Modified Cangfu Daotan decoction ameliorates polycystic ovary syndrome with insulin resistance via NF-κB/LCN-2 signaling pathway in inflammatory microenvironment.

This study explored the possible connection between the insulin resistance-targeting protein adipokine lipocalin-2 (LCN-2) and NF-κB signaling pathway in the inflammatory microenvironment in PCOS-IR model rats to determine the pharmacological mechanism of modified Cangfu Daotan decoction (MCDD) intervention for PCOS-IR. We used a high-fat diet (42 days) combined with letrozole (1 mg/kg/day, 42 days) to establish a PCOS-IR rat model. From the third week after modeling, the rats were given continuous administration of MCDD (high dose with 31.68 g/kg, medium dose with 15.84 g/kg, and low dose with 7.92 g/kg) for 28 days. Serum, ovarian tissue, liver, and adipose tissue were collected after the last gavage. Enzyme-linked immunosorbent assay, hematoxylin-eosin (HE) staining, Masson staining, qRT-PCR, and Western blot experiments were performed to detect various indicators. Our results showed that MCDD could reduce body weight and abdominal fat weight; restore normal estrous cycle and ovarian function; alleviate fatty liver; regulate HOMA-IR and OGTT index; reduce serum inflammatory factor levels, LCN-2 level, and gene expression; and regulate the insulin signal transduction and NF-κB pathways in PCOS-IR rats. Thus, MCDD may play a role in improving ovarian function in PCOS-IR rats by downregulating NF-κB/LCN-2 proteins and upregulating the gene expression of Insr/Irs-1/Glut4 in the insulin signaling pathway in the inflammatory environment.

Development and Validation of a Hypoxia-related Prognostic Model for Ovarian Cancer.

BACKGROUND: The high heterogeneity of ovarian cancer (OC) brings great difficulties to its early diagnosis and prognostic forecast. There is an urgent need to establish a prognostic model of OC based on clinicopathological features and genomics. METHODS: We identified hypoxia-related differentially expressed genes (DEGs) between OC tissues from The Cancer Genome Atlas (TCGA) and normal tissues from the Genotype-Tissue Expression (GTEx). LASSO Cox regression analysis was applied for building a prognostic model in the TCGA-GTEx cohorts, and its predictive value was validated in the GEO-OC cohort. Functional enrichment analysis was performed to investigate the underlying mechanisms. By constructing a hypoxia model of the SKOV3 cell line and applying qRT-PCR, we investigated the relationship between hypoxia with two novel genes in the prognostic model (ISG20 and ANGPTL4). RESULTS: Twelve prognostic hypoxia-related DEGs were identified, and nine of them were selected to establish a prognostic model. OC patients were stratified into two risk groups, and the high-risk group showed reduced survival time compared to the low-risk group upon survival analysis. Univariate and multivariate Cox regression analysis demonstrated that the risk score was an independent risk factor for overall survival. The biological function of the identified prognostic hypoxia-related gene signature was involved in immune cell infiltration. Low expression of ISG20 was observed in the CoCl2-mimicked hypoxic SKOV3 cell line and negatively correlated with HIF-1α. CONCLUSION: Our findings showed that this hypoxia-related gene signature could serve as a satisfactory prognostic classifier for OC and will be beneficial to the research and development of targeted therapeutic strategies.

3 CpG Methylation Biomarkers for the Diagnosis of Polycystic Ovary Syndrome (PCOS) in Blood Samples.

BACKGROUND: Polycystic ovary syndrome (PCOS) is a common endocrine disease in women that seriously interferes with patient's metabolic and reproductive functions. The current diagnostic criteria for PCOS are expert-based and still disputed. Previous studies have identified changes in DNA methylation in peripheral blood of women with PCOS, but their diagnostic potential for PCOS remains to be studied. OBJECTIVE: The present study aimed to identify potential methylation biomarkers for the diagnosis of PCOS in blood. METHODS: Methylation profiling of peripheral blood was downloaded from a public database, Gene Expression Omnibus (GEO), including 30 PCOS patients (diagnosed with the revised 2003 Rotterdam consensus criteria) and 30 age-matched healthy women recruited from Centre of Reproductive Medicine, Linyi People's Hospital, Shandong, China. Weighted gene co-expression network analysis (WGCNA) was utilized to identify PCOS-related co-methylation CpG sites (co- MPs). Functional enrichment analysis was performed on the localized genes of PCOS-related co- MPs. The least absolute shrinkage and selection operator (LASSO) regression was used to screen out CpG methylation signatures for PCOS diagnosis, and receiver operating characteristic (ROC) analysis was conducted to evaluate their diagnostic accuracy. To assess the accuracy of the combination of the investigated indicators, multivariate ROC analysis was performed on the predicted probability values obtained using binary logistic regression on the methylation levels of selected CpGs. RESULTS: Seven co-methylation modules were obtained, among which the turquoise module is the most relevant to PCOS, containing 194 co-MPs. The genes that these co-MPs located in were mainly associated with the immune-related pathway. According to LASSO regression, three Co- MPs (cg23464743, cg06834912, cg00103771) were identified as potential diagnostic biomarkers of PCOS. ROC analysis showed an AUC (area under curve) of 0.7556 (sensitivity 60.0%, specificity 83.3%) for cg23464743, 0.7822 (sensitivity 70.0%, specificity 80.0%) for cg06834912, and 0.7611 (sensitivity 63.3%, specificity 83.3%) for cg00103771. The diagnostic accuracy of the combination of these 3 indicators presented to be higher than any single one of them, with the AUC of 0.8378 (sensitivity 73.3%, specificity 93.3%). CONCLUSION: The combination of 3 CpG methylation signatures in blood was identified with a good diagnostic accuracy for PCOS, which may bring new insight into the development of PCOS diagnostic markers in the future.