Network Pharmacology and Data Mining Approach Reveal the Medication Rule of Traditional Chinese Medicine in the Treatment of Premenstrual Syndrome/Premenstrual Dysphoric Disorder

Abstract

Premenstrual syndrome (PMS) is a common disorder that affects women of reproductive age. It is characterized by periodic mental and somatic symptoms such as irritability, depression, and breast pain during the luteal phase. Premenstrual dysphoric disorder (PMDD) is the most severe form of PMS. In recent years, the incidence of PMS/PMDD has been increasing year after year. However, due to the complex symptoms and ambiguous classification of PMS/PMDD, the limitations of present treatments, such as their poor efficacy rate, have become increasingly apparent. With its unique benefits such as syndrome differentiation and high cure rate, traditional Chinese medicine (TCM) has sparked new diagnosing and treating of PMS/PMDD. This study uses data mining methods, and statistical analysis revealed that Xiaoyao San and Chaihu Shugan San were the commonly used TCM to treat PMS/PMDD. A detailed investigation of regularly used single herbs revealed that most TCM is used as cold herbs that penetrate the liver meridian, with predominant bitter, sweet, and pungent flavors. The network pharmacology method analyzes the interactions between diseases, targets, and herbs. Meanwhile, the deep action targets and molecular mechanisms of 10 commonly used herbs for the treatment of PMS/PMDD are studied, revealing that it involves several ingredients, many targets, and different pathways. This interaction provides insight into the mechanism of action of TCM in the synergistic treatment of PMS/PMDD. It is now clear that we can begin treating PMS/PMDD with TCM using the target and mechanism revealed by the abovementioned findings in the future. This serves as an essential reference for future research and clinical application of TCM in the treatment of PMS/PMDD.

Keywords: herbs; medication rule; network pharmacology; pharmacological mechanism; premenstrual dysphoric disorder; premenstrual syndrome; proprietary Chinese medicine; traditional Chinese medicine prescription.

FIGURE 1

Workflow figure of this study.

FIGURE 2

Year distribution table of TCM-related literature on PMS/PMDD. The abscissa indicates the year of publication (years of publication), and the ordinate indicates the number of publications in a specific year (number of publications). The blue line represents the Chinese literature on PMS/PMDD Chinese medicine treatment, and the red line represents the English literature on PMS/PMDD Chinese medicine treatment.

FIGURE 3

Frequency ratio of herb attribution meridian. Different colored plates represent different meridians.

FIGURE 4

Herb–herb combination–active ingredient–disease target network diagram. See Supplementary Tables S6, S7 for details. Ret stands for different combinations of herbs, as shown in Supplementary Table S8. Ret-1 contains ten herbs corresponding to two targets, ESR1/ESR2 (red line). Ret-3 contains the most significant number of common targets (blue line). The color gradually darkens (yellow–light green–dark green), and the degree of freedom (degree) increases.

FIGURE 5

Protein interaction network diagram for TCM active ingredient–disease intersection targets. The greater the number of adjacent nodes, the greater the probability of becoming a core gene.

FIGURE 6

GO enrichment bubble plot analysis for TCM active ingredient–disease intersection targets (BP, CC, and MF). (A) Analysis of biological processes. (B) Analysis of cellular components. (C) Analysis of molecular function. The x-axis is the RichFactor; the y-axis is the GO Term. The point size indicates the number of gene, and the color of the points is the most important, representing the p-value. The degree of enrichment is proportional to the size of the circle and inversely proportional to the p-value (the closer to the red, the higher the degree of enrichment).

FIGURE 7

Analysis of the KEGG pathway for TCM active ingredient–disease intersection targets. (A) Analysis of the top 20 KEGG enrichment pathways. The x-axis is the gene percent (%), and the y-axis is the pathway’s name. The color represents value; the degree of enrichment is inversely proportional to the p-value (the closer the dark blue, the higher the degree of enrichment). (B) KEGG pathway annotation. The x-axis is the number of genes. The y-axis is the pathway name (green represents metabolic, purple represents environmental information processing, blue represents the cellular processes, red represents organismal systems, and orange represents human diseases).

FIGURE 8

GO enrichment analysis of the predicted targets of 10 common herbs. The x-axis is the three functional groups (BP, CC, and MF) and the y-axis is the number of genes. Red represents the analysis of biological processes, among which the three functional groups, cellular process, biological regulation, and response to stimulus, contain the largest number of genes; green represents the analysis of cellular components, and cell and cell parts also account for more; blue represents molecular function enrichment analysis, binding, and molecular transducer activity functional group dominates absolutely.

FIGURE 9

KEGG pathway analysis of the predicted targets of 10 common herbs. (A) Analysis of the top 20 KEGG enrichment. X-axis is the gene percent (%) and the y-axis is the pathway’s name. The color represents value; the degree of enrichment is inversely proportional to the p-value (the closer the dark blue, the higher the degree of enrichment). (B) KEGG pathway annotation. The x-axis is the number of genes. The y-axis is the pathway name (green represents metabolic, purple represents environmental information processing, blue represents the cellular processes, red represents organismal systems, and orange represents the human diseases.).