Identification of the Shared Gene Signatures and Molecular Mechanisms Between Polycystic Ovarian Syndrome and Major Depressive Disorder: Evidence From Transcriptome Data

Identification of the Shared Gene Signatures and Molecular Mechanisms Between Polycystic Ovarian Syndrome and Major Depressive Disorder: Evidence From Transcriptome Data | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Identification of the Shared Gene Signatures and Molecular Mechanisms Between Polycystic Ovarian Syndrome and Major Depressive Disorder: Evidence From Transcriptome Data Zheng Zheng, Yuxing Wang, Xinmin Liu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3704976/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Polycystic Ovarian Syndrome (PCOS) is the most common metabolic and endocrine disorder in reproductive-age women, while Major Depressive Disorder (MDD) is a relatively common psychiatric condition. Previous studies have suggested a potential link between PCOS and MDD, but the underlying pathophysiological mechanisms remain unclear. This study aims to identify differential expression genes (DEGs) between PCOS and MDD using bioinformatics methods, explore the associated molecular mechanisms, elucidate the TF-mRNA-miRNA regulatory network involved, predict potential drug molecules, and validate them through molecular docking. Methods: Microarray datasets GSE34526 and GSE125664 were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of PCOS and MDD were analyzed using the GEO2R online tool to obtain shared DEGs to both. Next, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the shared DEGs were performed. Then, protein-protein interaction (PPI) network were constructed and the hub genes were identified using the STRING database and Cytoscape software. Next, NetworkAnalyst was used to construct network between target transcription factors (TFs), microRNAs (miRNAs), and hub genes. Finally, the DSigDB database was used to search for potential drug molecules for the treatment of PCOS combined with MDD, followed by molecular docking using the AutoDock Tools and visualization of the results using PyMol 2.4.0. Results: In the above two datasets, 158 shared DEGs were identified. GO and KEGG enrichment analyses showed that these shared DEGs were mainly enriched in pathways related to neural signaling, energy metabolism, and chronic inflammation with immune dysregulation. In addition, genes with greater than 2-fold median interaction number were further screened by Cytoscape's plugin, cytoNCA, and finally 6 hub genes were selected from the PPI network, ncluding GRIN1, CNR1, DNM1, SYNJ1, PLA2G4A and EPHB2. Then, through the construction of the TF-mRNA-miRNA regulatory network, it was concluded that hsa-miR-27a might be a strongly associated miRNA with the pathogenesis of PCOS and MDD, while TFAP2A might be a strongly associated TF. Finally, orlistat, docosahexaenoic acid (DHA), capsaicin, and myo-inositol were considered as potential drug molecules for the treatment of PCOS combined with MDD using the DSigDB database and related study finding, and then molecular docking was performed using AutoDock Tools. The drug-molecule combination with the lowest binding energy was visualized using PyMol software and it found to be well docked. Conclusions: In summary, we constructed a TF-mRNA-miRNA regulatory network for the first time to characterize the interactions among potential TFs, miRNAs, and hub genes associated with PCOS and MDD, and concluded that aberrant neuronal signaling, disturbed energy metabolism, and immune dysregulation with inflammatory response may be the common pathogenesis of PCOS and MDD. In addition, we identified potential drug molecules for the treatment of PCOS and MDD and performed molecular docking validation. This provides new insights to identify potential associations, potential biomarkers and therapeutic agents for PCOS and MDD. PCOS MDD bioinformatics analysis DEGs TF-mRNA-miRNA regulatory network potential drug prediction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Polycystic Ovary Syndrome (PCOS) is a common endocrine disorder that primarily affects ovarian function during the reproductive years and affects about 5–10% of women. It can occur in people of all ethnicities. The exact etiology of PCOS is unknown, but genetics, insulin resistance, and hormonal imbalances are thought to play significant roles in its development [ 1 ]. The clinical manifestations of PCOS varies from person to person but usually combines the following features: irregular menstrual cycles, ovulatory dysfunction, hyperandrogenism, polycystic ovaries, metabolic disorders and mood disorders. Prolonged PCOS can lead to adverse outcomes such as endometrial cancer, recurrent miscarriages, congenital malformations in offspring, type 2 diabetes, and cardiovascular damage [ 2 ]. Therefore, PCOS can have a significant impact on a person's overall health and quality of life, as well as increase the economic burden on the family and society. Major depressive disorder (MDD) is a highly prevalent mental health disorder worldwide. According to the World Health Organization (WHO), more than 264 million people worldwide suffer from depression, and it is one of the leading causes of disability globally [ 3 ]. MDD can affect individuals of all ages, but it is more commonly diagnosed in adults and is also more prevalent in women than in men [ 4 ]. As a complex disorder with multiple underlying causes, MDD is usually caused by a combination of genetic, biological, environmental and psychological factors. It is clinically characterized by persistent and pervasive feelings of sadness, hopelessness, and loss of interest or pleasure in once enjoyable activities. Untreated or poorly managed MDD can have serious and far-reaching consequences, including: impaired daily functioning, increased risk of disease (heart disease, diabetes, and chronic pain conditions, among others), risk of suicide, decreased quality of life, and increased financial burden [ 5 ]. In summary, MDD is a widespread and serious mental health condition with a significant impact on individuals' lives and society as a whole. The link between PCOS and MDD has been the subject of research, and to date, more and more studies have confirmed the important link between PCOS and MDD [ 6 – 7 ]. Studies have shown that people with PCOS are more likely to have symptoms of depression and anxiety compared to those without PCOS. And the comorbidity of PCOS and depression is more common, with the prevalence of depression in PCOS patients ranging from 42–52% [ 8 – 9 ], which is more likely to occur in overweight PCOS patients [ 10 ]. PCOS is characterized by hormonal imbalances, including elevated androgen levels and insulin resistance. These hormonal changes may lead to mood disorders, which can increase the risk of depression [ 11 – 12 ]. Although the relationship between PCOS and MDD has attracted great interest recently, leading to a large number of studies related to this topic, relevant genetic studies are still limited and need to be further explored. Therefore, there is a need for further research into the relationship between PCOS and MDD, as well as potential treatment options. In recent years, microarray technology and bioinformatics analysis have been widely used to screen for genetic alterations at the genome level. The GEO database is a public repository for high-throughput gene expression data and other functional genomics datasets. In this study, we completed the collection of shared DEGs for PCOS and MDD based on the GEO database. Subsequently, we explored the molecular mechanisms involved in the identified shared DEGs by GO analysis, KEGG analysis, and performed PPI analysis thus identifying the hub genes. We then predicted TFs and miRNAs strongly associated with the hub genes as well as potential drug molecules and performed molecular docking to validate the predictions. The aim of this study was to identify the hub genes and molecular mechanisms associated with PCOS and MDD and to provide potential clinical treatment options for patients with PCOS combined with MDD. Methods Collection of gene expression profiling data We collected two microarray datasets (GSE34526, GSE125664) containing PCOS, MDD and their control samples from the GEO database. Among them, the GSE34526 dataset includes gene expression profiles of seven PCOS patients and three normal controls; the GSE125664 dataset includes gene expression profiles of three MDD patients and three normal controls. Selection of DEGs The DEGs of PCOS and MDD were extracted and analyzed separately using the GEO2R online analysis tool ( https://www.ncbi.nlm.nih.gov/geo/geo2r/ ), and the volcano plots were plotted using the bioinformatics online platform ( https://www.bioinformatics.com.cn ). the thresholds for DEGs screening were∣log2 FC∣> 1.0 and p value < 0.05. In addition, Wayne plots were plotted using the bioinformatics platform to identify shared DEGs between PCOS and MDD. these shared DEGs were retained for subsequent analysis. Functional clustering and pathway enrichment of DEGs GO functional enrichment analysis, including biological process (BP), cellular component (CC) and molecular function (MF), and KEGG signaling pathway enrichment analysis were performed on the above shared DEGs. The enriched GO terms and KEGG pathways with p value < 0.05 were selected. Establishment of PPI network and identification of hub genes To further explore the interactions between the shared DEGs obtained above, the Search Tool for Retrieving Gene Interactions (STRING) ( http://string-db.org/ ) was used to construct the PPI network. Subsequently, the PPI network was visualized using Cytoscape software. Then, the betweenness centrality (BC) of the genes was calculated by applying the cytoNCA plug-in, and the genes with the top 15 BC values in the PPI network were screened, and the intersection was taken with the genes with greater than 2 times the median number of interactions to obtain the genes, which were the hub genes in this study. Determine the TFs, miRNAs associated with the hub genes NetworkAnalyst ( https://www.networkanalyst.ca/ ) was used to find TFs and miRNAs associated with hub genes. Finally, the TF-mRNA-miRNA regulatory network was mapped by Cytoscape software. Identification of potential drugs and molecular docking DSigDB ( http://dsigdb.tanlab.org/DSigDBv1.0/ ) is mainly used to study the mechanism of action of drugs and discover new drug targets. Hub genes were uploaded into the DSigDB database to find potential drug molecules targeting these genes for the treatment of PCOS and MDD. Drug molecules with p value 500 were selected and combined with relevant previous studies to finalize the potential drug molecules. Next, to explore the binding of the potential drug molecules to the proteins expressed by the hub genes, we obtained the 3D structures of the drug molecules from PubChem ( https://pubchem.ncbi.nlm.nih.gov/ ), retrieved the target proteins from PDB database ( https://www.rcsb.org/ ) crystal structure, and then molecular docking was performed using AutoDock Tools software. The results of molecular docking were visualized using PyMol software. Results Identification of DEGs in PCOS and MDD We downloaded the GSE34526 series dataset on PCOS and the GSE125664 series dataset on MDD from the GEO database. After screening with p value 1.0, 3003 DEGs were identified in the GSE34526 dataset, of which, 2192 were up-regulated and 811 were down-regulated, and 2448 DEGs were identified in the GSE125664 dataset, of which, 1702 were up-regulated and 746 were down-regulated. The DEGs in the two datasets were presented by drawing volcano plots, as shown in Fig. 1 A and Fig. 1 B, A Venn Diagram analysis was performed to assess the common DEGs between GSE34526 and GSE125664. 158 shared DEGs were found as shown in Fig. 1 C. of which, 125 were up-regulated and 33 were down-regulated. GO and KEGG analysis GO and KEGG analyses were performed using the DAVID database to better understand the biological functions of the identified DEGs. After filtering with a threshold of p value < 0.05, we selected the top five significantly enriched GO terms and the top five KEGG terms.The results showed that in terms of biological processes, DEGs were mainly enriched in signal transduction, actin cytoskeleton organization, brain development, peptidyl-serine phosphorylation, and chemical synaptic transmission (Fig. 2 A). In terms of cellular components, DEGs were mainly associated with the cytoplasm, plasma membrane, cytosol, integral component of plasma membrane, and Golgi apparatus (Fig. 2 B). Molecular functional analysis showed that DEGs were significantly enriched in ATP binding, calcium ion binding, GTPase activator activity, actin binding, and guanyl-nucleotide exchange factor activity (Fig. 2 C). The top five important KEGG pathways of DEGs were enriched in neuroactive ligand-receptor interaction, endocytosis, glutamatergic synapse, phospholipase D signaling pathway, and cAMP signaling pathway (Fig. 2 D). Construction of PPI network and identification of hub genes The construction of the PPI network was first performed based on the STRING database to identify the interactions between the shared DEGs. Then, the obtained results were imported into Cytoscape software for visualization and analysis (Fig. 3 A). And genes with greater than 2-fold median interaction number were shown (Fig. 3 B). The PPI network was analyzed by the Cytoscape plug-in cytoNCA, and the genes with the top 15 BC values were filtered as the candidate hub genes (Fig. 3 C). By taking the intersection of the two, six genes were identified as hub genes, namely, GRIN1, CNR1, DNM1, SYNJ1, PLA2G4A and EPHB2. prediction of related TFs, miRNAs and construction of TF-mRNA-miRNA regulatory network NetworkAnalyst database was used to predict the related TFs and miRNAs of the hub genes. We used Cytoscape software to construct the TF-mRNA-miRNA regulatory network, which consists of 180 nodes and 216 edges containing 49 TFs with 125 miRNAs (Fig. 4 ). Based on the cytoHubba plug-in in Cytoscape software, the most strongly associated TFs and miRNAs were selected based on the Maximal Clique Centrality (MCC) score. Hsa-miR-27a was the highest-scoring miRNA (MCC score of 3); TFAP2A was the highest-scoring TF ( MCC score of 3), and the rest of TFs and miRNAs had MCC scores of 2 and below, so they were not included. Prediction of potential drug molecules The identified hub genes for PCOS and MDD were uploaded to the DSigDB database, which provides a list of potential drug molecules targeting the genes. Drug molecules with a composite score > 500 were screened using a threshold of p value < 0.05. Four potential drug molecules were screened after manual deletion of duplicates, and review of relevant prior studies [ 13 – 16 ]. These drug molecules were: orlistat (composite score: 2284.21), docosahexaenoic acid (DHA) (composite score: 1882.08), capsaicin (composite score: 1077.01) and myo-inositol (composite score: 792.97). Molecular docking analysis Molecular docking was performed to assess the binding affinity of the four drug molecules to the six hub genes (Fig. 5 A). Lower free binding energy indicated stronger binding ability. Among them, Capsaicin had the lowest free binding energy of -7.2 kcal/mol with SYNJ1, and the docking details were visualized by PyMol software, which revealed that Capsaicin could form a hydrogen bond with amino acid residue ARG-717 of SYNJ1 at a distance of 2.2 Å, and amino acid residue ARG-819 at a distance of two hydrogen bonds with distances of 2.5 Å and 2.0 Å, respectively (Fig. 5 B). Discussion Nowadays, more and more studies confirm the correlation between PCOS and MDD. Studies have shown that people with PCOS are more likely to develop symptoms related to depression and anxiety compared to non-PCOS people [ 17 ]. Some studies have claimed that the risk of depression may be approximately two to three times higher in people with PCOS compared to non-PCOS populations [ 18 ]. PCOS is characterized by hormonal imbalances, including elevated androgen levels and insulin resistance. It is hypothesized that these hormonal changes play an important role in the development of mood disorders, including depression. Androgens, such as testosterone, may affect mood and behavior, and elevated androgen levels are common in patients with PCOS [ 19 ]. Several studies have explored the role of chronic inflammation in PCOS and depression. Chronic low-grade inflammation has been associated with mood disorders, and in some cases, PCOS has been associated with increased markers of inflammation [ 20 ]. To date, the mechanisms linking PCOS and MDD are not fully understood. Therefore, exploring the molecular mechanisms between PCOS and MDD for early identification and intervention is undoubtedly of great clinical significance. In this study, we performed a series of bioinformatics analysis on two independent gene chip databases for PCOS and MDD, and obtained 158 shared DEGs between PCOS and MDD based on the GEO database. We further analyzed these DEGs, and found that they were mainly closely associated with neuronal signaling, energy metabolism, and inflammatory response. Relationship between neuronal signaling and the pathogenesis of PCOS and MDD The endocrine system and the nervous system are interconnected and there is crosstalk between them. PCOS mainly affects the endocrine and reproductive systems, but it may also affect the nervous system, such as the regulation of neurotransmitters, which affects mood and behavior. In addition, the chronic stress state in which PCOS patients live can activate the hypothalamic-pituitary-adrenal (HPA) axis, thus affecting neural signaling [ 21 ]. MDD, which is mainly characterized by mood disorders, is nowadays more and more understood as a disorder with an important neurobiological basis. In MDD, neuroplasticity may be impaired, thereby affecting neural signaling and leading to the persistence of depressive symptoms. In addition, MDD is associated with imbalances in certain neurotransmitters, and these imbalances can affect neural signaling, leading to disruptions in mood regulation [ 22 ]. In this study, GO analysis suggested that the shared DEGs of PCOS and MDD were associated with signal transduction, brain development, peptidyl-serine phosphorylation, chemical synaptic transmission, and calcium ion binding. KEGG analysis suggested that they were associated with neuroactive ligand-receptor interaction, glutamatergic synapses, phospholipase D (PLD) signaling pathway, and cAMP signaling pathway. This suggests that abnormal neuronal signaling may be the common pathogenesis of PCOS and MDD. Among them, peptidyl-serine phosphorylation is a common mechanism for many signaling pathways. In the nervous system, peptidyl-serine phosphorylation is essential for processes such as synaptic plasticity and neurotransmission. Serine residue phosphorylation events regulate the activity of synaptic proteins, ion channels and receptors, and affect the strength and efficacy of neuronal signaling [ 23 ]. In neurons, calcium ions play a crucial role in the release of neurotransmitters from synaptic vesicles. When an action potential reaches the presynaptic terminal, voltage-gated calcium channels open, allowing calcium to enter the cell. This calcium influx triggers the fusion of the synaptic vesicle with the presynaptic membrane, leading to the release of neurotransmitters into the synaptic gap. Glutamatergic synapses are an important part of the nervous system, transmitting excitatory signals through the release of the neurotransmitter glutamate, and they are at the core of many cognitive processes [ 24 – 25 ]. the PLD signaling pathway can also affect intracellular calcium levels, which in turn affects neurotransmitter release and influences neuronal signaling [ 26 ]. cAMP regulates the release of neurotransmitters from the neuron and affects synaptic transmission [ 27 ]. In this study, six hub genes were identified through the construction of PPI network. Among them, GRIN1 is a gene encoding the subunit of n -methyl- d -aspartate (NMDA) receptor. NMDA receptor is an ionophilic glutamate receptor, which plays a crucial role in synaptic plasticity, learning, memory, and a variety of neurological processes. Dysregulation of NMDA receptor is closely related to MDD. The glutamatergic hypothesis of depression suggests that abnormalities in glutamatergic neurotransmission are associated with the pathophysiology of MDD. This hypothesis suggests that glutamate release, uptake, and receptor binding may be altered in depressed patients [ 28 ]. CNR1 is a gene that encodes a G-protein-coupled receptor known as CB1 (Cannabinoid Receptor Type 1). The CB1 receptor is found primarily in the central nervous system (CNS) and regulates the release of neurotransmitters, including glutamate, gamma -aminobutyric acid (GABA), and dopamine. This regulation affects neuronal communication and influences mood, cognition, and behavior.Activation of CB1 receptors can stimulate appetite, and obesity, which is often associated with patients with PCOS, may be related to this [ 29 ]. DNM1, a gene encoding dynamin 1, is particularly important in neurons, and plays a key role in synaptic vesicle cycling and neurotransmitter release [ 30 ]. Dysregulation of dynamin 1 may have important neurological implications, and its study has been an active area of research in neuroscience. SYNJ1 is a gene that encodes the Synaptojanin 1 protein. Synaptojanin 1 is an essential enzyme found primarily in neurons and plays a crucial role in synaptic vesicle cycling, which is particularly important for efficient neurotransmission and synaptic plasticity in particular [ 31 ]. EphB2 is a type of receptor tyrosine kinase that acts as a cell surface receptor and plays a crucial role in a variety of biological processes, especially in the development and regulation of the nervous system. In the adult nervous system, EphB2 is involved in the regulation of synaptic plasticity [ 32 ]. The relationship between energy metabolism and the pathogenesis of PCOS and MDD Metabolic dysregulation plays an important role in PCOS. Among them, insulin resistance, a hallmark of PCOS, is closely related to energy metabolism. Insulin is a key regulator of cellular uptake and utilization of glucose for energy, and high insulin levels increase fat storage, especially in the abdominal region, and promote weight gain. And obesity further exacerbates metabolic problems. Excess body fat, especially abdominal fat, leads to insulin resistance, which further disrupts energy metabolism. Obesity increases the risk of developing type 2 diabetes, cardiovascular disease and other metabolic complications. In addition, several studies have suggested that PCOS may be associated with alterations in basal metabolic rate (BMR), which is often decreased in patients with PCOS, and this is particularly evident in PCOS patients with comorbid insulin resistance [ 33 ]. BMR is the rate at which the body burns energy at rest.Any change in BMR affects energy expenditure. The relationship between MDD and energy metabolism is a complex bidirectional one, and there is evidence that depression affects energy metabolism, and disruption of energy metabolism is likewise an important pathogenesis of MDD [ 34 ]. MDD leads to changes in appetite and these changes affect energy balance and metabolism. Studies have shown that people with MDD are more likely to develop insulin resistance, which can affect glucose metabolism and energy utilization [ 35 ]. In this study, GO analysis suggested that the shared DEGs in PCOS and MDD were associated with ATP binding, GTPase activator activity, and guanyl-nucleotide exchange factor activity, and KEGG analysis suggested that they were associated with the cAMP signaling pathway. This suggests that the pathogenesis of PCOS and MDD is closely related to the dysregulation of energy metabolism. ATP is often referred to as the cell's "energy currency" because it is the main source of energy for many cellular processes. ATP is closely related to cellular energy metabolism. It serves as a substrate in various metabolic pathways in which enzymes bind to ATP to catalyze reactions that require energy input. GTP is a molecule that serves as the primary source of energy in the cell and, like ATP, is capable of storing and transferring energy. The high-energy phosphate bonds in GTP can be hydrolyzed to release the energy needed to drive a wide variety of cellular processes. GTPases are a group of proteins that can bind and hydrolyze GTP to act as molecular switches in a variety of cellular processes. GTPase activators act to regulate the activation and inactivation of GTPases, controlling the timing and duration of their signaling functions. Guanyl-nucleotide exchange factor (GEF) activates GTPases by facilitating the exchange of GDP for GTP on GTPases. GTPases are a family of proteins that act as molecular switches, cycling between an inactive GTP-bound state and an active GTP-bound state, thereby influencing energy metabolic processes. cAMP can influence glucose metabolism by regulating glycogenolysis and gluconeogenesis in the liver. Among the hub genes identified in this study, CNR1 is mainly expressed in the brain, but they are also expressed in peripheral tissues. CNR1 in peripheral tissues is known to play a role in the regulation of energy homeostasis and lipid metabolism [ 36 ]. Dysregulation of these processes may be associated with the development of PCOS. PLA2G4A, also known as phospholipase A2 group IVA, is a gene that encodes for an enzyme called cytoplasmic phospholipase A2- α (cPLA2α). This enzyme belongs to the phospholipase A2 (PLA2) family and plays a central role in lipid metabolism, especially in the hydrolysis of phospholipids. cPLA2α is involved in the release of fatty acids and bioactive lipids, which has a variety of physiological and pathological implications [ 37 ]. In this study, Hsa-miR-27a was found to be a strongly associated miRNA with the pathogenesis of PCOS and MDD. Hsa-miR-27a is a member of the microRNA family and plays a role in the post-transcriptional regulation of gene expression. Hsa-miR-27a is known to play a role in adipogenesis. It inhibits the expression of genes that promote adipocyte differentiation and is involved in the regulation of lipid metabolism. Dysregulation of hsa-miR-27a in adipose tissue is associated with obesity and metabolic disorders [ 38 ]. In addition, studies have shown that Hsa-miR-27a dysregulation can lead to insulin resistance [ 39 ]. Therefore, hsa-miR-27a dysregulation may ultimately contribute to the development of PCOS and MDD by affecting energy metabolism. The relationship between inflammatory response and the pathogenesis of PCOS and MDD A growing body of research suggests that inflammatory response is associated with PCOS [ 40 ]. Patients with PCOS typically exhibit chronic low-grade inflammation with elevated levels of inflammatory markers such as c-reactive protein (CRP) and proinflammatory cytokines. This inflammation is thought to be associated with insulin resistance and obesity, both of which are common features of PCOS. Immune cells play a role in this chronic inflammatory response. Studies have shown that immune cells can infiltrate adipose tissue in patients with PCOS. This infiltration may lead to localized inflammation in adipose tissue, which in turn affects insulin sensitivity and hormone regulation [ 41 ]. Insulin resistance is a hallmark of PCOS. It is believed that insulin resistance and hyperinsulinemia may trigger an inflammatory response and affect immune cell function. Chronic hyperinsulinemia may lead to the release of pro-inflammatory factors and contribute to inflammation in polycystic ovary syndrome [ 42 ]. The relationship between depression and inflammatory response is bidirectional. A large body of evidence suggests that depression can lead to inflammatory response. Conversely, the inflammatory response may also contribute to the development of depression [ 43 – 44 ]. Elevated levels of pro-inflammatory cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), have been observed in patients with MDD. This suggests a link between depression and inflammation. Chronic low-grade inflammation may lead to depressive symptoms. In this study, KEGG analysis suggested that the pathogenesis of PCOS with MDD is associated with the cAMP signaling pathway. Studies have shown that the cAMP signaling pathway is involved in inflammatory response [ 45 ]. As one of the pivotal genes identified in this study, PLA2G4A is involved in the inflammatory process, promoting the release of arachidonic acid and its subsequent conversion to inflammatory mediators, thereby contributing to the initiation and maintenance of inflammation in various tissues. TFAP2A was identified in this study as a strongly associated TF associated with the pathogenesis of PCOS and MDD. TFAP2A was found to play a role in the inflammatory response, affecting the expression of genes related to immune cell function [ 46 ]. Prediction of potential drug molecules In this study, orlistat, DHA, capsaicin and myo-inositol were identified as potential drugs for the treatment of PCOS and MDD. Among them, orlistat blocks pancreatic lipase activity and reduces the digestion and absorption of dietary fat. The prevalence of obesity in PCOS patients is about 64% [ 47 ]. Weight loss can improve insulin sensitivity, regulate the menstrual cycle, and lower androgen levels in some women with PCOS. Currently, oral orlistat has become an effective treatment for weight management in PCOS [ 13 ]. However, little has been reported about orlistat for MDD, and we look forward to future studies related to it. DHA is an omega-3 fatty acid that is an important part of the human diet and plays a vital role in maintaining overall health. DHA is highly concentrated in the gray matter and synaptic membranes of the brain, which is important for nerve cell signaling and overall cognitive function [ 48 ]. DHA may affect the production and function of neurotransmitters such as serotonin and dopamine, which play a crucial role. Although the exact mechanisms are not fully understood, DHA may help maintain the balance of these neurotransmitters in the brain. DHA may provide neuroprotection by enhancing the brain's ability to repair and maintain neuronal structure. These neuroprotective properties may be beneficial in the treatment of MDD. Currently, DHA is often used as an adjunct to conventional treatments for depression, such as psychotherapy or antidepressant medications. When used in combination with these treatments, DHA may increase its effectiveness. A meta-analysis showed that DHA was effective in improving several symptoms associated with depression, making it a beneficial complementary therapeutic agent [ 49 ]. DHA may also help reduce triglyceride levels and improve lipid metabolism. In addition, DHA has anti-inflammatory properties. It may help regulate the body's inflammatory response, which is important for immune function and reducing the risk of chronic inflammation [ 50 ]. By reducing inflammation, DHA may help reduce some of the inflammatory symptoms associated with PCOS and MDD. Studies have shown that DHA may be used as a raw material for the synthesis of specialized pro-resolving mediators (SPMs), which are involved in the repair of chronic inflammation due to PCOS [ 51 ]. PCOS is often associated with insulin resistance, which leads to elevated insulin levels and an increased risk of developing type 2 diabetes. DHA has been shown to improve insulin sensitivity. By improving insulin sensitivity, DHA may help improve metabolism and ovulation in PCOS patients [ 52 ]. Capsaicin is a lipophilic compound. Studies have shown that capsaicin may improve lipid metabolism by activating the TRPV1 receptor, thereby improving weight loss, which is beneficial in ameliorating obesity associated with PCOS [ 53 ]. Studies have shown that capsaicin may inhibit the production of pro-inflammatory cytokines and chemokines, potentially reducing inflammatory response [ 54 ]. Additionally, capsaicin promotes vasodilation, which helps to improve blood flow and reduce tissue inflammation. Therefore, capsaicin may treat PCOS and MDD by improving the inflammatory state of the body. Myo-inositol is a naturally occurring compound that plays an important role in various biological processes in the human body. Myo-inositol has been studied for the treatment of PCOS. Some studies have suggested that myo-inositol may help to reduce androgen levels, thereby improving symptoms such as hirsuteness and hair loss in PCOS, and improving follicle quality to increase the likelihood of successful fertilization and healthy pregnancies [ 55 ]. In addition, some studies have shown that myo-inositol as an insulin sensitizer can improve insulin resistance and hyperinsulinemia in patients with PCOS, which in turn improves the endocrine and metabolic conditions of patients with PCOS, promotes the maturation of oocytes in patients with PCOS, and improves the quality of embryos, etc [ 56 – 57 ]. There is evidence that myo-inositol may play a role in controlling depression [ 58 ]. Myo-inositol is involved in a variety of cellular processes, including neurotransmitter signaling in the brain. Several studies have suggested that myo-inositol supplementation may help regulate neurotransmitters, thereby improving symptoms associated with depression [ 59 ]. In addition, myo-inositol has antioxidant properties, which help to reduce oxidative stress in the body and improve the chronic inflammatory state, thereby treating PCOS and MDD [ 60 ]. Studies have shown that myo-inositol can effectively improve the psychological state of patients with PCOS combined with depression, and can be used as a first-line medication to improve depressive symptoms in PCOS patients [ 61 ]. Conclusion Our study identified a TF-mRNA-miRNA regulatory network that may be associated with PCOS and MDD. Six hub genes (including GRIN1, CNR1, DNM1, SYNJ1, PLA2G4A, and EPHB2) were identified as hub genes that are strongly associated with the pathogenesis of PCOS and MDD. TFAP2A was identified as a strongly associated TF. Hsa-miR-27a was identified as a strongly associated miRNA. Abnormal neuronal signaling, disturbed energy metabolism and inflammatory response may be the common pathogenesis of PCOS and MDD. Based on this, we identified potential drug molecules for the treatment of PCOS and MDD, namely: orlistat, DHA, capsaicin and myo-inositol. These findings may contribute to the development of early diagnostic strategies, prognostic markers and new therapeutic agents. Declarations Funding: National Natural Science Foundation of China (81674011); Major Research Project of Science and Technology Innovation Project of China Academy of Traditional Chinese Medicine (CI2021A02404). Author Contribution Zheng Zheng wrote the main manuscript text, Xinmin Liu reviewed the manuscript and Yuxing Wang prepared figures 1-5. All authors reviewed the manuscript. References Sadeghi HM, Adeli I, Calina D, et al. Polycystic Ovary Syndrome: A Comprehensive Review of Pathogenesis, Management, and Drug Repurposing. Int J Mol Sci. 2022;23(2):583. Azziz R. Polycystic Ovary Syndrome. Obstet Gynecol. 2018;132(2):321–36. Depression. (2020). Accessed: May 3, 2020: https://www.who.int/news-room/fact-sheets/detail/depression . Kuehner C. Gender differences in unipolar depression: an update of epidemiological findings and possible explanations. Acta Psychiatr Scand. 2003;108(3):163–74. Malhi GS, Mann JJ, Depression. Lancet. 2018;392(10161):2299–312. Cesta CE, Kuja-Halkola R, Lehto K, et al. Polycystic ovary syndrome, personality, and depression: A twin study. Psychoneuroendocrinology. 2017;85:63–8. Ramly B, Afiqah-Aleng N, Mohamed-Hussein ZA. Protein-Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome. Int J Mol Sci. 2019;20(12):2959. Lin H, Liu M, Zhong D, et al. The prevalence and factors associated with anxiety-like and depression-like behaviors in women with polycystic ovary syndrome[J]. Front Psychiatry. 2021;12:709674. Wang Y, Ni Z, Li K. The prevalence of anxiety and depression of different severity in women with polycystic ovary syndrome: a meta-analysis[J]. Gynecol Endocrinol. 2021;37(12):1072–8. Lee IO, Kim JC, Seo JW, et al. Risk of developing major depressive disorder in polycystic ovary syndrome: a retrospective cohort study. J Obstet Gynaecol. 2021;41(7):1157–61. Rodriguez-Paris D, Remlinger-Molenda A, Kurzawa R, et al. Psychiatric disorders in women with polycystic ovary syndrome. Psychiatr Pol. 2019;53(4):955–66. Ethirajulu A, Alkasabera A, Onyali CB, et al. Insulin Resistance, Hyperandrogenism, and Its Associated Symptoms Are the Precipitating Factors for Depression in Women With Polycystic Ovarian Syndrome. Cureus. 2021;13(9):e18013. Ozgen Saydam B, Yildiz BO. Weight management strategies for patients with PCOS: current perspectives. Expert Rev Endocrinol Metab. 2021;16(2):49–62. Lu L, Li X, Lv L, et al. Dietary and serum n-3 PUFA and polycystic ovary syndrome: a matched case-control study. Br J Nutr. 2022;128(1):114–23. Zheng J, Zheng S, Feng Q, et al. Dietary capsaicin and its anti-obesity potency: from mechanism to clinical implications. Biosci Rep. 2017;37(3):BSR20170286. Genazzani AD. Inositol as putative integrative treatment for PCOS. Reprod Biomed Online. 2016;33(6):770–80. Brutocao C, Zaiem F, Alsawas M, et al. Psychiatric disorders in women with polycystic ovary syndrome: a systematic review and meta-analysis. Endocrine. 2018;62(2):318–25. Vishnubhotla DS, Tenali SN, Fernandez M, et al. Evaluation of Prevalence of PCOS and Associated Depression, Nutrition, and Family History: A Questionnaire-based Assessment. Indian J Endocrinol Metab. 2022 Jul-Aug;26(4):341–7. Kolhe JV, Chhipa AS, Butani S, et al. PCOS and Depression: Common Links and Potential Targets. Reprod Sci. 2022;29(11):3106–23. Xing L, Xu J, Wei Y, et al. Depression in polycystic ovary syndrome: Focusing on pathogenesis and treatment. Front Psychiatry. 2022;13:1001484. Gonzalez D, Maidana P, Ibar C, et al. Hair cortisol in polycystic ovary syndrome. Sci Rep. 2022;12(1):10309. Nanou E, Catterall WA. Calcium Channels, Synaptic Plasticity, and Neuropsychiatric Disease. Neuron. 2018;98(3):466–81. Gomez-Castro F, Zappettini S, Pressey JC, et al. Convergence of adenosine and GABA signaling for synapse stabilization during development. Science. 2021;374(6568):eabk2055. Stepan J, Heinz DE, Dethloff F, et al. Hippo-released WWC1 facilitates AMPA receptor regulatory complexes for hippocampal learning. Cell Rep. 2022;41(10):111766. Hansen KB, Wollmuth LP, Bowie D, et al. Structure, Function, and Pharmacology of Glutamate Receptor Ion Channels. Pharmacol Rev. 2021;73(4):298–487. Sousa-Valente J, Varga A, Torres-Perez JV, et al. Inflammation of peripheral tissues and injury to peripheral nerves induce differing effects in the expression of the calcium-sensitive N-arachydonoylethanolamine-synthesizing enzyme and related molecules in rat primary sensory neurons. J Comp Neurol. 2017;525(8):1778–96. Yang L. Neuronal cAMP/PKA Signaling and Energy Homeostasis. Adv Exp Med Biol. 2018;1090:31–48. Wang HQ, Wang ZZ, Chen NH. The receptor hypothesis and the pathogenesis of depression: Genetic bases and biological correlates. Pharmacol Res. 2021;167:105542. Zagzoog A, Mohamed KA, Kim HJJ, et al. In vitro and in vivo pharmacological activity of minor cannabinoids isolated from Cannabis sativa. Sci Rep. 2020;10(1):20405. Bragato C, Pistocchi A, Bellipanni G, et al. Zebrafish dnm1a gene plays a role in the formation of axons and synapses in the nervous tissue. J Neurosci Res. 2023;101(8):1345–59. Al Zaabi N, Al Menhali N, Al-Jasmi F. SYNJ1 gene associated with neonatal onset of neurodegenerative disorder and intractable seizure. Mol Genet Genomic Med. 2018;6(1):109–13. Yuan L, Yi W, Sun C, et al. EphB2 activates CREB-dependent expression of Annexin A1 to regulate dendritic spine morphogenesis. Biochem Biophys Res Commun. 2021;584:107–15. Georgopoulos NA, Saltamavros AD, Vervita V, et al. Basal metabolic rate is decreased in women with polycystic ovary syndrome and biochemical hyperandrogenemia and is associated with insulin resistance. Fertil Steril. 2009;92(1):250–5. Gu X, Ke S, Wang Q, et al. Energy metabolism in major depressive disorder: Recent advances from omics technologies and imaging. Biomed Pharmacother. 2021;141:111869. Fernandes BS, Salagre E, Enduru N, et al. Insulin resistance in depression: A large meta-analysis of metabolic parameters and variation. Neurosci Biobehav Rev. 2022;139:104758. Liu LY, Alexa K, Cortes M, et al. Cannabinoid receptor signaling regulates liver development and metabolism. Development. 2016;143(4):609–22. Murakami M, Kudo I. Phospholipase A2. J Biochem. 2002;131(3):285–92. Lin XZ, Luo J, Zhang LP, et al. MiR-27a suppresses triglyceride accumulation and affects gene mRNA expression associated with fat metabolism in dairy goat mammary gland epithelial cells. Gene. 2013;521(1):15–23. Chen T, Zhang Y, Liu Y, et al. MiR-27a promotes insulin resistance and mediates glucose metabolism by targeting PPAR-γ-mediated PI3K/AKT signaling. Aging. 2019;11(18):7510–24. Rudnicka E, Suchta K, Grymowicz M, et al. Chronic Low Grade Inflammation in Pathogenesis of PCOS. Int J Mol Sci. 2021;22(7):3789. Rostamtabar M, Esmaeilzadeh S, Tourani M, et al. Pathophysiological roles of chronic low-grade inflammation mediators in polycystic ovary syndrome. J Cell Physiol. 2021;236(2):824–38. Matulewicz N, Karczewska-Kupczewska M. Insulin resistance and chronic inflammation. Postepy Hig Med Dosw (Online). 2016;70(0):1245–58. Beurel E, Toups M, Nemeroff CB. The Bidirectional Relationship of Depression and Inflammation: Double Trouble. Neuron. 2020;107(2):234–56. Cruz-Pereira JS, Rea K, Nolan YM, et al. Depression's Unholy Trinity: Dysregulated Stress, Immunity, and the Microbiome. Annu Rev Psychol. 2020;71:49–78. Tavares LP, Negreiros-Lima GL, Lima KM, et al. Blame the signaling: Role of cAMP for the resolution of inflammation. Pharmacol Res. 2020;159:105030. He J, Cao Q, Feng DD, et al. Transcription factor AP–2α negatively regulates thymic stromal lymphopoietin expression in respiratory syncytial virus infection. Mol Med Rep. 2020;22(2):1639–46. Tay CT, Teede HJ, Hill B, et al. Increased prevalence of eating disorders, low self-esteem, and psychological distress in women with polycystic ovary syndrome: a community-based cohort study[J]. Fertil Steril. 2019;112(2):353–61. Weiser MJ, Butt CM, Mohajeri MH. Docosahexaenoic Acid and Cognition throughout the Lifespan. Nutrients. 2016;8(2):99. Liao Y, Xie B, Zhang H, et al. Efficacy of omega-3 PUFAs in depression: A meta-analysis. Transl Psychiatry. 2019;9(1):190. Zhang TT, Xu J, Wang YM, et al. Health benefits of dietary marine DHA/EPA-enriched glycerophospholipids. Prog Lipid Res. 2019;75:100997. Regidor PA, Mueller A, Sailer M, et al. Chronic Inflammation in PCOS: The Potential Benefits of Specialized Pro-Resolving Lipid Mediators (SPMs) in the Improvement of the Resolutive Response. Int J Mol Sci. 2020;22(1):384. Capel F, Acquaviva C, Pitois E, et al. DHA at nutritional doses restores insulin sensitivity in skeletal muscle by preventing lipotoxicity and inflammation. J Nutr Biochem. 2015;26(9):949–59. Baskaran P, Krishnan V, Ren J, et al. Capsaicin induces browning of white adipose tissue and counters obesity by activating TRPV1 channel-dependent mechanisms. Br J Pharmacol. 2016;173(15):2369–89. Braga Ferreira LG, Faria JV, Dos Santos JPS, et al. Capsaicin: TRPV1-independent mechanisms and novel therapeutic possibilities. Eur J Pharmacol. 2020;887:173356. Pkhaladze L, Russo M, Unfer V, et al. Treatment of lean PCOS teenagers: a follow-up comparison between Myo-Inositol and oral contraceptives. Eur Rev Med Pharmacol Sci. 2021;25(23):7476–85. Cappelli V, Musacchio MC, Bulfoni A, et al. Natural molecules for the therapy of hyperandrogenism and metabolic disorders in PCOS[J]. Eur Rev Med Pharmacol Sci. 2017;21(2 Suppl):15–29. Facchinetti F, Bizzarri M, Benvenga S et al. Results from the international consensus conference on myo-inositol and D-chiro-inositol in obstetrics and gynecology: the link between metabolic syndrome and PCOS[J]. Eur J Obstet Gynecol Reprod Biol, 2015, 195(72–76). Mukai T, Kishi T, Matsuda Y, et al. A meta-analysis of inositol for depression and anxiety disorders. Hum Psychopharmacol. 2014;29(1):55–63. Kiani AK, Paolacci S, Calogero AE, et al. From Myo-inositol to D-chiro-inositol molecular pathways. Eur Rev Med Pharmacol Sci. 2021;25(5):2390–402. Simic D, Nikolic Turnic T, Dimitrijevic A, et al. Potential role of d-chiro-inositol in reducing oxidative stress in the blood of nonobese women with polycystic ovary syndrome. Can J Physiol Pharmacol. 2022;100(7):629–36. Cantelmi T, Lambiase E, Unfer VR, et al. Inositol treatment for psychological symptoms in Polycystic Ovary Syndrome women. Eur Rev Med Pharmacol Sci. 2021;25(5):2383–9. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3704976","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":256166664,"identity":"c83f36ce-3081-4c22-81e3-63bd2fa3f9e6","order_by":0,"name":"Zheng Zheng","email":"","orcid":"","institution":"Guang’anmen Hospital, China Academy of Chinese Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Zheng","middleName":"","lastName":"Zheng","suffix":""},{"id":256166665,"identity":"b1df59d3-6c77-42a9-b961-b4e25fda7a38","order_by":1,"name":"Yuxing Wang","email":"","orcid":"","institution":"The Second Affiliated Hospital of Dalian Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Yuxing","middleName":"","lastName":"Wang","suffix":""},{"id":256166666,"identity":"d9c22d24-d4a1-433f-822d-43959aca2b16","order_by":2,"name":"Xinmin Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACNvbG9h8f/9TI2c8/fIA4LXw8hxskZzYcMzaQYEsgToucRHqDNG8Dc+IGCR4DIh3GkNhgwLuDjXG7dM/HG28Y7OR0GwhqOdiQIHlGhtlyztnNlnMYko3NDhDSwtjYcMCAjY2N4UDuNmkehgOJ2whqYQbqSWBjBirOeUakFjbGZoaDbcwSBjdy2IjUwsPYxthw5piBZM8xY8s5BkT4RX7+82fMfypq6vvZmx/eeFNhJ0dQCwogOmqQtZCqYxSMglEwCkYEAADUuUHmDBKVvQAAAABJRU5ErkJggg==","orcid":"","institution":"Guang’anmen Hospital, China Academy of Chinese Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Xinmin","middleName":"","lastName":"Liu","suffix":""}],"badges":[],"createdAt":"2023-12-04 08:59:26","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3704976/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3704976/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":47718733,"identity":"58fca414-e8db-4bd1-b5a3-dfc918f6de20","added_by":"auto","created_at":"2023-12-06 14:04:43","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":403646,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of shared DEGs by GEO2R. \u003cstrong\u003e(A)\u003c/strong\u003e, The volcano plot of GSE34526 dataset. \u003cstrong\u003e(B)\u003c/strong\u003e, The volcano plot of GSE125664 dataset. \u003cstrong\u003e(C)\u003c/strong\u003e, The venn diagram of DEGs in GSE34526 and GSE125664 datasets. There are also two vertical dashed lines in the figure, representing log2 FC at −1 and 1; The horizontal dashed line represents p value at 0.05. Abbreviations: DEGs, differentially expressed genes; PCOS, Polycystic ovary syndrome; MDD, major depressive disorder.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-3704976/v1/9c27ce59081ac12999c075ab.png"},{"id":47717798,"identity":"7d3272cc-2fb2-46b1-ba70-59fa924870db","added_by":"auto","created_at":"2023-12-06 13:56:43","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":239890,"visible":true,"origin":"","legend":"\u003cp\u003eGO and KEGG analysis for shared DEGs. \u003cstrong\u003e(A)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eEnrichment results of GO biological process analysis. \u003cstrong\u003e(B)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eEnrichment results of GO cell component analysis. \u003cstrong\u003e(C)\u003c/strong\u003e, Enrichment results of GO molecular function analysis. \u003cstrong\u003e(D)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eEnrichment results of KEGG analysis. Abbreviations: GO, Gene Ontology; BP, biological process; CC, cellular component; MF, molecular function; KEGG, Kyoto Encyclopedia of Genes and Genomes.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-3704976/v1/83ca2224d00fc83d94a9c67c.png"},{"id":47718734,"identity":"815d7a6d-6273-41d8-a34d-edf35dd18972","added_by":"auto","created_at":"2023-12-06 14:04:43","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":826122,"visible":true,"origin":"","legend":"\u003cp\u003eIdentification of hub genes. \u003cstrong\u003e(A)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eThe PPI networks of shared DEGs. \u003cstrong\u003e(B)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eGenes with more than 2 times the median number of interactions. \u003cstrong\u003e(C)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eThe top 15 BC-value genes. In Figure 3A, red nodes: genes with more than 2 times the median number of interactions and top 15 BC-value (hub genes); yellow nodes: genes with more than 2 times the median number of interactions or top 15 BC-value; blue nodes: other DEGs.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-3704976/v1/c3559d9872dbc4422dd570c9.png"},{"id":47717799,"identity":"57e3ef28-414a-4ddc-a2f0-ce7386995cb2","added_by":"auto","created_at":"2023-12-06 13:56:43","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":973857,"visible":true,"origin":"","legend":"\u003cp\u003eThe TF-mRNA-miRNA regulatory network of hub genes. Orange nodes: hub genes; yellow nodes: TFs associated with hub genes; purple nodes: miRNAs associated with hub genes. Abbreviations: TFs, transcription factors; miRNAs：microRNAs.\u003c/p\u003e","description":"","filename":"Fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-3704976/v1/003e9fdaaef7cef9971e8ff4.png"},{"id":47717801,"identity":"9a4a7830-e016-407b-9322-08201796eabb","added_by":"auto","created_at":"2023-12-06 13:56:43","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":534611,"visible":true,"origin":"","legend":"\u003cp\u003eBinding energy demonstration of potential drug molecules. \u003cstrong\u003e(A)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eThe heat map of binding energy of potential drug molecules to hub genes. \u003cstrong\u003e(B)\u003c/strong\u003e,\u003cstrong\u003e \u003c/strong\u003eDetailed map of molecular docking of capsaicin with SYNJ1.\u003c/p\u003e","description":"","filename":"Fig5.png","url":"https://assets-eu.researchsquare.com/files/rs-3704976/v1/54f6368f0295be19084d108f.png"},{"id":47936896,"identity":"1808fbfd-5fa7-4d7a-8ef6-f7b8a3ca85d6","added_by":"auto","created_at":"2023-12-10 16:52:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2025591,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3704976/v1/f6d630f9-2cc4-4ce8-8782-fc6657f5c5a1.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of the Shared Gene Signatures and Molecular Mechanisms Between Polycystic Ovarian Syndrome and Major Depressive Disorder: Evidence From Transcriptome Data","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolycystic Ovary Syndrome (PCOS) is a common endocrine disorder that primarily affects ovarian function during the reproductive years and affects about 5\u0026ndash;10% of women. It can occur in people of all ethnicities. The exact etiology of PCOS is unknown, but genetics, insulin resistance, and hormonal imbalances are thought to play significant roles in its development [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The clinical manifestations of PCOS varies from person to person but usually combines the following features: irregular menstrual cycles, ovulatory dysfunction, hyperandrogenism, polycystic ovaries, metabolic disorders and mood disorders. Prolonged PCOS can lead to adverse outcomes such as endometrial cancer, recurrent miscarriages, congenital malformations in offspring, type 2 diabetes, and cardiovascular damage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Therefore, PCOS can have a significant impact on a person's overall health and quality of life, as well as increase the economic burden on the family and society.\u003c/p\u003e \u003cp\u003eMajor depressive disorder (MDD) is a highly prevalent mental health disorder worldwide. According to the World Health Organization (WHO), more than 264\u0026nbsp;million people worldwide suffer from depression, and it is one of the leading causes of disability globally [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. MDD can affect individuals of all ages, but it is more commonly diagnosed in adults and is also more prevalent in women than in men [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. As a complex disorder with multiple underlying causes, MDD is usually caused by a combination of genetic, biological, environmental and psychological factors. It is clinically characterized by persistent and pervasive feelings of sadness, hopelessness, and loss of interest or pleasure in once enjoyable activities. Untreated or poorly managed MDD can have serious and far-reaching consequences, including: impaired daily functioning, increased risk of disease (heart disease, diabetes, and chronic pain conditions, among others), risk of suicide, decreased quality of life, and increased financial burden [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In summary, MDD is a widespread and serious mental health condition with a significant impact on individuals' lives and society as a whole.\u003c/p\u003e \u003cp\u003eThe link between PCOS and MDD has been the subject of research, and to date, more and more studies have confirmed the important link between PCOS and MDD [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Studies have shown that people with PCOS are more likely to have symptoms of depression and anxiety compared to those without PCOS. And the comorbidity of PCOS and depression is more common, with the prevalence of depression in PCOS patients ranging from 42\u0026ndash;52% [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], which is more likely to occur in overweight PCOS patients [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. PCOS is characterized by hormonal imbalances, including elevated androgen levels and insulin resistance. These hormonal changes may lead to mood disorders, which can increase the risk of depression [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Although the relationship between PCOS and MDD has attracted great interest recently, leading to a large number of studies related to this topic, relevant genetic studies are still limited and need to be further explored. Therefore, there is a need for further research into the relationship between PCOS and MDD, as well as potential treatment options.\u003c/p\u003e \u003cp\u003eIn recent years, microarray technology and bioinformatics analysis have been widely used to screen for genetic alterations at the genome level. The GEO database is a public repository for high-throughput gene expression data and other functional genomics datasets. In this study, we completed the collection of shared DEGs for PCOS and MDD based on the GEO database. Subsequently, we explored the molecular mechanisms involved in the identified shared DEGs by GO analysis, KEGG analysis, and performed PPI analysis thus identifying the hub genes. We then predicted TFs and miRNAs strongly associated with the hub genes as well as potential drug molecules and performed molecular docking to validate the predictions. The aim of this study was to identify the hub genes and molecular mechanisms associated with PCOS and MDD and to provide potential clinical treatment options for patients with PCOS combined with MDD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of gene expression profiling data\u003c/h2\u003e \u003cp\u003eWe collected two microarray datasets (GSE34526, GSE125664) containing PCOS, MDD and their control samples from the GEO database. Among them, the GSE34526 dataset includes gene expression profiles of seven PCOS patients and three normal controls; the GSE125664 dataset includes gene expression profiles of three MDD patients and three normal controls.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eSelection of DEGs\u003c/h2\u003e \u003cp\u003eThe DEGs of PCOS and MDD were extracted and analyzed separately using the GEO2R online analysis tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/geo2r/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/geo2r/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and the volcano plots were plotted using the bioinformatics online platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.bioinformatics.com.cn\u003c/span\u003e\u003cspan address=\"https://www.bioinformatics.com.cn\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e ). the thresholds for DEGs screening were∣log2 FC∣\u0026gt; 1.0 and p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. In addition, Wayne plots were plotted using the bioinformatics platform to identify shared DEGs between PCOS and MDD. these shared DEGs were retained for subsequent analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional clustering and pathway enrichment of DEGs\u003c/h2\u003e \u003cp\u003eGO functional enrichment analysis, including biological process (BP), cellular component (CC) and molecular function (MF), and KEGG signaling pathway enrichment analysis were performed on the above shared DEGs. The enriched GO terms and KEGG pathways with p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eEstablishment of PPI network and identification of hub genes\u003c/h2\u003e \u003cp\u003eTo further explore the interactions between the shared DEGs obtained above, the Search Tool for Retrieving Gene Interactions (STRING) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to construct the PPI network. Subsequently, the PPI network was visualized using Cytoscape software. Then, the betweenness centrality (BC) of the genes was calculated by applying the cytoNCA plug-in, and the genes with the top 15 BC values in the PPI network were screened, and the intersection was taken with the genes with greater than 2 times the median number of interactions to obtain the genes, which were the hub genes in this study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eDetermine the TFs, miRNAs associated with the hub genes\u003c/h2\u003e \u003cp\u003eNetworkAnalyst (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.networkanalyst.ca/\u003c/span\u003e\u003cspan address=\"https://www.networkanalyst.ca/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to find TFs and miRNAs associated with hub genes. Finally, the TF-mRNA-miRNA regulatory network was mapped by Cytoscape software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of potential drugs and molecular docking\u003c/h2\u003e \u003cp\u003eDSigDB (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dsigdb.tanlab.org/DSigDBv1.0/\u003c/span\u003e\u003cspan address=\"http://dsigdb.tanlab.org/DSigDBv1.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) is mainly used to study the mechanism of action of drugs and discover new drug targets. Hub genes were uploaded into the DSigDB database to find potential drug molecules targeting these genes for the treatment of PCOS and MDD. Drug molecules with p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and composite score\u0026thinsp;\u0026gt;\u0026thinsp;500 were selected and combined with relevant previous studies to finalize the potential drug molecules. Next, to explore the binding of the potential drug molecules to the proteins expressed by the hub genes, we obtained the 3D structures of the drug molecules from PubChem (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://pubchem.ncbi.nlm.nih.gov/\u003c/span\u003e\u003cspan address=\"https://pubchem.ncbi.nlm.nih.gov/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), retrieved the target proteins from PDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rcsb.org/\u003c/span\u003e\u003cspan address=\"https://www.rcsb.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) crystal structure, and then molecular docking was performed using AutoDock Tools software. The results of molecular docking were visualized using PyMol software.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of DEGs in PCOS and MDD\u003c/h2\u003e \u003cp\u003eWe downloaded the GSE34526 series dataset on PCOS and the GSE125664 series dataset on MDD from the GEO database. After screening with p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and ∣log2 FC∣\u0026gt; 1.0, 3003 DEGs were identified in the GSE34526 dataset, of which, 2192 were up-regulated and 811 were down-regulated, and 2448 DEGs were identified in the GSE125664 dataset, of which, 1702 were up-regulated and 746 were down-regulated. The DEGs in the two datasets were presented by drawing volcano plots, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB, A Venn Diagram analysis was performed to assess the common DEGs between GSE34526 and GSE125664. 158 shared DEGs were found as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC. of which, 125 were up-regulated and 33 were down-regulated.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGO and KEGG analysis\u003c/h2\u003e \u003cp\u003eGO and KEGG analyses were performed using the DAVID database to better understand the biological functions of the identified DEGs.\u003c/p\u003e \u003cp\u003eAfter filtering with a threshold of p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, we selected the top five significantly enriched GO terms and the top five KEGG terms.The results showed that in terms of biological processes, DEGs were mainly enriched in signal transduction, actin cytoskeleton organization, brain development, peptidyl-serine phosphorylation, and chemical synaptic transmission (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In terms of cellular components, DEGs were mainly associated with the cytoplasm, plasma membrane, cytosol, integral component of plasma membrane, and Golgi apparatus (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Molecular functional analysis showed that DEGs were significantly enriched in ATP binding, calcium ion binding, GTPase activator activity, actin binding, and guanyl-nucleotide exchange factor activity (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). The top five important KEGG pathways of DEGs were enriched in neuroactive ligand-receptor interaction, endocytosis, glutamatergic synapse, phospholipase D signaling pathway, and cAMP signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eConstruction of PPI network and identification of hub genes\u003c/h2\u003e \u003cp\u003eThe construction of the PPI network was first performed based on the STRING database to identify the interactions between the shared DEGs. Then, the obtained results were imported into Cytoscape software for visualization and analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). And genes with greater than 2-fold median interaction number were shown (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The PPI network was analyzed by the Cytoscape plug-in cytoNCA, and the genes with the top 15 BC values were filtered as the candidate hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). By taking the intersection of the two, six genes were identified as hub genes, namely, GRIN1, CNR1, DNM1, SYNJ1, PLA2G4A and EPHB2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eprediction of related TFs, miRNAs and construction of TF-mRNA-miRNA regulatory network\u003c/h2\u003e \u003cp\u003eNetworkAnalyst database was used to predict the related TFs and miRNAs of the hub genes.\u003c/p\u003e \u003cp\u003eWe used Cytoscape software to construct the TF-mRNA-miRNA regulatory network, which consists of 180 nodes and 216 edges containing 49 TFs with 125 miRNAs (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBased on the cytoHubba plug-in in Cytoscape software, the most strongly associated TFs and miRNAs were selected based on the Maximal Clique Centrality (MCC) score. Hsa-miR-27a was the highest-scoring miRNA (MCC score of 3); TFAP2A was the highest-scoring TF ( MCC score of 3), and the rest of TFs and miRNAs had MCC scores of 2 and below, so they were not included.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of potential drug molecules\u003c/h2\u003e \u003cp\u003eThe identified hub genes for PCOS and MDD were uploaded to the DSigDB database, which provides a list of potential drug molecules targeting the genes. Drug molecules with a composite score\u0026thinsp;\u0026gt;\u0026thinsp;500 were screened using a threshold of p value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Four potential drug molecules were screened after manual deletion of duplicates, and review of relevant prior studies [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. These drug molecules were: orlistat (composite score: 2284.21), docosahexaenoic acid (DHA) (composite score: 1882.08), capsaicin (composite score: 1077.01) and myo-inositol (composite score: 792.97).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eMolecular docking analysis\u003c/h2\u003e \u003cp\u003eMolecular docking was performed to assess the binding affinity of the four drug molecules to the six hub genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA). Lower free binding energy indicated stronger binding ability. Among them, Capsaicin had the lowest free binding energy of -7.2 kcal/mol with SYNJ1, and the docking details were visualized by PyMol software, which revealed that Capsaicin could form a hydrogen bond with amino acid residue ARG-717 of SYNJ1 at a distance of 2.2 \u0026Aring;, and amino acid residue ARG-819 at a distance of two hydrogen bonds with distances of 2.5 \u0026Aring; and 2.0 \u0026Aring;, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eNowadays, more and more studies confirm the correlation between PCOS and MDD. Studies have shown that people with PCOS are more likely to develop symptoms related to depression and anxiety compared to non-PCOS people [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Some studies have claimed that the risk of depression may be approximately two to three times higher in people with PCOS compared to non-PCOS populations [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. PCOS is characterized by hormonal imbalances, including elevated androgen levels and insulin resistance. It is hypothesized that these hormonal changes play an important role in the development of mood disorders, including depression. Androgens, such as testosterone, may affect mood and behavior, and elevated androgen levels are common in patients with PCOS [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Several studies have explored the role of chronic inflammation in PCOS and depression. Chronic low-grade inflammation has been associated with mood disorders, and in some cases, PCOS has been associated with increased markers of inflammation [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. To date, the mechanisms linking PCOS and MDD are not fully understood. Therefore, exploring the molecular mechanisms between PCOS and MDD for early identification and intervention is undoubtedly of great clinical significance. In this study, we performed a series of bioinformatics analysis on two independent gene chip databases for PCOS and MDD, and obtained 158 shared DEGs between PCOS and MDD based on the GEO database. We further analyzed these DEGs, and found that they were mainly closely associated with neuronal signaling, energy metabolism, and inflammatory response.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between neuronal signaling and the pathogenesis of PCOS and MDD\u003c/h2\u003e \u003cp\u003eThe endocrine system and the nervous system are interconnected and there is crosstalk between them. PCOS mainly affects the endocrine and reproductive systems, but it may also affect the nervous system, such as the regulation of neurotransmitters, which affects mood and behavior. In addition, the chronic stress state in which PCOS patients live can activate the hypothalamic-pituitary-adrenal (HPA) axis, thus affecting neural signaling [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. MDD, which is mainly characterized by mood disorders, is nowadays more and more understood as a disorder with an important neurobiological basis. In MDD, neuroplasticity may be impaired, thereby affecting neural signaling and leading to the persistence of depressive symptoms. In addition, MDD is associated with imbalances in certain neurotransmitters, and these imbalances can affect neural signaling, leading to disruptions in mood regulation [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, GO analysis suggested that the shared DEGs of PCOS and MDD were associated with signal transduction, brain development, peptidyl-serine phosphorylation, chemical synaptic transmission, and calcium ion binding. KEGG analysis suggested that they were associated with neuroactive ligand-receptor interaction, glutamatergic synapses, phospholipase D (PLD) signaling pathway, and cAMP signaling pathway. This suggests that abnormal neuronal signaling may be the common pathogenesis of PCOS and MDD. Among them, peptidyl-serine phosphorylation is a common mechanism for many signaling pathways. In the nervous system, peptidyl-serine phosphorylation is essential for processes such as synaptic plasticity and neurotransmission. Serine residue phosphorylation events regulate the activity of synaptic proteins, ion channels and receptors, and affect the strength and efficacy of neuronal signaling [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. In neurons, calcium ions play a crucial role in the release of neurotransmitters from synaptic vesicles. When an action potential reaches the presynaptic terminal, voltage-gated calcium channels open, allowing calcium to enter the cell. This calcium influx triggers the fusion of the synaptic vesicle with the presynaptic membrane, leading to the release of neurotransmitters into the synaptic gap. Glutamatergic synapses are an important part of the nervous system, transmitting excitatory signals through the release of the neurotransmitter glutamate, and they are at the core of many cognitive processes [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. the PLD signaling pathway can also affect intracellular calcium levels, which in turn affects neurotransmitter release and influences neuronal signaling [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. cAMP regulates the release of neurotransmitters from the neuron and affects synaptic transmission [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, six hub genes were identified through the construction of PPI network. Among them, GRIN1 is a gene encoding the subunit of n -methyl- d -aspartate (NMDA) receptor. NMDA receptor is an ionophilic glutamate receptor, which plays a crucial role in synaptic plasticity, learning, memory, and a variety of neurological processes. Dysregulation of NMDA receptor is closely related to MDD. The glutamatergic hypothesis of depression suggests that abnormalities in glutamatergic neurotransmission are associated with the pathophysiology of MDD. This hypothesis suggests that glutamate release, uptake, and receptor binding may be altered in depressed patients [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. CNR1 is a gene that encodes a G-protein-coupled receptor known as CB1 (Cannabinoid Receptor Type 1). The CB1 receptor is found primarily in the central nervous system (CNS) and regulates the release of neurotransmitters, including glutamate, gamma -aminobutyric acid (GABA), and dopamine. This regulation affects neuronal communication and influences mood, cognition, and behavior.Activation of CB1 receptors can stimulate appetite, and obesity, which is often associated with patients with PCOS, may be related to this [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. DNM1, a gene encoding dynamin 1, is particularly important in neurons, and plays a key role in synaptic vesicle cycling and neurotransmitter release [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Dysregulation of dynamin 1 may have important neurological implications, and its study has been an active area of research in neuroscience. SYNJ1 is a gene that encodes the Synaptojanin 1 protein. Synaptojanin 1 is an essential enzyme found primarily in neurons and plays a crucial role in synaptic vesicle cycling, which is particularly important for efficient neurotransmission and synaptic plasticity in particular [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. EphB2 is a type of receptor tyrosine kinase that acts as a cell surface receptor and plays a crucial role in a variety of biological processes, especially in the development and regulation of the nervous system. In the adult nervous system, EphB2 is involved in the regulation of synaptic plasticity [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eThe relationship between energy metabolism and the pathogenesis of PCOS and MDD\u003c/h2\u003e \u003cp\u003eMetabolic dysregulation plays an important role in PCOS. Among them, insulin resistance, a hallmark of PCOS, is closely related to energy metabolism. Insulin is a key regulator of cellular uptake and utilization of glucose for energy, and high insulin levels increase fat storage, especially in the abdominal region, and promote weight gain. And obesity further exacerbates metabolic problems. Excess body fat, especially abdominal fat, leads to insulin resistance, which further disrupts energy metabolism. Obesity increases the risk of developing type 2 diabetes, cardiovascular disease and other metabolic complications. In addition, several studies have suggested that PCOS may be associated with alterations in basal metabolic rate (BMR), which is often decreased in patients with PCOS, and this is particularly evident in PCOS patients with comorbid insulin resistance [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. BMR is the rate at which the body burns energy at rest.Any change in BMR affects energy expenditure. The relationship between MDD and energy metabolism is a complex bidirectional one, and there is evidence that depression affects energy metabolism, and disruption of energy metabolism is likewise an important pathogenesis of MDD [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. MDD leads to changes in appetite and these changes affect energy balance and metabolism. Studies have shown that people with MDD are more likely to develop insulin resistance, which can affect glucose metabolism and energy utilization [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, GO analysis suggested that the shared DEGs in PCOS and MDD were associated with ATP binding, GTPase activator activity, and guanyl-nucleotide exchange factor activity, and KEGG analysis suggested that they were associated with the cAMP signaling pathway. This suggests that the pathogenesis of PCOS and MDD is closely related to the dysregulation of energy metabolism. ATP is often referred to as the cell's \"energy currency\" because it is the main source of energy for many cellular processes. ATP is closely related to cellular energy metabolism. It serves as a substrate in various metabolic pathways in which enzymes bind to ATP to catalyze reactions that require energy input. GTP is a molecule that serves as the primary source of energy in the cell and, like ATP, is capable of storing and transferring energy. The high-energy phosphate bonds in GTP can be hydrolyzed to release the energy needed to drive a wide variety of cellular processes. GTPases are a group of proteins that can bind and hydrolyze GTP to act as molecular switches in a variety of cellular processes. GTPase activators act to regulate the activation and inactivation of GTPases, controlling the timing and duration of their signaling functions. Guanyl-nucleotide exchange factor (GEF) activates GTPases by facilitating the exchange of GDP for GTP on GTPases. GTPases are a family of proteins that act as molecular switches, cycling between an inactive GTP-bound state and an active GTP-bound state, thereby influencing energy metabolic processes. cAMP can influence glucose metabolism by regulating glycogenolysis and gluconeogenesis in the liver.\u003c/p\u003e \u003cp\u003eAmong the hub genes identified in this study, CNR1 is mainly expressed in the brain, but they are also expressed in peripheral tissues. CNR1 in peripheral tissues is known to play a role in the regulation of energy homeostasis and lipid metabolism [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Dysregulation of these processes may be associated with the development of PCOS. PLA2G4A, also known as phospholipase A2 group IVA, is a gene that encodes for an enzyme called cytoplasmic phospholipase A2- α (cPLA2α). This enzyme belongs to the phospholipase A2 (PLA2) family and plays a central role in lipid metabolism, especially in the hydrolysis of phospholipids. cPLA2α is involved in the release of fatty acids and bioactive lipids, which has a variety of physiological and pathological implications [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this study, Hsa-miR-27a was found to be a strongly associated miRNA with the pathogenesis of PCOS and MDD. Hsa-miR-27a is a member of the microRNA family and plays a role in the post-transcriptional regulation of gene expression. Hsa-miR-27a is known to play a role in adipogenesis. It inhibits the expression of genes that promote adipocyte differentiation and is involved in the regulation of lipid metabolism. Dysregulation of hsa-miR-27a in adipose tissue is associated with obesity and metabolic disorders [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In addition, studies have shown that Hsa-miR-27a dysregulation can lead to insulin resistance [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Therefore, hsa-miR-27a dysregulation may ultimately contribute to the development of PCOS and MDD by affecting energy metabolism.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eThe relationship between inflammatory response and the pathogenesis of PCOS and MDD\u003c/h2\u003e \u003cp\u003eA growing body of research suggests that inflammatory response is associated with PCOS [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Patients with PCOS typically exhibit chronic low-grade inflammation with elevated levels of inflammatory markers such as c-reactive protein (CRP) and proinflammatory cytokines. This inflammation is thought to be associated with insulin resistance and obesity, both of which are common features of PCOS. Immune cells play a role in this chronic inflammatory response. Studies have shown that immune cells can infiltrate adipose tissue in patients with PCOS. This infiltration may lead to localized inflammation in adipose tissue, which in turn affects insulin sensitivity and hormone regulation [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Insulin resistance is a hallmark of PCOS. It is believed that insulin resistance and hyperinsulinemia may trigger an inflammatory response and affect immune cell function. Chronic hyperinsulinemia may lead to the release of pro-inflammatory factors and contribute to inflammation in polycystic ovary syndrome [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The relationship between depression and inflammatory response is bidirectional. A large body of evidence suggests that depression can lead to inflammatory response. Conversely, the inflammatory response may also contribute to the development of depression [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Elevated levels of pro-inflammatory cytokines, such as interleukin-6 (IL-6) and tumor necrosis factor-α (TNF-α), have been observed in patients with MDD. This suggests a link between depression and inflammation. Chronic low-grade inflammation may lead to depressive symptoms.\u003c/p\u003e \u003cp\u003eIn this study, KEGG analysis suggested that the pathogenesis of PCOS with MDD is associated with the cAMP signaling pathway. Studies have shown that the cAMP signaling pathway is involved in inflammatory response [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. As one of the pivotal genes identified in this study, PLA2G4A is involved in the inflammatory process, promoting the release of arachidonic acid and its subsequent conversion to inflammatory mediators, thereby contributing to the initiation and maintenance of inflammation in various tissues. TFAP2A was identified in this study as a strongly associated TF associated with the pathogenesis of PCOS and MDD. TFAP2A was found to play a role in the inflammatory response, affecting the expression of genes related to immune cell function [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003ePrediction of potential drug molecules\u003c/h2\u003e \u003cp\u003eIn this study, orlistat, DHA, capsaicin and myo-inositol were identified as potential drugs for the treatment of PCOS and MDD.\u003c/p\u003e \u003cp\u003eAmong them, orlistat blocks pancreatic lipase activity and reduces the digestion and absorption of dietary fat. The prevalence of obesity in PCOS patients is about 64% [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Weight loss can improve insulin sensitivity, regulate the menstrual cycle, and lower androgen levels in some women with PCOS. Currently, oral orlistat has become an effective treatment for weight management in PCOS [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, little has been reported about orlistat for MDD, and we look forward to future studies related to it.\u003c/p\u003e \u003cp\u003eDHA is an omega-3 fatty acid that is an important part of the human diet and plays a vital role in maintaining overall health. DHA is highly concentrated in the gray matter and synaptic membranes of the brain, which is important for nerve cell signaling and overall cognitive function [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. DHA may affect the production and function of neurotransmitters such as serotonin and dopamine, which play a crucial role. Although the exact mechanisms are not fully understood, DHA may help maintain the balance of these neurotransmitters in the brain. DHA may provide neuroprotection by enhancing the brain's ability to repair and maintain neuronal structure. These neuroprotective properties may be beneficial in the treatment of MDD. Currently, DHA is often used as an adjunct to conventional treatments for depression, such as psychotherapy or antidepressant medications. When used in combination with these treatments, DHA may increase its effectiveness. A meta-analysis showed that DHA was effective in improving several symptoms associated with depression, making it a beneficial complementary therapeutic agent [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. DHA may also help reduce triglyceride levels and improve lipid metabolism. In addition, DHA has anti-inflammatory properties. It may help regulate the body's inflammatory response, which is important for immune function and reducing the risk of chronic inflammation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. By reducing inflammation, DHA may help reduce some of the inflammatory symptoms associated with PCOS and MDD. Studies have shown that DHA may be used as a raw material for the synthesis of specialized pro-resolving mediators (SPMs), which are involved in the repair of chronic inflammation due to PCOS [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. PCOS is often associated with insulin resistance, which leads to elevated insulin levels and an increased risk of developing type 2 diabetes. DHA has been shown to improve insulin sensitivity. By improving insulin sensitivity, DHA may help improve metabolism and ovulation in PCOS patients [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCapsaicin is a lipophilic compound. Studies have shown that capsaicin may improve lipid metabolism by activating the TRPV1 receptor, thereby improving weight loss, which is beneficial in ameliorating obesity associated with PCOS [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. Studies have shown that capsaicin may inhibit the production of pro-inflammatory cytokines and chemokines, potentially reducing inflammatory response [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Additionally, capsaicin promotes vasodilation, which helps to improve blood flow and reduce tissue inflammation. Therefore, capsaicin may treat PCOS and MDD by improving the inflammatory state of the body.\u003c/p\u003e \u003cp\u003eMyo-inositol is a naturally occurring compound that plays an important role in various biological processes in the human body. Myo-inositol has been studied for the treatment of PCOS. Some studies have suggested that myo-inositol may help to reduce androgen levels, thereby improving symptoms such as hirsuteness and hair loss in PCOS, and improving follicle quality to increase the likelihood of successful fertilization and healthy pregnancies [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. In addition, some studies have shown that myo-inositol as an insulin sensitizer can improve insulin resistance and hyperinsulinemia in patients with PCOS, which in turn improves the endocrine and metabolic conditions of patients with PCOS, promotes the maturation of oocytes in patients with PCOS, and improves the quality of embryos, etc [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. There is evidence that myo-inositol may play a role in controlling depression [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. Myo-inositol is involved in a variety of cellular processes, including neurotransmitter signaling in the brain. Several studies have suggested that myo-inositol supplementation may help regulate neurotransmitters, thereby improving symptoms associated with depression [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. In addition, myo-inositol has antioxidant properties, which help to reduce oxidative stress in the body and improve the chronic inflammatory state, thereby treating PCOS and MDD [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Studies have shown that myo-inositol can effectively improve the psychological state of patients with PCOS combined with depression, and can be used as a first-line medication to improve depressive symptoms in PCOS patients [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOur study identified a TF-mRNA-miRNA regulatory network that may be associated with PCOS and MDD. Six hub genes (including GRIN1, CNR1, DNM1, SYNJ1, PLA2G4A, and EPHB2) were identified as hub genes that are strongly associated with the pathogenesis of PCOS and MDD. TFAP2A was identified as a strongly associated TF. Hsa-miR-27a was identified as a strongly associated miRNA. Abnormal neuronal signaling, disturbed energy metabolism and inflammatory response may be the common pathogenesis of PCOS and MDD. Based on this, we identified potential drug molecules for the treatment of PCOS and MDD, namely: orlistat, DHA, capsaicin and myo-inositol. These findings may contribute to the development of early diagnostic strategies, prognostic markers and new therapeutic agents.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eNational Natural Science Foundation of China (81674011); Major Research Project of Science and Technology Innovation Project of China Academy of Traditional Chinese Medicine (CI2021A02404).\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eZheng Zheng wrote the main manuscript text, Xinmin Liu reviewed the manuscript and Yuxing Wang prepared figures 1-5. All authors reviewed the manuscript.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSadeghi HM, Adeli I, Calina D, et al. Polycystic Ovary Syndrome: A Comprehensive Review of Pathogenesis, Management, and Drug Repurposing. Int J Mol Sci. 2022;23(2):583.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzziz R. Polycystic Ovary Syndrome. Obstet Gynecol. 2018;132(2):321\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDepression. (2020). Accessed: May 3, 2020: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/depression\u003c/span\u003e\u003cspan address=\"https://www.who.int/news-room/fact-sheets/detail/depression\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKuehner C. Gender differences in unipolar depression: an update of epidemiological findings and possible explanations. Acta Psychiatr Scand. 2003;108(3):163\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalhi GS, Mann JJ, Depression. Lancet. 2018;392(10161):2299\u0026ndash;312.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCesta CE, Kuja-Halkola R, Lehto K, et al. Polycystic ovary syndrome, personality, and depression: A twin study. Psychoneuroendocrinology. 2017;85:63\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamly B, Afiqah-Aleng N, Mohamed-Hussein ZA. Protein-Protein Interaction Network Analysis Reveals Several Diseases Highly Associated with Polycystic Ovarian Syndrome. Int J Mol Sci. 2019;20(12):2959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin H, Liu M, Zhong D, et al. The prevalence and factors associated with anxiety-like and depression-like behaviors in women with polycystic ovary syndrome[J]. Front Psychiatry. 2021;12:709674.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Y, Ni Z, Li K. The prevalence of anxiety and depression of different severity in women with polycystic ovary syndrome: a meta-analysis[J]. Gynecol Endocrinol. 2021;37(12):1072\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee IO, Kim JC, Seo JW, et al. Risk of developing major depressive disorder in polycystic ovary syndrome: a retrospective cohort study. J Obstet Gynaecol. 2021;41(7):1157\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRodriguez-Paris D, Remlinger-Molenda A, Kurzawa R, et al. Psychiatric disorders in women with polycystic ovary syndrome. Psychiatr Pol. 2019;53(4):955\u0026ndash;66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEthirajulu A, Alkasabera A, Onyali CB, et al. Insulin Resistance, Hyperandrogenism, and Its Associated Symptoms Are the Precipitating Factors for Depression in Women With Polycystic Ovarian Syndrome. Cureus. 2021;13(9):e18013.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOzgen Saydam B, Yildiz BO. Weight management strategies for patients with PCOS: current perspectives. Expert Rev Endocrinol Metab. 2021;16(2):49\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu L, Li X, Lv L, et al. Dietary and serum n-3 PUFA and polycystic ovary syndrome: a matched case-control study. Br J Nutr. 2022;128(1):114\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng J, Zheng S, Feng Q, et al. Dietary capsaicin and its anti-obesity potency: from mechanism to clinical implications. Biosci Rep. 2017;37(3):BSR20170286.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGenazzani AD. Inositol as putative integrative treatment for PCOS. Reprod Biomed Online. 2016;33(6):770\u0026ndash;80.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrutocao C, Zaiem F, Alsawas M, et al. Psychiatric disorders in women with polycystic ovary syndrome: a systematic review and meta-analysis. Endocrine. 2018;62(2):318\u0026ndash;25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVishnubhotla DS, Tenali SN, Fernandez M, et al. Evaluation of Prevalence of PCOS and Associated Depression, Nutrition, and Family History: A Questionnaire-based Assessment. Indian J Endocrinol Metab. 2022 Jul-Aug;26(4):341\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKolhe JV, Chhipa AS, Butani S, et al. PCOS and Depression: Common Links and Potential Targets. Reprod Sci. 2022;29(11):3106\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXing L, Xu J, Wei Y, et al. Depression in polycystic ovary syndrome: Focusing on pathogenesis and treatment. Front Psychiatry. 2022;13:1001484.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGonzalez D, Maidana P, Ibar C, et al. Hair cortisol in polycystic ovary syndrome. Sci Rep. 2022;12(1):10309.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNanou E, Catterall WA. Calcium Channels, Synaptic Plasticity, and Neuropsychiatric Disease. Neuron. 2018;98(3):466\u0026ndash;81.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGomez-Castro F, Zappettini S, Pressey JC, et al. Convergence of adenosine and GABA signaling for synapse stabilization during development. Science. 2021;374(6568):eabk2055.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStepan J, Heinz DE, Dethloff F, et al. Hippo-released WWC1 facilitates AMPA receptor regulatory complexes for hippocampal learning. Cell Rep. 2022;41(10):111766.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHansen KB, Wollmuth LP, Bowie D, et al. Structure, Function, and Pharmacology of Glutamate Receptor Ion Channels. Pharmacol Rev. 2021;73(4):298\u0026ndash;487.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSousa-Valente J, Varga A, Torres-Perez JV, et al. Inflammation of peripheral tissues and injury to peripheral nerves induce differing effects in the expression of the calcium-sensitive N-arachydonoylethanolamine-synthesizing enzyme and related molecules in rat primary sensory neurons. J Comp Neurol. 2017;525(8):1778\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang L. Neuronal cAMP/PKA Signaling and Energy Homeostasis. Adv Exp Med Biol. 2018;1090:31\u0026ndash;48.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang HQ, Wang ZZ, Chen NH. The receptor hypothesis and the pathogenesis of depression: Genetic bases and biological correlates. Pharmacol Res. 2021;167:105542.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZagzoog A, Mohamed KA, Kim HJJ, et al. In vitro and in vivo pharmacological activity of minor cannabinoids isolated from Cannabis sativa. Sci Rep. 2020;10(1):20405.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBragato C, Pistocchi A, Bellipanni G, et al. Zebrafish dnm1a gene plays a role in the formation of axons and synapses in the nervous tissue. J Neurosci Res. 2023;101(8):1345\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAl Zaabi N, Al Menhali N, Al-Jasmi F. SYNJ1 gene associated with neonatal onset of neurodegenerative disorder and intractable seizure. Mol Genet Genomic Med. 2018;6(1):109\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYuan L, Yi W, Sun C, et al. EphB2 activates CREB-dependent expression of Annexin A1 to regulate dendritic spine morphogenesis. Biochem Biophys Res Commun. 2021;584:107\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeorgopoulos NA, Saltamavros AD, Vervita V, et al. Basal metabolic rate is decreased in women with polycystic ovary syndrome and biochemical hyperandrogenemia and is associated with insulin resistance. Fertil Steril. 2009;92(1):250\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGu X, Ke S, Wang Q, et al. Energy metabolism in major depressive disorder: Recent advances from omics technologies and imaging. Biomed Pharmacother. 2021;141:111869.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernandes BS, Salagre E, Enduru N, et al. Insulin resistance in depression: A large meta-analysis of metabolic parameters and variation. Neurosci Biobehav Rev. 2022;139:104758.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu LY, Alexa K, Cortes M, et al. Cannabinoid receptor signaling regulates liver development and metabolism. Development. 2016;143(4):609\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMurakami M, Kudo I. Phospholipase A2. J Biochem. 2002;131(3):285\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLin XZ, Luo J, Zhang LP, et al. MiR-27a suppresses triglyceride accumulation and affects gene mRNA expression associated with fat metabolism in dairy goat mammary gland epithelial cells. Gene. 2013;521(1):15\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen T, Zhang Y, Liu Y, et al. MiR-27a promotes insulin resistance and mediates glucose metabolism by targeting PPAR-γ-mediated PI3K/AKT signaling. Aging. 2019;11(18):7510\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRudnicka E, Suchta K, Grymowicz M, et al. Chronic Low Grade Inflammation in Pathogenesis of PCOS. Int J Mol Sci. 2021;22(7):3789.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRostamtabar M, Esmaeilzadeh S, Tourani M, et al. Pathophysiological roles of chronic low-grade inflammation mediators in polycystic ovary syndrome. J Cell Physiol. 2021;236(2):824\u0026ndash;38.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatulewicz N, Karczewska-Kupczewska M. Insulin resistance and chronic inflammation. Postepy Hig Med Dosw (Online). 2016;70(0):1245\u0026ndash;58.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeurel E, Toups M, Nemeroff CB. The Bidirectional Relationship of Depression and Inflammation: Double Trouble. Neuron. 2020;107(2):234\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCruz-Pereira JS, Rea K, Nolan YM, et al. Depression's Unholy Trinity: Dysregulated Stress, Immunity, and the Microbiome. Annu Rev Psychol. 2020;71:49\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTavares LP, Negreiros-Lima GL, Lima KM, et al. Blame the signaling: Role of cAMP for the resolution of inflammation. Pharmacol Res. 2020;159:105030.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe J, Cao Q, Feng DD, et al. Transcription factor AP\u0026ndash;2α negatively regulates thymic stromal lymphopoietin expression in respiratory syncytial virus infection. Mol Med Rep. 2020;22(2):1639\u0026ndash;46.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTay CT, Teede HJ, Hill B, et al. Increased prevalence of eating disorders, low self-esteem, and psychological distress in women with polycystic ovary syndrome: a community-based cohort study[J]. Fertil Steril. 2019;112(2):353\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWeiser MJ, Butt CM, Mohajeri MH. Docosahexaenoic Acid and Cognition throughout the Lifespan. Nutrients. 2016;8(2):99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiao Y, Xie B, Zhang H, et al. Efficacy of omega-3 PUFAs in depression: A meta-analysis. Transl Psychiatry. 2019;9(1):190.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang TT, Xu J, Wang YM, et al. Health benefits of dietary marine DHA/EPA-enriched glycerophospholipids. Prog Lipid Res. 2019;75:100997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRegidor PA, Mueller A, Sailer M, et al. Chronic Inflammation in PCOS: The Potential Benefits of Specialized Pro-Resolving Lipid Mediators (SPMs) in the Improvement of the Resolutive Response. Int J Mol Sci. 2020;22(1):384.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCapel F, Acquaviva C, Pitois E, et al. DHA at nutritional doses restores insulin sensitivity in skeletal muscle by preventing lipotoxicity and inflammation. J Nutr Biochem. 2015;26(9):949\u0026ndash;59.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaskaran P, Krishnan V, Ren J, et al. Capsaicin induces browning of white adipose tissue and counters obesity by activating TRPV1 channel-dependent mechanisms. Br J Pharmacol. 2016;173(15):2369\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBraga Ferreira LG, Faria JV, Dos Santos JPS, et al. Capsaicin: TRPV1-independent mechanisms and novel therapeutic possibilities. Eur J Pharmacol. 2020;887:173356.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePkhaladze L, Russo M, Unfer V, et al. Treatment of lean PCOS teenagers: a follow-up comparison between Myo-Inositol and oral contraceptives. Eur Rev Med Pharmacol Sci. 2021;25(23):7476\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCappelli V, Musacchio MC, Bulfoni A, et al. Natural molecules for the therapy of hyperandrogenism and metabolic disorders in PCOS[J]. Eur Rev Med Pharmacol Sci. 2017;21(2 Suppl):15\u0026ndash;29.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFacchinetti F, Bizzarri M, Benvenga S et al. Results from the international consensus conference on myo-inositol and D-chiro-inositol in obstetrics and gynecology: the link between metabolic syndrome and PCOS[J]. Eur J Obstet Gynecol Reprod Biol, 2015, 195(72\u0026ndash;76).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMukai T, Kishi T, Matsuda Y, et al. A meta-analysis of inositol for depression and anxiety disorders. Hum Psychopharmacol. 2014;29(1):55\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKiani AK, Paolacci S, Calogero AE, et al. From Myo-inositol to D-chiro-inositol molecular pathways. Eur Rev Med Pharmacol Sci. 2021;25(5):2390\u0026ndash;402.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSimic D, Nikolic Turnic T, Dimitrijevic A, et al. Potential role of d-chiro-inositol in reducing oxidative stress in the blood of nonobese women with polycystic ovary syndrome. Can J Physiol Pharmacol. 2022;100(7):629\u0026ndash;36.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCantelmi T, Lambiase E, Unfer VR, et al. Inositol treatment for psychological symptoms in Polycystic Ovary Syndrome women. Eur Rev Med Pharmacol Sci. 2021;25(5):2383\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"PCOS, MDD, bioinformatics analysis, DEGs, TF-mRNA-miRNA regulatory network, potential drug prediction","lastPublishedDoi":"10.21203/rs.3.rs-3704976/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3704976/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePolycystic Ovarian Syndrome (PCOS) is the most common metabolic and endocrine disorder in reproductive-age women, while Major Depressive Disorder (MDD) is a relatively common psychiatric condition. Previous studies have suggested a potential link between PCOS and MDD, but the underlying pathophysiological mechanisms remain unclear. This study aims to identify differential expression genes (DEGs) between PCOS and MDD using bioinformatics methods, explore the associated molecular mechanisms, elucidate the TF-mRNA-miRNA regulatory network involved, predict potential drug molecules, and validate them through molecular docking.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods: \u003c/strong\u003eMicroarray datasets GSE34526 and GSE125664 were downloaded from the Gene Expression Omnibus (GEO) database. The differentially expressed genes (DEGs) of PCOS and MDD were analyzed using the GEO2R online tool to obtain shared DEGs to both. Next, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the shared DEGs were performed. Then, protein-protein interaction (PPI) network were constructed and the hub genes were identified using the STRING database and Cytoscape software. Next, NetworkAnalyst was used to construct network between target transcription factors (TFs), microRNAs (miRNAs), and hub genes. Finally, the DSigDB database was used to search for potential drug molecules for the treatment of PCOS combined with MDD, followed by molecular docking using the AutoDock Tools and visualization of the results using PyMol 2.4.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn the above two datasets, 158 shared DEGs were identified. GO and KEGG enrichment analyses showed that these shared DEGs were mainly enriched in pathways related to neural signaling, energy metabolism, and chronic inflammation with immune dysregulation. In addition, genes with greater than 2-fold median interaction number were further screened by Cytoscape's plugin, cytoNCA, and finally 6 hub genes were selected from the PPI network, ncluding GRIN1, CNR1, DNM1, SYNJ1, PLA2G4A and EPHB2. Then, through the construction of the TF-mRNA-miRNA regulatory network, it was concluded that hsa-miR-27a might be a strongly associated miRNA with the pathogenesis of PCOS and MDD, while TFAP2A might be a strongly associated TF. Finally, orlistat, docosahexaenoic acid (DHA), capsaicin, and myo-inositol were considered as potential drug molecules for the treatment of PCOS combined with MDD using the DSigDB database and related study finding, and then molecular docking was performed using AutoDock Tools. The drug-molecule combination with the lowest binding energy was visualized using PyMol software and it found to be well docked.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eIn summary, we constructed a TF-mRNA-miRNA regulatory network for the first time to characterize the interactions among potential TFs, miRNAs, and hub genes associated with PCOS and MDD, and concluded that aberrant neuronal signaling, disturbed energy metabolism, and immune dysregulation with inflammatory response may be the common pathogenesis of PCOS and MDD. In addition, we identified potential drug molecules for the treatment of PCOS and MDD and performed molecular docking validation. This provides new insights to identify potential associations, potential biomarkers and therapeutic agents for PCOS and MDD.\u003c/p\u003e","manuscriptTitle":"Identification of the Shared Gene Signatures and Molecular Mechanisms Between Polycystic Ovarian Syndrome and Major Depressive Disorder: Evidence From Transcriptome Data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-12-06 13:56:38","doi":"10.21203/rs.3.rs-3704976/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d315a727-99d5-4f29-ae2d-a030e0f331f0","owner":[],"postedDate":"December 6th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-12-10T16:44:13+00:00","versionOfRecord":[],"versionCreatedAt":"2023-12-06 13:56:38","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3704976","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3704976","identity":"rs-3704976","version":["v1"]},"buildId":"GqpaHPwrfC8PjnIFayRh5","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}