Identification of the shared gene signatures and pathways between polycystic ovary syndrome and endometrial cancer: An omics data based combined approach

Identification and analysis of biomarkers associated with oxidative stress and ferroptosis in recurrent miscarriage

Recurrent miscarriage (RM) has a huge impact on women. Both oxidative stress and ferroptosis play an important role in the pathogenesis of RM. Hence, it was vital to screen the ferroptosis oxidation-related biomarkers for the diagnosis and treatment of RM. We introduced transcript data to screen out differentially expressed genes (DEGs) in RM. Ferroptosis oxidation-related differentially expressed genes were obtained by overlapping DEGs and oxidative stress related genes with correlations >0.9 with ferroptosis-related genes. Least Absolute Shrinkage and Selectionator operator regression and support vector machine based recursive feature elimination algorithm were implemented to screen feature genes. The biomarkers associated with ferroptosis oxidation were screened via receiver operating characteristic curve analysis. We finally analyzed the competing endogenous RNAs regulatory network and potential drugs of biomarkers. We identified 1047 DEGs in RM. Then, 9 ferroptosis oxidation-related differentially expressed genes were obtained via venn diagram. Subsequently, 8 feature genes (PTPN6, GJA1, HMOX1, CPT1A, CREB3L1, SNCA, EPAS1, and TGM2) were identified via machine learning. Moreover, 4 biomarkers associated with ferroptosis oxidation, including PTPN6, GJA1, CPT1A, and CREB3L1, were screened via receiver operating characteristic curve analysis. We constructed the '227 long noncoding RNAs-4 mRNAs-36 microRNAs' network, in which hsa-miR-635 was associated with CREB3L1 and PTPN6. There were 11 drugs with therapeutic potential on 3 biomarkers associated with ferroptosis oxidation. We also observed higher expression of CPT1A and CREB3L1 in RM group compared to the healthy control group by quantitative real-time reverse transcription polymerase chain reaction. Overall, we obtained 4 biomarkers (PTPN6, GJA1, CPT1A, and CREB3L1) associated with ferroptosis and oxidative stress, which laid a theoretical foundation for the diagnosis and treatment of RM.

A Study on the Analysis of Important Gene Networks and Pathways Involved in Progression of Endometriosis to Ovarian Endometrioma Cyst

Endometriosis (EM) is a gynecological condition known by the manifestation of endometrium alike soft tissue external to the usual place affecting up to 10% of all womenfolk in the reproductively active stage. However, the pathological process of endometriosis is not identified fully. The study aims to investigate the genes associated with the progression of endometriosis and its pathways using bioinformatics tools and techniques. The gene expression profile of three sets was retrieved, and bioinformatics data analysis was carried out for the microarray samples using GEO, DAVID, and STICH. Differently expressed genes (DEGs) refer to genes that exhibit significant changes in their expression levels between different conditions or groups, such as between different cell types, treatments, disease states, or developmental stages. DEG was determined based on a significant cutoff resulting in 298 unique elements based on the GEO Venn diagram map. DAVID (database for annotation, visualization, and integrated discovery) helps understand the biological significance of the data by identifying overrepresented biological terms, pathways, and functional annotations among a set of genes or proteins of interest. DAVID analysis revealed positively and negatively associated genes and followed by target proteins. DAVID is helpful for getting results of molecular mechanisms and pathways associated with DEGs. The gene expression studies showed that the m-RNA expression of all the genes was upregulated in the PA1 cell line. The present study identified five genes (COMT, CYP19A1, GALT, LTA, and STAR) from 298 unique DEGs using microarray data analysis, and 5 protein targets were also identified that were linked with EM. The study concludes that this information may provide a bridging gap in understanding the progression of endometriosis.

Prediction of shared gene signatures and biological mechanisms between polycystic ovary syndrome and asthma: Based on weighted gene coexpression network analysis

OBJECTIVE

Several clinical studies have shown an association between polycystic ovary syndrome (PCOS) and asthma; however, the molecular link between these conditions remains unclear. In this study, we conducted a reanalysis and repurposing of existing databases in order to depict the common key genes, related signaling pathways, and similarity of the immune microenvironment between PCOS and asthma.

METHODS

PCOS and asthma data sets were downloaded, and common signal pathways were identified by using gene set enrichment analysis. Identified common susceptibility genes were explored by intersecting the weighted gene coexpression network analysis module genes for both diseases. Then, we performed protein-protein interaction, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes analyses of the common susceptibility genes. Finally, we analyzed the immune environment of PCOS and asthma.

RESULTS

We identified five hub genes, namely, MMP9, CDC42, CD44, CD19, and BCL2L1, and uncovered that these five hub genes showed a tendency to be upregulated in both PCOS and asthma and possessed good diagnostic ability. In addition, we revealed that both PCOS and asthma were significantly enriched in the FcεRI-mediated signaling pathway. Moreover, we found that both PCOS and asthma exhibited infiltration of similar types of immune cells, such as monocytes, suggesting that the two diseases have similar pathological features.

CONCLUSION

PCOS and asthma share common causative genes with a similar immune environment. Taken together, we uncovered previously unsuspected traits for comprehensive diagnosis and treatment of PCOS and asthma in the future.

Identification of Key Genes Associated with Polycystic Ovarian Syndrome and Endometrial and Ovarian Cancer through Bioinformatics

Background

Polycystic ovary syndrome (PCOS), a common endocrine disorder, is linked to increased risks of endometrial cancer (EC) and ovarian cancer (OC). Our study utilises bioinformatics analysis to identify shared gene signatures and elucidate biological processes between EC and OC and PCOS.

Aim

The objective of this research is to unveil the common molecular landscape shared by PCOS and EC and OC.

Settings and Design

An observational computational bioinformatics analysis.

Materials and Methods

Gene expression profiles for PCOS (GSE199225), EC (GSE215413) and OC (GSE174670) were obtained from the Gene Expression Omnibus database. Hub genes were identified through functional enrichment analysis and protein-protein interaction. Drug identification analyses were employed to find drugs targeting the hub genes.

Results

Key hub genes linking PCOS and EC includes RECQL4, RAD54L, ATR, CHTF18, WDHD1, CDT1, PLK1, PKMYT1, RAD18 and RPL3; for PCOS and OC, they include HMOX1, TXNRD1, NQO1, GCLC, GSTP1, PRDX1, SOD1, GPX3, BOP1 and BYSL. Gene Ontology analysis revealed DNA metabolic process in PCOS and EC, while in PCOS and OC, it identified the removal of superoxide radicals. Kyoto Encyclopaedia of Genes and Genomes pathway analysis highlighted cell cycle in PCOS and EC and hepatocellular carcinoma in PCOS and OC. Potential drugs for PCOS and EC include quercetin, calcitriol and testosterone; for PCOS and OC, eugenol and 1-chloro-2,4-dinitrobenzene are identified.

Conclusion

These findings offer insights into potential therapeutic targets and pathways linking PCOS with EC and OC, enhancing our understanding of the molecular mechanisms involved in these associations.

Identification of potential diagnostic biomarkers and therapeutic targets for endometriosis based on bioinformatics and machine learning analysis

PURPOSE

Endometriosis (EMs) is a major gynecological condition in women. Due to the absence of definitive symptoms, its early detection is very challenging; thus, it is crucial to find biomarkers to ease its diagnosis and therapy. Here, we aimed to identify potential diagnostic and therapeutic targets for EMs by constructing a regulatory network and using machine learning approaches.

METHODS

Three Gene Expression Omnibus (GEO) datasets were merged, and differentially expressed genes (DEGS) were identified after preprocessing steps. Using the DEGs, a transcription factor (TF)-mRNA-miRNA regulatory network was constructed, and hub genes were detected based on four different algorithms in CytoHubba. The hub genes were used to build a GaussianNB diagnostic model and also in docking analysis that were performed using Discovery Studio and AutoDock Vina software.

RESULTS

A total of 119 DEGs were identified between EMs and non-EMs samples. A regulatory network consisting of 52 mRNAs, 249 miRNAs, and 37 TFs was then constructed. The diagnostic model was introduced using the hub genes selected from the network (GATA6, HMOX1, HS3ST1, NFASC, and PTGIS) that its area under the curve (AUC) was 0.98 and 0.92 in the training and validation cohorts, respectively. Based on docking analysis, two chemical compounds, rofecoxib and retinoic acid, had potential therapeutic effects on EMs.

CONCLUSION

In conclusion, this study identified potential diagnostic and therapeutic targets for EMs which demand more experimental confirmations.

Deciphering novel common gene signatures for rheumatoid arthritis and systemic lupus erythematosus by integrative analysis of transcriptomic profiles

Rheumatoid Arthritis (RA) and Systemic Lupus Erythematosus (SLE) are the two highly prevalent debilitating and sometimes life-threatening systemic inflammatory autoimmune diseases. The etiology and pathogenesis of RA and SLE are interconnected in several ways, with limited knowledge about the underlying molecular mechanisms. With the motivation to better understand shared biological mechanisms and determine novel therapeutic targets, we explored common molecular disease signatures by performing a meta-analysis of publicly available microarray gene expression datasets of RA and SLE. We performed an integrated, multi-cohort analysis of 1088 transcriptomic profiles from 14 independent studies to identify common gene signatures. We identified sixty-two genes common among RA and SLE, out of which fifty-nine genes (21 upregulated and 38 downregulated) had similar expression profiles in the diseases. However, antagonistic expression profiles were observed for ACVR2A, FAM135A, and MAPRE1 genes. Thirty genes common between RA and SLE were proposed as robust gene signatures, with persistent expression in all the studies and cell types. These gene signatures were found to be involved in innate as well as adaptive immune responses, bone development and growth. In conclusion, our analysis of multicohort and multiple microarray datasets would provide the basis for understanding the common mechanisms of pathogenesis and exploring these gene signatures for their diagnostic and therapeutic potential.

Updates in diagnosing polycystic ovary syndrome-related infertility

INTRODUCTION

Polycystic ovary syndrome (PCOS) is a condition that affects approximately 13% of reproductive age women and is characterized by androgen excess, menstrual irregularity and altered ovarian morphology. PCOS presents a complex etiology and pathophysiology, which still requires a detailed investigation of biochemical signatures to identify the molecules and mechanisms that govern it.

AREAS COVERED

This narrative review summarizes the main molecular alterations found in the ovarian follicular fluid, endometrium and placenta of women with PCOS, and the genotypes potentially associated with the outcome of infertility treatments in PCOS.

EXPERT OPINION

PCOS is associated with multiple alterations in growth factors, sex steroid hormones, reactive oxygen species, proinflammatory cytokines and adipokines, which contribute to follicle arrest/ anovulation or suboptimal corpus luteum function, and ultimately to menstrual irregularity and hyperandrogenic symptoms. A panel of PCOS biomarkers should include, besides ovarian products, markers of adipose tissue function, insulin resistance, vascular health, and low-grade chronic inflammation. The effects of ovarian stimulation drugs on infertile women with PCOS are likely to be modified by genetic factors, but the available evidence is heterogeneous; therefore, future studies should evaluate standard treatments and pre-specified outcomes of interest to provide more conclusive answers.

Polycystic ovary syndrome and adipose tissue

Polycystic ovary syndrome (PCOS) is the most common endocrine metabolic disorder in women of reproductive age. Typically, it is associated with ovulatory dysfunction: dysovulation or anovulation, and symptoms of hyperandrogenism. It incurs risk of metabolic disorders such as diabetes, dyslipidemia and fatty liver. As a key endocrine organ in metabolic homeostasis, adipose tissue is often implicated in these complications. Studies of white adipose tissue (WAT) in PCOS have focused on the mechanism of insulin resistance in this tissue. Clinically, abnormalities in WAT distribution are seen, with decreased waist-to-hip ratio and increased ratio of adipose to lean mass. Such abnormalities are greater when total circulating androgens are elevated. At tissue level, white adipocyte hyperplasia occurs, along with infiltration of macrophages. Secretion of adipokines, cytokines and chemo-attractant proteins is increased in a pro-inflammatory manner, leading to reduced insulin sensitivity via alteration of glucose transporters, and hence decreased glucose uptake. The kinetics of non-esterified fatty acids (or free fatty acids) is also altered, leading to lipotoxicity. In recent years, brown adipose tissue (BAT) has been studied in women with PCOS. Although abundance is low in the body, BAT appears to play a significant role in energy expenditure and metabolic parameters. Both supra-clavicular skin temperature, which reflects BAT activity, and BAT mass are reduced in women with PCOS. Moreover, BAT mass and body mass index (BMI) are inversely correlated in patients. In the adipocyte, increased total circulating androgen levels reduce expression of uncoupling protein 1 (UCP1), a key protein in the brown adipocyte, leading to reduced biogenesis and mitochondrial respiration and hence a reduction in post-prandial thermogenesis. BAT is currently being investigated as a possible new therapeutic application.