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. 2022 Apr 28:2022:3145938.
doi: 10.1155/2022/3145938. eCollection 2022.

Identification of Bioactive Compounds and Potential Mechanisms of Kuntai Capsule in the Treatment of Polycystic Ovary Syndrome by Integrating Network Pharmacology and Bioinformatics

Affiliations

Identification of Bioactive Compounds and Potential Mechanisms of Kuntai Capsule in the Treatment of Polycystic Ovary Syndrome by Integrating Network Pharmacology and Bioinformatics

Xiushen Li et al. Oxid Med Cell Longev. .

Abstract

Objective: This study elucidates the potential therapeutic targets and molecular mechanisms of KTC in the treatment of PCOS.

Materials and methods: Using the Traditional Chinese Medicine System Pharmacology Database and Analysis Platform (TCMSP), the active ingredients and potential targets of KTC were obtained. The Gene Expression Omnibus (GEO) database was used to find differentially expressed genes (DEGs) related to PCOS. Search the CTD, DisGeNet, genecards, NCBI, OMIM, and PharmGKB databases for therapeutic targets related to PCOS. The intersection of potential targets, DEGs, and therapeutic targets was submitted to perform bioinformatics analysis by R language. Finally, the analyses' core targets and their corresponding active ingredients were molecularly docked.

Results: 88 potential therapeutic targets of KTC for PCOS were discovered by intersecting the potential targets, DEGs, and therapeutic targets. According to bioinformatics analysis, the mechanisms of KTC treatment for PCOS could be linked to IL-17 signaling route, p53 signaling pathway, HIF-1 signaling pathway, etc. The minimal binding energies of the 5 core targets and their corresponding ingredients were all less than -6.5. Further research found that quercetin may replace KTC in the treatment of PCOS. Discussion and Conclusions. We explored the active ingredients and molecular mechanisms of KTC in the treatment of PCOS and found that quercetin may be the core ingredient of KTC in the treatment of PCOS.

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Conflict of interest statement

The authors declare that they have no conflicts of interest.

Figures

Figure 1
Figure 1
Flow chart.
Figure 2
Figure 2
The network of the relationship between the active ingredients and the targets of KTC.
Figure 3
Figure 3
DEGs related to PCOS in the GEO dataset. (a) Volcano map of DEGs related to PCOS (GSE5850, GSE98421, and GSE34526). (b) Heat map of DEGs related to PCOS (GSE5850, GSE98421, and GSE34526).
Figure 4
Figure 4
Targets related to PCOS treatment. (a) The Venn diagram of PCOS therapeutic targets in 5 disease databases and GEO data sets. (b) The Venn diagram of the targets in at least two databases in (a) and the therapeutic targets of KTC.
Figure 5
Figure 5
The GO and KEGG enrichment analyses of KTC's therapeutic target. (a) GO enrichment analysis (the top 10 results of BP, CC, MF enrichment analysis respectively). (b) KEGG enrichment analysis of therapeutic targets (the top 30 results).
Figure 6
Figure 6
The PPI network of KTC's targets for the treatment of PCOS. (a) Analysis results of PPI network. (b) The core targets of the PPI network.
Figure 7
Figure 7
The results of molecular docking between the core targets of the PPI network and their corresponding active ingredients (the four results with the lowest binding energy). (a) Molecular docking results of TP53 and quercetin (binding energy -8.9). (b) Molecular docking results of TP53 and wogonin (binding energy -8.8). (c) Molecular docking results of MAPK1 and quercetin (binding energy -8.7). (d) Molecular docking results of MAPK8 and kaempferol (binding energy -8.7).
Figure 8
Figure 8
The bioinformatic analysis of quercetin's potential therapeutic target. (a) The Venn diagram of the potential therapeutic targets of quercetin and the therapeutic targets of KTC. (b) PPI network of therapeutic targets. (c) GO enrichment analysis of therapeutic targets (the top 10 results of BP, CC, MF enrichment analysis respectively). (d) KEGG enrichment analysis of therapeutic target (the top 30 results).

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