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.
AIMS: To identify the transcriptome expression profiles of peripheral blood mononuclear cells (PBMCs) in women with PCOS and controls. To investigate noninvasive diagnostic biomarkers and potential treatment targets to improve women's fertility.
METHODS: RNA sequencing (RNA-Seq) was conducted on PBMC samples from six patients with PCOS and six healthy controls. qRT-PCR validation was carried out in 68 subjects. Multivariate logistic regression was performed to assess the combined impact of biomarkers.
RESULTS: A total of 186 differentially expressed genes (DEG) were found between patients and controls (log2FC >1, p < 0.05). Enrichment analysis revealed cytokine-mediated signaling pathways, cytokine activity, and cytokine-cytokine receptor interaction. RNA sequencing showed consistency with qRT-PCR. Women with PCOS had significantly higher levels of AQP9 (p < 0.001), PROK2 (p = 0.001), and S100A12 (p < 0.001) expression compared to controls. AQP9 (AUC = 0.77), PROK2 (AUC = 0.71), and S100A12 (AUC = 0.82) adequately discriminated women with PCOS from healthy controls. In addition, multiple logistic regression on biomarkers resulted in a significant diagnostic power with an AUC = 0.89, 95 % CI: 0.81-0.97, p < 0.0001. Further associations were analyzed between relative gene expression and clinical, anthropometric, hormonal, and ultrasonographic data.
CONCLUSIONS: Dysregulated RNA expression in PBMCs may contribute to an increased risk of PCOS and serve as a potential diagnostic biomarker. The involvement of inflammatory and cytokine-related pathways supports the notion that PCOS is a chronic inflammatory condition.
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.