Integrated analysis of DNA methylation and transcriptome profiling of polycystic ovary syndrome

Analysis of Methylome, Transcriptome, and Lipid Metabolites to Understand the Molecular Abnormalities in Polycystic Ovary Syndrome

Purpose

This study aimed to identify differentially methylated genes (DMGs) and differentially expressed genes (DEGs) to investigate new biomarkers for the diagnosis and treatment of polycystic ovary syndrome (PCOS).

Methods

To explore the potential biomarkers of PCOS diagnosis and treatment, we performed methyl-binding domain sequencing (MBD-seq) and RNA sequencing (RNA-seq) on ovarian granulosa cells (GCs) from PCOS patients and healthy controls. MBD-seq was also performed on the ovarian tissue of constructed prenatally androgenized (PNA) mice. Differential methylation and expression analysis were implemented to identify DMGs and DEGs, respectively. The identified gene was further verified by real-time quantitative PCR (RT-qPCR) and methylation-specific PCR (MSP) in clinical samples. Furthermore, ultra-performance liquid chromatography-mass spectrometry (UPLC-MS) was carried out on PCOS patients and healthy controls to identify differential lipid metabolites.

Results

Compared to the control group, 13,526 DMGs related to the promoter region and 2429 DEGs were found. The function analysis of DMGs and DEGs showed that they were mainly enriched in glycerophospholipid, ovarian steroidogenesis, and other lipid metabolic pathways. Moreover, 5753 genes in DMGs related to the promoter region were screened in the constructed PNA mice. Integrating the DMGs data from PCOS patients and PNA mice, we identified the following 8 genes: CDC42EP4, ERMN, EZR, PIK3R1, ARHGEF18, NECTIN2, TSC2, and TACSTD2. RT-qPCR and MSP verification results showed that the methylation and expression of TACSTD2 were consistent with sequencing data. Additionally, 15 differential lipid metabolites were shown in the serum of PCOS patients. The differential lipids were involved in glycerophospholipid and glycerolipid metabolism.

Conclusion

Using integration of methylome and lipid metabolites profiling we identified 8 potential epigenetic markers and 15 potential lipid metabolite markers for PCOS. Our results suggest that aberrant DNA methylation and lipid metabolite disorders may provide novel insights into the diagnosis and etiology of PCOS.

Integrated Transcriptome Profiling Identifies Prognostic Hub Genes as Therapeutic Targets of Glioblastoma: Evidenced by Bioinformatics Analysis

Glioblastoma (GBM) is the most devastating and frequent type of primary brain tumor with high morbidity and mortality. Despite the use of surgical resection followed by radio- and chemotherapy as standard therapy, the progression of GBM remains dismal with a median overall survival of <15 months. GBM embodies a populace of cancer stem cells (GSCs) that is associated with tumor initiation, invasion, therapeutic resistance, and post-treatment reoccurrence. However, understanding the potential mechanisms of stemness and their candidate biomarkers remains limited. Hence in this investigation, we aimed to illuminate potential candidate hub genes and key pathways associated with the pathogenesis of GSC in the development of GBM. The integrated analysis discovered differentially expressed genes (DEGs) between the brain cancer tissues (GBM and GSC) and normal brain tissues. Multiple approaches, including gene ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis, were employed to functionally annotate the DEGs and visualize them through the R program. The significant hub genes were identified through the protein-protein interaction network, Venn diagram analysis, and survival analysis. We observed that the upregulated DEGs were prominently involved in the ECM-receptor interaction pathway. The downregulated genes were mainly associated with the axon guidance pathway. Five significant hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) were screened out through multiple analyses. GO and KEGG analyses of hub genes uncovered that these genes were primarily enriched in disease-associated pathways such as the inhibition of apoptosis and the DNA damage repair mechanism, activation of the cell cycle, EMT (epithelial-mesenchymal transition), hormone AR (androgen receptor), hormone ER (estrogen receptor), PI3K/AKT (phosphatidylinositol 3-kinase and AKT), RTK (receptor tyrosine kinase), and TSC/mTOR (tuberous sclerosis complex and mammalian target of rapamycin). Consequently, the epigenetic regulatory network disclosed that hub genes played a vital role in the progression of GBM. Finally, candidate drugs were predicted that can be used as possible drugs to treat GBM patients. Overall, our investigation offered five hub genes (CTNNB1, ITGB1, TNC, EGFR, and SHOX2) that could be used as precise diagnostic and prognostic candidate biomarkers of GBM and might be used as personalized therapeutic targets to obstruct gliomagenesis.

Construction and Analysis of a Joint Diagnosis Model of Random Forest and Artificial Neural Network for Obesity

Obesity is a significant global health concern since it is connected to a higher risk of several chronic diseases. As a consequence, obesity may be described as a condition that reduces human life expectancy and significantly impacts life quality. Because traditional obesity diagnosis procedures have several flaws, it is vital to design new diagnostic models to enhance current methods. More obesity-related markers have been discovered in recent years as a result of improvements and enhancements in gene sequencing technology. Using current gene expression profiles from the Gene Expression Omnibus (GEO) collection, we identified differentially expressed genes (DEGs) associated with obesity and found 12 important genes (CRLS1, ANG, ALPK3, ADSSL1, ABCC1, HLF, AZGP1, TSC22D3, F2R, FXN, PEMT, and SPTAN1) using a random forest classifier. ALPK3, HLF, FXN, and SPTAN1 are the only genes that have never been linked to obesity. We also used an artificial neural network to build a novel obesity diagnosis model and tested its diagnostic effectiveness using public datasets.

An epigenomic landscape of cervical intraepithelial neoplasia and cervical cancer using single-base resolution methylome and hydroxymethylome

BACKGROUND

Cervical cancer (CC) is the second leading cause of cancer death among women worldwide. Epigenetic regulation of gene expression through DNA methylation and hydroxymethylation plays a pivotal role during tumorigenesis. In this study, to analyze the epigenomic landscape and identify potential biomarkers for CCs, we selected a series of samples from normal to cervical intra-epithelial neoplasia (CINs) to CCs and performed an integrative analysis of whole-genome bisulfite sequencing (WGBS-seq), oxidative WGBS, RNA-seq, and external histone modifications profiling data.

RESULTS

In the development and progression of CC, there were genome-wide hypo-methylation and hypo-hydroxymethylation, accompanied by local hyper-methylation and hyper-hydroxymethylation. Hydroxymethylation prefers to distribute in the CpG islands and CpG shores, as displayed a trend of gradual decline from health to CIN2, while a trend of increase from CIN3 to CC. The differentially methylated and hydroxymethylated region-associated genes both enriched in Hippo and other cancer-related signaling pathways that drive cervical carcinogenesis. Furthermore, we identified eight novel differentially methylated/hydroxymethylated-associated genes (DES, MAL, MTIF2, PIP5K1A, RPS6KA6, ANGEL2, MPP, and PAPSS2) significantly correlated with the overall survival of CC. In addition, no any correlation was observed between methylation or hydroxymethylation levels and somatic copy number variations in CINs and CCs.

CONCLUSION

Our current study systematically delineates the map of methylome and hydroxymethylome from CINs to CC, and some differentially methylated/hydroxymethylated-associated genes can be used as the potential epigenetic biomarkers in CC prognosis.

Characterization of DNA Methylation and Screening of Epigenetic Markers in Polycystic Ovary Syndrome

Polycystic ovary syndrome (PCOS) is a heterogeneous endocrine and metabolic disorder in women, which is characterized by androgen excess, ovulation dysfunction, and polycystic ovary. Although the etiology of PCOS is largely unknown, many studies suggest that aberrant DNA methylation is an important contributing factor for its pathological changes. In this study, we investigated DNA methylation characteristics and their impact on gene expression in granulosa cells obtained from PCOS patients. Transcriptome analysis found that differentially expressed genes were mainly enriched in pathways of insulin resistance, fat cell differentiation, and steroid metabolism in PCOS. Overall DNA methylation level in granulosa cells was reduced in PCOS, and the first introns were found to be the major genomic regions that were hypomethylated in PCOS. Integrated analysis of transcriptome, DNA methylation, and miRNAs in ovarian granulosa cells revealed a DNA methylation and miRNA coregulated network and identified key candidate genes for pathogenesis of PCOS, including BMP4, ETS1, and IRS1. Our study shed more light on epigenetic mechanism of PCOS and provided valuable reference for its diagnosis and treatment.

Extracellular microRNAs: key players to explore the outcomes of in vitro fertilization

BACKGROUND

MicroRNAs (miRNAs) are small RNA molecules that modulate post-transcriptional gene regulation. They are often used as promising non-invasive biomarkers for the early diagnosis of cancer. However, their roles in assisted reproduction are still unknown.

METHODS

This prospective study was designed to evaluate the expression profiles of seven extracellular miRNAs (miR-7-5p, miR-202-5p, miR-378-3p, miR-224, miR-320a, miR-212-3p, and miR-21-5p) in human follicular fluid (FF) to explore the outcomes of in vitro fertilization (IVF). Of 255 women, 145 were without polycystic ovary syndrome (PCOS), and their ovarian assets were normal (NOR), while 110 were with normo-androgenic PCOS.

RESULTS

The combination of six FF miRNAs expression profile discriminated between PCOS and NOR women with a sensitivity of 79.2% and a specificity of 87.32% (AUC = 0.881 [0.61; 0.92], p = 0.001). MiR-202-5p significantly had a lower abundance level, and miR-378-3p had a high abundance level in pooled FF samples from patients treated with human menopausal gonadotropin (hMG) than those treated with recombinant follicle-stimulating hormone (rFSH) (p < 0.001). Our results showed that miRNA-320a was significantly different in top-quality embryos versus non-top-quality embryos on day 3 in NOR patients with a sensitivity of 80% and specificity of 71%, (AUC = [0.753 (0.651; 0.855)], p = 0.001). For clinical pregnancy outcome prediction, FF miRNA-21 exhibited high sensitivity (74.8%) and specificity (83.7%) with the AUC value of 0.774 (0.682; 0.865).

CONCLUSION

Conclusively, our results provide evidence that miR-7-5p, miR-378-3p, miR-224, miR-212-3p were a differentially high expression in normo-androgenic PCOS patients than NOR patients. While miRNA-320a was significantly different in top-quality embryos versus non-top-quality embryos on day 3 (p = 0.001). The expression level of FF miR-212-3p was significantly related to the probability of embryos to develop into a high-quality blastocyst in patients with normal ovarian reserve.