Integrative bioinformatics and transcriptomic analysis identifies biomarkers in Polycystic Ovary Syndrome through machine learning approach.

Background: Polycystic ovary syndrome (PCOS) is the most common metabolic and endocrinopathies disorder in women of reproductive age and non-alcoholic fatty liver (NAFLD) is one of the most common liver diseases worldwide. Previous research has indicated potential associations between PCOS and NAFLD, but the underlying pathophysiology is still not clear. The present study aims to identify the differentially expressed genes (DEGs) between PCOS and NAFLD through the bioinformatics method, and explore the associated molecular mechanisms. Methods: The microarray datasets GSE34526 and GSE63067 were downloaded from Gene Expression Omnibus (GEO) database and analyzed to obtain the DEGs between PCOS and NAFLD with the GEO2R online tool. Next, the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis for the DEGs were performed. Then, the protein-protein interaction (PPI) network was constructed and the hub genes were identified using the STRING database and Cytoscape software. Finally, NetworkAnalyst was used to construct the network between the targeted microRNAs (miRNAs) and the hub genes. Results: A total of 52 genes were identified as DEGs in the above two datasets. GO and KEGG enrichment analysis indicated that DEGs are mostly enriched in immunity and inflammation related pathways. In addition, nine hub genes, including TREM1, S100A9, FPR1, NCF2, FCER1G, CCR1, S100A12, MMP9, and IL1RN were selected from the PPI network by using the cytoHubba and MCODE plug-in. Then, four miRNAs, including miR-20a-5p, miR-129-2-3p, miR-124-3p, and miR-101-3p, were predicted as possibly the key miRNAs through the miRNA-gene network construction. Conclusion: In summary, we firstly constructed a miRNA-gene regulatory network depicting interactions between the predicted miRNA and the hub genes in NAFLD and PCOS, which provides novel insights into the identification of potential biomarkers and valuable therapeutic leads for PCOS and NAFLD.

BackgroundPatients with polycystic ovary syndrome (PCOS) often experience a range of metabolic comorbidities, suggesting a potential association between PCOS and metabolic syndrome (MetS). However, this potential link has not yet been fully elucidated.MethodsThis study employed transcriptomic analysis and machine learning techniques to identify key genes and signaling pathways associated with both PCOS and MetS. Differentially expressed genes (DEGs) were analyzed, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. Machine learning algorithms were used to identify hub genes, and their diagnostic potential was assessed using Receiver Operating Characteristic (ROC) curves.ResultsA total of 373 DEGs were identified in PCOS, and 516 DEGs in MetS, with 14 overlapping genes considered critical for both conditions. Six hub genes including Dihydropyrimidinase-like 4(DPYSL4), FBJ osteosarcoma oncogene(FOS), Jun dimerization protein 2(JDP2), Stearoyl-CoA desaturase(SCD), Tribbles pseudokinase 1(TRIB1), Zinc finger protein 331(ZNF331) were selected through various machine learning methods. Enrichment analyses revealed that these genes significantly influence apoptosis, TNF signaling, and lipid metabolism pathways, highlighting their roles in the pathogenesis of PCOS and MetS.ConclusionsThese findings suggest that these genes may serve as potential therapeutic targets for the prevention and treatment of comorbidities in patients with PCOS and MetS. The identified hub genes play significant roles in the development of PCOS and MetS, underscoring the need for further research on these genes. This study offers insights into molecular interactions and potential biomarkers for early diagnosis and therapeutic targets for these syndromes. Future studies should aim to validate these findings in larger cohorts to enhance their clinical applicability.Clinical trial numberNot applicable.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13048-025-01787-z.

Previous studies have shown that asthma is a risk factor for lung cancer, while the mechanisms involved remain unclear. We attempted to further explore the association between asthma and non-small cell lung cancer (NSCLC) via bioinformatics analysis. We obtained GSE143303 and GSE18842 from the GEO database. Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) groups were downloaded from the TCGA database. Based on the results of differentially expressed genes (DEGs) between asthma and NSCLC, we determined common DEGs by constructing a Venn diagram. Enrichment analysis was used to explore the common pathways of asthma and NSCLC. A protein-protein interaction (PPI) network was constructed to screen hub genes. KM survival analysis was performed to screen prognostic genes in the LUAD and LUSC groups. A Cox model was constructed based on hub genes and validated internally and externally. Tumor Immune Estimation Resource (TIMER) was used to evaluate the association of prognostic gene models with the tumor microenvironment (TME) and immune cell infiltration. Nomogram model was constructed by combining prognostic genes and clinical features. 114 common DEGs were obtained based on asthma and NSCLC data, and enrichment analysis showed that significant enrichment pathways mainly focused on inflammatory pathways. Screening of 5 hub genes as a key prognostic gene model for asthma progression to LUAD, and internal and external validation led to consistent conclusions. In addition, the risk score of the 5 hub genes could be used as a tool to assess the TME and immune cell infiltration. The nomogram model constructed by combining the 5 hub genes with clinical features was accurate for LUAD. Five-hub genes enrich our understanding of the potential mechanisms by which asthma contributes to the increased risk of lung cancer.

Background:The aim of this study was to find underlying genes and their interaction mechanism crucial to the polycystic ovarian syndrome (PCOS) by analyzing differentially expressed genes (DEGs) between PCOS and non-PCOS subjects.Methods:Gene expression data of PCOS and non-PCOS subjects were collected from gene expression omnibus (GEO) database. GEO2R were used to calculating P value and logFC. The screening threshold of DEGs was P < .05 and | FC | ≥ 1.2. GO annotation and Kyoto encyclopedia of genes and genomes (KEGG) signaling pathway enrichment analysis was performed by using DAVID (2021 Update). The protein-protein interaction (PPI) network of DEGs was constructed by using the STRING database, and the hub genes were recognized through Hubba plugin of Cytoscape software.Results:PCOS and non-PCOS subjects shared a total of 174 DGEs, including 14 upregulated and 160 downregulated genes. The GO biological processes enriched by DEGs mainly involved actin cytoskeleton organization, positive regulation of NF-κB signaling pathway, and positive regulation of canonical Wnt signaling pathway. The DEGs were significantly enriched in cytoplasm, nucleus and cytosol. Their molecular functions mainly focused on protein binding, calmodulin binding and glycerol-3-phosphate dehydrogenase activity. The PI3K/Akt signaling pathway and glycosaminoglycan biosynthesis were highlighted as critical pathways enriched by DEGs. 10 hub genes were screened from the constructed PPI network, of which EGF, FN1 and TLR4 were mainly enriched in the PI3K/Akt signaling pathway.Conclusion:In this study, a total of 174 DEGs and 10 hub genes were identified as new candidate targets for insulin resistance (IR) in PCOS individuals, which may provide a new direction for developing novel treatment strategies for PCOS.

Polycystic ovarian syndrome (PCOS) is classically characterized by the clinical triad of androgen excess, anovulation infertility and obesity. Anovulation occurs due to functional ovarian and/or adrenal hyperandrogenism. The etiology and patho physiology of PCOS is unknown .Proposed theories include excess of gonadotropins; the effect of which is amplified by disturbances in intrinsic regulatory peptides, such as inhibin or extrinsic regulatory peptides, such as insulin or insulin like growth factor ( IGF). For over 25 years insulin resistance has been known to be associated with PCOS. Improvement in insulin resistance with the use of insulin sensitizers, such as metformin and thiazoldinediones (TZDs) have been seen to be associated with better ovulation and reduced testosterone levels in patients with PCOS. Aims: The aim of the present review is to discuss the new concepts in the pathogenesis of PCOS and to know usefulness of insulin sensitizers in such patients. Methods: Over 50 articles extending the span of more than 25 years have been reviewed and an attempt has been made to know the etiopathogenesis of PCOS and also to assess the validity for the uses of insulin sensitizers in patients of PCOS. Results: With the advancement of knowledge regarding etiopathogenesis, the management of PCOS has changed in recent years. In view of positive association between hyperinsulinemia and PCOS, improvement in insulin resistance through weight loss and use of insulin sensitizing drugs has been recommended. Conclusions: Besides symptomatic treatment, recent studies recommend use of insulin sensitizers in management in PCOS for better outcome in them. Key words: Polycystic ovarian syndrome (PCOS), Insulin resistance (IR) Insulin sensitizers (IS).doi:10.3126/njog.v3i1.1431 NJOG 2008 May-June; 3(1): 3 - 9

A variant in the fibrillin-3 gene is associated with TGF-β and inhibin B levels in women with polycystic ovary syndrome

The current study demonstrated that oxidative stress (OS) is closely related to the pathogenesis of polycystic ovary syndrome (PCOS). However, there are numerous factors that lead to OS, therefore, identifying the key genes associated with PCOS that contribute to OS is crucial for elucidating the pathogenesis of PCOS and selecting appropriate treatment strategies. Four datasets (GSE95728, GSE106724, GSE138572, and GSE145296) were downloaded from the gene expression omnibus (GEO) database. GSE95728 and GSE106724 were combined to identify differentially expressed genes (DEGs) in PCOS. weighted gene correlation network analysis (WGCNA) was used to screen key module genes associated with PCOS. Differentially expressed OS related genes (DE-OSRGs) associated with PCOS were obtained by overlapping DEGs, key module genes, and OSRGs. Subsequently, the optimal machine model was obtained to identify key genes by comparing the performance of the random forest model (RF), support vector machine model (SVM), and generalized linear model (GLM). The molecular networks were constructed to reveal the non-coding regulatory mechanisms of key genes based on GSE138572 and GSE145296. The Drug-Gene Interaction Database (DGIdb) was used to predict the potential therapeutic agents of key genes for PCOS. Finally, the expression of key OSRGs was validated by RT-PCR. In this study, 8 DE-OSRGs were identified. Based on the residuals and root mean square error of the three models, the best performance of RF was derived and 7 key genes (TNFSF10, CBL, IFNG, CP, CASP8, APOA1, and DDIT3) were identified. The GSEA enrichment analysis revealed that TNFSF10, CP, DDIT3, and INFG are all enriched in the NOD-like receptor signaling pathway and natural killer cell-mediated cytotoxicity pathways. The molecular regulatory network uncovered that both TNFSF10 and CBL are regulated by non-coding RNAs. Additionally, 70 potential therapeutic drugs for PCOS were predicted, with ibuprofen associated with DDIT3 and IFNG. RT-qPCR validation confirmed the expression trends of key genes IFNG, DDIT3, and APOA1 were consistent with the dataset, and the observed differences were statistically significant (P < 0.05). The identification of seven key genes and molecular regulatory networks through bioinformatics analysis is of great significance for exploring the pathogenesis and therapeutic strategies of PCOS.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women. In recent years, endoplasmic reticulum (ER) stress has gained increasing attention in the pathogenesis of PCOS. This study aims to explore the potential role of ER stress in PCOS by constructing a predictive model based on ER stress-related genes, and further evaluate the characteristics of immune infiltration and screen potential drugs. Five algorithms, including Lasso, Support Vector Machine (SVM), Random Forest (RF), Gradient Boosting Algorithm (XGB), and Generalized Linear Model (GLM), were used to screen key genes associated with PCOS and endoplasmic reticulum (ER) stress. A predictive model was constructed to analyze its diagnostic value in PCOS. External validation of the model was conducted using different datasets to assess its predictive accuracy. Furthermore, immune infiltration analysis was performed to explore the relationship between ER stress-related genes and the immune microenvironment in PCOS, revealing their potential role in disease development through immune response regulation. Finally, molecular docking and drug screening platforms were utilized to identify potential drugs that can modulate the ER stress pathway, providing new drug targets for the clinical treatment of PCOS. Two downregulated genes, NQO1 and NPY, and three upregulated genes, TFEB, JUP, and ATF4, were identified in PCOS cases. The constructed nomogram model demonstrated that the area under the ROC curve for NQO1, TFEB, JUP, NPY, and ATF4 in the validation set were 0.629, 0.600, 0.629, 0.543, and 0.743, respectively, indicating that the PCOS diagnostic model built from these five hub genes has good reliability. Immune infiltration analysis revealed that the expression of the JUP gene was positively correlated with T lymphocyte infiltration, while the expression of TFEB and NPY was negatively correlated with T lymphocyte infiltration, suggesting their potential involvement in immune regulation in PCOS. Through molecular docking and drug screening, 66 potential drugs were identified, 18 of which are already approved for use, providing options for pharmacological treatment of PCOS. The results of this study suggest that endoplasmic reticulum (ER) stress-related genes play an important role in the pathogenesis and development of PCOS, and that accurate predictive models may provide new insights for early diagnosis of the disease. Immune infiltration analysis revealed the potential mechanisms of immune cell involvement in PCOS, while drug screening provides a theoretical basis for future targeted therapies for PCOS.

Polycystic ovary syndrome (PCOS) affects women of reproductive age globally with an incidence rate of 5% - 26%. Growing evidence reports important roles for microRNAs (miRNAs) in the pathophysiology of granulosa cells (GCs) in PCOS. The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated. Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs. Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs. The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.

Polycystic ovary syndrome (PCOS), a common endocrine disorder, is associated with impaired oocyte development, leading to infertility. However, the pathogenesis of PCOS has not been completely elucidated. This study aimed to determine the differentially expressed genes (DEGs) and epigenetic changes in the oocytes from a PCOS mouse model to identify the etiological factors. RNA-sequencing analysis revealed that 90 DEGs were upregulated and 27 DEGs were downregulated in mice with PCOS compared with control mice. DNA methylation analysis revealed 30 hypomethylated and 10 hypermethylated regions in the PCOS group. However, the DNA methylation status did not correlate with differential gene expression. The pathway enrichment analysis revealed that five DEGs (Rps21, Rpl36, Rpl36a, Rpl37a, and Rpl22l1) were enriched in ribosome-related pathways in the oocytes of mice with PCOS, and the immunohistochemical analysis revealed significantly upregulated expression levels of Rps21 and Rpl36. These results suggest that differential gene expression in the oocytes of mice in PCOS is related to impaired folliculogenesis. These findings improve our understanding of PCOS pathogenesis.

Identification of the core ontologies and signature genes of polycystic ovary syndrome (PCOS): A bioinformatics analysis

Identification of CCR7 as a potential biomarker in polycystic ovary syndrome through transcriptome sequencing and integrated bioinformatics

The role of vitamin D in polycystic ovary syndrome

Polycystic ovary syndrome (PCOS) is a common endocrine disorder predominantly affecting women of reproductive age. Clinical manifestations are diverse including hyperandrogenism, anovulation, infertility and increased risk of metabolic diseases besides psychosocial dysfunction. This review provides information on the problem of PCOS in India, its pathophysiology, genetics and an overview of current management options to instigate further research in this field. Prevalence of PCOS in India ranges from 3.7 to 22.5 per cent depending on the population studied and the criteria used for diagnosis. Abnormalities in leptin-adiponectin (adipocyte biology), oxidative stress and autoimmunity are among the mechanisms studied regarding pathogenesis of PCOS. Many candidate gene studies have shown associations with PCOS in various studies. Studies have consistently demonstrated the relationship between the well-known manifestation of hyperandrogenism among Indian PCOS women and the metabolic morbidities including insulin resistance, glucose intolerance and cardiovascular risk. Management of individual components of PCOS can be achieved by medications or surgical methods, though further clarification regarding pathogenesis of PCOS is needed to sharpen our therapeutic armamentarium.

The prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) among polycystic ovary syndrome (PCOS) is significantly higher than in the general population. However, the mechanisms underlying this remain obscure. This study aimed to explore the mechanisms by identifying the genetic signature of SARS-CoV-2 infection in PCOS. In the present study, a total of 27 common differentially expressed genes (DEGs) were selected for subsequent analyses. Functional analyses showed that immunity and hormone-related pathways collectively participated in the development and progression of PCOS and SARS-CoV-2 infection. Under these, 7 significant hub genes were identified, including S100A9, MMP9, TLR2, THBD, ITGB2, ICAM1, and CD86 by using the algorithm in Cytoscape. Furthermore, hub gene expression was confirmed in the validation set, PCOS clinical samples, and mouse model. Immune microenvironment analysis with the CIBERSORTx database demonstrated that the hub genes were significantly correlated with T cells, dendritic cells, mast cells, B cells, NK cells, and eosinophils and positively correlated with immune scores. Among the hub genes, S100A9, MMP9, THBD, ITGB2, CD86, and ICAM1 demonstrated potential as possible diagnostic markers for COVID-19 and PCOS. In addition, we established the interaction networks of ovary-specific genes, transcription factors, miRNAs, drugs, and chemical compounds with hub genes with NetworkAnalyst. This work uncovered the common pathogenesis and genetic signature of PCOS and SARS-CoV-2 infection, which might provide a theoretical basis and innovative ideas for further mechanistic research and drug discovery of the comorbidity of the two diseases.