Dataset Information

Novel Hub genes co-expression network mediates dysfunction in a model of polycystic ovary syndrome.

ABSTRACT:

Background

This study aimed to integrate DNA methylation, miRNA, and mRNA microarray data to construct a gene co-expression network for polycystic ovarian syndrome (PCOS).

Methods

The weighted gene co-expression network analysis (WGCNA) was conducted to construct a PCOS-related co-expression network by using the GEO public datasets. We performed Gene Ontology and KEGG pathway enrichment analyses for a further exploration of gene function in networks. Finally, the dysfunction module consisting of a co-expression network was mapped to the PCOS patients and tried to provide guidance to the PCOS phenotyping.

Results

Three modules (Midnightbule, Pink, and Red) were identified to be PCOS-related by WGCNA analysis. These module-related genes were enriched in cell response to stimulus, PI3K-Akt signaling pathway, insulin biological process, signaling pathway, and cytokine-cytokine receptor interaction biological processes. The multiple-factor network, including miRNA-lncRNA and DNA methylation-mRNA interaction, was closely associated with PCOS dysfunction.

Conclusion

Our study render a novel insight into the mechanisms and might provide candidate biomarkers and therapeutic targets for the classification of PCOS dysfunction.

SUBMITTER: Xi S

PROVIDER: S-EPMC8991170 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature

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Publications

Novel Hub genes co-expression network mediates dysfunction in a model of polycystic ovary syndrome.

Xi Sujuan S Li Weihao W Li Zaiyi Z Lin Wenjing W Chen Lin L Tian Chengzi C Yang Yazhu Y Ma Lin L

American journal of translational research 20220315 3

<h4>Background</h4>This study aimed to integrate DNA methylation, miRNA, and mRNA microarray data to construct a gene co-expression network for polycystic ovarian syndrome (PCOS).<h4>Methods</h4>The weighted gene co-expression network analysis (WGCNA) was conducted to construct a PCOS-related co-expression network by using the GEO public datasets. We performed Gene Ontology and KEGG pathway enrichment analyses for a further exploration of gene function in networks. Finally, the dysfunction modul ...[more]

PMID: 35422941

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Project description:Polycystic ovary syndrome (PCOS) is a common metabolic and reproductive disorder with an increasing risk for type 2 diabetes. Insulin resistance is a common feature of women with PCOS, but the underlying molecular mechanism remains unclear. This study aimed to screen critical long non-coding RNAs (lncRNAs) that might play pivotal roles in insulin resistance, which could provide candidate biomarkers and potential therapeutic targets for PCOS. Gene expression profiles of the skeletal muscle in patients with PCOS accompanied by insulin resistance and healthy patients were obtained from the publicly available Gene Expression Omnibus (GEO) database. A global triple network including RNA-binding protein, mRNA, and lncRNAs was constructed based on the data from starBase. Then, we extracted an insulin resistance-associated lncRNA-mRNA network (IRLMN) by integrating the data from starBase and GEO. We also performed a weighted gene co-expression network analysis (WGCNA) on the differentially expressed genes between the women with and without PCOS, to identify hub lncRNAs. Additionally, the findings of key lncRNAs were examined in an independent GEO dataset. The expression level of lncRNA RP11-151A6.4 in ovarian granulosa cells was increased in patients with PCOS compared with that in control women. Levels were also increased in PCOS patients with higher BMI, hyperinsulinemia, and higher HOMA-IR values. As a result, RP11-151A6.4 was identified as a hub lncRNA based on IRLMN and WGCNA and was highly expressed in ovarian granulosa cells, skeletal muscle, and subcutaneous and omental adipose tissues of patients with insulin resistance. This study showed the differences between lncRNA and mRNA profiles from healthy women and women with PCOS and insulin resistance. Here, we demonstrated that RP11-151A6.4 might play a vital role in insulin resistance, androgen excess, and adipose dysfunction in patients with PCOS. Further study concerning RP11-151A6.4 could elucidate the underlying mechanisms of insulin resistance.

Project description:IntroductionPolycystic ovary syndrome (PCOS) is a common metabolic and endocrine disorder prevalent among women of reproductive age. Recent studies show that autophagy participated in the pathogenesis of PCOS, including anovulation, hyperandrogenism, and metabolic disturbances. This study was designed to screen autophagy-related genes (ATGs) that may play a pivotal role in PCOS, providing potential biomarkers and identifying new molecular subgroups for therapeutic intervention.MethodsGene expression profiles of the PCOS and control samples were obtained from the publicly available Gene Expression Omnibus database. The gene lists of ATGs from databases were integrated. Then, the weighted gene co-expression network analysis was conducted to obtain functional modules and construct a multifactorial co-expression network. Gene Ontology and KEGG pathway enrichment analyses were performed for further exploration of ATG's function in the key modules. Differentially expressed ATGs were identified and validated in external datasets with the Limma R package. To provide guidance on PCOS phenotyping, the dysfunction module consists of a co-expression network mapped to PCOS patients. A PCOS-Autophagy-related co-expression network was established using Cytoscape, followed by identifying molecular subgroups using the Limma R package. ps. RNA-sequencing analysis was used to confirm the differential expression of hub ATGs, and the diagnostic value of hub ATGs was assessed by receiver operating characteristic curve analysis.ResultsThree modules (Brown, Turquoise, and Green) in GSE8157, three modules (Blue, Red, and Green) in GSE43264, and four modules (Blue, Green, Black, and Yellow) in GSE106724 were identified to be PCOS-related by WGCNA analysis. 29 ATGs were found to be the hub genes that strongly correlated with PCOS. These hub ATGs were mainly enriched in autophagy-related functions and pathways such as autophagy, endocytosis, apoptosis, and mTOR signaling pathways. The mRNA-miRNA-lncRNA multifactorial network was successfully constructed. And three new molecular subgroups were identified via the K-means algorithm.DiscussionWe provide a novel insight into the mechanisms behind autophagy in PCOS. BRCA1, LDLR, MAP1B, hsa-miR-92b-3p, hsa-miR-20b-5p, and NEAT1 might play a considerably important role in PCOS dysfunction. As a result, new potential biomarkers can be evaluated for use in PCOS diagnosis and treatment in the future.

Project description:Polycystic ovary syndrome (PCOS), a common endocrine and metabolic disorder affecting women in their reproductive years. Emerging evidence suggests that the maternal-fetal immune system is crucial for proper pregnancy. However, whether immune function is altered at the end of pregnancy in PCOS women and the underlying molecular mechanisms is currently unexplored. Herein, the basic maternal immune system was investigated (n = 136 in the control group; n = 103 in the PCOS group), and whole-transcriptome sequencing was carried out to quantify the mRNAs, miRNAs, and lncRNAs expression levels in fetal side placental tissue of women with PCOS. GO, KEGG, and GSEA analysis were employed for functional enrichment analysis. The process of identifying hub genes was conducted utilizing the protein-protein interaction network. CIBERSORT and Connectivity Map were deployed to determine immune cell infiltration and predict potential drugs, respectively. A network of mRNA-miRNA-lncRNA was constructed and then validated by qRT-PCR. First, red blood cell count, neutrophil count, lymphocyte count, hypersensitive C-reactive protein, and procalcitonin were significantly elevated, while placental growth factor was hindered in PCOS women. We identified 308 DEmRNAs, 77 DEmiRNAs, and 332 DElncRNAs in PCOS samples. Functional enrichment analysis revealed that there were significant changes observed in terms of the immune system, especially the chemokine pathway. Eight genes, including FOS, JUN, EGR1, CXCL10, CXCR1, CXCR2, CXCL11, and CXCL8, were considered as hub genes. Furthermore, the degree of infiltration of neutrophils was dramatically decreased in PCOS tissues. In total, 57 ceRNA events were finally obtained, and immune-related ceRNA networks were validated. Some potential drug candidates, such as enalapril and RS-100329, could have a function in PCOS therapy. This study represents the inaugural attempt to evaluate the immune system at the end of pregnancy and placental ceRNA networks in PCOS, indicating alterations in the chemokine pathway, which may impact fetal and placental growth, and provides new therapy targets.

Dataset Information

Novel Hub genes co-expression network mediates dysfunction in a model of polycystic ovary syndrome.

Background

Methods

Results

Conclusion

Publications

Novel Hub genes co-expression network mediates dysfunction in a model of polycystic ovary syndrome.

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