INTRODUCTION:

Polycystic Ovarian Syndrome (PCOS) is an endocrine and metabolic syndrome. 8-20% of women affected with this disorder and causes female infertility¹. It is represented by polycystic ovaries, oligo/anovulation, hyperandrogenism, irregular menstruation, low sex hormone-binding globulin, raised luteinizing hormone (LH) to follicle-stimulating hormone (FSH), and prolonged low-grade inflammation. PCOS raises the risk of type 2 diabetes mellitus (T2DM), atherosclerosis, cardiovascular disease, endometrial cancer, breast cancer, and other chronic problems in addition to endangering women's physical and emotional well-being². Nowadays, anti-androgen medications, insulin sensitizers, and ovulation-promoting medications are the conventional approaches for PCOS treatment. According to traditional herbal medicine, bioactive compounds derived from medicinal plants shown therapeutic potential in treating PCOS-related abnormalities, such as insulin resistance, hyperinsulinemia, hyperandrogenism, abnormal ovarian function, obesity, infertility, etc. According to study individuals suffering from PCOS, curcumin, the active component of curcuma longa, has positive benefits on weight reduction, glucose and lipid metabolism, and inflammation³. Likewise, the linolenic acid regulates TGF-β signalling in glomerulosclerosis. TGF-β in the ovary stimulates follicle-stimulating hormone (FSH) through thrombospondin 1 (TSP1) in kidney causes cell proliferation and apoptosis⁴. As per the study Cinnamaldehyde beneficial to control metabolic disorder⁵. Therapeutic benefit of glycyrrhizin acid decreases the ovarian cyst also improve the fertilization rate⁶.

Network pharmacology has recently emerged as a novel method and technique for understanding intricate pharmacological issues in the context of new drug discovery. It encompasses several fields and approaches, including genomics, proteomics, and systems biology⁷. This study intends to build the theoretical foundation for its practical applications by investigating the targets and possible mechanisms of traditional herbs and their effectiveness as a therapy for PCOS using network pharmacology approaches. It also integrates multi-target and multi-pathway analyses and provides an analytical approach to investigate the therapeutic potential of bioactive compounds. Although the current results show the expected functions of glycyrrhizin acid, linolenic acid, curcumin, and cinnamaldehyde, experimental confirmation is still needed to determine their exact processes. Network pharmacology has the potential to be a key component of precision medicine in the future by facilitating precise target identification, forecasting compound synergy, and speeding up drug discovery. Its combination with artificial intelligence and omics technology will also improve and understand intricate disease networks and identify multi-component therapies. In the end, network pharmacology provides a viable framework for converting herbal bioactive into scientifically supported treatment plans for complicated disorders.

The current study implemented network pharmacology and enrichment analysis to find possible hub genes and molecular targets linked to PCOS. However, these bioinformatic predictions need to be confirmed with more experiments. To validate the expression levels of significant hub genes (such as CYP17A1, CYP19A1, and HSD17B2) in clinical samples from PCOS patients and normal controls, quantitative real-time PCR (qRT-PCR) will be conducted initially. This will help figure out how they are differentially expressed and what their biological significance is in the disease. Second, we will create in vitro cell culture models using ovarian granulosa or theca cells to study how bioactive substances (glycyrrhizin acid, linolenic acid, curcumin, and cinnamaldehyde) affect molecular pathways related to PCOS. Lastly, the drugs will be studied to observe how they change the genome and transcription, which will help us understand how they work and how well they work as treatments. These experimental methods will help us understand the gene-drug interactions that were found in silico and make it possible to create target-based treatments for PCOS.

MATERIALS AND METHODS

1. Identification of Potential Molecular Targets in PCOS:

Firstly, we searched genes associated with PCOS by searching keywords “Polycystic ovary syndrome” from GeneCards (https://www.genecards.org/), OMIM (https://www.omim.org/) and CTD (https://ctdbase.org/) database. A higher score in the GeneCards database means that the target has a strong connection to the disease. For scientific and therapeutic purposes, OMIM (Online Mendelian Inheritance in Man) is a comprehensive database that lists all known human genes and genetic disorders, emphasizing the links between genes and diseases as well as inheritance patterns. The Comparative Toxicogenomic Database (CTD) is a public resource that helps academics understand how environmental exposures impact human health by offering information about the connections between chemicals, genes, and diseases. 993 protein-coding genes were retrieved from the mentioned database.

2. Construction of a PPI Network of Common Targets in PCOS:

Using STRING (Search Tool for the Retrieval of Interacting Genes/Proteins) software Protein-protein interaction (PPI) network was constructed for 993 diseases. By combining known and expected interactions from databases, quantitative analyses, and experimental data, this bioinformatics tool and database analyses and visualizes networks of protein-protein interactions. Cytoscape and plugins such as CytoHubba were used to identify hub genes.

3. Hub genes identification:

The process of identifying significant genes that are strongly linked and probably serve essential functions for regulating biological processes or disease mechanisms within a biological network (such as a network of gene co-expression or protein–protein interactions) is known as hub gene identification. To discover hub genes, topological criteria were used. Closeness centrality > average, Betweenness centrality > average, and Degree > average.

4. Screening of Bioactive Ingredients and Target Proteins:

Swiss Target Prediction predicts their targets based on the herbal medicines' structures. the most likely protein targets for specific chemicals or herbal treatments based on their molecular composition. It makes it easier to find potential interactions and mechanisms of action between drugs and their targets.

5. Intersection mapping Targets in PCOS and Herbal Medicines:

Identifying common biological targets that are impacted by both the active ingredients in herbal medicines and the genes and proteins linked to PCOS is known as intersection mapping of targets in PCOS and herbal medications. The Venny 2.1.0 software is used to map intersections.

6. Functional enrichment analysis:

Gene Ontology (GO) and KEGG pathway enrichment analyses were performed using tools such as g profiler. Significantly enhanced terms were those with a p-value < 0.05. The data were presented using bar graphs, bubble charts, or network diagrams to highlight the most relevant biological pathways and processes.

RESULTS:

1. Analysis of PPI Network and Hub genes identification: With the aim to investigate the landscape of molecular interactions underlying PCOS, 958 genes associated with PCOS were used to construct a Protein–Protein Interaction (PPI) network. The interaction data (confidence score ≥0.4) was obtained from the STRING database and evaluated using Eclipse Adoptium in Cytoscape (3.10.3) Java: 17.0.5. Proteins and their associated interactions were represented by the network's 2814 edges and 958 nodes, respectively. Each protein was functionally connected with roughly eight additional proteins on average, as indicated by the average number of neighbors, which was 8.101. The network structure showed a modest susceptibility for nodes to form clusters, with a clustering coefficient of 0.374 and a characteristic path length of 4.052. Multiple strongly connected sections may be present within the overall network, as indicated by the network diameter 10 and radius 6. While the network heterogeneity (1.054) and centralization (0.083) revealed the presence of a few highly connected hub genes, the network density (0.012) indicated a sparse but biologically significant interaction pattern. One significant component, which represented the central functional module of PCOS-associated genes, made up the majority of the 256 related components that were found. Two noticeable dense clusters were visible upon visual inspection of the network, encircled by a number of smaller subnetworks, indicating functional modularity among the interacting proteins.

Figure 1: The PPI network of genes linked to PCOS was used to create the hub gene network. Using Cytoscape, the network's 614 edges and 146 hub genes are displayed. Genes are represented by each node, while interactions between proteins are indicated by edges. Nodes' topological properties (degree, betweenness, and closeness centrality) are reflected in their size and colour intensity.

The topological parameters degree, betweenness centrality, and closeness centrality were used to identify 146 hub genes from the 958-gene network that was created. Hub genes were only chosen from nodes with values higher than the mean of the three metrics. The degree of a node's effect within the network is indicated by the number of direct connections it has. Potential "connector" or "bridge" genes that connect several functional modules are identified by the betweenness centrality, which calculates how frequently a node is on the shortest path between other nodes. A node's proximity centrality indicates its core position in the molecular network by indicating how quickly it may interact with every other node. The hub gene network that resulted has 614 edges and 146 nodes, with an average of 8.411 neighbors. While the network density (0.058) revealed moderate interconnection within a single, well-integrated component, the clustering coefficient (0.577) showed a considerable tendency for nodes to form closely connected clusters. The network width (10) and characteristic path length (4.015) demonstrated the existence of highly interactive hubs that efficiently link different molecular submodules. According to the network's centralization (0.158) and heterogeneity (0.662), there were a number of powerful hub nodes that had a big impact on network communication. The list of 146 gene added in supplementary file.

2. Assessment of Common Target Mapping Between PCOS and Herbal Interventions: The bioactive substances glycyrrhizin acid, linolenic acid, curcumin, and cinnamaldehyde were subjected to in silico target prediction using the Swiss Target Prediction database (https://swisstargetprediction.ch/) in order to investigate the possible therapeutic targets of specific phytoconstituents in PCOS. Swiss Target Prediction was used to upload each compound's standard SMILES or 2D structures, which were obtained from the PubChem database. Only the targets with the highest probability scores were taken into consideration for additional analysis, and the prediction was carried out using the Homo sapiens species setting. There were roughly 100 predicted target genes associated with each compound. Intersection mapping between the anticipated drug target genes and the 146 PCOS hub genes that were previously identified was done using the Venny 2.1.0 online tool (https://bioinfogp.cnb.csic.es/tools/venny/) in order to determine their relation to PCOS. The discovery of shared targets that may be influenced by both the bioactive substances and PCOS-related molecular hubs was made possible by this intersecting method. After duplicates were eliminated, the intersection analysis produced 25 common genes, as seen in (figure 02).

The essential biological link between the selected phytoconstituents and PCOS pathogenesis is represented by the 24 overlapping genes that were identified as compound PCOS core targets. Then, using Cytoscape software, a 21-gene interaction network was created by eliminating duplicate and non-interacting nodes. Strong molecular relationships between the identified targets were indicated by the network's dense and highly linked topology. Subsequent examination of the interactions between the four phytocompounds and the 21 important genes revealed that the main molecular targets that all four compounds had in common were CYP17A1, CYP19A1, and HSD17B2.

Table 2: Role of CYP17A1, CYP19A1, and HSD17B3 in ovarian steroid imbalance and hyperandrogenism associated with PCOS

S. N.	Gene Name	Activity involved PCOS	References
1	CYP17A1	CYP17A1 plays a central role in androgen biosynthesis, and its dysregulation in theca cells contributes to persistent hyperandrogenism, disrupted folliculogenesis, and insulin resistance. The rs743572 polymorphism has been widely associated with PCOS susceptibility, influencing steroidogenic activity and contributing to phenotypic heterogeneity, including variations in metabolic and reproductive features.	20–24
2	CYP19A1	In PCOS, CYP19A1 (aromatase) expression is tightly regulated by promoter-specific FSH signalling, epigenetic modifications, and non-coding RNA networks. Altered DNA methylation, histone marks, and circRNA/ceRNA-mediated mechanisms can dysregulate aromatase activity, leading to impaired androgen–estrogen conversion, abnormal oestradiol production, follicular arrest, and ovarian dysfunction. Thus, disrupted transcriptional and post-transcriptional control of CYP19A1 contributes significantly to steroidogenic imbalance and reproductive abnormalities in PCOS.	25–29
3	HSD17B3	HSD17B3 plays a critical role in ovarian steroidogenesis by converting androstenedione to testosterone. Dysregulation or increased activity of this enzyme may enhance ovarian androgen production, contributing to hyperandrogenism, disrupted folliculogenesis, and characteristic ovarian morphological changes seen in PCOS. Genetic variations in HSD17B3 may therefore influence androgen–estrogen balance and PCOS-related reproductive dysfunction.	30,31

Table 3: Biological Process (BP) and Gene Ontology (GO) enrichment analysis of target genes associated with PCOS.

Biological Process	Adjusted P value	Genes
steroid metabolic process	6.05E-14	TNF, APP, HSD17B3, LIPC, CYP51A1, HMGCR, SRD5A2, CYP19A1, CYP17A1, CYP11B2, CYP11B1, SRD5A1
steroid biosynthetic process	8.35E-13	TNF, HSD17B3, CYP51A1, HMGCR, SRD5A2, CYP19A1, CYP17A1, CYP11B2, CYP11B1, SRD5A1
lipid metabolic process	2.37E-12	TNF, EGFR, APP, HSD17B3, LIPC, CYP51A1, HMGCR, SRD5A2, CYP19A1, CYP17A1, PPARA, PPARG, CYP11B2, CYP11B1, SRD5A1, F2
steroid hormone biosynthetic process	2.11E-11	HSD17B3, SRD5A2, CYP19A1, CYP17A1, CYP11B2, CYP11B1, SRD5A1
regulation of hormone levels	3.06E-11	TNF, EGFR, HSD17B3, IL6, SRD5A2, CYP19A1, CYP17A1, PPARG, CYP11B2, CYP11B1, SRD5A1, F2
hormone biosynthetic process	4.46E-10	HSD17B3, SRD5A2, CYP19A1, CYP17A1, CYP11B2, CYP11B1, SRD5A1
lipid biosynthetic process	9.60E-10	TNF, HSD17B3, LIPC, CYP51A1, HMGCR, SRD5A2, CYP19A1, CYP17A1, PPARA, CYP11B2, CYP11B1, SRD5A1
androgen metabolic process	1.23E-07	HSD17B3, SRD5A2, CYP19A1, CYP17A1, SRD5A1
androgen biosynthetic process	2.27E-07	HSD17B3, SRD5A2, CYP17A1, SRD5A1
hormone metabolic process	3.77E-06	HSD17B3, SRD5A2, CYP19A1, CYP17A1, CYP11B2, CYP11B1, SRD5A1
cholesterol metabolic process	5.29E-06	APP, LIPC, CYP51A1, HMGCR, CYP11B2, CYP11B1
testosterone biosynthetic process	0.000207537	HSD17B3, SRD5A2, CYP19A1
glucocorticoid biosynthetic process	0.000702108	CYP17A1, CYP11B2, CYP11B1
hormone secretion	0.000740508	TNF, EGFR, IL6, CYP19A1, PPARG, F2
hormone transport	0.00090399	TNF, EGFR, IL6, CYP19A1, PPARG, F2
steroid catabolic process	0.002524606	SRD5A2, CYP19A1, SRD5A1
lipid storage	0.002735757	TNF, IL6, PPARA, PPARG
glucocorticoid metabolic process	0.002867028	CYP17A1, CYP11B2, CYP11B1
androgen catabolic process	0.01395835	CYP19A1, SRD5A1
C21-steroid hormone metabolic process	7.82E-05	CYP17A1, CYP11B2, CYP11B1, SRD5A1

DISCUSSION:

The protein–protein interaction (PPI) network was constructed in this study in order to identify important biological targets linked to PCOS. With 958 nodes and 2814 edges, the network showed a highly integrated system, indicating that the proteins linked to PCOS have close functional relationships. Moderate clustering and effective information transfer throughout the network were suggested by topological measures including the average number of neighbors (8.10) and clustering coefficient (0.374)^32–34. The primary molecular framework of PCOS-associated genes is represented by the densely connected core region of the hub gene network (Figure 02), which is encircled by peripheral nodes that play auxiliary regulatory roles. High connection and centrality scores were shown by genes including INS, IL6, TNF, TP53, AKT1, EGFR, ESR1, TLR4, and BDNF, indicating their crucial role in biological processes such oxidative stress, insulin resistance, inflammation, apoptosis, and hormonal imbalance^35–37The main molecular nodes that act as important regulators within the PCOS-associated PPI network were generally identified by the hub gene analysis. Because of their crucial significance in preserving the structural and functional integrity of the PCOS molecular network, these hub genes may serve as prospective biomarkers or therapeutic targets³⁸.

An important mechanistic insight into the possible therapeutic significance of these phytoconstituents in the treatment of PCOS is provided by the discovery that glycyrrhizin acid, linolenic acid, curcumin, and cinnamaldehyde mostly target CYP17A1, CYP19A1, and HSD17B2. The steroidogenic pathway, which is essential for regulating the hormonal balance of the ovaries, involves important enzymes that are encoded by these genes. In women with PCOS, dysregulation of these enzymes has been widely documented to contribute to ovarian dysfunction, estrogen imbalance, and hyperandrogenism. The low density and considerable heterogeneity of the network, which show its scale-free nature, emphasize how important these hub genes are to preserving network stability^39–42. All things considered, PPI network analysis helped identify possible treatment targets for additional experimental validation and offered insightful information about the molecular complexity of PCOS^35,43. The results of the study provide a thorough molecular understanding of the complex pathophysiology of PCOS, highlighting the interrelated functions of inflammation, metabolic dysfunction, and steroidogenesis. According to the GO enrichment studies, the 21 genes linked to PCOS are mostly engaged in metabolic regulation, oxidation-reduction, and steroid biosynthesis, with additional functions associated with receptor-signalling and cytokine-mediated pathways. Future research on biomarkers and pharmacological targets will be well-founded by the results we have obtained, which provide molecular evidence that PCOS is a systemic condition involving intricate interactions between the immunological, metabolic, and hormonal networks^44,45.

The majority of genes were linked to ovarian follicle development, lipid metabolism, and steroid synthesis, all of which are important processes in the pathophysiology of PCOS, according to the Gene Ontology (GO) enrichment. Significant enrichment in oxidoreductase activity, enzyme binding, and steroid hydroxylase activity has been identified by the Molecular Function (MF) analysis, highlighting the roles of these genes in steroidogenesis and hormone regulation. Similar to this, biological process (BP) words like cholesterol metabolism, hormone stimulus response, and reproductive process control enhance their roles in androgen excess and ovulatory dysfunction, which are characteristics of PCOS⁴⁶. These results are consistent with previous studies showing that steroidogenic enzyme dysregulation, including those of CYP17A1, CYP19A1, and HSD17B2, disturbs the balance between estrogen and androgen, leading to follicular arrest and hyperandrogenism^47,48. Thus, functional enrichment emphasizes these genes' potential as therapeutic targets for PCOS patients in order to restore metabolic and endocrine balance.

CONCLUSION:

The application of network pharmacology, through systematic data mining and multi-target, multi-pathway analysis, has enhanced our understanding of the potential therapeutic relevance of glycyrrhizin acid, linolenic acid, curcumin, and cinnamaldehyde in PCOS. The current work primarily provides predictive insights into the interactions of these compounds with nuclear signalling pathways. Nonetheless, comprehensive experimental studies are essential to validate their precise mechanisms of action. In the future, these bioactive molecules may contribute to the development of more precise and targeted therapeutic strategies for PCOS management. Future research should focus on confirming these mechanisms through well-designed In vitro, In vivo, and clinical studies to establish efficacy, safety, and translational relevance, thereby facilitating the development of evidence-based targeted therapies for PCOS.

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Received on 25.08.2025 Revised on 22.11.2025

Accepted on 23.01.2026 Published on 06.07.2026

Available online from July 20, 2026

Asian J. Pharm. Res. 2026; 16(3):325-333.

DOI: 10.52711/2231-5691.2026.00048

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License. Creative Commons License.

S. N.	Compound Name	PCOS Mechanistic Relevance	References
1	Linolenic Acid	Linolenic acid reduces pro-inflammatory cytokines and improves lipid and metabolic signalling (e.g., GPR120-cAMP, gut microbiota), thereby indirectly modulating ovarian steroidogenesis. Although it does not directly inhibit CYP17A1 or CYP19A1, it helps restore androgen–estrogen balance by improving the inflammatory and metabolic environment in PCOS.	8–10
2	Glycyrrhizin Acid	Glycyrrhizin acid suppresses NF-κB/MAPK signalling and improves insulin resistance, reducing chronic inflammation linked to hyperandrogenism. Through these systemic effects, it indirectly influences CYP17A1 and CYP19A1 activity and ovarian steroid balance.	11–13
3	Curcumin	Curcumin modulates steroidogenic gene expression and inhibits androgen receptor signalling, potentially reducing HSD17B3-mediated testosterone synthesis and improving hormonal imbalance in PCOS.	14–16
4	Cinnamaldehyde	Cinnamaldehyde decreases ovarian inflammation and oxidative stress, which may reduce inflammation-induced upregulation of steroidogenic enzymes such as HSD17B3, thereby lowering androgen excess indirectly.	17–19


Figure 2: Intersection mapping using Venny 2.1.0 using 146 PCOS hub genes. Twenty-one common genes have been identified from the Venn diagram, demonstrating overlapping genes.	Figure 3: Interaction of drugs with 21 main genes (linolenic acid (LA), glycyrrhizin acid (GA), cinnamaldehyde (CA) and curcumin (CU).