Identification of Critical Genes and Signaling Pathways in Human Monocytes Following High-Intensity Exercise

Background: Monocytes are critical components, not only for innate immunity, but also for the activation of the adaptive immune system. Many studies in animals and humans have demonstrated that monocytes may be closely associated with chronic inflammatory diseases and be proved to be pivotal in the association between high-intensity exercise and anti-inflammation response. However, the underlying molecular mechanisms driving this are barely understood. The present study aimed to screen for potential hub genes and candidate signaling pathways associated with the effects of high-intensity exercise on human monocytes through bioinformatics analysis. Materials and Methods: The GSE51835 gene expression dataset was downloaded from the Gene Expression Omnibus database. The dataset consists of 12 monocyte samples from two groups of pre-exercise and post-exercise individuals. Identifying differentially expressed genes (DEGs) with R software, and functional annotation and pathway analyses were then performed with related web databases. Subsequently, a protein–protein interaction (PPI) network which discovers key functional protein and a transcription factors-DEGs network which predicts upstream regulators were constructed. Results: A total of 146 differentially expressed genes were identified, including 95 upregulated and 51 downregulated genes. Gene Ontology analysis indicated that in the biological process functional group, these DEGs were mainly involved in cellular response to hydrogen peroxide, response to unfolded protein, negative regulation of cell proliferation, cellular response to laminar fluid shear stress, and positive regulation of protein metabolic process. The top five enrichment pathways in a Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were the FoxO signaling pathway, protein processing in the endoplasmic reticulum, influenza A, the ErbB signaling pathway, and the MAPK signaling pathway. TNF, DUSP1, ATF3, CXCR4, NR4A1, BHLHE40, CDKN1B, SOCS3, TNFAIP3, and MCL1 were the top 10 potential hub genes. The most important modules obtained in the PPI network were performed KEGG pathway analysis, which showed that these genes were mainly involved in the MAPK signaling pathway, the IL-17 signaling pathway, the TNF signaling pathway, osteoclast differentiation, and apoptosis. A transcription factor (TF) target network illustrated that FOXJ2 was a critical regulatory factor. Conclusions: This study identified the essential genes and pathways associated with exercise and monocytes. Among these, four essential genes (TNF, DUSP1, CXCR4, and NR4A1) and the FoxO signaling pathway play vital roles in the immune function of monocytes. High-intensity exercise may improve the resistance of chronic inflammatory diseases by regulating the expression of these genes.

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Gene Expression Profiling of Type 2 Diabetes Mellitus by Bioinformatics Analysis

Computational and Mathematical Methods in Medicine ◽

10.1155/2020/9602016 ◽

2020 ◽

Vol 2020 ◽

pp. 1-10

Author(s):

Huijing Zhu ◽

Xin Zhu ◽

Yuhong Liu ◽

Fusong Jiang ◽

Miao Chen ◽

...

Keyword(s):

Gene Expression ◽

Diabetes Mellitus ◽

Type 2 Diabetes ◽

Candidate Genes ◽

Signaling Pathway ◽

Bioinformatics Analysis ◽

Kegg Pathway ◽

Ppi Network ◽

Kegg Pathway Analysis

Objective. The aim of this study was to identify the candidate genes in type 2 diabetes mellitus (T2DM) and explore their potential mechanisms. Methods. The gene expression profile GSE26168 was downloaded from the Gene Expression Omnibus (GEO) database. The online tool GEO2R was used to obtain differentially expressed genes (DEGs). Gene Ontology (GO) term enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed by using Metascape for annotation, visualization, and comprehensive discovery. The protein-protein interaction (PPI) network of DEGs was constructed by using Cytoscape software to find the candidate genes and key pathways. Results. A total of 981 DEGs were found in T2DM, including 301 upregulated genes and 680 downregulated genes. GO analyses from Metascape revealed that DEGs were significantly enriched in cell differentiation, cell adhesion, intracellular signal transduction, and regulation of protein kinase activity. KEGG pathway analysis revealed that DEGs were mainly enriched in the cAMP signaling pathway, Rap1 signaling pathway, regulation of lipolysis in adipocytes, PI3K-Akt signaling pathway, MAPK signaling pathway, and so on. On the basis of the PPI network of the DEGs, the following 6 candidate genes were identified: PIK3R1, RAC1, GNG3, GNAI1, CDC42, and ITGB1. Conclusion. Our data provide a comprehensive bioinformatics analysis of genes, functions, and pathways, which may be related to the pathogenesis of T2DM.

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Mechanisms of Berberine for the Treatment of Atherosclerosis Based on Network Pharmacology

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/3568756 ◽

2020 ◽

Vol 2020 ◽

pp. 1-11 ◽

Cited By ~ 2

Author(s):

Xuejiao Xie ◽

Xingyu Ma ◽

Siyu Zeng ◽

Wansi Tang ◽

Liucheng Xiao ◽

...

Keyword(s):

Signaling Pathway ◽

Pathway Analysis ◽

Kegg Pathway ◽

Functional Enrichment ◽

Network Pharmacology ◽

Foam Cell Formation ◽

Ppi Network ◽

The Core ◽

Ppi Networks ◽

Kegg Pathway Analysis

Atherosclerosis is a common metabolic disease characterized by lipid metabolic disorder. The processes of atherosclerosis include endothelial dysfunction, new endothelial layer formation, lipid sediment, foam cell formation, plaque formation, and plaque burst. Owing to the adverse effects of first-line medications, it is urgent to discover new medications to deal with atherosclerosis. Berberine is one of the most promising natural products derived from traditional Chinese medicine. However, the panoramic mechanism of berberine against atherosclerosis has not been discovered clearly. In this study, we used network pharmacology to investigate the interaction between berberine and atherosclerosis. We identified potential targets related to berberine and atherosclerosis from several databases. A total of 31 and 331 putative targets for berberine and atherosclerosis were identified, respectively. Then, we constructed berberine and atherosclerosis targets with PPI data. Berberine targets network with PPI data had 3204 nodes and 79437 edges. Atherosclerosis targets network with PPI data had 5451 nodes and 130891 edges. Furthermore, we merged the two PPI networks and obtained the core PPI network from the merged PPI network. The core PPI network had 132 nodes and 3339 edges. At last, we performed functional enrichment analyses including GO and KEGG pathway analysis in David database. GO analysis indicated that the biological processes were correlated with G1/S transition of mitotic cells cycle. KEGG pathway analysis found that the pathways directly associated with berberine against atherosclerosis were cell cycle, ubiquitin mediated proteolysis, MAPK signaling pathway, and PI3K-Akt signaling pathway. After combining the results in context with the available treatments for atherosclerosis, we considered that berberine inhibited inflammation and cell proliferation in the treatment of atherosclerosis. Our study provided a valid theoretical foundation for future research.

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Network analyses of circular RNAs expression profiles and their hub genes in pancreatic ductal adenocarcinoma

10.21203/rs.2.15313/v1 ◽

2019 ◽

Author(s):

Yanyan Tang ◽

Ping Zhang

Keyword(s):

Gene Expression ◽

Pancreatic Ductal Adenocarcinoma ◽

Signaling Pathway ◽

Pathway Analysis ◽

Kegg Pathway ◽

Expression Profiles ◽

Ductal Adenocarcinoma ◽

Ppi Network ◽

Hub Genes ◽

Kegg Pathway Analysis

Abstract Pancreatic ductal adenocarcinoma (PDAC) is one of the most common malignant tumor in digestive system. CircRNAs involve in lots of biological processes through interacting with miRNAs and their targeted mRNA. We obtained the circRNA gene expression profiles from Gene Expression Omnibus (GEO) and identified differentially expressed genes (DEGs) between PDAC samples and paracancerous tissues. Bioinformatics analyses, including GO analysis, KEGG pathway analysis and PPI network analysis, were conducted for further investigation. We also constructed circRNA‑microRNA-mRNA co-expression network. A total 291 differentially expressed circRNAs were screened out. The GO enrichment analysis revealed that up-regulated DEGs were mainly involved metabolic process, biological regulation, and gene expression, and down-regulated DEGs were involved in cell communication, single-organism process, and signal transduction. The KEGG pathway analysis, the upregulated circRNAs were enriched cGMP-PKG signaling pathway, and HTLV-I infection, while the downregulated circRNAs were enriched in protein processing in endoplasmic reticulum, insulin signaling pathway, regulation of actin cytoskeleton, etc. Four genes were identified from PPI network as both hub genes and module genes, and their circRNA‑miRNA-mRNA regulatory network also be constructed. Our study indicated possible involvement of dysregulated circRNAs in the development of PDAC and promoted our understanding of the underlying molecular mechanisms.

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Identification of Core Genes and Pathways in Major Depressive Disorder Applying Bioinformatics

10.21203/rs.3.rs-42127/v1 ◽

2020 ◽

Author(s):

Wenshan Yang ◽

Hong Yin ◽

Yichen Wang ◽

Ping Liu ◽

Yuan Hu

Keyword(s):

Major Depressive Disorder ◽

Depressive Disorder ◽

Signaling Pathway ◽

Kegg Pathway ◽

Enrichment Analysis ◽

Ppi Network ◽

Biological Mechanisms ◽

Major Depressive ◽

Kegg Pathway Analysis ◽

Depressive Patients

Abstract Background: Although extensive study efforts on major depressive disorder (MDD), the pathogenesis related to the biological factors are not fully understood and present therapeutic regimen are ineffective in some depressive patients. This study aims to identify key genes and pathways associated with the molecular biological mechanisms of major depressive disorder through bioinformatics analysis in the Gene Expression Omnibus (GEO) public database of the National Center for Biotechnology Information (NCBI) website.Materials and methods: The whole-transcriptome brain expression profile dataset (GSE101521) was obtained from the GEO database. Differentially-expressed genes (DEGs) in normal group (non-psychiatric human) and MDD group (depressive patients) were identified applying Networkanalyst online database. Gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed to function annotation and enrichment analysis. After that, STRING online database was conducted to protein–protein interaction (PPI) network, and Cytoscape.3.7.2 software was performed to module analysis. Results: Out of the 41 DEGs identified from normal tissue samples and MDD, 39 were upregulated and 2 were downregulated. GO enrichment analysis discovered that DEGs were primarily involved in inflammatory response, and KEGG pathway analysis suggested that the most chiefly pathway related to MDD were IL-17 signaling pathway, TNF signaling pathway and NOD-like receptor signaling pathway. Six hub genes (IL6, CXCL8, IL1B, FOS, CCL2 and CXCL2) were identified by PPI network and module analysis. Conclusion: Our current study detected novel markers and targets involved immune system, which are involved in pivotal biological mechanisms related to the pathogenesis of major depression. Looking forward, these findings still need to be validated in future experimental studies.

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Exploration of the Mechanism and Active Targets of Chinese Herbal Formula, JTTZ, for the Treatment of Type 2 Diabetes with Obesity and Hyperlipidemia Based on Network Pharmacology

10.21203/rs.3.rs-147873/v1 ◽

2021 ◽

Author(s):

Haiyu Zhang ◽

Xuedong An ◽

De Jin ◽

Jiaxing Tian ◽

Wenke Liu ◽

...

Keyword(s):

Inflammatory Response ◽

Signaling Pathway ◽

Pathway Analysis ◽

Kegg Pathway ◽

Therapeutic Targets ◽

Mapk Signaling ◽

Network Pharmacology ◽

Bile Acid Metabolism ◽

Ppi Networks ◽

Kegg Pathway Analysis

Abstract BackgroundPrevious studies have indicated that the JTTZ formula exhibits clinical benefit in T2D with obesity and hyperlipidemia such as lowering blood glucose, blood lipids, weight, and ameliorating symptoms as well as regulating islet function. However, their mechanism of action remains unclear. T2D with obesity and hyperlipidemia is associated with a severely poor management duo to difficulty in achieving the clinical goals and lack of effective multi-targeted therapies. In this study, we explored its potential mechanisms and therapeutic targets by network pharmacology. MethodsThe active ingredients and targets of JTTZ were obtained in the TCMSP, TCMID, TCM Database@Taiwan, PubChem and Swiss Target Prediction. And the therapeutic targets were searched from TTD, DrugBank Database and DisGeNET. Then, topology analysis were used as secondary screens to identify key hubs of the network. Finally, the data was integrated by Cytoscape software to construct a common network module. PPI networks were visualized to identify the interaction of the candidate targets. GO and KEGG pathway analysis were implemented. Rerult: 110 active compounds and 166 candidate targets of JTTZ against T2D with obesity and hyperlipidemia were obtained to construct compound-targets network. And, the therapeutic targets AKT2, RELA, NFKB1 and GSK3B were identified. GO and KEGG pathway analysis indicated that the biological processes related to inflammatory response, insulin secretion, steroid and bile acid metabolism, and 13 pathways mainly including adipocytokine signaling pathway, cAMP signaling pathway and cGMP-PKG signaling pathway were enriched. ConclusionOur data established that JTTZ intervenes with adipose tissue dysfunction via regulating to the adipocytokine (leptin and adiponectin), AMPK signaling pathway, cAMP and cGMP-PKG signaling pathway, inhibits systematic inflammatory response by NF-κB and MAPK signaling pathway, and ameliorates insulin resistance through PI3K/AKT2 pathway, all of which could thus offer a promising therapeutic strategy. In addition, AKT2, RELA, NFKB1 and GSK3B were identified to be regarded as potential therapeutic targets as well.

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MYLK, CNN1, TAGLN and LMOD1 identified as potential prognostic biomarkers for bladder cancer using bioinformatics analysis

10.21203/rs.3.rs-379852/v1 ◽

2021 ◽

Author(s):

zhiyong tan ◽

Xuhua Qiao ◽

Shi Fu ◽

Xianzhong Duan ◽

Yigang Zuo ◽

...

Keyword(s):

Gene Expression ◽

Extracellular Matrix ◽

Bladder Cancer ◽

Signaling Pathway ◽

Kegg Pathway ◽

Mapk Signaling ◽

Prognostic Biomarkers ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Hub Genes

Abstract Background: Bladder cancer (BCa) is a challenge carcinoma that occurs on the bladder mucosa, which is the most common malignant neoplasm of the urinary system. Great efforts have been made to elucidate its pathogenesis. However, the molecular mechanisms involved in BCa remain unclear. Therefore, there is an urgent need to identify effective biomarkers to accurately predict the progression and prognosis of BCa.Material and methods: To investigate potential prognostic biomarkers of BCa, we download the GSE23732 expression profile from Gene Expression Omnibus (GEO) database. The GEO2R analysis tool was performed to identify the DEGs between BCa and normal bladder mucosae tissue. Gene Ontology (GO) functional annotation analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed for the screened DEGs by the Database for Annotation, Visualization, and Integrated Discovery (DAVID) online tool. We employed the Search Tool for the Retrieval of Interacting Genes (STRING) database to construct the protein-protein interaction (PPI) network of DEGs. Subsequently, the PPI network’s information was visualized by Cytoscape software. The Gene Expression Profiling Interactive Analysis (GEPIA) resource was used to describe the OS and DFS outcomes in bladder cancer patients based on the hub genes expression levels.Results: A total of 396 DEGs comprising 344 upregulated genes and 52 downregulated genes were screened. The results of the GO analysis showed that DEG was mainly enriched in proteinaceous extracellular matrix, extracellular matrix, heparin binding and extracellular matrix organization. In addition, KEGG pathway analysis showed that DEGs were mainly enriched in PI3K-Akt signaling pathway, Focal adhesion, MAPK signaling pathway. A PPI network was constructed using the 396 DEGs, 10 hub genes were selected and 4 of them including MYLK, CNN1, TAGLN and LMOD1 were associated with overall survival and disease-free survival.Conclusion: MYLK, CNN1, TAGLN and LMOD1 may represent promising prognostic biomarkers and potential therapeutic option for BCa.

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Network Pharmacology and Molecular Docking Study on the Potential Mechanism of Yi-Qi-Huo-Xue-Tong-Luo Formula in Treating Diabetic Peripheral Neuropathy

Journal of Diabetes Research ◽

10.1155/2021/9941791 ◽

2021 ◽

Vol 2021 ◽

pp. 1-16

Author(s):

Yixuan Lin ◽

Chuqiao Shen ◽

Fanjing Wang ◽

Zhaohui Fang ◽

Guoming Shen

Keyword(s):

Molecular Docking ◽

Peripheral Neuropathy ◽

Signaling Pathway ◽

Diabetic Peripheral Neuropathy ◽

Kegg Pathway ◽

Network Pharmacology ◽

Chemical Components ◽

Ppi Network ◽

Active Ingredients ◽

Kegg Pathway Analysis

Objective. To investigate the potential mechanism of action of Yi-Qi-Huo-Xue-Tong-Luo formula (YQHXTLF) in the treatment of diabetic peripheral neuropathy (DPN). Methods. Network pharmacology and molecular docking techniques were used in this study. Firstly, the active ingredients and the corresponding targets of YQHXTLF were retrieved using the Traditional Chinese Medicine Systems Pharmacology (TCMSP) platform; subsequently, the targets related to DPN were retrieved using GeneCards, Online Mendelian Inheritance in Man (OMIM), Pharmgkb, Therapeutic Target Database (TTD) and Drugbank databases; the common targets of YQHXTLF and DPN were obtained by Venn diagram; afterwards, the “YQHXTLF Pharmacodynamic Component-DPN Target” regulatory network was visualized using Cytoscape 3.6.1 software, and Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis were performed on the potential targets using R 3.6.3 software. Finally, molecular docking of the main chemical components in the PPI network with the core targets was verified by Autodock Vina software. Results. A total of 86 active ingredients and 229 targets in YQHXTLF were screened, and 81 active ingredients and 110 targets were identified to be closely related to diabetic peripheral neuropathy disease. PPI network mapping identified TP53, MAPK1, JUN, and STAT3 as possible core targets. KEGG pathway analysis showed that these targets are mostly involved in AGE-RAGE signaling pathway in diabetic complications, TNF signaling pathway, and MAPK signaling pathway. The molecular docking results showed that the main chemical components of YQHXTLF have a stable binding activity to the core pivotal targets. Conclusion. YQHXTLF may act on TP53, MAPK1, JUN, and STAT3 to regulate inflammatory response, apoptosis, or proliferation as a molecular mechanism for the treatment of diabetic peripheral neuropathy, reflecting its multitarget and multipathway action, and providing new ideas to further uncover its pharmacological basis and mechanism of action.

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Network Pharmacology-Based Analysis of the Underlying Mechanism of Hyssopus cuspidatus Boriss. for Antiasthma: A Characteristic Medicinal Material in Xinjiang

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2021/7671247 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Rongchang Liu ◽

Yan Mao ◽

Zhengyi Gu ◽

Jinhua He

Keyword(s):

Signaling Pathways ◽

Pathway Analysis ◽

Kegg Pathway ◽

Underlying Mechanism ◽

Mapk Signaling ◽

Network Pharmacology ◽

Interleukin 17 ◽

Ppi Network ◽

Active Compounds ◽

Kegg Pathway Analysis

Background. Hyssopus cuspidatus Boriss. (Shen Xiang Cao (SXC)), a traditional medicine herb in Xinjiang, has a long history of being used by minorities to treat asthma. However, its active antiasthmatic compounds and underlying mechanism of action are still unknown. The aim of this study was to investigate the bioactive compounds and explore the molecular mechanism of SCX in the treatment of asthma using network pharmacology. Methods. The compounds of SCX were collected by a literature search, and Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) and SwissTargetPrediction were used to predict targets and screen active compounds. Moreover, asthma-related targets were obtained based on DisGeNET, Herb, and GeneCards databases, and a protein-protein interaction (PPI) network was built by the STRING database. Furthermore, the topological analysis of the PPI and SXC-compound-target networks were analyzed and established by Cytoscape software. Finally, the RStudio software package was used for carrying out Gene Ontology (GO) function enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. AutoDock tools and AutoDock Vina were used to molecularly dock the active compounds and key targets. Results. A total of 8 active compounds and 258 potential targets related to SXC were predicted, and PPI network screened out key targets, including IL-6, JUN, TNF, IL10, and CXCL8. GO enrichment analysis involved cell responses to reactive oxygen species, oxidative stress, chemical stress, etc. In addition, KEGG pathway analysis showed that SXC effectively treated asthma through regulation of mitogen-activated protein kinases (MAPK) signaling pathways, interleukin 17 (IL-17) signaling pathways, toll-like receptor (TLR) signaling pathways, and tumor necrosis factor (TNF) signaling pathways. Conclusion. The preliminary study that was based on multiple compounds, multiple targets, and multiple pathways provides a scientific basis for further elucidating the molecules involved and the underlying antiasthma-related mechanisms of SXC.

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Studies of the Anti-Diabetic Mechanism of Pueraria lobata Based on Metabolomics and Network Pharmacology

Processes ◽

10.3390/pr9071245 ◽

2021 ◽

Vol 9 (7) ◽

pp. 1245

Author(s):

Shu Zhang ◽

Qi Ge ◽

Liang Chen ◽

Keping Chen

Keyword(s):

Signaling Pathway ◽

Diabetes Complications ◽

Mapk Signaling ◽

Network Pharmacology ◽

Pueraria Lobata ◽

Insulin Deficiency ◽

Active Ingredients ◽

Foxo Signaling Pathway ◽

Anti Inflammation

Diabetes mellitus (DM), as a chronic disease caused by insulin deficiency or using obstacles, is gradually becoming a principal worldwide health problem. Pueraria lobata is one of the traditional Chinese medicinal and edible plants, playing roles in improving the cardiovascular system, lowering blood sugar, anti-inflammation, anti-oxidation, and so on. Studies on the hypoglycemic effects of Pueraria lobata were also frequently reported. To determine the active ingredients and related targets of Pueraria lobata for DM, 256 metabolites were identified by LC/MS non targeted metabonomics, and 19 active ingredients interacting with 51 DM-related targets were screened. The results showed that puerarin, quercetin, genistein, daidzein, and other active ingredients in Pueraria lobata could participate in the AGE-RAGE signaling pathway, insulin resistance, HIF-1 signaling pathway, FoxO signaling pathway, and MAPK signaling pathway by acting on VEGFA, INS, INSR, IL-6, TNF and AKT1, and may regulate type 2 diabetes, inflammation, atherosis and diabetes complications, such as diabetic retinopathy, diabetic nephropathy, and diabetic cardiomyopathy.

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System Pharmacological Mechanism and Compatibility Laws of Jianghuang from Traditional Chinese Medicine In Blood-Regulating Formulae

10.21203/rs.3.rs-29958/v1 ◽

2020 ◽

Author(s):

Zhiqiang Liu ◽

Bolong Wang

Keyword(s):

Chinese Medicine ◽

Traditional Chinese Medicine ◽

Kegg Pathway ◽

Inflammatory Diseases ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Pharmacological Effects ◽

Ppi Network ◽

Pathway Enrichment ◽

Pharmacological Mechanism

Abstract Background: Jianghuang (JH) is a popular ingredient in blood-regulating traditional Chinese Medicine (TCM) that could be effective for the treatment of various diseases. We demonstrate the compatibility laws and system pharmacological mechanisms of the key formula containing JH by leveraging data mining of bioinformatics databases.Material/Methods: The compatibility laws of blood-regulating formulae containing JH from the Chinese Traditional Medicine Formula Dictionary were analyzed using a generalized rule induction (GRI) algorithm implemented. The putative target gene and miRNA were retrieved via a combination of the Arrowsmith knowledge discovery tool and FunRich 3.1.3. System pharmacological mechanisms are traced by their protein-protein interaction (PPI) network, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis was conducted using Uniprot, the Human Protein Atlas (HPA), STRING 11.0, and KOBAS 3.0.Results: We found that the JH-CX-DG formula (Ligusticum chuanxiong-Angelica sinensis) could represent a key formula containing JH in blood-regulating TCM formulae. The JH-CX-DG formula was observed to directly target AKT, TLR4, caspase-3, PI3K, mTOR, p38 MAPK, VEGF, iNOS, Nrf2, BDNF, NF-κB, Bcl-2, and Bax 13 targets and regulate targets through 13 miRNA. The PPI network and KEGG pathway enrichment analysis showed that the JH-CX-DG formula possess potential pharmacological effects including anti-inflammatory, improving microcirculation, and anti-tumor through the regulation of multiple pathways including PI3K/Akt, MAPK, Toll-like receptor, T cell receptor, EGFR, VEGFR, Apoptosis, HIF-1 (p < 0.05).Conclusion: The JH-CX-DG formula can exert beneficial pharmacological effects through multi-target and multi-pathway interactions. It can be effectively administered for the treatment of inflammatory diseases, microcirculation disorders, cardiovascular disease, and cancer. We found a new effective drug formula through analyzing the compatibility law and systemic pharmacological mechanism of JH. Our study provides a theoretical basis and directions for subsequent research on the JH-CX-DG formula.

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