Bioinformatics analysis of the molecular mechanism of obesity in polycystic ovary syndrome

Integrated bioinformatics analysis elucidates granulosa cell whole-transcriptome landscape of PCOS in China

BACKGROUND

Polycystic ovary syndrome (PCOS) is a common reproductive, neuroendocrine, and metabolic disorder in women of reproductive age that affects up to 5-10% of women of reproductive age. The aetiology of follicle development arrest and critical issues regarding the abnormal follicular development in PCOS remain unclear. The present study aims to systematically evaluate granulosa cell whole-transcriptome sequencing data to gain more insights into the transcriptomic landscape and molecular mechanism of PCOS in China.

METHODS

In the present study, the microarray datasets GSE138518, GSE168404, GSE193123, GSE138572, GSE95728, and GSE145296 were downloaded from the Gene Expression Omnibus (GEO) database. Subsequently, differential expression analysis was performed on the PCOS and control groups, followed by functional interaction prediction analysis to investigate gene-regulatory circuits in PCOS. Finally, hub genes and their associated ncRNAs were validated by qPCR in human-luteinized granulosa (hGL) cells and were correlated with the clinical characteristics of the patients.

RESULTS

A total of 200 differentially expressed mRNAs, 3 differentially expressed miRNAs, 52 differentially expressed lncRNAs, and 66 differentially expressed circRNAs were found in PCOS samples compared with controls. GO and KEGG enrichment analyses indicated that the DEGs were mostly enriched in phospholipid metabolic processes, steroid biosynthesis and inflammation related pathways. In addition, the upregulated miRNA hsa-miR-205-5p was significantly enriched in the ceRNA network, and two hub genes, MVD and PNPLA3, were regulated by hsa-miR-205-5p, which means that hsa-miR-205-5p may play a fundamental role in the pathogenesis of PCOS. We also found that MVD and PNPLA3 were related to metabolic processes and ovarian steroidogenesis, which may be the cause of the follicle development arrest in PCOS patients.

CONCLUSIONS

In summary, we systematically constructed a ceRNA network depicting the interactions between the ncRNAs and the hub genes in PCOS and control subjects and correlated the hub genes with the clinical characteristics of the patients, which provides valuable insights into the granulosa cell whole-transcriptome landscape of PCOS in China.

The Implication of Mechanistic Approaches and the Role of the Microbiome in Polycystic Ovary Syndrome (PCOS): A Review

As a complex endocrine and metabolic condition, polycystic ovarian syndrome (PCOS) affects women's reproductive health. These common symptoms include hirsutism, hyperandrogenism, ovulatory dysfunction, irregular menstruation, and infertility. No one knows what causes it or how to stop it yet. Alterations in gut microbiota composition and disruptions in secondary bile acid production appear to play a causative role in developing PCOS. PCOS pathophysiology and phenotypes are tightly related to both enteric and vaginal bacteria. Patients with PCOS exhibit changed microbiome compositions and decreased microbial diversity. Intestinal microorganisms also alter PCOS patient phenotypes by upregulating or downregulating hormone release, gut-brain mediators, and metabolite synthesis. The human body's gut microbiota, also known as the "second genome," can interact with the environment to improve metabolic and immunological function. Inflammation is connected to PCOS and may be caused by dysbiosis in the gut microbiome. This review sheds light on the recently discovered connections between gut microbiota and insulin resistance (IR) and the potential mechanisms of PCOS. This study also describes metabolomic studies to obtain a clear view of PCOS and ways to tackle it.

Contribution of labor related gene subtype classification on heterogeneity of polycystic ovary syndrome

OBJECTIVE

As one of the most common endocrine disorders in women of reproductive age, polycystic ovary syndrome (PCOS) is highly heterogeneous with varied clinical features and diverse gestational complications among individuals. The patients with PCOS have 2-fold higher risk of preterm labor which is associated with substantial infant morbidity and mortality and great socioeconomic cost. The study was designated to identify molecular subtypes and the related hub genes to facilitate the susceptibility assessment of preterm labor in women with PCOS.

METHODS

Four mRNA datasets (GSE84958, GSE5090, GSE43264 and GSE98421) were obtained from Gene Expression Omnibus database. Twenty-eight candidate genes related to preterm labor or labor were yielded from the researches and our unpublished data. Then, we utilized unsupervised clustering to identify molecular subtypes in PCOS based on the expression of above candidate genes. Key modules were generated with weighted gene co-expression network analysis R package, and their hub genes were generated with CytoHubba. The probable biological function and mechanism were explored through Gene Ontology analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis. In addition, STRING and Cytoscape software were used to identify the protein-protein interaction (PPI) network, and the molecular complex detection (MCODE) was used to identify the hub genes. Then the overlapping hub genes were predicted.

RESULTS

Two molecular subtypes were found in women with PCOS based on the expression similarity of preterm labor or labor-related genes, in which two modules were highlighted. The key modules and PPI network have five overlapping five hub genes, two of which, GTF2F2 and MYO6 gene, were further confirmed by the comparison between clustering subgroups according to the expression of hub genes.

CONCLUSIONS

Distinct PCOS molecular subtypes were identified with preterm labor or labor-related genes, which might uncover the potential mechanism underlying heterogeneity of clinical pregnancy complications in women with PCOS.

Quantitative Proteomics Reveals That a Prognostic Signature of the Endometrium of the Polycystic Ovary Syndrome Women Based on Ferroptosis Proteins

OBJECTIVE

We aimed to study the relationship between ferroptosis proteins and reproductive outcomes of infertile patients with PCOS and construct the related prognostic model.

METHODS

These endometrium samples of the study were collected from 33 women with PCOS and 7 control women with successful pregnancies at the Reproductive Center of Lanzhou University Second Hospital, September 2019 to September 2020. The 40 patients' endometrium was identified the differentially expressed proteins (DEPs) using liquid chromatography tandem mass spectrometry. The Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis and Gene Ontology (GO) showed that the DEPs related pathways and functions between PCOS and controls. Subsequently, univariate Cox regression analysis and Lasso regression were used to identifying independent prognostic ferroptosis proteins, which were utilized to establish a prognostic model. Then the performance of the prognostic model was evaluated by receiver operating characteristic curve (ROC) and decision curve analysis (DCA). Then clinical data and prognostic model were used to predict the reproductive outcomes of PCOS patients by constructing the nomograms. Finally, we performed the single sample gene set enrichment analysis (ssGSEA) to explore the correlation between risk scores and immune status.

RESULTS

A total of 5331 proteins were identified, 391 proteins were differentially expressed in the PCOS and controls. The KEGG analysis revealed that the ferroptosis pathway was significantly different between PCOS and controls. 5 ferroptosis proteins (GPX4, DPP4, G6PD, PCBP1, and PCBP2) prognostic model (FerSig) was constructed via Cox regression and Lasso regression. Patients were separated into high and low-risk groups according to the FerSig. Kaplan-Meier curve showed that patients in the low-risk group had much better reproductive outcomes than those in the high-risk group. The DCA showed that the risk score was an independent predictive factor for reproductive outcomes. Compared with clinical data, ROC curve analysis indicated the FerSig proteins as a potential diagnostic and prognostic factor in PCOS patients. Functional analysis revealed that the FerSig proteins and immune microenvironment were correlated to the prognosis of PCOS.

CONCLUSION

The prognostic model focused on the FerSig proteins could predict the reproductive outcomes of PCOS patients with decreased endometrial receptivity, and provided theoretical basis for individualized treatment.

Metabolic Syndrome and PCOS: Pathogenesis and the Role of Metabolites

Polycystic ovary syndrome (PCOS) is one of the most common endocrine diseases among women of reproductive age and is associated with many metabolic manifestations, such as obesity, insulin resistance (IR) and hyperandrogenism. The underlying pathogenesis of these metabolic symptoms has not yet been fully elucidated. With the application of metabolomics techniques, a variety of metabolite changes have been observed in the serum and follicular fluid (FF) of PCOS patients and animal models. Changes in metabolites result from the daily diet and occur during uncommon physiological routines. However, some of these metabolite changes may provide evidence to explain possible mechanisms and new approaches for prevention and therapy. This article reviews the pathogenesis of PCOS metabolic symptoms and the relationship between metabolites and the pathophysiology of PCOS. Furthermore, the potential clinical application of some specific metabolites will be discussed.