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In this study, the chemical substances of Heiguteng Zhuifeng Huoluo Capsule (HZFC) and its potential active ingredients for the treatment of rheumatoid arthritis (RA) were characterized and analyzed by medicinal chemistry combined with bioinformatics methods. Also, the potential active ingredients of HZFC against RA were verified by lipopolysaccharide (LPS)-induced macrophage activation model. The results showed that 79 chemical constituents were successfully identified, mainly including phenylpropanoids, flavonoids, and alkaloids. Among them, 13 active components were closely related to the nine core targets (FASN, ALOX5, EGFR, MMP1, CYP2D6, CNR1, AR, MAOA, and FKBP5) of HZFC in the treatment of RA. Molecular docking further proved that 13 active components had strong docking activity with 9 core targets. In the verification experiment of the LPS-induced RAW 264.7 macrophage model, the verified components (magnoflorine, N-feruloyltyramine, canadine, rutin, quercetin-3-O-glucoside, and pseudocolumbamine) all showed a clear inhibitory effect on the secretion of inflammatory factors in model cells. The above research results suggest that 13 components such as stepharanine, rutin, quercetin-3-O-glucoside, corydine methyl ether, canadine, 8-oxoepiberberine, disinomenine, deosinomenine glucoside, tuduranine, magnoflorine, isosinomenine, pseudocolumbamine, and N-feruloyltyramine may be the main active substances of HZFC in the treatment of RA.
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Background: Rheumatoid arthritis (RA) is a chronic systemic autoimmune disease characterized by inflammatory cell infiltration, which can lead to chronic disability, joint destruction and loss of function. At present, the pathogenesis of RA is still unclear. The purpose of this study is to explore the potential biomarkers and immune molecular mechanisms of rheumatoid arthritis through machine learning-assisted bioinformatics analysis, in order to provide reference for the early diagnosis and treatment of RA disease. Methods: RA gene chips were screened from the public gene GEO database, and batch correction of different groups of RA gene chips was performed using Strawberry Perl. DEGs were obtained using the limma package of R software, and functional enrichment analysis such as gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), disease ontology (DO), and gene set (GSEA) were performed. Three machine learning methods, least absolute shrinkage and selection operator regression (LASSO), support vector machine recursive feature elimination (SVM-RFE) and random forest tree (Random Forest), were used to identify potential biomarkers of RA. The validation group data set was used to verify and further confirm its expression and diagnostic value. In addition, CIBERSORT algorithm was used to evaluate the infiltration of immune cells in RA and control samples, and the correlation between confirmed RA diagnostic biomarkers and immune cells was analyzed. Results: Through feature screening, 79 key DEGs were obtained, mainly involving virus response, Parkinson's pathway, dermatitis and cell junction components. A total of 29 hub genes were screened by LASSO regression, 34 hub genes were screened by SVM-RFE, and 39 hub genes were screened by Random Forest. Combined with the three algorithms, a total of 12 hub genes were obtained. Through the expression and diagnostic value verification in the validation group data set, 7 genes that can be used as diagnostic biomarkers for RA were preliminarily confirmed. At the same time, the correlation analysis of immune cells found that γδT cells, CD4+ memory activated T cells, activated dendritic cells and other immune cells were positively correlated with multiple RA diagnostic biomarkers, CD4+ naive T cells, regulatory T cells and other immune cells were negatively correlated with multiple RA diagnostic biomarkers. Conclusions: The results of novel characteristic gene analysis of RA showed that KYNU, EVI2A, CD52, C1QB, BATF, AIM2 and NDC80 had good diagnostic and clinical value for the diagnosis of RA, and were closely related to immune cells. Therefore, these seven DEGs may become new diagnostic markers and immunotherapy markers for RA.
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ETHNOPHARMACOLOGICAL RELEVANCE: Traditional Chinese medicine and ethnic medicine together play an important value in the modern medicine system that is different from that of chemical drugs. Chinese medicine and ethnic medicine with hemostatic effect have unique advantages and development potential in the prevention and treatment of clinical hemorrhagic diseases, reflecting multi-component, multi-target and multi-pathway effects. AIM OF THE STUDY: In this paper, the active ingredients related to the hemostatic effect of traditional Chinese medicine and ethnic medicine are taken as the starting point, and the traditional Chinese medicine and ethnic medicine with traditional hemostatic purposes are reviewed, and the existing research progress on the active ingredients and their mechanism of action of these drugs is systematically expounded, aiming to provide theoretical reference for the development of traditional hemostatic drugs, the discovery of hemostatic active ingredients and the research of new hemostatic methods. MATERIALS AND METHODS: Hemostatic chinese medicine and ethnic medicine were collected and summarized from the classic books of Materia Medica, public literature database and doctoral or master's thesis repositories. At the same time, we discussed the classification of various types of hemostatic active ingredients of traditional Chinese medicine and ethnic medicine according to the different mechanisms of hemostasis. RESULTS: A total of 436 traditional Chinese medicine and ethnic medicine with hemostatic effects have been collected, and their hemostatic active ingredients include alkaloids, quinones, flavonoids, phenylpropanoids, organic acids, amino acids, terpenoids, steroids, phenols, tannins, esters, polysaccharides and herbal extracts, etc. These active ingredients accelerate the formation of hemostasis by improving endogenous and exogenous hemostatic pathways mainly through enhancing vascular wall contraction, increasing platelet aggregation, promoting coagulation system activation and inhibiting fibrinolysis. CONCLUSIONS: This article reviews the previous data on various aspects of the hemostatic effect of traditional Chinese medicine and ethnomedicine. Many traditional hemostatic drugs have been discovered and many active ingredients and mechanisms have been reported. However, although there are a large number of drugs with traditional hemostatic effects, there are still few developed and applied. At the same time, the hemostatic components of many drugs still remain in the study of the activity of their total extracts, and the potential link between some drug components achieving hemostatic effects through different mechanisms remains to be elucidated.