Biomarker potential of competing endogenous RNA networks in Polycystic Ovary Syndrome (PCOS).

Polycystic ovary syndrome (PCOS) is the most common condition affecting women of reproductive age globally. PCOS continues to be the largest contributing factor to female infertility despite significant progress in our knowledge of the molecular underpinnings and treatment of the condition. The fact that PCOS is a very diverse condition makes it one of the key reasons why we haven't been able to overcome it. Non-coding RNAs (ncRNAs) are implicated in the development of PCOS, according to growing evidence. However, it is unclear how the complex regulatory relationships between the many ncRNA types contribute to the growth of this malignancy. Competing endogenous RNA (ceRNA), a recently identified mechanism in the RNA world, suggests regulatory interactions between various RNAs, including long non-coding RNAs (lncRNAs), microRNAs (miRNAs), transcribed pseudogenes, and circular RNAs (circRNAs). Recent studies on PCOS have shown that dysregulation of multiple ceRNA networks (ceRNETs) between these ncRNAs plays crucial roles in developing the defining characteristics of PCOS development. And it is believed that such a finding may open a new door for a deeper comprehension of PCOS's unexplored facets. In addition, it may be able to provide fresh biomarkers and effective therapy targets for PCOS. This review will go over the body of information that exists about the primary roles of ceRNETs before highlighting the developing involvement of several newly found ceRNETs in a number of PCOS characteristics.

Weighted Gene Co-Expression Network Analysis (WGCNA) Discovered Novel Long Non-Coding RNAs for Polycystic Ovary Syndrome.

Polycystic ovary syndrome (PCOS) affects reproductive-age women. This condition causes infertility, insulin resistance, obesity, and heart difficulties. The molecular basis and mechanism of PCOS might potentially generate effective treatments. Long non-coding RNAs (lncRNAs) show control over multifactorial disorders' growth and incidence. Numerous studies have emphasized its significance and alterations in PCOS. We used bioinformatic methods to find novel dysregulated lncRNAs in PCOS. To achieve this objective, the gene expression profile of GSE48301, comprising PCOS patients and normal control tissue samples, was evaluated using the R limma package with the following cut-off criterion: p-value < 0.05. Firstly, weighted gene co-expression network analysis (WGCNA) was used to determine the co-expression genes of lncRNAs; subsequently, hub gene identification and pathway enrichment analysis were used. With the defined criteria, nine novel dysregulated lncRNAs were identified. In WGCNA, different colors represent different modules. In the current study, WGCNA resulted in turquoise, gray, blue, and black co-expression modules with dysregulated lncRNAs. The pathway enrichment analysis of these co-expressed modules revealed enrichment in PCOS-associated pathways, including gene expression, signal transduction, metabolism, and apoptosis. In addition, CCT7, EFTUD2, ESR1, JUN, NDUFAB1, CTTNB1, GRB2, and CTNNB1 were identified as hub genes, and some of them have been investigated in PCOS. This study uncovered nine novel PCOS-related lncRNAs. To confirm how these lncRNAs control translational modification in PCOS, functional studies are required.

Unveiling Key Biomarkers and Therapeutic Drugs in Polycystic Ovary Syndrome (PCOS) Through Pathway Enrichment Analysis and Hub Gene-miRNA Networks.

Background: Polycystic ovary syndrome (PCOS) affects women of reproductive age globally with an incidence rate of 5% - 26%. Growing evidence reports important roles for microRNAs (miRNAs) in the pathophysiology of granulosa cells (GCs) in PCOS. Objectives: The objectives of this study were to identify the top differentially expressed miRNAs (DE-miRNAs) and their corresponding targets in hub gene-miRNA networks, as well as identify novel DE-miRNAs by analyzing three distinct microarray datasets. Additionally, functional enrichment analysis was performed using bioinformatics approaches. Finally, interactions between the 5 top-ranked hub genes and drugs were investigated. Methods: Using bioinformatics approaches, three GC profiles from the gene expression omnibus (GEO), namely gene expression omnibus series (GSE)-34526, GSE114419, and GSE137684, were analyzed. Targets of the top DE-miRNAs were predicted using the multiMiR R package, and only miRNAs with validated results were retrieved. Genes that were common between the "DE-miRNA prediction results" and the "existing tissue DE-mRNAs" were designated as differentially expressed genes (DEGs). Gene ontology (GO) and pathway enrichment analyses were implemented for DEGs. In order to identify hub genes and hub DE-miRNAs, the protein-protein interaction (PPI) network and miRNA-mRNA interaction network were constructed using Cytoscape software. The drug-gene interaction database (DGIdb) database was utilized to identify interactions between the top-ranked hub genes and drugs. Results: Out of the top 20 DE-miRNAs that were retrieved from the GSE114419 and GSE34526 microarray datasets, only 13 of them had "validated results" through the multiMiR prediction method. Among the 13 DE-miRNAs investigated, only 5, namely hsa-miR-8085, hsa-miR-548w, hsa-miR-612, hsa-miR-1470, and hsa-miR-644a, demonstrated interactions with the 10 hub genes in the hub gene-miRNA networks in our study. Except for hsa-miR-612, the other 4 DE-miRNAs, including hsa-miR-8085, hsa-miR-548w, hsa-miR-1470, and hsa-miR-644a, are novel and had not been reported in PCOS pathogenesis before. Also, GO and pathway enrichment analyses identified "pathogenic E. coli infection" in the Kyoto encyclopedia of genes and genomes (KEGG) and "regulation of Rac1 activity" in FunRich as the top pathways. The drug-hub gene interaction network identified ACTB, JUN, PTEN, KRAS, and MAPK1 as potential targets to treat PCOS with therapeutic drugs. Conclusions: The findings from this study might assist researchers in uncovering new biomarkers and potential therapeutic drug targets in PCOS treatment.

A comprehensive bioinformatic analysis revealed novel MicroRNA biomarkers of Parkinson's disease.

Parkinson's disease (PD) is categorized as a neurodegenerative disorder. Different studies have focused on the role of microRNAs (miRNAs) on PD progression. Due to its complexity in initiation and progression, a considerable requirement has arisen to identify novel miRNA biomarkers in a noninvasive manner. In silico analysis has been used to select differentially expressed miRNAs (DE-miRNAs) and key pathways in this disease. In this manner, several data sets of different neurodegenerative diseases have been analyzed to purify the findings of the present study. Totally, 15 DE miRNAs showed significant changes compared to healthy controls and other neurodegenerative diseases. Then, the targets of the miRNAs were predicted through miRTarBase and TargetScan databases. Besides, enrichment analysis was implemented for predicted target genes. Most of the target genes were enriched in the TRAIL signaling pathway, Regulation of nucleobase, nucleoside, nucleotide and nucleic acid metabolism, protein serine/threonine kinase activity, and Cytoplasm. Moreover, a protein-protein interaction network was constructed to find the most key DE miRNAs and targets in this disease. The results of the present study may help researchers shed light on the discovery of novel biomarkers for PD.

A Review on CYP11A1, CYP17A1, and CYP19A1 Polymorphism Studies: Candidate Susceptibility Genes for Polycystic Ovary Syndrome (PCOS) and Infertility.

Polycystic ovary syndrome is a multifactorial condition associated with reproductive and endocrine organs and might cause infertility and metabolic abnormalities in childbearing age. PCOS seems to be a multifactorial disorder resulting from the combination of several genetic and environmental factors. Little research has been conducted to date on the impact of polymorphisms in infertility. We aim to review the appearance of polymorphisms in females of diverse ethnicities and their effect on infertility in the population with polycystic ovary syndrome. There have been numerous reports of the importance of the steroidogenesis pathway and genetic variants in PCOS pathogenesis. The most important genes that play a role in the aetiology of PCOS are CYP11A1, CYP17A1, and CYP19A1. We evaluated the occurrence of polymorphisms in various ethnicities in the CYP11A1, CYP17A1, and CYP19A1 genes and their efficacy on increasing PCOS risk with infertility. Our findings revealed that polymorphisms in various ethnicities are associated with the risk of PCOS with infertility. Although conflicting results regarding CYP11A1, CYP17A1, and CYP19A1 polymorphisms and their influence on PCOS with infertility have been reported in a small number of papers, the authors feel this may be attributable to the sample size and ethnic composition of the examined populations. In conclusion, our study strongly suggests that the CYP11A1, CYP17A1, and CYP19A1 genes might significantly enhance the probability of developing PCOS with infertility.

Association of Vitamin D receptor gene polymorphisms and clinical/severe outcomes of COVID-19 patients.

INTRODUCTION: Growing evidence documented the critical impacts of vitamin D (VD) in the prognosis of COVID-19 patients. The functions of VD are dependent on the vitamin D receptor (VDR) in the VD/VDR signaling pathway. Therefore, we aimed to assess the association of VDR gene polymorphisms with COVID-19 outcomes. METHODS: In the present study, eight VDR single nucleotide polymorphisms (SNPs) were genotyped by polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) in 500 COVID-19 patients in Iran, including 160 asymptomatic, 250 mild/moderate, and 90 severe/critical cases. The association of these polymorphisms with severity, clinical outcomes, and comorbidities were evaluated through the calculation of the Odds ratio (OR). RESULTS: Interestingly, significant associations were disclosed for some of the SNP-related alleles and/or genotypes in one or more genetic models with different clinical data in COVID-19 patients. Significant association of VDR-SNPs with signs, symptoms, and comorbidities was as follows: ApaI with shortness of breath (P ˂ 0.001) and asthma (P = 0.034) in severe/critical patients (group III); BsmI with chronic renal disease (P = 0.010) in mild/moderate patients (group II); Tru9I with vomiting (P = 0.031), shortness of breath (P = 0.04), and hypertension (P = 0.030); FokI with fever and hypertension (P = 0.027) in severe/critical patients (group III); CDX2 with shortness of breath (P = 0.022), hypertension (P = 0.036), and diabetes (P = 0.042) in severe/critical patients (group III); EcoRV with diabetes (P ˂ 0.001 and P = 0.045 in mild/moderate patients (group II) and severe/critical patients (group III), respectively). However, the association of VDR TaqI and BglI polymorphisms with clinical symptoms and comorbidities in COVID-19 patients was not significant. CONCLUSION: VDR gene polymorphisms might play critical roles in the vulnerability to infection and severity of COVID-19, probably by altering the risk of comorbidities. However, these results require further validation in larger studies with different ethnicities and geographical regions.