Therapeutic effects and potential mechanisms of caffeine on obese polycystic ovary syndrome: bioinformatic analysis and experimental validation

Therapeutic effects and potential mechanisms of caffeine on obese polycystic ovary syndrome: bioinformatic analysis and experimental validation | 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 Article Therapeutic effects and potential mechanisms of caffeine on obese polycystic ovary syndrome: bioinformatic analysis and experimental validation Bai Tonglin, Hu Yue, Luo Ling, Liu Yuhuan, Yu Chaoqin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4917244/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 26 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Background The risk of PCOS is significantly increased in obese women, and studies have shown that weight loss can improve the symptoms of PCOS. Coffee has been shown to effectively reduce body weight. In this study, we focused on the SLC16A6 gene through bioinformatics and searched for coffee and its monomers through reverse network pharmacology. Materials and Methods The Gene Expression Omnibus (GEO) database was searched to screen differentially expressed genes (DEGs) with PCOS patients. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were subsequently performed. The effects of caffeine on body weight, the estrous cycle, ovarian pathology, the serum insulin concentration and the insulin resistance index, and the expression of the SLC16A6 transporter gene in the ovarian tissues of obese PCOS rats were observed. Results The common differentially expressed gene SLC16A6 was identified in this study, and animal experiments confirmed the effectiveness of caffeine in the treatment of obese PCOS rats. Conclusions Caffeine can effectively improve the symptoms of obese PCOS rats. The mechanism by which caffeine can treat obese patients with PCOS is related to increasing the expression of the SLC16A6 gene. Biological sciences/Computational biology and bioinformatics Health sciences/Endocrinology/Endocrine system and metabolic diseases Health sciences/Diseases/Endocrine system and metabolic diseases Health sciences/Diseases/Metabolic disorders Health sciences/Diseases/Reproductive disorders Polycystic ovary syndrome bioinformatics analysis network pharmacology caffeine SLC16A6 Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Introduction Polycystic ovary syndrome (PCOS) is a common gynecological endocrine disease in women of reproductive age, with a global incidence of approximately 5–18%[ 1 ]. It clinically manifests as a menstrual disorder, infertility, hyperandrogenism, insulin resistance (IR), and obesity [ 2 , 3 ]. The clinical manifestations of PCOS are highly heterogeneous and complex. The diagnostic criteria are different, and treatment is difficult, which seriously affects the physical and mental health of women [ 4 ]. Studies have shown that approximately 50% of women with PCOS are obese [ 5 ]. A meta-analysis of 16 studies revealed that women with PCOS have a greater risk of metabolic diseases [ 6 ]. A prospective study in Spain revealed that 28% of obese and overweight women of childbearing age have PCOS [ 7 ]. The risk of PCOS increases with increasing BMI. Although the etiology of PCOS is still unclear, obesity may be the driving factor for PCOS in people at risk, and weight loss is considered an important treatment for improving the symptoms of PCOS patients [ 5 , 8 ]. However, researchers still do not know why some women suffer from PCOS with obesity and whether some genetic disorders contribute. Therefore, a better understanding of the pathophysiological mechanism of PCOS and active explorations of new molecular mechanisms and potential therapeutic targets for obese patients with PCOS are important. In recent years, bioinformatics and microarray technology have been widely used to identify potential genes involved in diseases [ 9 ], providing new methods for the prevention and treatment of diseases. In this study, bioinformatics analysis was used to screen the SLC16A6 gene potentially related to the pathogenesis of obese PCOS patients. This gene belongs to the human solute carrier (SLC) transporter family, which includes 400 genes and 52 subfamilies [ 10 ] and has been shown to function in metabolic diseases [ 11 , 12 ]. Since most SLCs contribute to the transport of small organic molecules, several drugs targeting SLCs have been approved for marketing [ 13 ]. The study of SLCs has not advanced at present, and no study has shown that the SLC family can be used as a therapeutic target for the treatment of polycystic ovary syndrome. Current research focuses on the role of SLC transporters in metabolic diseases and on determining whether a subtype of the SLC protein family interacts with the endocrine and metabolic problems associated with PCOS. These findings may help to identify or treat this complex reproductive endocrine disease. Through reverse network pharmacology, this study predicted that coffee would have a potential therapeutic effect on obese PCOS patients. A clinical trial revealed that healthy subjects who drank black coffee daily for 2 weeks experienced significant reductions in body weight and BMI. In addition, waist circumference and abdominal fat were reduced [ 14 ]. The most important component of coffee is caffeine. Varillas-Delgado et al. reported that caffeine promotes fat oxidative metabolism and participates in lipid metabolism in the body [ 15 ], and a prospective cohort study confirmed that caffeine can effectively reduce the risk of obesity [ 16 ]. Caffeine reduces the risk of type 2 diabetes by ameliorating disorders in the central insulin signaling pathway [ 17 ]. Therefore, this study used bioinformatics analysis combined with in vivo experiments to prove that SLC16A6 has a regulatory effect on PCOS and that coffee and its effective monomers can have a therapeutic effect on PCOS by regulating SLC16A6, providing certain life advice for obese women with PCOS and exploring new therapeutic targets for obese women with PCOS. 2. Materials and Methods 2.1 Bioinformatics: 2.1.1 Analysis of differentially expressed genes and ROC curve analysis of obese PCOS patients After searching the GEO database, "GSE10946" and "GSE193812" were selected as the main research datasets. In GSE10946, 5 samples of granulosa cells from obese non-PCOS patients and 7 samples of granulosa cells from obese PCOS patients were used. In GSE193812, abdominal fat samples from 4 obese non-PCOS patients and 4 obese PCOS patients were used. The R packages "limma", "edgeR" and "DESeq2" were used to analyze the DEGs in the two datasets, and the criteria were set as P < 0.05 and absolute logFC ≥ 1. The intersection of the DEGs was obtained using a Venn diagram. The GSE10946 dataset was used for the ROC curve analysis of the intersection genes, and the "PROC" package was used. 2.1.2 Analysis of differentially expressed genes and enrichment analysis of the SLC16A6 high- and low-expression groups The "DESeq2" R package was used to select the samples with high and low SLC16A6 expression for the analysis of differentially expressed genes. After the DEGs were obtained, "clusterProfiler" was used for GO and KEGG enrichment analyses. 2.2. Network Pharmacology: 2.2.1 Reverse network pharmacology analysis of SLC16A6 The herb database ( http://herb.ac.cn/ ) was used to search for natural compounds related to SLC16A6 in the database by entering "SLC16A6" and performing target queries. 2.2.2 Target analysis of active coffee ingredients The effective active ingredients of coffee were obtained through literature retrieval, the active ingredients were included in the herb database and TCMSP database for queries, and the active ingredients with corresponding targets were saved. 2.2.3 PCOS-related targets and potential targets of coffee in the treatment of PCOS The PCOS targets were obtained from the following two databases: GeneCards[ 18 ] and OMIM[ 19 ]. The key words "polycystic ovarian syndrome" and "PCOS" were searched in the two databases to obtain disease targets. Online Wayne figure tools ( https://bioinfogp.cnb.csic.es/tools/venny/ ) were used to match PCOS disease-related genes and corresponding coffee target intersections to identify potential coffee targets for the treatment of PCOS. 2.2.4 Protein‒protein interactions Studying the interactions between protein networks can help to identify the core genes involved. A protein‒protein interaction (PPI) analysis was performed using STRING, the potential targets obtained from the Venn diagram were input into the "Multiple proteins" analysis in STRING, and the species was limited to " Homo sapiens ". The default confidence of the platform was adopted to improve the reliability of the data. The degree values of each node in the PPI network were obtained using the analysis plug-in of Cytoscape 3.8.0, in which the target of the height value played a pivotal role. Through this step, the core targets of coffee were obtained. 2.2.5 Network construction The data on the effective components of coffee and potential targets for the treatment of PCOS were collated. The obtained data were imported into Cytoscape 3.8.0 software to visualize the drug–active ingredient–disease target genes network, and the number of targets corresponding to each active ingredient was calculated. 2.2.6 Enrichment analysis The DAVID tool [ 20 ] was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Biological processes, cellular components, molecular functions and key signaling pathways were identified, and the core mechanism of coffee and related biological pathways were explored. The functional annotations with P values less than 0.05 in the enrichment results were further analyzed. A bubble map of the enrichment results was drawn, and three pathways, namely, insulin, fat and oocyte development, were extracted for visualization. 2.3 Experimental verification 2.3.1 Preparation and grouping of the obese PCOS rat model Thirty-six SPF female Sprague–Dawley rats (weight 160 g–180 g) were randomly selected as the study subjects. SD rats were provided by Beijing Weitong Lichina Laboratory Animal Co., Ltd. [license number: SCXK (Beijing) 2016-0006]. The animals were housed in the animal room of the Experimental Center of Shanghai Changhai Hospital [license number: SCXK (Shanghai) 2017-0005]. The animals were fed in separate cages and had free access to food and water. The temperature of the animal room in the experimental center was 28 ± 1.5°C, the relative humidity was 40 ± 5%, and the light and dark time was 12 hours each during the day and night. This experiment was reviewed and approved by the Experimental Animal Ethics Committee of the Medical Ethics Committee of Shanghai Changhai Hospital (ethical review number: CHEC (A.E.) 2022-011). All experimental procedures were performed in accordance with the relevant regulations for animal experiment ethics. At the age of 5 weeks, female Sprague‒Dawley rats were randomly divided into a PCOS model group (model group, n = 22) and a control group (control group, n = 8). According to a previous study, the rats in the model group were administered 1 mg/kg letrozole by gavage daily and fed a high-fat diet (energy composition of the animal high-fat diet formula: 60.65% fat, 21.22% carbohydrate, and 18.14% protein from Synbiotics (XTHF60)). The control group was administered 1 mg/kg normal saline by gavage once a day and fed a normal diet. After 28 days of the experiment, the body weight of the rats was measured, and the body weight of the model group increased by 20% compared with the control group, indicating that the obesity model was successfully established [ 21 ]. The rats with a body weight increase of more than 20% were further divided into a model group (n = 8), a CFYN group (n = 6) and a GLP-1 group (n = 8). The model group was administered 1 mg/kg normal saline by gavage once a day, the CFYN group was administered 10 mg/kg caffeine by gavage once a day, and the GLP-1 group was administered 0.63 mg/kg semaglutide by gavage once a day, along with a high-fat diet for 21 days. 2.3.2 Analysis of reproductive and metabolic phenotypes and blood and tissue sampling After the establishment of the model, the body masses of the rats in each group were measured every week and were summarized. From the 15th day of the estrous cycle, vaginal exfoliative cytology was continuously performed at 8:00 every day until the end of the experiment. The estrous cycle of the rats was evaluated by performing methylene blue staining of vaginal exfoliated cells. The estrous cycle table was generated, and the rate of estrous cycle disorder was calculated. At the end of the experiment (week 7), all rats were anesthesia with a Pentobarbital sodium, and blood and ovarian tissues were harvested. After the experiment, the rats were euthanized by high dose of pentobarbital sodium (150ཞ200mg/kg). Whole blood was centrifuged, and serum was collected and stored at − 80°C. The left ovary was fixed in 4% paraformaldehyde, and the right ovary was frozen for mRNA assays. 2.3.3 Morphology of the rat ovary tissue The ovaries were fixed with 4% paraformaldehyde and dehydrated with different concentrations of ethanol. The representative ovaries were embedded in paraffin, sectioned into 4 µm thick sections, and then stained with hematoxylin and eosin. The fixed sections of rat ovarian tissue were observed under a microscope and photographed, and the number of cystic follicles and corpus luteum were counted. 2.3.4 Rat serum analysis using enzyme-linked immunosorbent assay (ELISA) The levels of follicle stimulating hormone (FSH), luteinizing hormone (LH), fasting insulin (FINS) and fasting blood glucose (GLU) in the serum were detected using enzyme-linked immunosorbent assay (ELISA) kits, and the ratio of LH to FSH and the insulin resistance index (HOMA-IR) were calculated. The specific calculation method used was FINS*FBG/22.5. ELISAs were performed according to the instructions of the ELISA detection kits. 2.3.5 RT‒PCR was used to detect the expression of related genes in rat tissues The tissue to be tested was removed from the frozen ovary, and RNA was extracted using the TRIzol lysis method (according to the TRIzol operating instructions from Invitrogen Company) and reverse transcribed into cDNA (according to the operating instructions of RR047A from TAKARA Company). The mRNA expression of the target gene was detected using real-time PCR (according to the operating instructions of TAKARA RR420A). After the reaction, the amplification curve and melting curve of real-time PCR were confirmed, and the data were analyzed. 2.3.6 Statistical analysis The means ± standard deviations were used for statistical descriptions. ANOVA was used to compare more than two groups of normally distributed data. The Wilcoxon rank sum test was used to compare the sequencing data that were not normally distributed. All the statistical tests were two-tailed tests. When P < 0.05, the difference was considered statistically significant. The methods section include involving live animals to be reported as described by the ARRIVE guidelines (PLoS Bio 8(6), e1000412,2010​).: (i) identifying the institutional and/or licensing committee approving the experiments, including any relevant details; (ii) confirming that all experiments were performed in accordance with relevant guidelines and regulations. 3. Results 3.1 Bioinformatics From the GSE193812 dataset, a total of 472 differentially expressed genes in adipose tissue were identified, as shown in Figure 1A (Table 1). In the GSE10946 dataset, a total of 61 DEGs were found in the granule cell cluster, as shown in Figure 1B (Table 2). The common differentially expressed gene was SLC16A6 (Figure 1C). The GSE10946 dataset was selected to verify the expression of SLC16A6 and perform a ROC curve analysis. SLC16A6 was significantly increased in the granulosa cells of the PCOS group (P < 0.05) (Figure 1D). The AUC was 0.866, indicating that SLC16A6 expression in the granulosa cells of the PCOS group was strongly reliable (Figure 1E). A total of 356 DEGs were detected between the SLC16A6 high- and low-expression groups (Figure 1F). GO and KEGG analyses were performed on 356 DEGs, and a total of 380 significant GO terms were identified (Table 3). The top 30 GO terms are shown in Figure 1G. These pathways included "insulin-like growth factor receptor binding", "insulin-like growth factor II binding", insulin-like growth factor I binding, estradiol 17-beta-dehydrogenase activity, steroid dehydrogenase activity, etc., and "sex hormone-related pathways" (Figure 1G). Twenty significantly different pathways were identified according to the KEGG enrichment results (Table 4): "TGF-beta signaling pathway", "ovarian steroidogenesis", "proline signaling pathway", "regulation of lipolysis in" "adipocytes", "type II diabetes mellitus" and other inflammation-, intraovarian sterol-, and diabetes-related pathways (Figure 1H). 3.2, Network pharmacology A total of 15 corresponding components associated with SLC16A6 (Table 5), which contained three coffee-related substances, namely, "coffee acid", "caffeic", and "trans-caffeic acid", were obtained from the herb database. Seven effective substances in coffee were collected, namely, "coffee acid", "caffeic", "trans-caffeic acid", "cafestol", "trigonelline", "caffeine", and "chlorogenic acid". A total of 147 targets corresponding to 7 substances were identified (Table 6) (Figure 2A). The database revealed 3240 PCOS genes and 73 intersecting genes between active coffee ingredients and PCOS (Table 7) (Figure 2B). The PPI network diagram of the intersecting genes was constructed (Figure 2C), and the core targets of the PPI network diagram were analyzed. The top three targets were "INS", "TP53", and "TNF" (Figure 2D). A network diagram of coffee components and therapeutic targets for PCOS treatment was constructed (Figure 2E), and the corresponding targets of each component were statistically analyzed. Forty-six targets corresponding to caffeine were identified (Figure 2F), which was far more than the number of other components, indicating its core role. KEGG and GO analyses were performed on the 73 intersecting genes. Figure 3A shows the involvement of genes in each pathway, and the INS genes were involved in multiple pathways. Figure 3B shows the top 30 GO terms, including "regulation of insulin secretion", "glucose transmembrane transporter activity", "D-glucose transmembrane transporter" activity" and "other insulin or glucose-related pathways". After the KEGG analysis, the intersecting gene enrichment pathways were closely related to fat metabolism, insulin, and oocyte development, as shown in Figure 3C. 3.3. Experimental verification 3.3.1 Reproductive and metabolic phenotypes of obese PCOS rats In this study, the obese PCOS group was confirmed by changes in body weight. By the end of the induction (4 weeks), the body weight of the model group increased significantly (339.5±29.8 g) compared with that of the control group (234.7±11.8 g) (Fig. 4A). Compared with the control group (35.04%), the weight change in the model group was significantly greater (95.35%) (Figure 4B).The vaginal pictures of the control group suggested that the control group had a regular, complete estrous cycle of 4 to 5 days, while the vaginal smear of the model group showed aperiorism, mainly in the interestrus period (Figure 4C.4D). The HE-stained sections from the control group had a normal ovarian morphology. Microscopy revealed that the ovaries of the control group had multiple corpora lutea and follicles at various stages of development, and multilayer granulosa cells were neatly arranged. The HE-stained sections of the ovaries in the model group showed an obvious polycystic morphology with typical polycystic pathological changes under the microscope, with a large number of dilated cystic follicles, no or little luteal tissue, corona radiata and oocytes, and a significant reduction in the number of granulosa cell layers (Fig. 4E). In conclusion, rats subjected to long-term intragastric administration of letrozole and a high-fat diet (model group) exhibited obvious obese PCOS-like changes. 3.3.2 Caffeine treatment restored the estrous cycle in obese PCOS rats Vaginal exfoliative cytology was performed on the four groups of rats, and continuous observations were recorded until the end of the experiment. The results of vaginal smears showed that all rats in the control group had a regular and complete estrous cycle, which lasted for 4-5 days. In the model group, the estrous cycle was disrupted in 6 rats, and the normal rate of the estrous cycle was only 25%, which was mainly manifested as the prolongation of interestrus. The CFYN group and GLP-1 group were similar to the control group. Five rats in the CFYN group had a normal estrous cycle after the experiment, and the normal rate of the estrous cycle was 83%. Six rats in the GLP-1 group had normal estrous cycles at the end of the experiment, and the percentage of normal estrous cycles was 75% (Fig. 6A). Based on histological HE staining, the study revealed that the model group still exhibited significant PCOS-like changes in the ovaries, and the CFYN group and the GLP-1 group exhibited a reduced number of cystic follicles, an increased number of corpus lutea, and improvements in polycystic changes (Figure 6B). 3.3.3 Effects of caffeine on body weight and endocrine metabolism in obese PCOS rats In this study, the successful establishment of an obese PCOS model was confirmed by changes in body weight. After the establishment of the model (after 4 weeks), the obese PCOS rats were randomly divided into three groups: the untreated obese PCOS group (model group), caffeine gavage treatment group (CFYN group), and oral semaglutide gavage treatment group (GLP-1 group). After 3 weeks of treatment, no significant difference in body weight was observed between the CFYN group (373.3±66.2 g) and the model group (401.5±31.7 g), while the GLP-1 group (281.8±36.3 g) and the model group (401.5±31.7 g) showed a significant reduction in body weight (Fig. 5A). Serum samples were collected after a 12 h fast. The GLP-1, fasting plasma glucose and fasting insulin levels were compared among the model, CFYN, GLP-1 and control groups, and the corresponding insulin resistance index was calculated. A significant difference was observed between the model group and the control group (P<0.05). The FINS level of the CFYN group was significantly improved compared with the model group (P<0.05), and a significant difference was not observed between the CFYN group and the GLP-1 treatment group (Fig. 5B-E). In this study, the serum testosterone, FSH, and LH levels were analyzed in each group (Figure 5F-H). The data of this study showed that the testosterone level of the model group (11.74±1.2 nmol/L) was significantly higher than that of the control group (7.23±1.35 nmol/L) (P<0.05), which showed the typical hyperandrogenemia characteristics of PCOS. A significant difference in testosterone levels was not observed between the CFYN group and the GLP-1 group. (P<0.05) (Fig. 5F). The FSH level in the model group (5.9±0.84 U/l) was significantly higher than that in the control group (2.79±0.38 U/l) (P<0.05), and a significant difference was detected between the CFYN group and the model group (P<0.05); however, no significant difference was observed between the CFYN group and the GLP-1 group (Fig. 5G). Moreover, the LH level in the model group (8.69±1.49 mIU/ml) was significantly higher than that in the control group (3.69±1.27 mIU/ml) (P<0.05), which is a typical characteristic of PCOS. A significant difference was observed between the CFYN group and the model group (P<0.05), but no significant difference was detected between the CFYN group and the GLP-1 group (Fig. 5H), which indicated that CFYN could significantly restore the ovarian function of PCOS rats and improve their symptoms. 3.3.4 Effect of caffeine on the expression of the SLC16A6 transporter gene in obese PCOS rats RT‒PCR was used to detect the gene expression of the SLC16A6 transporter in different groups of rats. Compared with the normal control group, the expression of the SLC16A6 gene in the model group was significantly decreased, and significant differences were not observed among the control group, CFYN group and GLP-1 group. However, significant differences were noted between the CON group, CFYN group and GLP-1 group and the model group (P<0.05) (Fig. 7). 4. Discussion PCOS is a common heterogeneous endocrine and metabolic disorder in women of childbearing age, and its pathogenesis is not yet fully understood [ 22 ]. However, recent studies have shown that the reproductive and metabolic levels of PCOS patients with obesity are more likely to be affected, and obesity exacerbates reproductive and metabolic disorders in PCOS patients [ 23 , 24 ]. Excessive weight gain can lead to insulin resistance and disorders in the levels of other hormones. Additionally, obesity and elevated androgen levels can also affect female reproductive function [ 25 , 26 ]. Moreover, studies have shown that obesity and insulin resistance are high-risk factors affecting female reproductive function, and eliminating these risk factors may help to reduce or treat female reproductive disorders [ 27 ]. In recent years, studies on obesity and polycystic ovary syndrome have become increasingly extensive. According to the treatment recommendations of the international evidence-based guidelines for PCOS, lifestyle interventions, including diet, exercise, and behavior, are recommended as first-line management for obese PCOS patients [ 28 ]. However, simple changes in diet structure and lifestyle have a relatively high dropout rate most of the time, and obese PCOS patients are unable to easily and effectively adhere to these changes to achieve weight control. Therefore, the use of adjuvant drug therapy is very important to help obese PCOS patients control weight and improve their endocrine function. Current research shows that the key factor in the treatment of obese PCOS patients is controlling body weight, and excess weight leads to endocrine disorders in obese PCOS patients, affecting reproductive fertility [ 27 ]. Caffeine, one of the main components of coffee, has not been directly confirmed to affect female reproduction, but some studies suggest that women should not consume excess caffeine during pregnancy [ 29 ]. Related studies have shown that caffeine intake is related to waist circumference and body mass index. Caffeine can better help women lose weight [ 30 ], but in other studies, caffeine intake was shown to lead to obesity in children [ 31 ]. In the present study, obese PCOS model rats exhibited estrous cycle disorders, which were significantly improved after weight loss. Consistent with current clinical research and treatment, weight loss and lifestyle improvements can improve the symptoms of obese PCOS patients [ 32 – 34 ]. Moreover, obese PCOS rats have various hormone and metabolic disorders, which further aggravate the symptoms of PCOS in obese rats. After caffeine treatment, the obese PCOS rats showed significant improvements in the corresponding symptoms and the estrous cycle also recovered, but the obese PCOS rats treated with caffeine did not show significant differences in body weight or the metabolic level compared with the obese PCOS rats without intervention, which may be related to caffeine intake. Follow-up experiments are needed for verification to explore whether different levels of caffeine exert different metabolic effects on obese PCOS models. Studies have shown that caffeine can effectively reverse histopathological damage, cell death, inflammation and the antioxidant status in PCOS rats [ 35 ]. The results of this part of the previous study were also mutually verified with the experimental results of this study, which proved the credibility of the results and further supported the therapeutic mechanism of caffeine in the treatment of PCOS rats. Clinically, if caffeine must be used to treat obese PCOS patients, exercise and lifestyle changes or other drugs should be used at the same time. This study also investigated whether the gene expression profile of the SLC16A6 transporter was associated with obese PCOS patients. SLC transporters mediate the transport of a variety of essential nutrients and metabolites, such as glucose, amino acids, vitamins, neurotransmitters, and inorganic/metal ions. The SLC16 subfamily is composed of 14 members of the monocarboxylic acid transporter (MCT) family, which plays crucial roles in the transport of important cellular nutrients, cell metabolism, and pH regulation [ 36 ]. In transgenic animal models, SLC transporters are involved in many important metabolic processes, including nutrient supply, metabolic transformation, and energy homeostasis [ 37 ]. In the preliminary experimental preparation of this study, data on the possible correlation between SLC16A6 and obese PCOS patients were screened, and the expression of SLC16A6 in fat and granule cells of obese PCOS patients was increased. Considering the physiological function of SLC16A6, this increase may be a form of physiological compensation, and further exploration is needed. The relationship between SLC16A6 expression and obese PCOS patients was confirmed in an animal experiment. This result may suggest that the expression of the SLC16A6 transporter is correlated with changes in reproductive function, and further verification is needed to confirm this hypothesis. In general, the results of the experimental verification of this study show that caffeine can improve the apparent symptoms of obese patients with PCOS and the corresponding changes in gene expression. The experimental results of this study still need to be further investigated through subsequent experimental verification to determine the corresponding specific mechanism and pathway. For example, how caffeine improves the symptoms of obese PCOS patients, whether it also improves the symptoms of nonobese PCOS patients, or whether caffeine improves the reproductive function of obese PCOS patients still need to be further verified. At present, a variety of hypotheses and conjectures have been proposed on the relationship between the etiology and development of obesity and polycystic ovary syndrome, but researchers have not clearly determined which disease is dominant. The pathological changes associated with obesity and polycystic ovary syndrome affect each other, causing further development of the disease and subsequently affecting reproductive function in women. In the present study, caffeine treatment significantly improved the recovery rate of the estrous cycle and improved the symptoms of PCOS in obese PCOS rats by regulating endocrine function and ovarian function. In summary, the results of this study indicate that patients can receive more targeted drugs for clinical treatment to achieve better therapeutic effects. Moreover, these findings suggest a possible relationship between the SLC16A6 transporter and reproductive function, which provides new ideas for the study of the occurrence and development of PCOS in obese patients. Declarations Conflict of interest: The authors declare no competing interests. Funding: This work was supported by the National Natural Science Foundation of China(81973896, 82004408), Shanghai Sailing Program༈20YF1448600༉, Natural Science Foundation of Shanghai༈23ZR1478600༉, Traditional Chinese Medicine Research Project of Shanghai Municipal Health Commission༈2022CX003༉ Author Contribution T.-L.B. ,Y.H and L.L. contributed equally to this work. T.-L.B. ,Y.H.and L.L. designed the experiments, performed the experiments, analyzed the data, and wrote the manuscript. C.-Q.Y. and Y.-H.L. contributed to the study design and manuscript preparation. Data Availability The data underlying this article will be shared upon reasonable request to the corresponding author. References Joham, A. E. et al. Polycystic ovary syndrome. The Lancet. Diabetes Endocrinol. 10 (9), 668–680 (2022). Azziz, R. et al. The prevalence and features of the polycystic ovary syndrome in an unselected population. J. Clin. Endocrinol. Metab. 89 (6), 2745–2749 (2004). Carmina, E. Diagnosis of polycystic ovary syndrome: from NIH criteria to ESHRE-ASRM guidelines. Minerva Ginecol. 56 (1), 1–6 (2004). Escobar-Morreale, H. F. Polycystic ovary syndrome: definition, aetiology, diagnosis and treatment. Nat. Rev. Endocrinol. 14 (5), 270–284 (2018). Glueck, C. J. & Goldenberg, N. Characteristics of obesity in polycystic ovary syndrome: Etiology, treatment, and genetics. Metab. Clin. Exp. 92 , 108–120 (2019). Moran, L. J. et al. Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis. Hum. Reprod. Update . 16 (4), 347–363 (2010). Alvarez-Blasco, F. et al. Prevalence and characteristics of the polycystic ovary syndrome in overweight and obese women. Arch. Intern. Med. 166 (19), 2081–2086 (2006). Hu, L. et al. Efficacy of Bariatric Surgery in the Treatment of Women With Obesity and Polycystic Ovary Syndrome. J. Clin. Endocrinol. Metab. 107 (8), e3217–e3229 (2022). Zheng, K. et al. Association Between RSK2 and Clinical Indexes of Primary Breast Cancer: A Meta-Analysis Based on mRNA Microarray Data. Front. Genet. 12 , 770134 (2021). Zhang, Y. et al. The SLC transporter in nutrient and metabolic sensing, regulation, and drug development . J. Mol. Cell Biol. , 11 (1). (2019). Dupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. Nat. Genet. 42 (2), 105–116 (2010). Nikitin, A. G. et al. Association of polymorphic markers of genes FTO, KCNJ11, CDKAL1, SLC30A8, and CDKN2B with type 2 diabetes mellitus in the Russian population. PeerJ . 5 , e3414 (2017). Jones, R. S. & Morris, M. E. Monocarboxylate Transporters: Therapeutic Targets and Prognostic Factors in Disease. Clin. Pharmacol. Ther. 100 (5), 454–463 (2016). Revuelta-Iniesta, R. & Al-Dujaili, E. A. S. Consumption of green coffee reduces blood pressure and body composition by influencing 11β-HSD1 enzyme activity in healthy individuals: a pilot crossover study using green and black coffee. BioMed Research International, 2014: p. 482704. (2014). Varillas-Delgado, D. et al. Effect of 3 and 6 mg/kg of caffeine on fat oxidation during exercise in healthy active females. Biology Sport . 40 (3), 827–834 (2023). Henn, M. et al. Changes in Coffee Intake, Added Sugar and Long-Term Weight Gain - Results from Three Large Prospective US Cohort Studies (The American Journal of Clinical Nutrition, 2023). Song, X. et al. Current therapeutic targets and multifaceted physiological impacts of caffeine (PTR, 2023). Rebhan, M. et al. GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. Bioinf. (Oxford England) . 14 (8), 656–664 (1998). Amberger, J. S. et al. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res. 43 (Database issue), D789–D798 (2015). Dennis, G. et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. Genome Biol. 4 (5), P3 (2003). Lu, K. et al. Preparation of a nano emodin transfersome and study on its anti-obesity mechanism in adipose tissue of diet-induced obese rats. J. Translational Med. 12 , 72 (2014). Lie Fong, S. et al. Polycystic ovarian morphology and the diagnosis of polycystic ovary syndrome: redefining threshold levels for follicle count and serum anti-Mullerian hormone using cluster analysis. Human Reproduction (Oxford, England), 32(8): pp. 1723–1731. (2017). Arya, S. et al. Metabolic syndrome in obesity: treatment success and adverse pregnancy outcomes with ovulation induction in polycystic ovary syndrome . Am. J. Obstet. Gynecol. , 225 (3). (2021). Silvestris, E. et al. Obesity as disruptor of the female fertility 16p. 22 (RB&E, 2018). 1. Fornes, R. et al. The effect of androgen excess on maternal metabolism, placental function and fetal growth in obese dams. Sci. Rep. 7 (1), 8066 (2017). Luke, B. Adverse effects of female obesity and interaction with race on reproductive potential. Fertil. Steril. 107 (4), 868–877 (2017). Venkatesh, S. S. et al. Obesity and risk of female reproductive conditions: A Mendelian randomisation study. PLoS Med. 19 (2), e1003679 (2022). Teede, H. J. et al. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. Hum. Reprod. (Oxford England) . 33 (9), 1602–1618 (2018). Boedt, T. et al. Preconception lifestyle advice for people with infertility. Cochrane Database Syst. Rev. 4 (4), CD008189 (2021). Alkhatib, A. et al. Caffeine Optimizes HIIT Benefits on Obesity-associated Metabolic Adversity in Women. Med. Sci. Sports. Exerc. 52 (8), 1793–1800 (2020). McCormick, D. P., Reyna, L. & Reifsnider, E. Calories, Caffeine and the Onset of Obesity in Young Children. Acad. Pediatr. 20 (6), 801–808 (2020). Elkind-Hirsch, K. E. et al. Liraglutide 3 mg on weight, body composition, and hormonal and metabolic parameters in women with obesity and polycystic ovary syndrome: a randomized placebo-controlled-phase 3 study. Fertil. Steril. 118 (2), 371–381 (2022). Lyu, X. et al. The Antiobesity Effect of GLP-1 Receptor Agonists Alone or in Combination with Metformin in Overweight /Obese Women with Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis. International Journal of Endocrinology, 2021: p. 6616693. (2021). Ma, R. L. et al. Short-term combined treatment with exenatide and metformin for overweight/obese women with polycystic ovary syndrome. Chin. Med. J. 134 (23), 2882–2889 (2021). Raoofi, A. et al. Therapeutic potentials of the caffeine in polycystic ovary syndrome in a rat model: Via modulation of proinflammatory cytokines and antioxidant activity. Allergol. Immunopathol. 50 (6), 137–146 (2022). Felmlee, M. A. et al. Monocarboxylate Transporters (SLC16): Function, Regulation, and Role in Health and Disease. Pharmacol. Rev. 72 (2), 466–485 (2020). Li, L., Bai, S. & Sheline, C. T. Erratum. hZnT8 (Slc30a8) Transgenic Mice That Overexpress the R325W Polymorph Have Reduced Islet Zn2 + and Proinsulin Levels, Increased Glucose Tolerance After a High-Fat Diet, and Altered Levels of Pancreatic Zinc Binding Proteins. Diabetes ; 66:551–559. Diabetes, 2019. 68(7): p. 1536. (2017). Tables Tables 1 to 8 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Tables18.docx Cite Share Download PDF Status: Published Journal Publication published 26 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 08 Oct, 2024 Reviews received at journal 06 Oct, 2024 Reviews received at journal 30 Sep, 2024 Reviewers agreed at journal 17 Sep, 2024 Reviewers agreed at journal 13 Sep, 2024 Reviewers invited by journal 08 Sep, 2024 Editor assigned by journal 08 Sep, 2024 Editor invited by journal 05 Sep, 2024 Submission checks completed at journal 04 Sep, 2024 First submitted to journal 15 Aug, 2024 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-4917244","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":363061703,"identity":"d74eba32-e043-46d0-ab60-5548e59fee97","order_by":0,"name":"Bai Tonglin","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Bai","middleName":"","lastName":"Tonglin","suffix":""},{"id":363061704,"identity":"81aac249-1459-4050-a7c1-2a2c58628611","order_by":1,"name":"Hu Yue","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Hu","middleName":"","lastName":"Yue","suffix":""},{"id":363061705,"identity":"01812f77-06e4-4c6c-ab73-1032fa25b8a4","order_by":2,"name":"Luo Ling","email":"","orcid":"","institution":"Naval Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Luo","middleName":"","lastName":"Ling","suffix":""},{"id":363061706,"identity":"e645489f-415b-4500-a5f2-786fa31dbbbb","order_by":3,"name":"Liu Yuhuan","email":"","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Yuhuan","suffix":""},{"id":363061707,"identity":"def4536a-9349-412a-bfdf-165b7f478226","order_by":4,"name":"Yu Chaoqin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAsElEQVRIiWNgGAWjYDACCRBRISHHxt5+gBQtZyyM+XjOJJCghbGtInGehIMBcTr4Zzdvk+Y5I5HeJsGQwPCjYhsRltw5VibNUyGR2ybdeICx58xtwloMJHLMQLbktskcSGBmbCNWC2+bRDqbRIIBaVoSiNcicSOt2HLOGQnDNmAgHyTKL/wzkjfeeFNRJy/f3n7wwY8KIrQAgYkEjHWAKPVAYPyBWJWjYBSMglEwQgEAVqU3SAlzA9cAAAAASUVORK5CYII=","orcid":"","institution":"the First Affiliated Hospital of Naval Medical University","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Chaoqin","suffix":""}],"badges":[],"createdAt":"2024-08-15 05:46:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4917244/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4917244/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-93890-w","type":"published","date":"2025-04-26T15:57:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":67130571,"identity":"71e4c256-65dc-4038-a229-afc37ec017a9","added_by":"auto","created_at":"2024-10-21 12:44:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2108290,"visible":true,"origin":"","legend":"\u003cp\u003eBioinformatics analysis. (A) Differentially expressed genes in adipose tissue. (B) Differentially expressed genes in granular cells. (C) Shared differentially expressed genes in adipose tissue and granulosa cells. (D) Differential expression of the SCL16A6 gene in granulosa cells. (E) ROC curve of SCL16A6 expression in granulosa cells. (F) Differentially expressed genes between the SCL16A6 high- and low-expression groups. (G) GO enrichment analysis of the top 30 genes. (H) KEGG analysis of the top 20 differentially expressed genes.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/3a9d8ea303b3db09593aabb7.png"},{"id":67129299,"identity":"3aa594d5-6609-4396-94e4-ea5a57a1e89c","added_by":"auto","created_at":"2024-10-21 12:36:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2108290,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork pharmacology of coffee. A. Target map of each component of coffee. B. Related targets of coffee in the treatment of PCOS. C. PPI network of intersecting targets. D. HUBBA map of core targets. E. Therapeutic target–active component network diagram. F. Ranking of component targets.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/5ed2a41ce0fef87c646a9ac8.png"},{"id":67130938,"identity":"b6108d59-c7af-48c9-b95a-85f4660e709e","added_by":"auto","created_at":"2024-10-21 12:52:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2108290,"visible":true,"origin":"","legend":"\u003cp\u003eGO/KEGG analyses of the intersecting genes. A. Sankey diagram of pathway targets. B. Target GO map. C. KEGG map of action targets.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/c27a45087b87f02746c03636.png"},{"id":67129295,"identity":"694d574c-3774-40f6-a6ba-df5e017e2274","added_by":"auto","created_at":"2024-10-21 12:36:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":803739,"visible":true,"origin":"","legend":"\u003cp\u003eThe establishment of an obese PCOS rat model was determined by weight changes. (A) Body weight and (B) body weight change (% initial body weight). The data are presented as the means ± SDs (significance level * P\u0026lt;0.05). Estrous cycle and ovarian morphological changes in obese PCOS rats. (C, D) Estrous cycles of two representative rats: E (estrus), M (metestrus), D (diestrus), and P (proestrus). (E) Photographs of representative ovarian morphology.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/0ff64c669b0234ed504c711c.png"},{"id":67129298,"identity":"ac4ce1be-6649-4b7e-ac53-1fba9c79158d","added_by":"auto","created_at":"2024-10-21 12:36:15","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":200893,"visible":true,"origin":"","legend":"\u003cp\u003eCaffeine treatment and the improvement of metabolism in obese PCOS rats. (A) Body weights, (B) FBG levels, (C) FINS levels, (D) GLP-1 levels, and (E) HOMA-IR. Caffeine treatment improved hormone levels in obese PCOS rats. (F) T levels, (G) LH levels, and (H) FSH levels. The data are presented as the means ± SDs (significance level: * model vs. CON, P \u0026lt; 0.05; #CFYN group and GLP-1 group compared with the model group, P \u0026lt; 0.05).\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/425c1d0b7c8e300abe27fd92.png"},{"id":67130573,"identity":"47478df2-7338-4ce3-b383-2415acc74ba3","added_by":"auto","created_at":"2024-10-21 12:44:15","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":551969,"visible":true,"origin":"","legend":"\u003cp\u003eEstrous cycle and ovarian morphological changes in obese PCOS rats treated with CFYN. (A) Estrous cycles of four representative rats: E (estrus), M (metestrus), D (diestrus), and P (proestrus). (B) Photographs of representative ovarian morphology.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/0ea4d7daf5a2b1dc913c1ad7.png"},{"id":67129297,"identity":"db66703c-9b0a-475f-b6e6-4a04f46b3a5b","added_by":"auto","created_at":"2024-10-21 12:36:15","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":13561,"visible":true,"origin":"","legend":"\u003cp\u003eCaffeine treatment affects SLC16A6 gene expression in obese PCOS rats. The data are presented as the means ± SDs (significance level * P\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/7b14945049fa3baf60d63065.png"},{"id":81570325,"identity":"36300cd4-4ddb-4105-afc5-c70022cd0f38","added_by":"auto","created_at":"2025-04-28 16:13:16","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3803295,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/460971d0-9d1f-47cc-bde3-eb5cdff96c33.pdf"},{"id":67129293,"identity":"e92f15ce-55b0-4a9a-a60c-91643282beea","added_by":"auto","created_at":"2024-10-21 12:36:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":236085,"visible":true,"origin":"","legend":"","description":"","filename":"Tables18.docx","url":"https://assets-eu.researchsquare.com/files/rs-4917244/v1/55470311f7fc9891cf9118ec.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Therapeutic effects and potential mechanisms of caffeine on obese polycystic ovary syndrome: bioinformatic analysis and experimental validation","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS) is a common gynecological endocrine disease in women of reproductive age, with a global incidence of approximately 5\u0026ndash;18%[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It clinically manifests as a menstrual disorder, infertility, hyperandrogenism, insulin resistance (IR), and obesity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The clinical manifestations of PCOS are highly heterogeneous and complex. The diagnostic criteria are different, and treatment is difficult, which seriously affects the physical and mental health of women [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Studies have shown that approximately 50% of women with PCOS are obese [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. A meta-analysis of 16 studies revealed that women with PCOS have a greater risk of metabolic diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A prospective study in Spain revealed that 28% of obese and overweight women of childbearing age have PCOS [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The risk of PCOS increases with increasing BMI. Although the etiology of PCOS is still unclear, obesity may be the driving factor for PCOS in people at risk, and weight loss is considered an important treatment for improving the symptoms of PCOS patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. However, researchers still do not know why some women suffer from PCOS with obesity and whether some genetic disorders contribute. Therefore, a better understanding of the pathophysiological mechanism of PCOS and active explorations of new molecular mechanisms and potential therapeutic targets for obese patients with PCOS are important.\u003c/p\u003e \u003cp\u003eIn recent years, bioinformatics and microarray technology have been widely used to identify potential genes involved in diseases [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], providing new methods for the prevention and treatment of diseases. In this study, bioinformatics analysis was used to screen the SLC16A6 gene potentially related to the pathogenesis of obese PCOS patients. This gene belongs to the human solute carrier (SLC) transporter family, which includes 400 genes and 52 subfamilies [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and has been shown to function in metabolic diseases [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Since most SLCs contribute to the transport of small organic molecules, several drugs targeting SLCs have been approved for marketing [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The study of SLCs has not advanced at present, and no study has shown that the SLC family can be used as a therapeutic target for the treatment of polycystic ovary syndrome. Current research focuses on the role of SLC transporters in metabolic diseases and on determining whether a subtype of the SLC protein family interacts with the endocrine and metabolic problems associated with PCOS. These findings may help to identify or treat this complex reproductive endocrine disease. Through reverse network pharmacology, this study predicted that coffee would have a potential therapeutic effect on obese PCOS patients. A clinical trial revealed that healthy subjects who drank black coffee daily for 2 weeks experienced significant reductions in body weight and BMI. In addition, waist circumference and abdominal fat were reduced [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The most important component of coffee is caffeine. Varillas-Delgado et al. reported that caffeine promotes fat oxidative metabolism and participates in lipid metabolism in the body [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], and a prospective cohort study confirmed that caffeine can effectively reduce the risk of obesity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Caffeine reduces the risk of type 2 diabetes by ameliorating disorders in the central insulin signaling pathway [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, this study used bioinformatics analysis combined with in vivo experiments to prove that SLC16A6 has a regulatory effect on PCOS and that coffee and its effective monomers can have a therapeutic effect on PCOS by regulating SLC16A6, providing certain life advice for obese women with PCOS and exploring new therapeutic targets for obese women with PCOS.\u003c/p\u003e"},{"header":"2. Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Bioinformatics:\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Analysis of differentially expressed genes and ROC curve analysis of obese PCOS patients\u003c/h2\u003e \u003cp\u003eAfter searching the GEO database, \"GSE10946\" and \"GSE193812\" were selected as the main research datasets. In GSE10946, 5 samples of granulosa cells from obese non-PCOS patients and 7 samples of granulosa cells from obese PCOS patients were used. In GSE193812, abdominal fat samples from 4 obese non-PCOS patients and 4 obese PCOS patients were used. The R packages \"limma\", \"edgeR\" and \"DESeq2\" were used to analyze the DEGs in the two datasets, and the criteria were set as P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and absolute logFC\u0026thinsp;\u0026ge;\u0026thinsp;1. The intersection of the DEGs was obtained using a Venn diagram. The GSE10946 dataset was used for the ROC curve analysis of the intersection genes, and the \"PROC\" package was used.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Analysis of differentially expressed genes and enrichment analysis of the SLC16A6 high- and low-expression groups\u003c/h2\u003e \u003cp\u003eThe \"DESeq2\" R package was used to select the samples with high and low SLC16A6 expression for the analysis of differentially expressed genes. After the DEGs were obtained, \"clusterProfiler\" was used for GO and KEGG enrichment analyses.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Network Pharmacology:\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Reverse network pharmacology analysis of SLC16A6\u003c/h2\u003e \u003cp\u003eThe herb database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://herb.ac.cn/\u003c/span\u003e\u003cspan address=\"http://herb.ac.cn/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) was used to search for natural compounds related to SLC16A6 in the database by entering \"SLC16A6\" and performing target queries.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Target analysis of active coffee ingredients\u003c/h2\u003e \u003cp\u003eThe effective active ingredients of coffee were obtained through literature retrieval, the active ingredients were included in the herb database and TCMSP database for queries, and the active ingredients with corresponding targets were saved.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 PCOS-related targets and potential targets of coffee in the treatment of PCOS\u003c/h2\u003e \u003cp\u003eThe PCOS targets were obtained from the following two databases: GeneCards[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and OMIM[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The key words \"polycystic ovarian syndrome\" and \"PCOS\" were searched in the two databases to obtain disease targets. Online Wayne figure tools (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bioinfogp.cnb.csic.es/tools/venny/\u003c/span\u003e\u003cspan address=\"https://bioinfogp.cnb.csic.es/tools/venny/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used to match PCOS disease-related genes and corresponding coffee target intersections to identify potential coffee targets for the treatment of PCOS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Protein‒protein interactions\u003c/h2\u003e \u003cp\u003eStudying the interactions between protein networks can help to identify the core genes involved. A protein‒protein interaction (PPI) analysis was performed using STRING, the potential targets obtained from the Venn diagram were input into the \"Multiple proteins\" analysis in STRING, and the species was limited to \"\u003cem\u003eHomo sapiens\u003c/em\u003e\". The default confidence of the platform was adopted to improve the reliability of the data. The degree values of each node in the PPI network were obtained using the analysis plug-in of Cytoscape 3.8.0, in which the target of the height value played a pivotal role. Through this step, the core targets of coffee were obtained.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.2.5 Network construction\u003c/h2\u003e \u003cp\u003eThe data on the effective components of coffee and potential targets for the treatment of PCOS were collated. The obtained data were imported into Cytoscape 3.8.0 software to visualize the drug\u0026ndash;active ingredient\u0026ndash;disease target genes network, and the number of targets corresponding to each active ingredient was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.2.6 Enrichment analysis\u003c/h2\u003e \u003cp\u003eThe DAVID tool [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e] was used to perform Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Biological processes, cellular components, molecular functions and key signaling pathways were identified, and the core mechanism of coffee and related biological pathways were explored. The functional annotations with P values less than 0.05 in the enrichment results were further analyzed. A bubble map of the enrichment results was drawn, and three pathways, namely, insulin, fat and oocyte development, were extracted for visualization.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Experimental verification\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1 Preparation and grouping of the obese PCOS rat model\u003c/h2\u003e \u003cp\u003eThirty-six SPF female Sprague\u0026ndash;Dawley rats (weight 160 g\u0026ndash;180 g) were randomly selected as the study subjects. SD rats were provided by Beijing Weitong Lichina Laboratory Animal Co., Ltd. [license number: SCXK (Beijing) 2016-0006]. The animals were housed in the animal room of the Experimental Center of Shanghai Changhai Hospital [license number: SCXK (Shanghai) 2017-0005]. The animals were fed in separate cages and had free access to food and water. The temperature of the animal room in the experimental center was 28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.5\u0026deg;C, the relative humidity was 40\u0026thinsp;\u0026plusmn;\u0026thinsp;5%, and the light and dark time was 12 hours each during the day and night. This experiment was reviewed and approved by the Experimental Animal Ethics Committee of the Medical Ethics Committee of Shanghai Changhai Hospital (ethical review number: CHEC (A.E.) 2022-011). All experimental procedures were performed in accordance with the relevant regulations for animal experiment ethics.\u003c/p\u003e \u003cp\u003eAt the age of 5 weeks, female Sprague‒Dawley rats were randomly divided into a PCOS model group (model group, n\u0026thinsp;=\u0026thinsp;22) and a control group (control group, n\u0026thinsp;=\u0026thinsp;8). According to a previous study, the rats in the model group were administered 1 mg/kg letrozole by gavage daily and fed a high-fat diet (energy composition of the animal high-fat diet formula: 60.65% fat, 21.22% carbohydrate, and 18.14% protein from Synbiotics (XTHF60)). The control group was administered 1 mg/kg normal saline by gavage once a day and fed a normal diet. After 28 days of the experiment, the body weight of the rats was measured, and the body weight of the model group increased by 20% compared with the control group, indicating that the obesity model was successfully established [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. The rats with a body weight increase of more than 20% were further divided into a model group (n\u0026thinsp;=\u0026thinsp;8), a CFYN group (n\u0026thinsp;=\u0026thinsp;6) and a GLP-1 group (n\u0026thinsp;=\u0026thinsp;8). The model group was administered 1 mg/kg normal saline by gavage once a day, the CFYN group was administered 10 mg/kg caffeine by gavage once a day, and the GLP-1 group was administered 0.63 mg/kg semaglutide by gavage once a day, along with a high-fat diet for 21 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2 Analysis of reproductive and metabolic phenotypes and blood and tissue sampling\u003c/h2\u003e \u003cp\u003eAfter the establishment of the model, the body masses of the rats in each group were measured every week and were summarized. From the 15th day of the estrous cycle, vaginal exfoliative cytology was continuously performed at 8:00 every day until the end of the experiment. The estrous cycle of the rats was evaluated by performing methylene blue staining of vaginal exfoliated cells. The estrous cycle table was generated, and the rate of estrous cycle disorder was calculated. At the end of the experiment (week 7), all rats were anesthesia with a Pentobarbital sodium, and blood and ovarian tissues were harvested. After the experiment, the rats were euthanized by high dose of pentobarbital sodium (150ཞ200mg/kg). Whole blood was centrifuged, and serum was collected and stored at \u0026minus;\u0026thinsp;80\u0026deg;C. The left ovary was fixed in 4% paraformaldehyde, and the right ovary was frozen for mRNA assays.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3 Morphology of the rat ovary tissue\u003c/h2\u003e \u003cp\u003eThe ovaries were fixed with 4% paraformaldehyde and dehydrated with different concentrations of ethanol. The representative ovaries were embedded in paraffin, sectioned into 4 \u0026micro;m thick sections, and then stained with hematoxylin and eosin. The fixed sections of rat ovarian tissue were observed under a microscope and photographed, and the number of cystic follicles and corpus luteum were counted.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.3.4 Rat serum analysis using enzyme-linked immunosorbent assay (ELISA)\u003c/h2\u003e \u003cp\u003eThe levels of follicle stimulating hormone (FSH), luteinizing hormone (LH), fasting insulin (FINS) and fasting blood glucose (GLU) in the serum were detected using enzyme-linked immunosorbent assay (ELISA) kits, and the ratio of LH to FSH and the insulin resistance index (HOMA-IR) were calculated. The specific calculation method used was FINS*FBG/22.5. ELISAs were performed according to the instructions of the ELISA detection kits.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.3.5 RT‒PCR was used to detect the expression of related genes in rat tissues\u003c/h2\u003e \u003cp\u003eThe tissue to be tested was removed from the frozen ovary, and RNA was extracted using the TRIzol lysis method (according to the TRIzol operating instructions from Invitrogen Company) and reverse transcribed into cDNA (according to the operating instructions of RR047A from TAKARA Company). The mRNA expression of the target gene was detected using real-time PCR (according to the operating instructions of TAKARA RR420A). After the reaction, the amplification curve and melting curve of real-time PCR were confirmed, and the data were analyzed.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.3.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations were used for statistical descriptions. ANOVA was used to compare more than two groups of normally distributed data. The Wilcoxon rank sum test was used to compare the sequencing data that were not normally distributed. All the statistical tests were two-tailed tests. When P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, the difference was considered statistically significant.\u003c/p\u003e \u003cp\u003e The methods section include involving live animals to be reported as described by the ARRIVE guidelines (PLoS Bio 8(6), e1000412,2010​).: (i) identifying the institutional and/or licensing committee approving the experiments, including any relevant details; (ii) confirming that all experiments were performed in accordance with relevant guidelines and regulations.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e3.1 Bioinformatics\u003c/p\u003e\n\u003cp\u003eFrom the GSE193812 dataset, a total of 472 differentially expressed genes in adipose tissue were identified, as shown in Figure 1A (Table 1). In the GSE10946 dataset, a total of 61 DEGs were found in the granule cell cluster, as shown in Figure 1B (Table 2). The common differentially expressed gene was SLC16A6 (Figure 1C). The GSE10946 dataset was selected to verify the expression of SLC16A6 and perform a ROC curve analysis. SLC16A6 was significantly increased in the granulosa cells of the PCOS group (P \u0026lt; 0.05) (Figure 1D). The AUC was 0.866, indicating that SLC16A6 expression in the granulosa cells of the PCOS group was strongly reliable (Figure 1E). A total of 356 DEGs were detected between the SLC16A6 high- and low-expression groups (Figure 1F). GO and KEGG analyses were performed on 356 DEGs, and a total of 380 significant GO terms were identified (Table\u0026nbsp;3). The top 30 GO terms are shown in Figure 1G. These pathways included \u0026quot;insulin-like growth factor receptor binding\u0026quot;, \u0026quot;insulin-like growth factor II binding\u0026quot;, insulin-like growth factor I binding, estradiol 17-beta-dehydrogenase activity, steroid dehydrogenase activity, etc., and \u0026quot;sex hormone-related pathways\u0026quot; (Figure 1G). Twenty significantly different pathways were identified according to the KEGG enrichment results (Table\u0026nbsp;4): \u0026quot;TGF-beta signaling pathway\u0026quot;, \u0026quot;ovarian steroidogenesis\u0026quot;, \u0026quot;proline signaling pathway\u0026quot;, \u0026quot;regulation of lipolysis in\u0026quot; \u0026quot;adipocytes\u0026quot;, \u0026quot;type II diabetes mellitus\u0026quot; and other inflammation-, intraovarian sterol-, and diabetes-related pathways (Figure 1H).\u003c/p\u003e\n\u003cp\u003e3.2, Network pharmacology\u003c/p\u003e\n\u003cp\u003eA total of 15 corresponding components associated with SLC16A6 (Table\u0026nbsp;5), which contained three coffee-related substances, namely, \u0026quot;coffee acid\u0026quot;, \u0026quot;caffeic\u0026quot;, and \u0026quot;trans-caffeic acid\u0026quot;, were obtained from the herb database.\u0026nbsp;Seven effective substances in coffee were collected, namely, \u0026quot;coffee acid\u0026quot;, \u0026quot;caffeic\u0026quot;, \u0026quot;trans-caffeic acid\u0026quot;, \u0026quot;cafestol\u0026quot;, \u0026quot;trigonelline\u0026quot;, \u0026quot;caffeine\u0026quot;, and \u0026quot;chlorogenic acid\u0026quot;. A total of 147 targets corresponding to 7 substances were identified (Table\u0026nbsp;6) (Figure 2A). The database revealed 3240 PCOS genes and 73 intersecting genes between active coffee ingredients and PCOS (Table\u0026nbsp;7) (Figure 2B). The PPI network diagram of the\u0026nbsp;intersecting genes was constructed (Figure 2C), and the core targets of the PPI network diagram were analyzed. The top three targets were \u0026quot;INS\u0026quot;, \u0026quot;TP53\u0026quot;, and \u0026quot;TNF\u0026quot; (Figure 2D). A network diagram of coffee components and therapeutic targets for PCOS treatment was constructed (Figure 2E), and the corresponding targets of each component were statistically analyzed. Forty-six targets corresponding to caffeine were identified (Figure 2F), which was far more than the number of other components, indicating its core role. KEGG and GO analyses were performed on the 73 intersecting genes. Figure 3A shows the involvement of genes in each pathway, and the INS genes were involved in multiple pathways. Figure 3B shows the top 30 GO terms, including \u0026quot;regulation of insulin secretion\u0026quot;, \u0026quot;glucose transmembrane transporter activity\u0026quot;, \u0026quot;D-glucose transmembrane transporter\u0026quot; activity\u0026quot; and \u0026quot;other insulin or glucose-related pathways\u0026quot;. After the KEGG analysis, the intersecting gene enrichment pathways were closely related to fat metabolism, insulin, and oocyte development, as shown in Figure 3C.\u003c/p\u003e\n\u003cp\u003e3.3. Experimental verification\u003c/p\u003e\n\u003cp\u003e3.3.1 Reproductive and metabolic phenotypes of obese PCOS rats\u003c/p\u003e\n\u003cp\u003eIn this study, the obese PCOS group was confirmed by changes in body weight. By the end of the induction (4 weeks), the body weight of the model group increased significantly (339.5\u0026plusmn;29.8 g) compared with that of the control group (234.7\u0026plusmn;11.8 g) (Fig. 4A). Compared with the control group (35.04%), the weight change in the model group was significantly greater (95.35%) (Figure 4B).The vaginal pictures of the control group suggested that the control group had a regular, complete estrous cycle of 4 to 5 days, while the vaginal smear of the model group showed aperiorism, mainly in the interestrus period (Figure 4C.4D). The HE-stained sections from the control group had a normal ovarian morphology. Microscopy revealed that the ovaries of the control group had multiple corpora lutea and follicles at various stages of development, and multilayer granulosa cells were neatly arranged. The HE-stained sections of the ovaries in the model group showed an obvious polycystic morphology with typical polycystic pathological changes under the microscope, with a large number of dilated cystic follicles, no or little luteal tissue, corona radiata and oocytes, and a significant reduction in the number of granulosa cell layers (Fig. 4E).\u003c/p\u003e\n\u003cp\u003eIn conclusion, rats subjected to long-term intragastric administration of letrozole and a high-fat diet (model group) exhibited obvious obese PCOS-like changes.\u003c/p\u003e\n\u003cp\u003e3.3.2 Caffeine treatment restored the estrous cycle in obese PCOS rats\u003c/p\u003e\n\u003cp\u003eVaginal exfoliative cytology was performed on the four groups of rats, and continuous observations were recorded until the end of the experiment. The results of vaginal smears showed that all rats in the control group had a regular and complete estrous cycle, which lasted for 4-5 days. In the model group, the estrous cycle was disrupted in 6 rats, and the normal rate of the estrous cycle was only 25%, which was mainly manifested as the prolongation of interestrus. The CFYN group and GLP-1 group were similar to the control group. Five rats in the CFYN group had a normal estrous cycle after the experiment, and the normal rate of the estrous cycle was 83%. Six rats in the GLP-1 group had normal estrous cycles at the end of the experiment, and the percentage of normal estrous cycles was 75% (Fig. 6A). Based on histological HE staining, the study revealed that the model group still exhibited significant PCOS-like changes in the ovaries, and the CFYN group and the GLP-1 group exhibited a reduced number of cystic follicles, an increased number of corpus lutea, and improvements in polycystic changes (Figure 6B).\u003c/p\u003e\n\u003cp\u003e3.3.3 Effects of caffeine on body weight and endocrine metabolism in obese PCOS rats\u003c/p\u003e\n\u003cp\u003eIn this study, the successful establishment of an obese PCOS model was confirmed by changes in body weight. After the establishment of the model (after 4 weeks), the obese PCOS rats were randomly divided into three groups: the untreated obese PCOS group (model group), caffeine gavage treatment group (CFYN group), and oral semaglutide gavage treatment group (GLP-1 group). After 3 weeks of treatment, no significant difference in body weight was observed between the CFYN group (373.3\u0026plusmn;66.2 g) and the model group (401.5\u0026plusmn;31.7 g), while the GLP-1 group (281.8\u0026plusmn;36.3 g) and the model group (401.5\u0026plusmn;31.7 g) showed a significant reduction in body weight (Fig. 5A).\u003c/p\u003e\n\u003cp\u003eSerum samples were collected after a 12 h fast. The GLP-1, fasting plasma glucose and fasting insulin levels were compared among the model, CFYN, GLP-1 and control groups, and the corresponding insulin resistance index was calculated. A significant difference was observed between the model group and the control group (P\u0026lt;0.05). The FINS level of the CFYN group was significantly improved compared with the model group (P\u0026lt;0.05), and a significant difference was not observed between the CFYN group and the GLP-1 treatment group (Fig. 5B-E).\u003c/p\u003e\n\u003cp\u003eIn this study, the serum testosterone, FSH, and LH levels were analyzed in each group (Figure 5F-H). The data of this study showed that the testosterone level of the model group (11.74\u0026plusmn;1.2 nmol/L) was significantly higher than that of the control group (7.23\u0026plusmn;1.35 nmol/L) (P\u0026lt;0.05), which showed the typical hyperandrogenemia characteristics of PCOS. A significant difference in testosterone levels was not observed between the CFYN group and the GLP-1 group. (P\u0026lt;0.05) (Fig. 5F). The FSH level in the model group (5.9\u0026plusmn;0.84 U/l) was significantly higher than that in the control group (2.79\u0026plusmn;0.38 U/l) (P\u0026lt;0.05), and a significant difference was detected between the CFYN group and the model group (P\u0026lt;0.05); however, no significant difference was observed between the CFYN group and the GLP-1 group (Fig. 5G). Moreover, the LH level in the model group (8.69\u0026plusmn;1.49 mIU/ml) was significantly higher than that in the control group (3.69\u0026plusmn;1.27 mIU/ml) (P\u0026lt;0.05), which is a typical characteristic of PCOS. A significant difference was observed between the CFYN group and the model group (P\u0026lt;0.05), but no significant difference was detected between the CFYN group and the GLP-1 group (Fig. 5H), which indicated that CFYN could significantly restore the ovarian function of PCOS rats and improve their symptoms.\u003c/p\u003e\n\u003cp\u003e3.3.4 Effect of caffeine on the expression of the SLC16A6 transporter gene in obese PCOS rats\u003c/p\u003e\n\u003cp\u003eRT‒PCR was used to detect the gene expression of the SLC16A6 transporter in different groups of rats. Compared with the normal control group, the expression of the SLC16A6 gene in the model group was significantly decreased, and significant differences were not observed among the control group, CFYN group and GLP-1 group. However, significant differences were noted between the CON group, CFYN group and GLP-1 group and the model group (P\u0026lt;0.05) (Fig. 7).\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003ePCOS is a common heterogeneous endocrine and metabolic disorder in women of childbearing age, and its pathogenesis is not yet fully understood [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, recent studies have shown that the reproductive and metabolic levels of PCOS patients with obesity are more likely to be affected, and obesity exacerbates reproductive and metabolic disorders in PCOS patients [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. Excessive weight gain can lead to insulin resistance and disorders in the levels of other hormones. Additionally, obesity and elevated androgen levels can also affect female reproductive function [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]. Moreover, studies have shown that obesity and insulin resistance are high-risk factors affecting female reproductive function, and eliminating these risk factors may help to reduce or treat female reproductive disorders [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n\u003cp\u003eIn recent years, studies on obesity and polycystic ovary syndrome have become increasingly extensive. According to the treatment recommendations of the international evidence-based guidelines for PCOS, lifestyle interventions, including diet, exercise, and behavior, are recommended as first-line management for obese PCOS patients [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]. However, simple changes in diet structure and lifestyle have a relatively high dropout rate most of the time, and obese PCOS patients are unable to easily and effectively adhere to these changes to achieve weight control. Therefore, the use of adjuvant drug therapy is very important to help obese PCOS patients control weight and improve their endocrine function.\u003c/p\u003e\n\u003cp\u003eCurrent research shows that the key factor in the treatment of obese PCOS patients is controlling body weight, and excess weight leads to endocrine disorders in obese PCOS patients, affecting reproductive fertility [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Caffeine, one of the main components of coffee, has not been directly confirmed to affect female reproduction, but some studies suggest that women should not consume excess caffeine during pregnancy [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]. Related studies have shown that caffeine intake is related to waist circumference and body mass index. Caffeine can better help women lose weight [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e], but in other studies, caffeine intake was shown to lead to obesity in children [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. In the present study, obese PCOS model rats exhibited estrous cycle disorders, which were significantly improved after weight loss. Consistent with current clinical research and treatment, weight loss and lifestyle improvements can improve the symptoms of obese PCOS patients [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]. Moreover, obese PCOS rats have various hormone and metabolic disorders, which further aggravate the symptoms of PCOS in obese rats. After caffeine treatment, the obese PCOS rats showed significant improvements in the corresponding symptoms and the estrous cycle also recovered, but the obese PCOS rats treated with caffeine did not show significant differences in body weight or the metabolic level compared with the obese PCOS rats without intervention, which may be related to caffeine intake. Follow-up experiments are needed for verification to explore whether different levels of caffeine exert different metabolic effects on obese PCOS models. Studies have shown that caffeine can effectively reverse histopathological damage, cell death, inflammation and the antioxidant status in PCOS rats [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]. The results of this part of the previous study were also mutually verified with the experimental results of this study, which proved the credibility of the results and further supported the therapeutic mechanism of caffeine in the treatment of PCOS rats. Clinically, if caffeine must be used to treat obese PCOS patients, exercise and lifestyle changes or other drugs should be used at the same time.\u003c/p\u003e\n\u003cp\u003eThis study also investigated whether the gene expression profile of the SLC16A6 transporter was associated with obese PCOS patients. SLC transporters mediate the transport of a variety of essential nutrients and metabolites, such as glucose, amino acids, vitamins, neurotransmitters, and inorganic/metal ions. The SLC16 subfamily is composed of 14 members of the monocarboxylic acid transporter (MCT) family, which plays crucial roles in the transport of important cellular nutrients, cell metabolism, and pH regulation [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]. In transgenic animal models, SLC transporters are involved in many important metabolic processes, including nutrient supply, metabolic transformation, and energy homeostasis [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]. In the preliminary experimental preparation of this study, data on the possible correlation between SLC16A6 and obese PCOS patients were screened, and the expression of SLC16A6 in fat and granule cells of obese PCOS patients was increased. Considering the physiological function of SLC16A6, this increase may be a form of physiological compensation, and further exploration is needed. The relationship between SLC16A6 expression and obese PCOS patients was confirmed in an animal experiment. This result may suggest that the expression of the SLC16A6 transporter is correlated with changes in reproductive function, and further verification is needed to confirm this hypothesis. In general, the results of the experimental verification of this study show that caffeine can improve the apparent symptoms of obese patients with PCOS and the corresponding changes in gene expression. The experimental results of this study still need to be further investigated through subsequent experimental verification to determine the corresponding specific mechanism and pathway. For example, how caffeine improves the symptoms of obese PCOS patients, whether it also improves the symptoms of nonobese PCOS patients, or whether caffeine improves the reproductive function of obese PCOS patients still need to be further verified.\u003c/p\u003e\n\u003cp\u003eAt present, a variety of hypotheses and conjectures have been proposed on the relationship between the etiology and development of obesity and polycystic ovary syndrome, but researchers have not clearly determined which disease is dominant. The pathological changes associated with obesity and polycystic ovary syndrome affect each other, causing further development of the disease and subsequently affecting reproductive function in women. In the present study, caffeine treatment significantly improved the recovery rate of the estrous cycle and improved the symptoms of PCOS in obese PCOS rats by regulating endocrine function and ovarian function. In summary, the results of this study indicate that patients can receive more targeted drugs for clinical treatment to achieve better therapeutic effects. Moreover, these findings suggest a possible relationship between the SLC16A6 transporter and reproductive function, which provides new ideas for the study of the occurrence and development of PCOS in obese patients.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e \u003ch2\u003eConflict of interest:\u003c/h2\u003e \u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003c/p\u003e\u003ch2\u003eFunding:\u003c/h2\u003e \u003cp\u003eThis work was supported by the National Natural Science Foundation of China(81973896, 82004408), Shanghai Sailing Program༈20YF1448600༉, Natural Science Foundation of Shanghai༈23ZR1478600༉, Traditional Chinese Medicine Research Project of Shanghai Municipal Health Commission༈2022CX003༉\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eT.-L.B. ,Y.H and L.L. contributed equally to this work. T.-L.B. ,Y.H.and L.L. designed the experiments, performed the experiments, analyzed the data, and wrote the manuscript. C.-Q.Y. and Y.-H.L. contributed to the study design and manuscript preparation.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data underlying this article will be shared upon reasonable request to the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eJoham, A. E. et al. \u003cem\u003ePolycystic ovary syndrome.\u003c/em\u003e The Lancet. \u003cem\u003eDiabetes Endocrinol.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e (9), 668\u0026ndash;680 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzziz, R. et al. The prevalence and features of the polycystic ovary syndrome in an unselected population. \u003cem\u003eJ. Clin. Endocrinol. Metab.\u003c/em\u003e \u003cb\u003e89\u003c/b\u003e (6), 2745\u0026ndash;2749 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCarmina, E. Diagnosis of polycystic ovary syndrome: from NIH criteria to ESHRE-ASRM guidelines. \u003cem\u003eMinerva Ginecol.\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e (1), 1\u0026ndash;6 (2004).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEscobar-Morreale, H. F. Polycystic ovary syndrome: definition, aetiology, diagnosis and treatment. \u003cem\u003eNat. Rev. Endocrinol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e (5), 270\u0026ndash;284 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlueck, C. J. \u0026amp; Goldenberg, N. Characteristics of obesity in polycystic ovary syndrome: Etiology, treatment, and genetics. \u003cem\u003eMetab. Clin. Exp.\u003c/em\u003e \u003cb\u003e92\u003c/b\u003e, 108\u0026ndash;120 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMoran, L. J. et al. Impaired glucose tolerance, type 2 diabetes and metabolic syndrome in polycystic ovary syndrome: a systematic review and meta-analysis. \u003cem\u003eHum. Reprod. Update\u003c/em\u003e. \u003cb\u003e16\u003c/b\u003e (4), 347\u0026ndash;363 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlvarez-Blasco, F. et al. Prevalence and characteristics of the polycystic ovary syndrome in overweight and obese women. \u003cem\u003eArch. Intern. Med.\u003c/em\u003e \u003cb\u003e166\u003c/b\u003e (19), 2081\u0026ndash;2086 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu, L. et al. Efficacy of Bariatric Surgery in the Treatment of Women With Obesity and Polycystic Ovary Syndrome. \u003cem\u003eJ. Clin. Endocrinol. Metab.\u003c/em\u003e \u003cb\u003e107\u003c/b\u003e (8), e3217\u0026ndash;e3229 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, K. et al. Association Between RSK2 and Clinical Indexes of Primary Breast Cancer: A Meta-Analysis Based on mRNA Microarray Data. \u003cem\u003eFront. Genet.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 770134 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, Y. et al. \u003cem\u003eThe SLC transporter in nutrient and metabolic sensing, regulation, and drug development\u003c/em\u003e. \u003cem\u003eJ. Mol. Cell Biol.\u003c/em\u003e, \u003cb\u003e11\u003c/b\u003e(1). (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDupuis, J. et al. New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk. \u003cem\u003eNat. Genet.\u003c/em\u003e \u003cb\u003e42\u003c/b\u003e (2), 105\u0026ndash;116 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNikitin, A. G. et al. Association of polymorphic markers of genes FTO, KCNJ11, CDKAL1, SLC30A8, and CDKN2B with type 2 diabetes mellitus in the Russian population. \u003cem\u003ePeerJ\u003c/em\u003e. \u003cb\u003e5\u003c/b\u003e, e3414 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones, R. S. \u0026amp; Morris, M. E. Monocarboxylate Transporters: Therapeutic Targets and Prognostic Factors in Disease. \u003cem\u003eClin. Pharmacol. Ther.\u003c/em\u003e \u003cb\u003e100\u003c/b\u003e (5), 454\u0026ndash;463 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRevuelta-Iniesta, R. \u0026amp; Al-Dujaili, E. A. S. \u003cem\u003eConsumption of green coffee reduces blood pressure and body composition by influencing 11β-HSD1 enzyme activity in healthy individuals: a pilot crossover study using green and black coffee.\u003c/em\u003e BioMed Research International, 2014: p. 482704. (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVarillas-Delgado, D. et al. Effect of 3 and 6 mg/kg of caffeine on fat oxidation during exercise in healthy active females. \u003cem\u003eBiology Sport\u003c/em\u003e. \u003cb\u003e40\u003c/b\u003e (3), 827\u0026ndash;834 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHenn, M. et al. \u003cem\u003eChanges in Coffee Intake, Added Sugar and Long-Term Weight Gain - Results from Three Large Prospective US Cohort Studies\u003c/em\u003e (The American Journal of Clinical Nutrition, 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSong, X. et al. \u003cem\u003eCurrent therapeutic targets and multifaceted physiological impacts of caffeine\u003c/em\u003e (PTR, 2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRebhan, M. et al. GeneCards: a novel functional genomics compendium with automated data mining and query reformulation support. \u003cem\u003eBioinf. (Oxford England)\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (8), 656\u0026ndash;664 (1998).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAmberger, J. S. et al. OMIM.org: Online Mendelian Inheritance in Man (OMIM\u0026reg;), an online catalog of human genes and genetic disorders. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (Database issue), D789\u0026ndash;D798 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDennis, G. et al. DAVID: Database for Annotation, Visualization, and Integrated Discovery. \u003cem\u003eGenome Biol.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (5), P3 (2003).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu, K. et al. Preparation of a nano emodin transfersome and study on its anti-obesity mechanism in adipose tissue of diet-induced obese rats. \u003cem\u003eJ. Translational Med.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 72 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLie Fong, S. et al. \u003cem\u003ePolycystic ovarian morphology and the diagnosis of polycystic ovary syndrome: redefining threshold levels for follicle count and serum anti-Mullerian hormone using cluster analysis.\u003c/em\u003e Human Reproduction (Oxford, England), 32(8): pp. 1723\u0026ndash;1731. (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArya, S. et al. \u003cem\u003eMetabolic syndrome in obesity: treatment success and adverse pregnancy outcomes with ovulation induction in polycystic ovary syndrome\u003c/em\u003e. \u003cem\u003eAm. J. Obstet. Gynecol.\u003c/em\u003e, \u003cb\u003e225\u003c/b\u003e(3). (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSilvestris, E. et al. \u003cem\u003eObesity as disruptor of the female fertility\u003c/em\u003e16p. 22 (RB\u0026amp;E, 2018). 1.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFornes, R. et al. The effect of androgen excess on maternal metabolism, placental function and fetal growth in obese dams. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e (1), 8066 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLuke, B. Adverse effects of female obesity and interaction with race on reproductive potential. \u003cem\u003eFertil. Steril.\u003c/em\u003e \u003cb\u003e107\u003c/b\u003e (4), 868\u0026ndash;877 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVenkatesh, S. S. et al. Obesity and risk of female reproductive conditions: A Mendelian randomisation study. \u003cem\u003ePLoS Med.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (2), e1003679 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTeede, H. J. et al. Recommendations from the international evidence-based guideline for the assessment and management of polycystic ovary syndrome. \u003cem\u003eHum. Reprod. (Oxford England)\u003c/em\u003e. \u003cb\u003e33\u003c/b\u003e (9), 1602\u0026ndash;1618 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBoedt, T. et al. Preconception lifestyle advice for people with infertility. \u003cem\u003eCochrane Database Syst. Rev.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e (4), CD008189 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlkhatib, A. et al. Caffeine Optimizes HIIT Benefits on Obesity-associated Metabolic Adversity in Women. \u003cem\u003eMed. Sci. Sports. Exerc.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e (8), 1793\u0026ndash;1800 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcCormick, D. P., Reyna, L. \u0026amp; Reifsnider, E. Calories, Caffeine and the Onset of Obesity in Young Children. \u003cem\u003eAcad. Pediatr.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e (6), 801\u0026ndash;808 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElkind-Hirsch, K. E. et al. Liraglutide 3 mg on weight, body composition, and hormonal and metabolic parameters in women with obesity and polycystic ovary syndrome: a randomized placebo-controlled-phase 3 study. \u003cem\u003eFertil. Steril.\u003c/em\u003e \u003cb\u003e118\u003c/b\u003e (2), 371\u0026ndash;381 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLyu, X. et al. \u003cem\u003eThe Antiobesity Effect of GLP-1 Receptor Agonists Alone or in Combination with Metformin in Overweight /Obese Women with Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis.\u003c/em\u003e International Journal of Endocrinology, 2021: p. 6616693. (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMa, R. L. et al. Short-term combined treatment with exenatide and metformin for overweight/obese women with polycystic ovary syndrome. \u003cem\u003eChin. Med. J.\u003c/em\u003e \u003cb\u003e134\u003c/b\u003e (23), 2882\u0026ndash;2889 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaoofi, A. et al. Therapeutic potentials of the caffeine in polycystic ovary syndrome in a rat model: Via modulation of proinflammatory cytokines and antioxidant activity. \u003cem\u003eAllergol. Immunopathol.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (6), 137\u0026ndash;146 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFelmlee, M. A. et al. Monocarboxylate Transporters (SLC16): Function, Regulation, and Role in Health and Disease. \u003cem\u003ePharmacol. Rev.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e (2), 466\u0026ndash;485 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, L., Bai, S. \u0026amp; Sheline, C. T. \u003cem\u003eErratum. hZnT8 (Slc30a8) Transgenic Mice That Overexpress the R325W Polymorph Have Reduced Islet Zn2\u0026thinsp;+\u0026thinsp;and Proinsulin Levels, Increased Glucose Tolerance After a High-Fat Diet, and Altered Levels of Pancreatic Zinc Binding Proteins. Diabetes\u003c/em\u003e ; 66:551\u0026ndash;559. Diabetes, 2019. 68(7): p. 1536. (2017).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 to 8 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Polycystic ovary syndrome, bioinformatics analysis, network pharmacology, caffeine, SLC16A6","lastPublishedDoi":"10.21203/rs.3.rs-4917244/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4917244/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe risk of PCOS is significantly increased in obese women, and studies have shown that weight loss can improve the symptoms of PCOS. Coffee has been shown to effectively reduce body weight. In this study, we focused on the SLC16A6 gene through bioinformatics and searched for coffee and its monomers through reverse network pharmacology.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eThe Gene Expression Omnibus (GEO) database was searched to screen differentially expressed genes (DEGs) with PCOS patients. Gene Ontology (GO) functional enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were subsequently performed. The effects of caffeine on body weight, the estrous cycle, ovarian pathology, the serum insulin concentration and the insulin resistance index, and the expression of the SLC16A6 transporter gene in the ovarian tissues of obese PCOS rats were observed.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe common differentially expressed gene SLC16A6 was identified in this study, and animal experiments confirmed the effectiveness of caffeine in the treatment of obese PCOS rats.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eCaffeine can effectively improve the symptoms of obese PCOS rats. The mechanism by which caffeine can treat obese patients with PCOS is related to increasing the expression of the SLC16A6 gene.\u003c/p\u003e","manuscriptTitle":"Therapeutic effects and potential mechanisms of caffeine on obese polycystic ovary syndrome: bioinformatic analysis and experimental validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-21 12:36:10","doi":"10.21203/rs.3.rs-4917244/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-08T04:49:15+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-06T21:15:42+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-09-30T18:32:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"99504223626501523806751634146320315469","date":"2024-09-17T07:48:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"17429454587785083324708392107323991204","date":"2024-09-13T15:32:49+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-08T15:28:33+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-09-08T14:59:32+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-09-05T19:43:53+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-09-05T03:52:01+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-08-15T05:43:03+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"97c2c132-327c-48c5-93ef-d23ec9b90ccd","owner":[],"postedDate":"October 21st, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":38617225,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":38617226,"name":"Health sciences/Endocrinology/Endocrine system and metabolic diseases"},{"id":38617227,"name":"Health sciences/Diseases/Endocrine system and metabolic diseases"},{"id":38617228,"name":"Health sciences/Diseases/Metabolic disorders"},{"id":38617229,"name":"Health sciences/Diseases/Reproductive disorders"}],"tags":[],"updatedAt":"2025-04-28T16:10:26+00:00","versionOfRecord":{"articleIdentity":"rs-4917244","link":"https://doi.org/10.1038/s41598-025-93890-w","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-04-26 15:57:11","publishedOnDateReadable":"April 26th, 2025"},"versionCreatedAt":"2024-10-21 12:36:10","video":"","vorDoi":"10.1038/s41598-025-93890-w","vorDoiUrl":"https://doi.org/10.1038/s41598-025-93890-w","workflowStages":[]},"version":"v1","identity":"rs-4917244","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4917244","identity":"rs-4917244","version":["v1"]},"buildId":"omnImTCwR2MFx8CMYfrG7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}