Metabonomic-Transcriptome Integration Analysis on Osteoarthritis and Rheumatoid Arthritis

Purpose. This study is aimed at exploring the potential metabolite/gene biomarkers, as well as the differences between the molecular mechanisms, of osteoarthritis (OA) and rheumatoid arthritis (RA). Methods. Transcriptome dataset GSE100786 was downloaded to explore the differentially expressed genes (DEGs) between OA samples and RA samples. Meanwhile, metabolomic dataset MTBLS564 was downloaded and preprocessed to obtain metabolites. Then, the principal component analysis (PCA) and linear models were used to reveal DEG-metabolite relations. Finally, metabolic pathway enrichment analysis was performed to investigate the differences between the molecular mechanisms of OA and RA. Results. A total of 976 DEGs and 171 metabolites were explored between OA samples and RA samples. The PCA and linear module analysis investigated 186 DEG-metabolite interactions including Glycogenin 1- (GYG1-) asparagine_54, hedgehog acyltransferase- (HHAT-) glucose_70, and TNF receptor-associated factor 3- (TRAF3-) acetoacetate_35. Finally, the KEGG pathway analysis showed that these metabolites were mainly enriched in pathways like gap junction, phagosome, NF-kappa B, and IL-17 pathway. Conclusions. Genes such as HHAT, GYG1, and TRAF3, as well as metabolites including glucose, asparagine, and acetoacetate, might be implicated in the pathogenesis of OA and RA. Metabolites like ethanol and tyrosine might participate differentially in OA and RA progression via the gap junction pathway and phagosome pathway, respectively. TRAF3-acetoacetate interaction may be involved in regulating inflammation in OA and RA by the NF-kappa B and IL-17 pathway.

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Molecular remission at T cell level in patients with rheumatoid arthritis

Scientific Reports ◽

10.1038/s41598-021-96300-z ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Jun Inamo ◽

Katsuya Suzuki ◽

Masaru Takeshita ◽

Yasushi Kondo ◽

Yuumi Okuzono ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

T Cells ◽

T Cell ◽

Disease Control ◽

Expression Profiling ◽

Molecular Mechanisms ◽

Principal Component ◽

Cell Level ◽

Molecular Remission ◽

Signature Genes

AbstractWhile numerous disease-modifying anti-rheumatic drugs (DMARDs) have brought about a dramatic paradigm shift in the management of rheumatoid arthritis (RA), unmet needs remain, such as the small proportion of patients who achieve drug-free status. The aim of this study was to explore key molecules for remission at the T cell level, which are known to be deeply involved in RA pathogenesis, and investigate the disease course of patients who achieved molecular remission (MR). We enrolled a total of 46 patients with RA and 10 healthy controls (HCs). We performed gene expression profiling and selected remission signature genes in CD4+ T cells and CD8+ T cells from patients with RA using machine learning methods. In addition, we investigated the benefits of achieving MR on disease control. We identified 9 and 23 genes that were associated with clinical remission in CD4+ and CD8+ T cells, respectively. Principal component analysis (PCA) demonstrated that their expression profiling was similar to those in HCs. For the remission signature genes in CD4+ T cells, the PCA result was reproduced using a validation cohort, indicating the robustness of these genes. A trend toward better disease control was observed during 12 months of follow-up in patients treated with tocilizumab in deep MR compared with those in non-deep MR, although the difference was not significant. The current study will promote our understanding of the molecular mechanisms necessary to achieve deep remission during the management of RA.

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Analysis of MCM Proteins’ Role as a Potential Target of Statins in Patients with Acute Type A Aortic Dissection through Bioinformatics

Genes ◽

10.3390/genes12030387 ◽

2021 ◽

Vol 12 (3) ◽

pp. 387

Author(s):

Zheyong Liang ◽

Yongjian Zhang ◽

Qiang Chen ◽

Junjun Hao ◽

Haichen Wang ◽

...

Keyword(s):

Aortic Dissection ◽

Molecular Mechanisms ◽

Vascular Diseases ◽

Enrichment Analysis ◽

Type A ◽

Protective Role ◽

Gene Expression Omnibus ◽

Pathway Enrichment Analysis ◽

Acute Type ◽

Type A Aortic Dissection

Acute aortic dissection is one of the most severe vascular diseases. The molecular mechanisms of aortic expansion and dissection are unclear. Clinical studies have found that statins play a protective role in aortic dissection development and therapy; however, the mechanism of statins’ effects on the aorta is unknown. The Gene Expression Omnibus (GEO) dataset GSE52093, GSE2450and GSE8686 were analyzed, and genes expressed differentially between aortic dissection samples and normal samples were determined using the Networkanalyst and iDEP tools. Weight gene correlation network analysis (WGCNA), functional annotation, pathway enrichment analysis, and the analysis of the regional variations of genomic features were then performed. We found that the minichromosome maintenance proteins (MCMs), a family of proteins targeted by statins, were upregulated in dissected aortic wall tissues and play a central role in cell-cycle and mitosis regulation in aortic dissection patients. Our results indicate a potential molecular target and mechanism for statins’ effects in patients with acute type A aortic dissection.

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1H NMR-Based Analysis of Serum Metabolites in Monocrotaline-Induced Pulmonary Arterial Hypertensive Rats

Disease Markers ◽

10.1155/2016/5803031 ◽

2016 ◽

Vol 2016 ◽

pp. 1-11 ◽

Cited By ~ 5

Author(s):

Taijie Lin ◽

Jinping Gu ◽

Caihua Huang ◽

Suli Zheng ◽

Xu Lin ◽

...

Keyword(s):

Intraperitoneal Injection ◽

Ventricular Hypertrophy ◽

Principal Component ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Metabolic Profiles ◽

Metabolic Dysfunction ◽

Sprague Dawley ◽

Mean Pulmonary Arterial Pressure ◽

Pulmonary Arterial

Aims. To study the changes of the metabolic profile during the pathogenesis in monocrotaline (MCT) induced pulmonary arterial hypertension (PAH).Methods. Forty male Sprague-Dawley (SD) rats were randomly divided into 5 groups (n=8, each). PAH rats were induced by a single dose intraperitoneal injection of 60 mg/kg MCT, while 8 rats given intraperitoneal injection of 1 ml normal saline and scarified in the same day (W0) served as control. Mean pulmonary arterial pressure (mPAP) was measured through catherization. The degree of right ventricular hypertrophy and pulmonary hyperplasia were determined at the end of first to fourth weeks; nuclear magnetic resonance (NMR) spectra of sera were then acquired for the analysis of metabolites. Principal component analysis (PCA) and orthogonal partial least-squares discriminant analysis (OPLS-DA) were used to discriminate different metabolic profiles.Results. The prominent changes of metabolic profiles were seen during these four weeks. Twenty specific metabolites were identified, which were mainly involved in lipid metabolism, glycolysis, energy metabolism, ketogenesis, and methionine metabolism. Profiles of correlation between these metabolites in each stage changed markedly, especially in the fourth week. Highly activated methionine and betaine metabolism pathways were selected by the pathway enrichment analysis.Conclusions. Metabolic dysfunction is involved in the development and progression of PAH.

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Elucidation of the mechanisms and molecular targets of Qishen Yiqi formula for the treatment of pulmonary arterial hypertension using a bioinformatics/network topology-based strategy

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207323666201019145354 ◽

2020 ◽

Vol 23 ◽

Author(s):

Peiliang Wu ◽

Xiaona Xie ◽

Mayun Chen ◽

Junwei Sun ◽

Luqiong Cai ◽

...

Keyword(s):

Pulmonary Arterial Hypertension ◽

Arterial Hypertension ◽

Network Topology ◽

Molecular Mechanisms ◽

Molecular Targets ◽

Enrichment Analysis ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Bioactive Ingredients ◽

Pulmonary Arterial

Background and Objective: Qishen Yiqi formula (QSYQ) is used to treat cardiovascular disease in the clinical practice of traditional Chinese medicine. However, few studies have explored whether QSYQ affects pulmonary arterial hypertension (PAH), and the mechanisms of action and molecular targets of QSYQ for the treatment of PAH are unclear. A bioinformatics/network topology-based strategy was used to identify the bioactive ingredients, putative targets, and molecular mechanisms of QSYQ in PAH. Methods: A network pharmacology-based strategy was employed by integrating active component gathering, target prediction, PAH gene collection, network topology, and gene enrichment analysis to systematically explore the multicomponent synergistic mechanisms. Results: In total, 107 bioactive ingredients of QSYQ and 228 ingredient targets were identified. Moreover, 234 PAH-related differentially expressed genes with a |fold change| >2 and an adjusted P value < 0.005 were identified between the PAH patient and control groups, and 266 therapeutic targets were identified. The pathway enrichment analysis indicated that 85 pathways, including the PI3K-Akt, MAPK, and HIF-1 signaling pathways, were significantly enriched. TP53 was the core target gene, and 7 other top genes (MAPK1, RELA, NFKB1, CDKN1A, AKT1, MYC, and MDM2) were the key genes in the gene-pathway network based on the effects of QSYQ on PAH. Conclusion: An integrative investigation based on network pharmacology may elucidate the multicomponent synergistic mechanisms of QSYQ in PAH and lay a foundation for further animal experiments, human clinical trials and rational clinical applications of QSYQ.

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Bioinformatic analysis identifies potential biomarkers and therapeutic targets of septic-shock-associated acute kidney injury

Hereditas ◽

10.1186/s41065-021-00176-y ◽

2021 ◽

Vol 158 (1) ◽

Author(s):

Yun Tang ◽

Xiaobo Yang ◽

Huaqing Shu ◽

Yuan Yu ◽

Shangwen Pan ◽

...

Keyword(s):

Gene Expression ◽

Septic Shock ◽

Acute Kidney Injury ◽

Molecular Mechanisms ◽

Therapeutic Targets ◽

Kidney Injury ◽

Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Blood Samples ◽

Potential Biomarkers

Abstract Background Sepsis and septic shock are life-threatening diseases with high mortality rate in intensive care unit (ICU). Acute kidney injury (AKI) is a common complication of sepsis, and its occurrence is a poor prognostic sign to septic patients. We analyzed co-differentially expressed genes (co-DEGs) to explore relationships between septic shock and AKI and reveal potential biomarkers and therapeutic targets of septic-shock-associated AKI (SSAKI). Methods Two gene expression datasets (GSE30718 and GSE57065) were downloaded from the Gene Expression Omnibus (GEO). The GSE57065 dataset included 28 septic shock patients and 25 healthy volunteers and blood samples were collected within 0.5, 24 and 48 h after shock. Specimens of GSE30718 were collected from 26 patients with AKI and 11 control patents. AKI-DEGs and septic-shock-DEGs were identified using the two datasets. Subsequently, Gene Ontology (GO) functional analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis, and protein-protein interaction (PPI) network analysis were performed to elucidate molecular mechanisms of DEGs. We also evaluated co-DEGs and corresponding predicted miRNAs involved in septic shock and AKI. Results We identified 62 DEGs in AKI specimens and 888, 870, and 717 DEGs in septic shock blood samples within 0.5, 24 and 48 h, respectively. The hub genes of EGF and OLFM4 may be involved in AKI and QPCT, CKAP4, PRKCQ, PLAC8, PRC1, BCL9L, ATP11B, KLHL2, LDLRAP1, NDUFAF1, IFIT2, CSF1R, HGF, NRN1, GZMB, and STAT4 may be associated with septic shock. Besides, co-DEGs of VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 coupled with corresponding predicted miRNAs, especially miR-29b-3p, miR-152-3p, and miR-223-3p may be regarded as promising targets for the diagnosis and treatment of SSAKI in the future. Conclusions Septic shock and AKI are related and VMP1, SLPI, PTX3, TIMP1, OLFM4, LCN2, and S100A9 genes are significantly associated with novel biomarkers involved in the occurrence and development of SSAKI.

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Tissue-based metabolomics profiling reveals metabolic signatures and major metabolic pathways of gastric cancer

10.21203/rs.3.rs-326163/v1 ◽

2021 ◽

Author(s):

Yaqin Wang ◽

Wenchao Chen ◽

Kun Li ◽

Gang Wu ◽

Wei Zhang ◽

...

Keyword(s):

Gastric Cancer ◽

Predictive Ability ◽

Principal Component ◽

Enrichment Analysis ◽

Pentose Phosphate ◽

P Value ◽

Pathway Enrichment Analysis ◽

Dodecanoic Acid ◽

Orthogonal Projections ◽

Latent Structures

Abstract Purpose This study was aimed to screen differential metabolites between gastric cancer (GC) and paracancerous (PC) tissues and find new biomarkers of GC. Methods GC (n = 28) and matched PC (n = 28) tissues were collected and LC-MS/MS analyses were performed to detect metabolites of GC and PC tissues in positive and negative models. Principal component analysis (PCA) and orthogonal projections to latent structures-discriminate analysis (OPLS-DA) were conducted to describe distribution of origin data and general separation and estimate the robustness and the predictive ability of our mode. Differential metabolites were screened based on criterion of variables with p value < 0.05 and VIP (variable importance in the projection) > 1.0. Receiver operating characteristic (ROC) analysis was performed to evaluate the diagnostic power of differential metabolites. Kyoto Encyclopedia of Genes and Genomes (KEGG) was performed to search for metabolite pathways and MetaboAnalyst was used for pathway enrichment analysis. Results Several metabolites were significantly changed in GC group compared with PC group. Thirteen metabolites with high VIP were chose and among which 1-methylnicotinamide, dodecanoic acid and sphinganine possessed high AUC values (AUC > 0.8) indicating an excellent discriminatory ability on GC. Pathways such as pentose phosphate pathway and histidine metabolism were focused based on differential metabolites demonstrating their effects on progress of GC. Conclusions In conclusion, we investigated the tissue-based metabolomics profile of GC and several differential metabolites and signaling pathways were focused. Further study is needed to verify those results.

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Gene Expression Profile of Active HE4 Stimulation in Epithelial Ovarian Cancer Cells: Microarray Study and Comprehensive Bioinformatics Analysis

10.21203/rs.3.rs-46014/v1 ◽

2020 ◽

Author(s):

Liancheng Zhu ◽

Mingzi Tan ◽

Haoya Xu ◽

Bei Lin

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Clinical Significance ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Coexpression Analysis ◽

Pathway Enrichment

Abstract Background.Human Epididymis Protein 4 (HE4) is a novel serum biomarker for diagnosis of epithelial ovarian cancer (EOC) with high specificity and sensitivity compared with CA125, and the increasing researches have been carried out on its roles in promoting carcinogenesis and chemoresistance in EOC in recent years, however, its underlying molecular mechanisms remain poorly understood. The aim of this study was to elucidate the molecular mechanisms of HE4 stimulation and to identify the key genes and pathways mediating carcinogenesis in EOC using microarray and bioinformatics analysis.Methods. We established a stable HE4-silence ES-2 ovarian cancer cell line labeled as “S”, and its active HE4 protein stimulated cells labeled as “S4”. Human whole genome microarray analysis was used to identify deferentially expressed genes (DEGs) from triplicate samples of S4 and S cells. “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis (GSEA) were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal for WFDC2 coexpression analysis. GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction (qRT-PCR) was applied for validation. The protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape. Results.In total, 713 DEGs were found (164 up regulated and 549 down regulated) and further analyzed by GO, pathway enrichment and PPI analyses. We found that MAPK pathway accounted for a significant portion of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2 coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) that were also dramatically changed in S4 cells and validated by dataset GSE51088. Kaplan–Meier survival statistics revealed clinical significance for all of the 10 target genes. Finally, PPI was constructed, sixteen hub genes and eight molecular complex detections (MCODEs) were identified, the seeds of five most significant MCODEs were subjected to GO and KEGG enrichment analysis and their clinical significance was evaluated.Conclusions.By applying microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network of active HE4 stimulation in EOC cells. We offered several possible mechanisms and identified therapeutic and prognostic targets of HE4 in EOC.

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Gene expression profiles following active HE4 stimulation in epithelial ovarian cancer cells: microarray study and comprehensive bioinformatics analysis

10.21203/rs.3.rs-100248/v1 ◽

2020 ◽

Author(s):

Liancheng Zhu ◽

Mingzi Tan ◽

Haoya Xu ◽

Bei Lin

Keyword(s):

Ovarian Cancer ◽

Epithelial Ovarian Cancer ◽

Molecular Mechanisms ◽

Bioinformatics Analysis ◽

Enrichment Analysis ◽

Gene Set Enrichment Analysis ◽

Pathway Enrichment Analysis ◽

Ppi Network ◽

Coexpression Analysis ◽

Pathway Enrichment

Abstract Background: Human epididymis protein 4 (HE4) is a novel serum biomarker for diagnosing epithelial ovarian cancer (EOC) with high specificity and sensitivity, compared with CA125. Recent studies have focused on the roles of HE4 in promoting carcinogenesis and chemoresistance in EOC; however, the molecular mechanisms underlying its action remain poorly understood. This study was conducted to determine the molecular mechanisms underlying HE4 stimulation and identifying key genes and pathways mediating carcinogenesis in EOC by microarray and bioinformatics analysis.Methods: We established a stable HE4-silenced ES-2 ovarian cancer cell line labeled as “S”; the S cells were stimulated with the active HE4 protein, yielding cells labeled as “S4”. Human whole-genome microarray analysis was used to identify differentially expressed genes (DEGs) in S4 and S cells. The “clusterProfiler” package in R, DAVID, Metascape, and Gene Set Enrichment Analysis were used to perform gene ontology (GO) and pathway enrichment analysis, and cBioPortal was used for WFDC2 coexpression analysis. The GEO dataset (GSE51088) and quantitative real-time polymerase chain reaction were used to validate the results. Protein–protein interaction (PPI) network and modular analyses were performed using Metascape and Cytoscape, respectively. Results: In total, 713 DEGs were identified (164 upregulated and 549 downregulated) and further analyzed by GO, pathway enrichment, and PPI analyses. We found that the MAPK pathway accounted for a significant large number of the enriched terms. WFDC2 coexpression analysis revealed ten WFDC2-coexpressed genes (TMEM220A, SEC23A, FRMD6, PMP22, APBB2, DNAJB4, ERLIN1, ZEB1, RAB6B, and PLEKHF1) whose expression levels were dramatically altered in S4 cells; this was validated using the GSE51088 dataset. Kaplan–Meier survival statistics revealed that all 10 target genes were clinically significant. Finally, in the PPI network, 16 hub genes and 8 molecular complex detections (MCODEs) were identified; the seeds of the five most significant MCODEs were subjected to GO and KEGG enrichment analyses and their clinical relevance was evaluated.Conclusions: Through microarray and bioinformatics analyses, we identified DEGs and determined a comprehensive gene network following active HE4 stimulation in EOC cells. We proposed several possible mechanisms underlying the action of HE4 and identified the therapeutic and prognostic targets of HE4 in EOC.

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A Systems Pharmacology Approach for Identifying the Multiple Mechanisms of Action for the Rougui-Fuzi Herb Pair in the Treatment of Cardiocerebral Vascular Diseases

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/5196302 ◽

2020 ◽

Vol 2020 ◽

pp. 1-17

Author(s):

Chun Li ◽

Xia Du ◽

Yang Liu ◽

Qi-Qi Liu ◽

Wen Bing Zhi ◽

...

Keyword(s):

Molecular Mechanisms ◽

Chinese Herb ◽

Vascular Diseases ◽

Enrichment Analysis ◽

Chinese Herbs ◽

Pathway Enrichment Analysis ◽

High Morbidity ◽

Active Ingredients ◽

Active Compounds ◽

System Pharmacology

Cardiocerebral vascular diseases (CCVDs) are the main reasons for high morbidity and mortality all over the world, including atherosclerosis, hypertension, myocardial infarction, stroke, and so on. Chinese herbs pair of the Cinnamomum cassia Presl (Chinese name, rougui) and the Aconitum carmichaelii Debx (Chinese name, fuzi) can be effective in CCVDs, which is recorded in the ancient classic book Shennong Bencao Jing, Mingyibielu and Thousand Golden Prescriptions. However, the active ingredients and the molecular mechanisms of rougui-fuzi in treatment of CCVDs are still unclear. This study was designed to apply a system pharmacology approach to reveal the molecular mechanisms of the rougui-fuzi anti-CCVDs. The 163 candidate compounds were retrieved from Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP). And 84 potential active compounds and the corresponding 42 targets were obtained from systematic model. The underlying mechanisms of the therapeutic effect for rougui-fuzi were investigated with gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. Then, component-target-disease (C-T-D) and target-pathway (T-P) networks were constructed to further dissect the core pathways, potential targets, and active compounds in treatment of CCVDs for rougui-fuzi. We also constituted protein-protein in interaction (PPI) network by the reflect target protein of the crucial pathways against CCVDs. As a result, 21 key compounds, 8 key targets, and 3 key pathways were obtained for rougui-fuzi. Afterwards, molecular docking was performed to validate the reliability of the interactions between some compounds and their corresponding targets. Finally, UPLC-Q-Exactive-MSE and GC-MS/MS were analyzed to detect the active ingredients of rougui-fuzi. Our results may provide a new approach to clarify the molecular mechanisms of Chinese herb pair in treatment with CCVDs at a systematic level.

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Integrating Network Pharmacology with Molecular Docking to Unravel the Active Compounds and Potential Mechanism of Simiao Pill Treating Rheumatoid Arthritis

Evidence-based Complementary and Alternative Medicine ◽

10.1155/2020/5786053 ◽

2020 ◽

Vol 2020 ◽

pp. 1-16

Author(s):

Mengshi Tang ◽

Xi Xie ◽

Pengji Yi ◽

Jin Kang ◽

Jiafen Liao ◽

...

Keyword(s):

Rheumatoid Arthritis ◽

Molecular Docking ◽

Signaling Pathway ◽

Target Genes ◽

Enrichment Analysis ◽

Binding Activity ◽

New Combination ◽

Network Pharmacology ◽

Pathway Enrichment Analysis ◽

Potential Mechanism

Objective. To explore the main components and unravel the potential mechanism of simiao pill (SM) on rheumatoid arthritis (RA) based on network pharmacological analysis and molecular docking. Methods. Related compounds were obtained from TCMSP and BATMAN-TCM database. Oral bioavailability and drug-likeness were then screened by using absorption, distribution, metabolism, and excretion (ADME) criteria. Additionally, target genes related to RA were acquired from GeneCards and OMIM database. Correlations about SM-RA, compounds-targets, and pathways-targets-compounds were visualized through Cytoscape 3.7.1. The protein-protein interaction (PPI) network was constructed by STRING. Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed via R packages. Molecular docking analysis was constructed by the Molecular Operating Environment (MOE). Results. A total of 72 potential compounds and 77 associated targets of SM were identified. The compounds-targets network analysis indicated that the 6 compounds, including quercetin, kaempferol, baicalein, wogonin, beta-sitosterol, and eugenol, were linked to ≥10 target genes, and the 10 target genes (PTGS1, ESR1, AR, PGR, CHRM3, PPARG, CHRM2, BCL2, CASP3, and RELA) were core target genes in the network. Enrichment analysis indicated that PI3K-Akt, TNF, and IL-17 signaling pathway may be a critical signaling pathway in the network pharmacology. Molecular docking showed that quercetin, kaempferol, baicalein, and wogonin have good binding activity with IL6, VEGFA, EGFR, and NFKBIA targets. Conclusion. The integrative investigation based on bioinformatics/network topology strategy may elaborate on the multicomponent synergy mechanisms of SM against RA and provide the way out to develop new combination medicines for RA.

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