysis. Furthermore, the function prediction methods purposely revealed that TPG therapy may be involved in flavonoid biosynthesis, which have anti-inflammatory properties. In addition, in vivo studies revealed that TPG exposure was found to not only attenuate the production of peripheral inflammatory cytokines but also inhibit the activation of microglia and astrocytes in substantia nigra of PD rats. Through network pharmacology and in vivo experiment-combined approach, the mechanisms of TPG in the treatment of PD were revealed, and the role of TPG in the regulation of gut microbiota and inflammatory response was confirmed.Parkinson’s disease (PD) is the second most common neurodegenerative disease. Tianqi Pingchan Granule (TPG) is a clinically effective formula of traditional Chinese medicine to treat PD. However, the therapeutic effect and underlying mechanisms of TPG in PD remain unclear. Based on network pharmacology, the corresponding targets of TPG were identified using the Traditional Chinese Medicine Database and Analysis Platform Database. Differentially expressed genes in PD were obtained from the Therapeutic Target Database, Online Mendelian Inheritance in Man, GeneCards, and DrugBank databases. The protein-protein interaction (PPI) networks of intersected targets were constructed using the STRING database and visualized using Cytoscape. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses were performed, and the pathways directly related to the pathogenesis of PD were integrated manually. Furthermore,  Parkinson’s disease (PD) is one of the most common neurodegenerative disorders commonly seen among the elderly . PD is cCurrent therapies for PD selectively modulate established targets, such as levodopa and dopamine agonists, have declined efficacy after long-term use . The oldL-dopa-induced dyskinesia, and is safe for consumption. However, the underlying molecular mechanisms of TPG in treating PD remain unclear and warrant further investigation. However, the precise protective role and mechanism of TPG against PD remain unclear.Tianqi Pingchan Granule (TPG) is a clinically effective formula of TCM that has been used in our hospital for the management of PD. TPG is modified based on classic TCM prescriptions “Mi Fang Ding Zhen Wan” compiled by Kentang Wang of the Ming Dynasty , which iin vivo  3.8.0 1 . The com1 , 10 were.) 3.8.0 .3 (4 (5 (6 (Disease-related genes were obtained from the Therapeutic Target Database (TTD)3 , Online 3  network , betweenness (BC), closeness (CC), eigenvector (EC), local average connectivity-based method (LAC), and network (NC) as screen parameters to construct core PPI network.Subsequently, the effect of TPG on PD was further analyzed by making Venn maps of 235 TPG predicted targets and 767 PD-related genes obtained using the above methods. The intersection was determined using Venny ver. 2.1 network . We sele9 The bubble chart was plotted with R ver. 4.0.3 10 and the ggplot2 package.Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of TPG’s core potential therapeutic targets were performed using metascape.Astragalus mongholicus Bunge [Fabaceae] (HQ), Rehmannia glutinosa (Gaertn.) DC. [Orobanchaceae] (SD), Paeonia lactiflora Pall. [Paeoniaceae] (BS), Angelica sinensis (Oliv.) Diels [Apiaceae] (DG), Uncaria rhynchophylla (Miq.) Miq. [Rubiaceae] (GT), and Gastrodia elata Blume [Orchidaceae] (TM), and one kind of animal-derived medicine, such as Bombyx batryticatus [Bombycidae] (JC), the dried larva of Bombyx mori L. (4th–5th instars) infected with Beauveria bassiana Vuill. These seven medicines were used to synthesize TPG in Sichuan New GreenMedicine Science and Technology Development Co., Ltd.,  under the guidance of the Chinese Pharmacopeia 2020 edition. HQ was picked from China’s Gansu province in spring and autumn. SD was picked from China’s Henan province in autumn. BS was picked from China’s Anhui province in summer and autumn. DG was picked from China’s Gansu province in late autumn. GT was picked from China’s Jiangxi province in spring and autumn. TM was picked from China’s Anhui province during the period from the beginning of winter to the next year before Qingming Festival. JC was collected from China’s Zhejiang province in autumn and winter. TPG was dissolved in pure water and injected into intragastric. The doses of TPG in our animal studies were selected based on our previous clinical study, and dose ranges were therapeutically relevant.Tianqi Pingchan Granule is one of the TCM composed of six kinds of plant-derived medicines, such as L-dopa, and benserazide were obtained from Sigma Chemical Co., .6-Hydroxydopamine (6-OHDA), apomorphine, Adult male Sprague-Dawley rats weighing 200–250 g were used, and all rats were kept under standard laboratory conditions as previously described . The proTo establish a rat model of PD, 6-OHDA was injected into the right middle forebrain bundle of each rat. Animal surgical procedures were performed as previously reported . Three wn = 4 rats per group): sham, PD, and TPG. TPG group rats were gavaged with TPG (5.6 g/kg) once daily at the same time. The control group of sham-lesioned rats and PD group rats was gavaged with sterile saline alone. The dose of TPG was based on our previous clinical study  according to the instructions of the manufacturer. The integrity of genomic DNA was detected through agarose gel electrophoresis, and the concentration and purity of genomic DNA were detected through the Nanodrop 2000 and Qubit3.0 Spectrophotometer. Multiple spike-ins with identical conserved regions to natural 16S rRNA genes, and variable regions replaced by random sequences with ∼40% GC content were artificially synthesized. Then, an appropriate proportion of spike-ins mixture with known gradient copy numbers were added to the sample DNA. The V3-V4 hypervariable regions of the 16S rRNA gene and spike-ins were amplified with the primers 341F (5′-CCTACGGGNGGCWGCAG-3′) and 805R (5′-GACTACHVGGGTATCTAATCC-3′), and then sequenced using Illumina NovaSeq 6000 sequencer.Absolute quantification of 16S rRNA amplicon sequencing was performed by Genesky Biotechnologies Inc., Shanghai, (China). Total genomic DNA was extracted using the FastDNAThe raw read sequences were processed in QIIME2 . The adaFunctional prediction analysis was assessed using PICRUSt2.g for 20 min at 4°C, and the supernatant was collected for the further measurement of interleukin (IL)-1, IL-2, IL-4, IL-6, and tumor necrosis factor-ɑ (TNF-ɑ) using ELISA kits .ELISA was utilized to analyze the concentrations of the serum inflammatory cytokines according to instructions of the manufacturer. The whole blood was centrifuged at 2,000 × Immunohistochemistry (IHC) was performed as previously described . Rat brap < 0.05.Data were analyzed using GraphPad Prism ver. 6.0 software. Data of the Western blot, IHC data, and ELISA are presented as mean ± SEM and analyzed by one-way ANOVA followed by Sidak’s multiple-comparisons test. Statistical significance was set at The sample size determination in the animal study was calculated based on our preexperiment data and the power test for sample size analysis by using PASS ver. 15.0 software. The power for the primary endpoint serum concentrations of inflammatory cytokines was calculated based on a one-way ANOVA test with a significance level of 5%. With an average sample size of 4 subjects and a total sample size of 12 subjects, the animal study will have more than 90% power to detect a difference among three groups.In our initial search of the TCMSP database, we identified 73 active ingredients of potential relevance . Of thesThe gene targets of the 63 active compounds in TPG were retrieved from the TCMSP database, and 1,542 targets were retrieved. After eliminating the overlaps, we identified 235 TPG targets, which included TNF, IL-1β, IL-2, IL-4, and IL-6. The detailed information is shown in O-methylisomucronulatol. To explore the interactions among the above targets in-depth, we constructed a PPI network. The primary network had 98 nodes and 1,275 edges  from TCMSP and identified putative TPG- and PD-related target genes from multiple databases. Then, we obtained TPG-compound-target-PD network to identify PD-related targets in TPG. Furthermore, we constructed the PPI network that identified TNF, IL-1β, and IL-6 as core targets in the context of TPG-mediated PD treatment. The network pharmacology-based findings from KEGG pathway and GO enrichment revealed that TPG -treated PD were mechanically associated with inflammatory response and related signaling pathway, such as NF-κB signaling pathway, T cell receptor signaling pathway, and lipopolysaccharide-mediated signaling pathway. It is well known that the NF-κB and T cell receptor signaling pathway has great effects on peripheral inflammatory response and glia-mediated neuroinflammation –32. Consin vivo. In this study, the composition of the gut microbiota after TPG treatment was screened using absolute quantification of 16S rRNA. Consistent with previous studies showing that gut microbial dysbiosis exists in patients with PD  and propagates neuroinflammation in the brain ain axis , 23. Simvia the construction of multicomponent networks. Using network pharmacology and 16S rRNA sequencing-combined approach, the mechanisms of TPG against PD are revealed.In conclusion, the network pharmacological analysis of TPG identified 73 compounds and 235 target genes associated with PD. TNF-ɑ, IL-1β, IL-6, IL-4, and IL-2 were recognized as essential targets. According to the results of pathway enrichment analysis, we verified that TPG can improve gut microbiota and inhibit the inflammatory response in PD. Network pharmacology strategies can match candidate compounds with potential targets The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The protocols of this study were reviewed and approved by the Ethical Committee of the Medical School of Shanghai Jiao Tong University .ZGL completed the design of the framework of this manuscript and the experiments. JZ conducted the network pharmacology study. ZHL, SY, YZ, and LS completed the animal experiments. ZHL analyzed the data and prepared the manuscript. NW was responsible for supervision, review, and editing. All authors read and approved the final manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Periodontitis is a chronic inflammatory oral disease that affects approximately 42% of adults 30 years of age or older in the United States. In response to microbial dysbiosis within the periodontal pockets surrounding teeth, the host immune system generates an inflammatory environment in which soft tissue and alveolar bone destruction occur. The objective of this study was to identify diagnostic biomarkers and the mechanistic drivers of inflammation in periodontitis to identify drugs that may be repurposed to treat chronic inflammation. A meta-analysis comprised of two independent RNA-seq datasets was performed. RNA-seq analysis, signal pathway impact analysis, protein-protein interaction analysis, and drug target analysis were performed to identify the critical pathways and key players that initiate inflammation in periodontitis as well as to predict potential drug targets. Seventy-eight differentially expressed genes, 10 significantly impacted signaling pathways, and 10 hub proteins in periodontal gingival tissue were identified. The top 10 drugs that may be repurposed for treating periodontitis were then predicted from the gene expression and pathway data. The efficacy of these drugs in treating periodontitis has yet to be investigated. However, this analysis indicates that these drugs may serve as potential therapeutics to treat inflammation in gingival tissue affected by periodontitis. Porphyromonas gingivalis, Tannerella forsythia, and Treponema denticola . Th. Thp-valanalysis . An inde2 fold change (log2FC) of −3.78. Fifty-two of the 78 significant DEGs in our current study were immunoglobulin domains, and four DEGs were predicted as transcribed pseudogenes . The remThe Signaling Pathway Impact Analysis (SPIA) algorithm was used to determine whether any known intracellular signaling pathways were enriched in DEGs. This robust approach uses a permutation-based analysis to generate a null distribution for each pathway, which yielded 10 pathways that were significantly affected in periodontitis gingival tissues . SeveralThe next analysis step consisted of determining whether any of the affected signaling pathways contained proteins that are known targets of existing small molecules, monoclonal antibodies, and/or peptides that could be repurposed as potential therapeutics. Using the output from SPIA, 335 proteins in significant pathways were identified that are targets for 500 known drugs . It was An unbiased approach was then applied to identify the drug targets that would be most likely to reverse the observed signaling pathway phenotype. Such a therapeutic approach could reduce or reverse some of the clinical signs and symptoms associated with the disease phenotype. To do so, the protein–protein interaction (PPI) network of drug targets that mapped back to all the statistically significant signaling pathways identified as playing a role in periodontitis gingival tissue were visualized. The initial PPI network, which was constructed using the online STRING database, consisted of 7462 edges and 304 nodes. CytoHubba was then used to reduce this initial network to the top 10 “central hub” proteins based on degrees  . Out of 500 drug candidates identified, 10 drugs that targeted the central hub proteins were identified as top candidates since they would be most likely to reverse the observed periodontitis phenotype . A drug The aim of this study was to identify inflammatory biomarkers and the mechanistic drivers of inflammation, as well as to predict potential therapeutics for various aspects of periodontal disease. For this purpose, two independent RNA-seq datasets were retrieved from NCBI Gene Expression Omnibus (GEO) for RNA-seq meta-analysis. Dataset GSE80715 was previously analyzed in a published study that identified novel gene expression and splicing patterns in periodontitis gingival biopsies , whereasThe signaling pathway impact analysis provided a higher level of analysis of how these differentially expressed genes contribute to the pathogenesis of periodontitis. For instance, the osteoclast differentiation pathway was significantly impacted and activated in periodontal gingival tissue. This is noteworthy since alveolar bone loss is a characteristic of severe periodontitis. Bone-resorbing osteoclasts work in conjunction with bone-forming osteoblasts during bone remodeling through cell–cell interactions and the secretion of signaling proteins  to influence the activation and differentiation of each other ,51. DysrP. gingivalis, are major contributors to microbial dysbiosis and inflammation in periodontitis, inhibiting the LPS-induced activation of TLR4 may be a potential mechanism to reduce inflammation in periodontitis.Using transcriptomics to identify central hubs in protein–protein interaction networks is a novel approach in the field of periodontitis. The best 10 scoring results from this analysis included proteins such as IL-6, toll-like receptor 4 (TLR4), tumor growth factor-beta (TGF-β), and others. IL-6 and IL-1β are pro-inflammatory cytokines that are associated with chronic inflammation, periodontitis, and osteoclast bone resorption ,53,54,55When identifying the top 10 drug targets to treat inflammation in periodontitis, drugs that target hub genes and those drugs which have been FDA-approved were prioritized. Although one prior clinical study has evaluated the post-surgical use of systemic Doxycycline, we are not aware of other studies that have sought to investigate potential therapeutic treatments for periodontal disease. All of the drugs that are reported have been investigated for at least one indication, and some of them are approved for the treatment of chronic inflammatory , autoimmune , and dysregulated bone resorption diseases (Denosumab). Seven of the 10 drug candidates are monoclonal antibodies used to inhibit the signaling pathways of inflammatory cytokines. Satralizumab, Denosumab, and Brodalumab (Siliq) are monoclonal antibodies FDA-approved to treat anti-aquaporin-4 (AQP4) antibody-positive neuromyelitis optica spectrum disorder (NMOSD) ,87, postClazakizumab (NCT03744910), Gevokizumab , Mavrilimumab (NCT01706926), and Gimsilumab (NCT04205851) are monoclonal antibodies that have not been FDA-approved to treat any conditions, but they either have been or are currently being investigated to treat kidney transplant rejection, type I and II diabetes, rheumatoid arthritis, and ankylosing spondylitis, respectively. The three remaining drugs are inhibitors that block cytokine signaling. Interferon beta-1b (Betaseron/Extavia) is a recombinant human interferon that binds to type I interferon receptors (IFNAR1 and IFNAR2) and is FDA-approved as an immunosuppressant to treat relapsing-remitting forms of multiple sclerosis ,101, andThe efficacy of these drugs in treating periodontitis has yet to be investigated. However, this analysis indicates that these drugs may serve as potential therapeutics, either individually or in combination, to treat inflammation in periodontitis gingival tissue. All of these drugs in some way mitigate the inflammatory response or bone resorption via their mechanism of action. As noted above, reducing the inflammation induced by periodontitis may also reduce the severity of other systemic diseases by reducing the number of inflammatory cytokines and activated immune cells that circulate or migrate to other tissues throughout the body. Thus, additional experimentation is justified to further investigate the efficacy of these drugs in reducing the inflammation in the oral cavity of animal models for periodontitis, such as ligature-induced periodontitis in mice.A total of 26 periodontal disease SRA files, which were comprised of 22 periodontitis RNA samples, as well as 22 healthy RNA samples, were downloaded from two series (GSE173082 and GSE80715) retrieved from NCBI Gene Expression Omnibus (GEO). The RNA samples from the GSE80715 series were isolated from nine periodontal-healthy patients , four moderate-periodontitis patients (one patient donating two samples), and three severe-periodontitis patients (two patients donating two samples each for a sum of 10 periodontitis samples). Patient information about the RNA samples from the GSE173082 series was not provided by the investigators on NCBI GEO. Information on the titles of each series, platforms used, library construction, sample type, diagnostic criteria, sample prep, and PubMed ID are provided in p-values (p < 0.05) as calculated by edgeR, together with their fold-change values, were then used as the input for the signaling pathway impact analysis (SPIA) algorithm [All of the Ensembl Gene IDs from edgeR were converted into their corresponding NCBI Gene identifiers utilizing the BioMart database . The NCBlgorithm .The SPIA output file was used as the input for drug target analysis which was performed using the Pathways2Targets R script that had previously been developed . Brieflyhttp://string-db.org/, accessed on 7 March 2022). The PPI network was visualized in the Cytoscape [Drug targets were used to construct the PPI network using the Search Tool for the Retrieval of Interacting Genes (STRING)  databaseytoscape  softwareytoscape , and MCO


Triple-negative breast cancer (TNBC) is one of the most lethal subtypes of breast cancer (BC), and it accounts for approximately 10%–20% of all invasive BCs diagnosed worldwide. The survival rate of TNBC in stages III and IV is very low, and a large number of patients are diagnosed in these stages. Therefore, the purpose of this study was to identify TNBC-causing molecular signatures and anti-TNBC drug agents for early diagnosis and therapies. Five microarray datasets that contained 304 TNBC and 109 control samples were collected from the Gene Expression Omnibus (GEO) database, and RNA-Seq data with 116 tumor and 124 normal samples were collected from TCGA database to identify differentially expressed genes (DEGs) between TNBC and control samples. A total of 64 DEGs were identified, of which 29 were upregulated and 35 were downregulated, by using the statistical limma R-package. Among them, seven key genes (KGs) were commonly selected from microarray and RNA-Seq data based on the high degree of connectivity through PPI (protein–protein interaction) and module analysis. Out of these seven KGs, six KGs  were upregulated and one (EGFR) was downregulated. We also investigated their differential expression patterns with different subtypes and progression stages of BC by the independent datasets of RNA-seq profiles from UALCAN database, which indicated that they may be potential biomarkers for early diagnosis. Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses with the proposed DEGs were performed using the online Enrichr database to investigate the pathogenetic processes of TNBC highlighting KGs. Then, we performed gene regulatory network analysis and identified three transcriptional  and three post-transcriptional  regulators of KGs. Finally, we proposed five KG-guided repurposable drug molecules  for TNBC through network pharmacology and molecular docking analyses. These drug molecules also showed significant binding performance with some cancer-related PTM-sites  of top-ranked four key proteins . Therefore, the findings of this computational study may play a vital role in early diagnosis and therapies against TNBC by wet-lab validation. Breast cancer (BC) is a major public health concern as it is one of the most common types of hormone-sensitive cancers and the most commonly diagnosed cancer in women worldwide. GLOBOCAN estimates approximately 2,261,419  newly diagnosed cases, and 684,996  deaths were reported due to BC in 2020 . There ade novo technique . Two datasets (GSE65216 and GSE76275) are based on the platform GPL570 Affymetrix Human Genome U133 Plus 2.0 Array, two datasets (GSE27447 and GSE61724) are based on the platform GPL6244 Affymetrix Human Gene 1.0 ST Array, and the last one (GSE38959) is based on the platform GPL4133 Agilent-014850 Whole Human Genome Microarray. More details of the datasets are given in A total of 304 TNBC samples and 109 normal samples were collected from five sets of omics data (gene expression profiles data) with accession numbers GSE65216 , GSE76270)  in both tumor and control groups) versus the alternative hypothesis (H1)  between tumor and control groups). We have considered the threshold to identify the up- and down-regulated DEGs for the combined data as follows:p-value: adjusted p-values and Log2FC: Log2 (fold change).Background correction, standardization, and median polish summarization were performed on the raw data through the robust multichip average (RMA) algorithm analysis . Then, wWe used the online database Search Tool for the Retrieval of Interacting Genes (STRING) to construct the PPI network of DEGs and visualized it using Cytoscape software . Module The association of KGs with different subtypes and progression stages of BC.http://gepia.cancer-pku.cn/index.html) (We investigated the association of KGs with different subtypes and progression stages of BC using a box plot of their independent RNA-Seq profiles from the UALCAN online database (ex.html) . This RNetc. GO functional and KEGG pathway enrichment analyses were performed using the online tool Enrichr (Gene Ontology (GO) analysis was performed to define and describe genes across species in three categories including biological process (BP), cellular component (CC), and molecular function (MF). The Kyoto Encyclopedia of Genes and Genomes (KEGG) is the databases of drugs, genomes, biological pathways, enzymes  Enrichr .We performed the regulatory interaction network (TF-KG-miRNA) to identify the transcriptional and post-transcriptional regulators of KGs. The regulatory interaction network was constructed using the online tool “NetworkAnalyst” (version: 3.0) . The ChETo propose candidate drug molecules for treatment against TNBC, we performed network pharmacology and molecular docking analyses. We considered our proposed KGs as drug target receptor proteins. KG-guided drug agents were detected by the Drug–Gene Interaction Database (DGIdb) through network pharmacology analysis . Molecul, and 306 DEGs including 68 upregulated and 238 downregulated for RNA-Seq data in We identified a total of 64 DEGs, including 29 up- and 35 down-regulated DEGs between 304 TNBC samples and 109 normal samples in p-value < 1.0e-16. The PPI network of DEGs for TCGA data included 298 nodes, 1,371 edges, average node degree 9.2, and PPI enrichment p-value < 1.0e-16. The PPI network of DEGs for NCBI data included 64 nodes, 427 edges, average node degree of 13.3, and PPI enrichment To investigate the similarity and consistency of different computational results with KGs, we summarized them in To investigate the association of KGs  with different subtypes and progression stages of BC by the independent datasets, we performed box plot analysis based on independent RNA-Seq profiles from the UALCAN online database. p < 0.01 was considered. The top 10 significantly enriched terms for each category are presented in p = 2.4E-15, DEGs involved = 25%), microtubule cytoskeleton organization involved in mitosis , intracellular membrane-bound organelle , nucleus , kinase binding , and protein kinase binding  were significantly enriched GO terms in TNBC in p = 9.6E-10, DEGs involved = 21%), estrogen signaling pathway , and pathways in cancer  were significantly enriched KEGG pathways terms in GO and KEGG enrichment analysis showed that our identified DEGs were enriched by 63 BP-terms, 14 CC-terms, 19 MF-terms, and 48 KEGG-terms, and the threshold The regulatory interaction network (TF-KG-miRNA) is shown in First, we collected a total of 115 FDA-approved drugs related to KGs from the DGIdb database and published articles and considered them as drug agents for further analysis. Then, structural interactions between KGs and drug agents were performed and binding affinity scores (BAS)  were calculated by molecular docking analysis. We presented the molecular docking results of the top-ranked interaction with BAS < −7 in https://www.musite.net/) and SuccinSite (http://systbio.cau.edu.cn/SuccinSite/). We found seven, seven, four, and six phosphorylated PTM sites in chain A and three, five, four, and six PTM sites in chain B for proteins EGFR, AURKB, BIRC5, and TOP2A, respectively ((in ). We observed that almost all active sites of blind docking are common with the predicted PTM sites  by their docking analysis, we predicted the their PTM sites  by the web-based prediction models MusiteDeep , where six KGs  were upregulated and only one gene (EGFR) was downregulated  was reported to be responsible for TNBC  and kinase binding  are significant functional term for TNBC progression  (We performed KG–disease interactions to check the enrichment of KGs in diseases from the DisGeNET database through the online based tool Enrichr (nrichr/) , as showTo discover the potential repurposable candidate drug molecules for the treatment against TNBC, 115 KG-associated FDA-approved drugs were collected from the DGIdb database and published articles. Among them, five drugs  have been proposed as the candidate drug molecules for TNBC through the molecular docking analysis . Then, wThe selection of few potential target proteins and drug agents from a huge number of alternatives are equally important in drug discovery by wet-lab experiments. In this study, we identified triple-negative breast cancer (TNBC) causing seven KGs  based on five NCBI-GEO microarray gene expression datasets and TCGA RNA-Seq datasets for early diagnosis and therapies of TNBC by using the integrated bioinformatics and systems biology approaches. We also confirmed their higher differential expression between normal and TNBC compared to other subtypes of BC from the other independent data source. The enrichment analysis of GO-terms and KEGG pathways with the key genes detected some crucial TNBC-related biological processes, molecular functions, cellular components, and pathways. For example, the detected mitotic spindle organization function and estrogen signaling pathway are both significantly associated with TNBC progression. The key gene (KG) regulatory network analysis detected some transcriptional and post-transcriptional regulators of KGs that are associated with TNBC progression. Finally, we proposed five KG-guided repurposable drug molecules  for TNBC through network pharmacology and molecular docking analyses. Therefore, our findings would be more reliable for the wet-lab researchers and medical doctors in TNBC diagnosis and therapies at the earlier stage.