Identification of novel candidate biomarkers and immune infiltration in polycystic ovary syndrome

Metabolism-related proteins as biomarkers for predicting prognosis in polycystic ovary syndrome

OBJECTIVE

The study aimed to explore the role of metabolism-related proteins and their correlation with clinical data in predicting the prognosis of polycystic ovary syndrome (PCOS).

METHODS

This research involves a secondary analysis of proteomic data derived from endometrial samples collected from our study group, which includes 33 PCOS patients and 7 control subjects. A comprehensive identification and analysis of 4425 proteins were conducted to screened differentially expressed proteins (DEPs). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were subsequently performed on the DEPs. To identify independent prognostic metabolism-related proteins, univariate Cox regression and LASSO regression were applied. The expression levels of these proteins were then used to develop a prognostic model, with their predictive accuracy evaluated through receiver operating characteristic (ROC) curves, decision curve analysis (DCA), and calibration curves. Furthermore, we also investigate the correlation between clinical data and prognostic proteins.

RESULTS

The study identified 285 DEPs between the PCOS and control groups. GO enrichment analysis revealed significant involvement in metabolic processes, while KEGG pathway analysis highlighted pathways such as glycolysis/gluconeogenesis and glucagon signaling. Ten key metabolism-related proteins (ACSL5, ANPEP, CYB5R3, ENOPH1, GLS, GLUD1, LDHB, PLCD1, PYCR2, and PYCR3) were identified as significant predictors of PCOS prognosis. Patients were separated into high and low-risk groups according to the risk score. The ROC curves for predicting outcomes at 6, 28, and 37 weeks demonstrated excellent predictive performance, with AUC values of 0.98, 1.0, and 1.0, respectively. The nomogram constructed from these proteins provided a reliable tool for predicting pregnancy outcomes. DCA indicated a net benefit of the model across various risk thresholds, and the calibration curve confirmed the model's accuracy. Additionally, we also found BMI exhibited a significant negative correlation with the expression of GLS (r =-0.44, p = 0.01) and CHO showed a significant positive correlation with the expression of LDHB (r = 0.35, p = 0.04).

CONCLUSION

The identified metabolism-related proteins provide valuable insights into the prognosis of PCOS. The protein based prognostic model offers a robust and reliable tool for risk stratification and personalized management of PCOS patients.

Unveiling the molecular landscape of PCOS: identifying hub genes and causal relationships through bioinformatics and Mendelian randomization

Background

Polycystic ovary syndrome (PCOS) is a complex endocrine disorder with various contributing factors. Understanding the molecular mechanisms underlying PCOS is essential for developing effective treatments. This study aimed to identify hub genes and investigate potential molecular mechanisms associated with PCOS through a combination of bioinformatics analysis and Mendelian randomization (MR).

Methods

This study employed bioinformatics analysis in conjunction with MR methods using publicly available databases to identify hub genes. We employed complementary MR methods, including inverse-variance weighted (IVW), to determine the causal relationship between the hub genes and PCOS. Sensitivity analyses were performed to ensure results reliability. Enrichment analysis and immune infiltration analysis were further conducted to assess the role and mechanisms of hub genes in the development of PCOS. Additionally, we validated hub gene expression in both an animal model and serum samples from PCOS patients using qRT-PCR.

Results

IVW analysis revealed significant associations between 10 hub genes and the risk of PCOS: CD93 [P= 0.004; OR 95%CI= 1.150 (1.046, 1.264)], CYBB [P= 0.013; OR 95%CI= 1.650 (1.113,2.447)], DOCK8 [P= 0.048; OR 95%CI= 1.223 (1.002,1.494)], IRF1 [P= 0.036; OR 95%CI= 1.343 (1.020,1.769)], MBOAT1 [P= 0.033; OR 95%CI= 1.140 (1.011,1.285)], MYO1F [P= 0.012; OR 95%CI= 1.325 (1.065,1.649)], NLRP1 [P= 0.020; OR 95%CI= 1.143 (1.021,1.280)], NOD2 [P= 0.002; OR 95%CI= 1.139 (1.049,1.237)], PIK3R1 [P= 0.040; OR 95%CI= 1.241 (1.010,1.526)], PTER [P= 0.015; OR 95%CI= 0.923 (0.866,0.984)]. No heterogeneity and pleiotropy were observed. Hub genes mainly enriched in positive regulation of cytokine production and TNF signaling pathway, and exhibited positive or negative correlations with different immune cells in individuals with PCOS. qRT-PCR validation in both the rat model and patient serum samples confirmed hub gene expression trends consistent with our combined analysis results.

Conclusions

Our bioinformatics combined with MR analysis revealed that CD93, CYBB, DOCK8, IRF1, MBOAT1, MYO1F, NLRP1, NOD2, PIK3R1 increase the risk of PCOS, while PTER decreases the risk of PCOS. This discovery has implications for clinical decision-making in terms of disease diagnosis, prognosis, treatment strategies, and opens up novel avenues for drug development.

Development of an Endoplasmic Reticulum Stress-Related Diagnostic Signature in Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a prevalent endocrine and metabolic disorder in premenopausal women. This investigation was to elucidate the underlying mechanism of endoplasmic reticulum stress (ERS) activation in granulosa cells, which has been implicated in the etiology of PCOS. Differentially expressed genes (DEGs) between PCOS and control groups were integrated with ERS gene lists from databases to identify DE-ERS genes, and functional analyses were performed. Univariate regression analysis and the LASSO method were used to select diagnostic factors, followed by establishing a DE-ERS gene-based diagnostic model. A nomogram model was further generated to predict the risk of PCOS. The correlation between ERS gene expression and immune cell proportion was assessed. A total of 14 DE-ERS genes associated with "protein processing in endoplasmic reticulum", "ferroptosis", and "glycerophospholipid metabolism" were selected as PCOS-related factors. An eight-DE-ERS genes-based diagnostic model was developed and displayed satisfactory performance in the training (Area under curve (AUC) = 0.983) and validation datasets (AUC = 0.802). High risk of PCOS can be accurately predicted, which might contribute to clinical decision-making. Moreover, EDEM1 expression was significantly positively correlated with naive B cell infiltration, while PDIA6 was negatively correlated with neutrophil proportion (P < 0.001). We identified eight novel molecules and developed an ERS gene-based diagnostic model in PCOS, which might provide novel insight for finding biomarkers and treatment methods.

Validation of mitochondrial biomarkers and immune dynamics in polycystic ovary syndrome

PROBLEM

Polycystic ovary syndrome (PCOS), a prevalent endocrine-metabolic disorder, presents considerable therapeutic challenges due to its complex and elusive pathophysiology.

METHOD OF STUDY

We employed three machine learning algorithms to identify potential biomarkers within a training dataset, comprising GSE138518, GSE155489, and GSE193123. The diagnostic accuracy of these biomarkers was rigorously evaluated using a validation dataset using area under the curve (AUC) metrics. Further validation in clinical samples was conducted using PCR and immunofluorescence techniques. Additionally, we investigate the complex interplay among immune cells in PCOS using CIBERSORT to uncover the relationships between the identified biomarkers and various immune cell types.

RESULTS

Our analysis identified ACSS2, LPIN1, and NR4A1 as key mitochondria-related biomarkers associated with PCOS. A notable difference was observed in the immune microenvironment between PCOS patients and healthy controls. In particular, LPIN1 exhibited a positive correlation with resting mast cells, whereas NR4A1 demonstrated a negative correlation with monocytes in PCOS patients.

CONCLUSION

ACSS2, LPIN1, and NR4A1 emerge as PCOS-related diagnostic biomarkers and potential intervention targets, opening new avenues for the diagnosis and management of PCOS.

Biomarkers in Polycystic Ovary Syndrome

Polycystic Ovary Syndrome (PCOS) is the most common endocrine disorder in reproductive-age women. PCOS is diagnosed by the presence of two of the following three characteristics: hyperandrogenemia and/or hyperandrogenism, oligo/amenorrhea, and polycystic ovarian morphology. PCOS is associated with reproductive and non-reproductive complications, including obesity, insulin resistance and diabetes, dyslipidemia, and increased blood pressure. There is an urgent need for biomarkers that address both the reproductive and non-reproductive aspects of this complex syndrome. This review focuses on biomarkers, or potential ones, associated with the reproductive and non-reproductive aspects of PCOS, including anthropometric and clinical biomarkers, insulin and the IGF-1 system, lipids, anti-Müllerian hormone and gonadotropins, steroids, inflammatory and renal injury biomarkers, oxidative stress, and non-coding RNAs. We expect that this review will bring some light on the recent updates in the field and encourage researchers to join the exciting and promising field of PCOS biomarkers.

A novel model based on necroptosis to assess progression for polycystic ovary syndrome and identification of potential therapeutic drugs

Background

Polycystic ovary syndrome (PCOS), a common endocrine and reproductive disorder, lacks precise diagnostic strategies. Necroptosis was found to be crucial in reproductive and endocrine disorders, but its function in PCOS remains unclear. We aimed to identify differentially diagnostic genes for necroptosis (NDDGs), construct a diagnostic model to assess the progression of PCOS and explore the potential therapeutic drugs.

Methods

Gene expression datasets were combined with weighted gene co-expression network analysis (WGCNA) and necroptosis gene sets to screen the differentially expressed genes for PCOS. Least absolute shrinkage and selection operator (LASSO) regression analysis was used to construct a necroptosis-related gene signatures. Independent risk analyses were performed using nomograms. Pathway enrichment of NDDGs was conducted with the GeneMANIA database and gene set enrichment analysis (GSEA). Immune microenvironment analysis was estimated based on ssGSEA algorithm analysis. The Comparative Toxicogenomics Database (CTD) was used to explore potential therapeutic drugs for NDDGs. The expression of NDDGs was validated in GSE84958, mouse model and clinical samples.

Results

Four necroptosis-related signature genes, IL33, TNFSF10, BCL2 and PYGM, were identified to define necroptosis for PCOS. The areas under curve (AUC) of receiver operating characteristic curve (ROC) for training set and validation in diagnostic risk model were 0.940 and 0.788, respectively. Enrichment analysis showed that NDDGs were enriched in immune-related signaling pathways such as B cells, T cells, and natural killer cells. Immune microenvironment analysis revealed that NDDGs were significantly correlated with 13 markedly different immune cells. A nomogram was constructed based on features that would benefit patients clinically. Several compounds, such as resveratrol, tretinoin, quercetin, curcumin, etc., were mined as therapeutic drugs for PCOS. The expression of the NDDGs in the validated set, animal model and clinical samples was consistent with the results of the training sets.

Conclusion

In this study, 4 NDDGs were identified to be highly effective in assessing the progression and prognosis of PCOS and exploring potential targets for PCOS treatment.

Identification of novel biomarkers and immune infiltration features of recurrent pregnancy loss by machine learning

Recurrent pregnancy loss (RPL) is a complex reproductive disorder. The incompletely understood pathophysiology of RPL makes early detection and exact treatment difficult. The purpose of this work was to discover optimally characterized genes (OFGs) of RPL and to investigate immune cell infiltration in RPL. It will aid in better understanding the etiology of RPL and in the early detection of RPL. The RPL-related datasets were obtained from the Gene Expression Omnibus (GEO), namely GSE165004 and GSE26787. We performed functional enrichment analysis on the screened differentially expressed genes (DEGs). Three machine learning techniques are used to generate the OFGs. A CIBERSORT analysis was conducted to examine the immune infiltration in RPL patients compared with normal controls and to investigate the correlation between OFGs and immune cells. Between the RPL and control groups, 42 DEGs were discovered. These DEGs were found to be involved in cell signal transduction, cytokine receptor interactions, and immunological response, according to the functional enrichment analysis. By integrating OFGs from the LASSO, SVM-REF, and RF algorithms (AUC > 0.880), we screened for three down-regulated genes: ZNF90, TPT1P8, FGF2, and an up-regulated FAM166B. Immune infiltration study revealed that RPL samples had more monocytes (P < 0.001) and fewer T cells (P = 0.005) than controls, which may contribute to RPL pathogenesis. Additionally, all OFGs linked with various invading immune cells to varying degrees. In conclusion, ZNF90, TPT1P8, FGF2, and FAM166B are potential RPL biomarkers, offering new avenues for research into the molecular mechanisms of RPL immune modulation and early detection.

N6-methyladenosine regulator-mediated methylation modification patterns and immune infiltration characterization in Polycystic Ovary Syndrome (PCOS)

BACKGROUND

Polycystic ovary syndrome (PCOS) is a multisystem-related disease whose pathophysiology is still unclear. Several regulators of N6-methyladenosine (m6A) modification were confirmed to play a regulatory role in PCOS. Nonetheless, the roles of m6A regulators in PCOS are not fully demonstrated.

MATERIALS AND METHODS

Four mRNA expression profiling microarrays were obtained from the Gene Expression Omnibus (GEO) database. Differentially expressed m6A regulators between PCOS and normal patients were identified by R software. A random forest modal and nomogram were developed to assess the relationship between m6A regulators and the occurrence risk of PCOS. A consensus clustering method was utilized to distinctly divide PCOS patients into two m6A subtypes (m6A cluster A/B). The patterns of differential expression and immune infiltration were explored between the two m6A clusters.

RESULTS

In this study, 22 significant m6A regulators were identified between healthy controls and PCOS patients. The random forest model determined three optimal m6A regulators which are related to the occurrence risk of PCOS, including YTHDF1, RBM15 and METTL14. A nomogram was established based on these genes, and its predictive reliability was validated by decision curve analysis. The consensus clustering algorithm distinctly divided PCOS cases into two m6A subtypes. The ssGSEA algorithm found that the immune infiltration was markedly enriched in m6A cluster B than in cluster A. The m6A-pattern related differentially expressed genes (DEGs) of the two m6A subtypes were demonstrated by differential expression analysis. We found that they were enriched in immune-related genes and various infection pathways. Based on the m6A-pattern related DEGs, the PCOS patients were classified into two m6A-pattern related genomic subtypes (gene clusters A and B).

CONCLUSIONS

The present study provided evidence concerning the different modification patterns of m6A regulators in PCOS compared with normal patients. This study will help clarify the overall impact of m6A modification patterns and related immune infiltration on PCOS.

Identification and immune features of cuproptosis-related molecular clusters in polycystic ovary syndrome

Polycystic ovary syndrome (PCOS), a common reproductive endocrine disease, has clinically heterogeneous characteristics. Recently, cuproptosis causes several diseases by killing cells. Hence, we aimed to explore cuproptosis-related molecular clusters in PCOS and construct a prediction model. Based on the GSE5090, GSE43264, GSE98421, and GSE124226 datasets, an analysis of cuproptosis regulators and immune features in PCOS was conducted. In 25 cases of PCOS, the molecular clusters of cuproptosis-related genes and the immune cell infiltration associated with PCOS were investigated. Weighted gene co-expression network analysis was used to identify differentially expressed genes within clusters. Next, we compared the performance of the random forest model, support vector machine model, generalized linear model, and eXtreme Gradient Boosting for deciding the optimum machine model. Validation of the predictive effectiveness was accomplished through nomogram, calibration curve, decision curve analysis, and using other two datasets. PCOS and non-PCOS controls differed in the dysregulation of cuproptosis-related genes and the activation of immunoreaction. Two cuproptosis-related molecular clusters associated with PCOS were identified. Significant heterogeneity was noted in immunity between the two clusters based on the analysis of immune infiltration. The immune-related pathways related to cluster-specific differentially expressed genes in Cluster1 were revealed by functional analysis. With a relatively low residual error and root mean square error and a higher area under the curve (1.000), the support vector machine model demonstrated optimal discriminative performance. An ultimate 5-gene-based support vector machine model was noted to perform satisfactorily in the other two validation datasets (area under the curve = 1.000 for both). Moreover, the nomogram, calibration curve, and decision curve analysis showed that PCOS subtypes can be accurately predicted. Our study results helped demonstrate a comprehensive understanding of the complex relationship between cuproptosis and PCOS and establish a promising prediction model for assessing the risk of cuproptosis in patients with PCOS.