Dataset Information

Polycystic Ovary Syndrome: Novel and Hub lncRNAs in the Insulin Resistance-Associated lncRNA-mRNA Network.

ABSTRACT: 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.

SUBMITTER: Zhao J

PROVIDER: S-EPMC6715451 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Polycystic Ovary Syndrome: Novel and Hub lncRNAs in the Insulin Resistance-Associated lncRNA-mRNA Network.

Zhao Jun J Huang Jiayu J Geng Xueying X Chu Weiwei W Li Shang S Chen Zi-Jiang ZJ Du Yanzhi Y

Frontiers in genetics 20190822

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 pat ...[more]

PMID: 31507635

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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:Background Polycystic ovary syndrome (PCOS) is a common multifactorial metabolic and endocrine disorder in women of reproductive age. Increasingly, evidence indicates that the microRNA (miRNA)-mRNA axis contributes to the development of PCOS. Methods To investigate the molecular mechanisms through which miRNA-mRNA expression affects hyperandrogenism, ovarian tissue samples from prenatally androgenized (PNA) mice were subjected to miRNA-seq and RNA-seq analysis. Analyses were performed to identify differentially expressed microRNAs (DEmiRs) and differentially expressed genes (DEGs). In combination with our previous data obtained from clinical samples, we have performed an integrated miRNA-mRNA analysis of PNA mice and granulosa cells (GCs) from patients with PCOS. The changes in expression were validated by RT-qPCR in more mouse models and clinical samples. Results In total, 3,432 genes and 16 miRNAs were differentially expressed in PNA mice compared with the control mice. We investigated the regulation pattern of miRNAs-mRNAs and observed a total of 12 miRNA-mRNA pairs involved in negative regulation. Functional analysis concentrated on insulin resistance, the T cell receptor signaling pathway, and other inflammation-related pathways. Verification of these results by RT-qPCR showed that the expression of miR-106-5p and miR-155-5p in clinical GCs was consistent with that in PNA mice. After predicting the target genes of miR-106-5p and miR-155-5p and performing negative regulation analysis, six target genes were obtained in GCs. The integration analysis showed that the network of miR-106-5p/miR-155-5p targets was mostly concentrated in pathway related to insulin resistance and inflammation. In addition, the upregulation of the inflammatory genes Il18/IL18 and Socs3/SOCS3 was validated in the PNA mice and GCs from patients compared with the appropriate control sample. The in vitro experiments showed that the regulatory relationship observed may be related to the direct stimulation of DHT. Conclusions Our results showed that the miRNA-mRNA regulatory network in PCOS was associated with markers of insulin sensitivity and inflammation. Our study provides a new genetic basis and novel insight regarding the pathogenesis of PCOS.

Project description:Polycystic ovary syndrome (PCOS), a common and frustrating syndrome in women of reproductive age, is characterized by symptoms including hyperandrogenemia, ovulation dysfunction, and polycystic ovaries. The role of competitive endogenous RNA (ceRNA) networks is receiving increasing attention and has been reported in multiple complicated diseases, such as various carcinomas, endometriosis, and tubal factor infertility. However, the association of ceRNA networks with the pathogenesis of PCOS remains unclear. This study aimed to construct a ceRNA network orchestrated by exosomal lnRNA and circRNA in PCOS. We screened RNA data of 34 samples from the Gene Expression Omnibus (GEO) database for differentially expressed lncRNAs (DELs), miRNAs (DEMs), mRNAs (DEGs), and circRNA associated with the progression of PCOS (PCOS, n = 17 vs. normal, n = 17). A protein-protein interaction (PPI) network, gene set enrichment analysis (GSEA), and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted. Importantly, the function of the ceRNA network was explored using GO and KEGG enrichment analyses. We identified 46 DELs (25 upregulated and 21 downregulated), 31 DEMs (20 upregulated and 11 downregulated), 165 DEGs (52 upregulated and 113 downregulated), and 1 differentially expressed circRNA. The PPI network had 79 nodes and 112 edges. The GSEA results showed that these genes were mainly related to oxidative phosphorylation; TNF signaling pathways; and valine, leucine, and isoleucine degradation. GO and KEGG analyses revealed that the DEGs were significantly enriched in lipid metabolism, peroxisome proliferator-activated receptor (PPAR) signaling pathways, and fatty acid metabolism. Additionally, we constructed a novel PCOS-associated lncRNA-miRNA-mRNA ceRNA triple network and a circRNA-related network. Thereafter, we described the potential roles played by follicular fluid exosomes in PCOS. Our present study describes the molecular pathogenesis of PCOS in human ovarian granulosa cells at the post-transcriptional level, which provides new insights for the clinical diagnosis and treatment of PCOS and further scientific research.

Dataset Information

Polycystic Ovary Syndrome: Novel and Hub lncRNAs in the Insulin Resistance-Associated lncRNA-mRNA Network.

Publications

Polycystic Ovary Syndrome: Novel and Hub lncRNAs in the Insulin Resistance-Associated lncRNA-mRNA Network.

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