Machine Learning and Network Methods for Biology and Medicine 2021View this Special Issue
Bioinformatics Analysis of ceRNA Network Related to Polycystic Ovarian Syndrome
Introduction. Polycystic ovary syndrome (PCOS) is caused by the hormonal environment in utero, abnormal metabolism, and genetics, and it is common in women of childbearing age. A large number of studies have reported that lncRNA is important to the biological process of cancer and can be used as a potential prognostic biomarker. Thus, we studied lncRNAs’ roles in PCOS in this article. Methods. We obtained mRNAs’, miRNAs’, and lncRNAs’ expression profiles in PCOS specimens and normal specimens from the National Biotechnology Information Gene Expression Comprehensive Center database. The EdgeR software package is used to distinguish the differentially expressed lncRNAs, miRNAs, and mRNAs. Functional enrichment analysis was carried out by the clusterProfiler R Package, and the lncRNA-miRNA-mRNA interaction ceRNA network was built in Cytoscape plug-in BiNGO and Database for Annotation, Visualization, and Integration Discovery (DAVID), respectively. Results. We distinguished differentially expressed RNAs, including 1087 lncRNAs, 14 miRNAs, and 566 mRNAs in PCOS. Among them, 410 lncRNAs, 11 miRNAs, and 185 mRNAs were contained in the ceRNA regulatory network. The outcomes from Gene Ontology (GO) analysis showed that the differentially expressed mRNAs (DEMs) were mainly enriched in response to the maternal process involved in female pregnancy, morphogenesis of embryonic epithelium, and the intracellular steroid hormone receptor signaling pathway. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis data showed that DEMs were primarily enriched in pathways related to the TGF-β signaling pathway, Type I diabetes mellitus, and glycolysis/gluconeogenesis. In addition, we chose NONHSAT123397, ENST00000564619, and NONHSAT077997 as key lncRNAs due to their high bearing on PCOS. Conclusion. ceRNA networks play an important role in PCOS. The research indicated that specific lncRNAs were related to PCOS development. NONHSAT123397, ENST00000564619, and NONHSAT077997 could be regarded as potential diagnostic mechanisms and biomarkers for PCOS. This discovery might provide more effective and more novel insights into the mechanisms of PCOS worthy of further exploration.
Polycystic ovary syndrome (PCOS) is caused by the hormonal environment in utero, abnormal metabolism, and genetics, and its incidence is related to race and eating habits [1–3]. The disease is very common in women of childbearing age, and its common clinical symptoms include menstrual disorders, hirsutism, obesity, and infertility [4–10]. Among them, according to relevant statistics, obesity accounts for 30% to 60% of PCOS patients. PCOS is often accompanied by other diseases, such as diabetes, cardiovascular disease (CVDS), and other complications [11–13]. The current treatment methods are mainly oral contraceptives and drugs to reduce hyperandrogenemia , ovulation-stimulating drugs [15, 16], surgical treatment, and in vitro fertilization . Since the cause of the disease is not yet clear, the clinical treatment of PCOS patients also has limitations. Therefore, more research on the pathogenesis of PCOS is needed to find efficient biomarkers.
Long noncoding RNAs (lncRNAs) are noncoding RNAs longer than 200 nucleotides [18, 19]. A large number of researches have shown that lncRNA is important to the biological process of cancer and can be used as a potential prognostic biomarker. The different mechanisms of lncRNA action in cancer will lead to different expression patterns in cancer cells. For example, according to related reports by Li et al., lncRNA GAS5 was upregulated in PCOS and could participate in the occurrence of diseases by regulating cell apoptosis and IL-6 expression . Qin et al. proved for the first time that lncRNA H19 was associated with PCOS, which was a useful biomarker for early endocrine and metabolic abnormalities in PCOS . Liu et al. found that the expression of lncRNA-Xist was related to the pathogenesis of PCOS. Knocking down the expression of this gene in PCOS could lead to the proliferation and migration of cancer cells . Further secrets about lncRNA in cancers remain to be discovered.
There is increasing evidence that noncoding RNA (ncRNA) plays a key role in the development of human diseases . Many studies have shown that these ncRNAs participate in competitive regulatory interactions ; that is, a network of competitive endogenous RNAs (ceRNAs) and lncRNAs can act as microRNA bait to regulate gene expression . These interactions are usually interconnected, so any abnormal expression of network components may derail complex regulatory circuits and ultimately lead to the development and progression of cancer. According to reports, XLOC_006390 acts as a ceRNA and reversely regulates the expression of miR-331-3p and miR-338-3p, thereby promoting the occurrence and metastasis of cervical cancer . MT1JP regulates the progression of gastric cancer by acting as a ceRNA to competitively bind to miR-92a-3p and regulate the expression of FBXW7 .
Herein, just as Figure 1 shows, we build a global triple network through the National Center for Biotechnology Information and Gene Expression Omnibus (NCBI and GEO) data. Research design is based on the internal competitive endogenous RNA (ceRNA) theory, and bioinformatics analysis is conducted to explore PCOS in the lncRNA-miRNA-mRNA network.
2. Materials and Methods
2.1. PCOS Data
GEO (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi), an open-access functional genomics database, offers support for the submission of MIAME-compatible data. GEO could provide data based on arrays and sequences as well as tools to help users download gene expression profiles. We downloaded the human miRNA real-time PCR array database in GEO (GSE37425).
2.2. lncRNAs’, miRNAs’, and mRNAs’ Differentially Expressed Screening
The differentially expressed lncRNAs (DELs), miRNAs (DEMis), and mRNAs (DEMs) between normal tissue and PCOS tissue are set out through a two-level differential method. Then, the differentially expressed genes were screened by -test. In light of values less than 0.05 and fold change more than 2, we screened the data from DELs, DEMis, and DEMs.
2.3. lncRNAs and mRNAs of DEMis Determination
RNAhybrid and miRanda were employed to determine lncRNAs’ miRNA targets and measure free energy (MFE) of miRNA-lncRNA double-stranded bodies’ minimum value. We found miRNA sequences in miRBase (http://www.mirbase.org/) and checked lncRNA sequences in NCBI (https://www.ncbi.nlm.nih.gov/) nucleotides. miRNA target binding sites were determined across the total lncRNA sequence. We chose lncRNAs with perfect nucleotides to pair at the 2nd and 8th ends of miRNA sequences to gain high-quality lncRNAs, and we then selected these lncRNAs to act as miRNA targets. We downloaded data about miRNA-mRNA interactions from miRTarBase (http://mirtarbase.cuhk.edu.cn/php/index.php) and miRWalk (http://mirwalk.umm.uni-heidelberg.de/).
2.4. Building of the lncRNA-miRNA-mRNA Network
On the basis of the ceRNA theory to build the lncRNA-miRNA-mRNA network, the methods were as follows: (1) We selected Pearson’s correlation coefficient (PCC) to determine the correlation between the DELs’ and DEMs’ expression. The coexpressed lncRNA-mRNA pairs’ standard was PCC more than 0.99 and less than 0.05. (2) The coexpression of a competition triplet was identified when mRNA and lncRNA in a pair were both targeted to a certain common miRNA and negatively expressed. (3) Next, coexpression competing triads were assembled to reconstruct the lncRNA-miRNA-mRNA network using Cytoscape software for visualization. Meanwhile, the degrees of all nodes in the miRNA-lncRNA-mRNA network were determined.
2.5. Analysis of Functional Enrichment
In functional enrichment analysis, the Cytoscape plug-in BiNGO and Database for Annotation, Visualization, and Integration Discovery Database (DAVID, https://david.ncifcrf.gov/) were used to perform Gene Ontology (GO) biological process terminology and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses on mRNAs in the lncRNA-miRNA-mRNA network, respectively. Then, we used Cytoscape plug-in BiNGO to rebuild the GO interactive network.
2.6. Key lncRNA-miRNA-mRNA Subnetwork Rebuilding
We extracted each lncRNA of the global triple network and its connected miRNAs and mRNAs, which were prepared for Cytoscape software to rebuild the new subnetwork. At the same time, the lncRNA-miRNA first and second relationship pairs’ number was calculated. Next, we collected key lncRNAs by lncRNA node number, first and second relationship pairs’ number, and lncRNA-miRNA’s quantity. Further, every last key lncRNA was used to perform GO and pathway annotations. Then, Cytoscape plug-in BiNGO was selected to reconstruct the GO interactive network.
3.1. Data Preprocessing
The human miRNA real-time PCR array database included 14 miRNAs, and 10 coexpression miRNAs were selected in this study. There were 566 mRNAs and 1087 lncRNAs selected in the human lncRNA/mRNA microarray data.
3.2. Results of DELs’, DEMs’, and DEMis’ Screening
We chose to preprocess data by values less than 0.05 and fold change more than 2. 566 differentially expressed mRNAs (DEMs), 1087 differentially expressed lncRNAs (DELs), and 14 differentially expressed miRNAs (DEMis) were filtered. Next, we merged the DEMs and DELs with the target mRNAs and lncRNAs of DEMis, respectively. We got 410 coexpression lncRNAs, 185 coexpression mRNAs, and 10 DEMis. Lastly, we chose these genes for reconstructing the lncRNA-miRNA-mRNA network.
3.3. lncRNA-miRNA-mRNA Network
For evaluating the lncRNAs’ features as targets of miRNA, we first reconstructed and then visualized the network between lncRNAs and miRNAs. As shown in Figure 2, there were 410 lncRNAs, 11 miRNAs, and 691 edges in the network. Then, we also reconstructed the network between miRNAs and mRNA. As shown in Figure 3, there were 10 miRNAs, 185 mRNAs, and 449 edges in this network. Finally, we rebuilt the lncRNAs’, miRNAs’, and mRNAs’ networks. As shown in Figure 4, there were 410 lncRNAs, 185 mRNAs, 10 miRNAs, and 1079 edges in the lncRNA-miRNA-mRNA network.
3.4. Prediction of lncRNA Function Based on lncRNA-miRNA-mRNA Network
We observed that one or more mRNAs surround and bind to lncRNAs in the lncRNA-miRNA-mRNA network. Thus, we could infer each lncRNA function according to the connected mRNAs’ features. We analyzed the DEL functions by all DEMs. For a deeper understanding of the function of DEMs in PCOS, we used the BiNGO plug-in to enrich the functions of these DEMs. The results of GO analysis revealed that the DEMs were enriched in 526 biological process (BP) terms, particularly in sodium ion transport, response to interferon-gamma, neural tube development, morphogenesis of embryonic epithelium, leukocyte chemotaxis, antigen processing, presentation of peptide, and more. The DEMs were enriched in 104 molecular function (MF) terms, such as virus receptor activity, Ras guanyl-nucleotide exchange factor activity, and guanyl-nucleotide exchange factor activity. The DEMs were enriched in 91 cellular component (CC) terms, such as filopodium, lamellipodium, and axon terminus. The top thirty significant GO terms are listed in Figure 5 according to value. Additionally, KEGG pathway analysis indicated that 97 pathways especially related to TGF-β signaling, Type I diabetes mellitus, and glycolysis/gluconeogenesis were obviously enriched. The top thirty significant KEGG pathways are shown in Figure 6 according to value.
3.5. Topological Analysis of the PCOS-Related lncRNA-miRNA-mRNA Network
Hub nodes are important to biological networks. Thus, we explored all nodes’ degrees in the lncRNA-miRNA-mRNA network. Based on the previous research from Hanet al., a node degree exceeding 5 was defined as a hub node. As shown in Table 1 and Figure 7, 30 nodes containing 10 lncRNAs, 10 miRNAs, and 10 mRNAs could be selected as hub nodes in this study. Moreover, the quantity of the lncRNA-miRNA pairs and the miRNA-mRNA pairs are counted and shown in Table 2. We observed NONHSAT123397, ENST00000564619, and NONHSAT077997. We found that they had higher degrees of nodes and a higher quantity of pairs. These results suggested that they played a key role in the launch and progress of PCOS; thus, they were selected as the key lncRNAs.
3.6. Subnetwork of Key lncRNA-miRNA-mRNA
As we all know, lncRNAs and mRNAs have a common coexpression mode in the ceRNA network. Therefore, we picked up the key lncRNAs and their linked mRNAs and miRNAs from the previous lncRNA-miRNA-mRNA network and reconstructed the subnetworks of key lncRNAs-miRNAs-mRNAs. As presented in Figures 8–10, the lncRNA NONHSAT123397-miRNA-mRNA subnetwork covered 7 miRNA nodes, 143 mRNAs, and 358 edges; the lncRNA ENST00000564619-miRNA-mRNA subnetwork covered 6 miRNAs, 140 mRNAs, and 304 edges; and the lncRNA NONHSAT077997-miRNA-mRNA subnetwork covered 6 miRNAs, 139 mRNAs, and 320 edges. Analysis of the GO terms and KEGG pathways demonstrated that 550 GO terms and 80 pathways were enriched in the subnetwork of NONHSAT123397, 553 GO terms and 82 pathways were enriched in the subnetwork of ENST00000564619, and 533 GO terms and 80 pathways were enriched in the subnetwork of NONHSAT077997. The top 30 significantly enriched GO terms and KEGG pathways of each subnetwork are listed in Figures 11–13.
PCOS has become a common disease in women, and the metabolic abnormalities of many PCOS patients often lead to the risk of cardiovascular disease [28, 29]. In recent years, there have been many controversies about the oncogene of PCOS. Many studies have shown that insulin deficiency may be the main cause of PCOS [30, 31]. In addition, obesity is also considered the main cause. More than 30% of PCOS patients suffer from obesity, and obesity makes the clinical treatment of PCOS more difficult. However, the exact reason for PCOS is still unclear. Currently, therapies like oral contraceptives can regulate menstruation and reduce the production of adrenal androgens [32, 33], but they can only be used for women who have no plan to be pregnant. In short, more research and therapies targeting the pathogenesis and pathophysiology of PCOS need to be explored.
Different from coding RNAs, lncRNAs’ functions have not been well studied, and the exploration of lncRNAs’ functions is full of challenges. Recently, accumulated data have found that lncRNAs had abnormal expressions in many diseases , such as PCOS, which indicates that lncRNA might have a special role in disease progression. So far, some studies have found that lncRNAs exert their functions by regulating mRNA expression or binding with miRNAs. For example, Luo et al. found lncRNA CASC11 might increase the capability of bladder cancer cell proliferation, and the roles of lncRNA CASC11 are probably through miRNA-150 . Wang et al. showed that lnc00152 slicing repressed the growth and invasiveness of hemangioma cells by regulating miR-139-5p . Besides, some studies point out that the relation among lncRNA, miRNA, and mRNA is worth exploring in cancer development [37, 38]. Similarly, Wu et al. discussed the therapeutic extent and role of miRNA, lncRNA, and circRNA in osteoarthritis .
The ceRNA (competing endogenous RNA) hypothesis prompts a novel mechanism of RNAs’ interaction . miRNA can silence genes through binding to mRNA , and ceRNA can adjust gene expression through binding to miRNA competitively . Compared with the miRNA regulatory network, the ceRNA regulatory network is more sophisticated and complex, involving more RNA molecules, including mRNA, pseudogenes of coding genes, long noncoding RNAs, and miRNAs . It provides a new perspective for scientific researchers to conduct transcriptome research. Thus, the purpose of this research is to determine the function and inner mechanism of lncRNAs as ceRNAs in PCOS through the lncRNA-miRNA-mRNA network.
Herein, we downloaded PCOS data in the NCBI GEO database. Based on a theory about ceRNA that the lncRNAs and mRNAs share the same miRNAs in triplets, we then established a global triple network by these data about PCOS. We determined 1087 lncRNAs, 14 miRNAs, and 566 mRNAs as differentially expressed RNAs. Meanwhile, we discovered that the lncRNA-miRNA-mRNA network was composed of 410 lncRNAs, 11 miRNAs, and 185 mRNAs. We evaluated the biological functions enriched in differentially expressed coding genes through Gene Ontology (GO) analysis and pathway analysis. We chose GO analysis as a control track for exploring the differentially expressed genes’ function and depicted the distribution of genes and gene products. According to the precise test of KEGG and Fisher’s and the significance threshold defined by the value, pathway analysis was applied to the differentially expressed genes’ location. The GO analysis’ results showed that the differentially expressed mRNAs (DEMs) were mainly rich in the following aspects: response to the maternal process involved in female pregnancy, morphogenesis of embryonic epithelium, and the intracellular steroid hormone receptor signaling pathway. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis results indicated that DEMs were mostly enriched in the TGF-β signaling pathway, Type I diabetes mellitus, and glycolysis/gluconeogenesis.
These important GO clauses involved in the maternal course were involved in response to interferon-gamma , neural tube development , morphogenesis of embryonic epithelium, leukocyte chemotaxis, antigen processing, presentation of peptide, and more [46–48]. The pathway analysis indicated that 97 pathways got rich, and it mainly involved the TGF-β signaling pathway [49, 50], Type I diabetes mellitus , and glycolysis/gluconeogenesis [52, 53]. All pathways determined were important to PCOS. Research on lncRNAs is getting much deeper, due to the expression of lncRNA related to its functions. Thus, lncRNAs are more suitable as reliable and effective biomarkers and therapeutic targets. Lately, many researchers have found several lncRNA-focused features to increase the cure rate of certain diseases, but lncRNAs’ diagnostic role in PCOS has not been fully studied. We used the hub nodes and the relationship pairs’ quantity to find key lncRNAs as new potential biomarkers for PCOS diagnosis and prognosis. Some studies have shown that hub nodes, which are characterized by their high connectivity with others, can be regarded as the network for accessing genes of importance . Generally, lncRNAs with more relationship pairs show that lncRNAs are hubs involved in ceRNA interplay [55, 56]. Therefore, lncRNA is indispensable and important for network organization.
It has been observed that NONHSAT123397, ENST00000564619, and NONHSAT077997 are the key nodes of the topology. The amount of lncRNA-miRNA and miRNA-mRNA pairs and the number of nodes far exceed other lncRNAs. These lncRNAs are important to PCOS and can be regarded as key lncRNAs. In the midst of these key lncRNAs, NONHSAT123397 is a rarely reported lncRNA. It has many functions, including connecting many mRNAs and interacting with many miRNAs known to be involved with PCOS. NONHSAT123397 is also related to PCOS due to the results of GO and pathway analyses, so these indicate that NONHSAT123397 is a key lncRNA to PCOS. However, the function of NONHSAT123397 to PCOS is not currently indicated from the current studies. On the basis of the subnetwork of NONHSAT123397-miRNA-mRNA, we speculate that NONHSAT123397 might compete with certain miRNA families to cause changes in the expression of downstream mRNAs linked to PCOS, like the miR-3135b and miR-3188 families. In order to confirm our guess, experiments in recent years have shown that miR-3135b and miR-3188 families are critical for PCOS’s development . For example, Wang et al. indicated that miR-3188 and miR-3135b in granulosa cells of PCOS patients were negatively correlated with FSH, while miR-3188 was positively correlated with BMI, and hsa-miR-3188/3135b improved the prediction accuracy of PCOS . In addition, studies show that the TGF-β signaling pathway is important to PCOS. Meanwhile, the target genes corresponding to miRNA-3135b are highly enriched in the TGF-β signaling pathway and insulin secretion. Downregulation of miR-486-5p expression in cumulus cells of metaphase II oocytes in patients with the polycystic ovary syndrome could control the proliferation of cumulus cells by activating PI3K/Akt.
This study has some limitations. The current experimental data are not enough to enable us to have a comprehensive understanding of the mechanism of action of these three lncRNAs, so we need to continue to explore. In future studies, we will collect more clinical samples, and we will explore the correlation between the expression of NONHSAT123397, ENST00000564619, and NONHSAT077997 and clinical parameters (including age, clinical stage, and survival time). Secondly, we will explore the regulation of the proliferation and metastasis of PCOS by NONHSAT123397, ENST00000564619, and NONHSAT077997. Finally, the ceRNA network identified in this study will be verified through experiments, such as through the dual-luciferase assay and the RNA immunoprecipitation assay.
Studies have shown that the development trend of PCOS in the human body will be affected by specific lncRNAs. Our research revealed that the ceRNA network, which involves a new type of interaction between lncRNAs, miRNAs, and mRNAs, have the potential to influence the occurrence and development of PCOS disease. The significant differential expression of NONHSAT123397, ENST00000564619, and NONHSAT077997 in PCOS means that these three genes may play an important role in PCOS. Then, we have a further breakthrough in the mechanism of PCOS. In short, in this experiment, we have discovered a new therapeutic target for PCOS, which is a new breakthrough in the treatment of clinical PCOS patients.
The data that support the findings of this study are available with approval from the authors.
Conflicts of Interest
The authors declare that they have no conflicts of interest.
We greatly appreciate the grants from the National Natural Science Foundation of China (grant no. 81674012) and the Scientific Research and Practice Innovation Plan for Postgraduates in Jiangsu Province (no. KYCX20_1465).
R. Azziz, K. S. Woods, R. Reyna, T. J. Key, E. S. Knochenhauer, and B. O. Yildiz, “The prevalence and features of the polycystic ovary syndrome in an unselected population,” The Journal of Clinical Endocrinology and Metabolism, vol. 89, no. 6, pp. 2745–2749, 2004.View at: Publisher Site | Google Scholar
W. Wolf, R. Wattick, O. Kinkade, and M. Olfert, “Geographical prevalence of polycystic ovary syndrome as determined by region and race/ethnicity,” International Journal of Environmental Research and Public Health, vol. 15, no. 11, p. 2589, 2018.View at: Publisher Site | Google Scholar
A. Y. Chang, J. Oshiro, C. Ayers, and R. J. Auchus, “Influence of race/ethnicity on cardiovascular risk factors in polycystic ovary syndrome, the Dallas Heart Study,” Clinical Endocrinology, vol. 85, no. 1, pp. 92–99, 2016.View at: Publisher Site | Google Scholar
L. A. Brinton, K. S. Moghissi, C. L. Westhoff, E. J. Lamb, and B. Scoccia, “Cancer risk among infertile women with androgen excess or menstrual disorders (including polycystic ovary syndrome),” Fertility and Sterility, vol. 94, no. 5, pp. 1787–1792, 2010.View at: Publisher Site | Google Scholar
A. Badawy and A. Elnashar, “Treatment options for polycystic ovary syndrome,” International Journal of Women's Health, vol. 3, pp. 25–35, 2011.View at: Publisher Site | Google Scholar
H. F. Escobar-Morreale, E. Carmina, D. Dewailly et al., “Epidemiology, diagnosis and management of hirsutism: a consensus statement by the Androgen Excess and Polycystic Ovary Syndrome Society,” Human Reproduction Update, vol. 18, no. 2, pp. 146–170, 2012.View at: Publisher Site | Google Scholar
L. J. Moran, R. Pasquali, H. J. Teede, K. M. Hoeger, and R. J. Norman, “Treatment of obesity in polycystic ovary syndrome: a position statement of the Androgen Excess and Polycystic Ovary Syndrome Society,” Fertility and Sterility, vol. 92, no. 6, pp. 1966–1982, 2009.View at: Publisher Site | Google Scholar
A. Gambineri, C. Pelusi, V. Vicennati, U. Pagotto, and R. Pasquali, “Obesity and the polycystic ovary syndrome,” International Journal of Obesity and Related Metabolic Disorders, vol. 26, no. 7, pp. 883–896, 2002.View at: Publisher Site | Google Scholar
T. A. Elhadd, T. Fiad, and L. Meer, “Ovarian stockpiling in polycystic ovary syndrome, infertility, and the combined use of rosiglitazone and metformin,” Diabetes Care, vol. 29, no. 10, pp. 2330-2331, 2006.View at: Publisher Site | Google Scholar
Z. Bu, W. Dai, Y. Guo, Y. Su, J. Zhai, and Y. Sun, “Overweight and obesity adversely affect outcomes of assisted reproductive technologies in polycystic ovary syndrome patients,” International Journal of Clinical and Experimental Medicine, vol. 6, no. 10, pp. 991–995, 2013.View at: Google Scholar
E. Carmina, “Obesity, adipokines and metabolic syndrome in polycystic ovary syndrome,” Frontiers of Hormone Research, vol. 40, pp. 40–50, 2013.View at: Publisher Site | Google Scholar
A. Gateva and Z. Kamenov, “Cardiovascular risk factors in Bulgarian patients with polycystic ovary syndrome and/or obesity,” Obstetrics and Gynecology International, vol. 2012, Article ID 306347, 11 pages, 2012.View at: Publisher Site | Google Scholar
F. Orio, T. Cascella, F. Giallauria et al., “The polycystic ovary syndrome: an example of obesity-related cardiovascular complication affecting young women,” Monaldi Archives for Chest Disease, vol. 66, no. 1, pp. 48–53, 2006.View at: Publisher Site | Google Scholar
R. S. Legro, R. Bentley-Lewis, D. Driscoll, S. C. Wang, and A. Dunaif, “Insulin resistance in the sisters of women with polycystic ovary syndrome: association with hyperandrogenemia rather than menstrual irregularity,” The Journal of Clinical Endocrinology and Metabolism, vol. 87, no. 5, pp. 2128–2133, 2002.View at: Publisher Site | Google Scholar
M. Luque-Ramirez, F. Alvarez-Blasco, and H. F. Escobar-Morreale, “Antiandrogenic contraceptives increase serum adiponectin in obese polycystic ovary syndrome patients,” Obesity (Silver Spring), vol. 17, no. 1, pp. 3–9, 2009.View at: Publisher Site | Google Scholar
T. Strowitzki, D. Seehaus, M. Korell, and H. Hepp, “Low-dose follicle stimulating hormone for ovulation induction in polycystic ovary syndrome,” The Journal of Reproductive Medicine, vol. 39, no. 7, pp. 499–503, 1994.View at: Google Scholar
K. L. Marquard, S. M. Stephens, E. S. Jungheim et al., “Polycystic ovary syndrome and maternal obesity affect oocyte size in in vitro fertilization/intracytoplasmic sperm injection cycles,” Fertility and Sterility, vol. 95, no. 6, pp. 2146–2149.e1, 2011.View at: Publisher Site | Google Scholar
R. Bonasio and R. Shiekhattar, “Regulation of transcription by long noncoding RNAs,” Annual Review of Genetics, vol. 48, no. 1, pp. 433–455, 2014.View at: Publisher Site | Google Scholar
O. Karlsson and A. A. Baccarelli, “Environmental health and long non-coding RNAs,” Current Environmental Health Reports, vol. 3, no. 3, pp. 178–187, 2016.View at: Publisher Site | Google Scholar
L. Li, J. Zhu, F. Ye et al., “Upregulation of the lncRNA SRLR in polycystic ovary syndrome regulates cell apoptosis and IL-6 expression,” Cell Biochemistry and Function, vol. 38, no. 7, pp. 880–885, 2020.View at: Publisher Site | Google Scholar
L. Qin, C. C. Huang, X. M. Yan, Y. Wang, Z. Y. Li, and X. C. Wei, “Long non-coding RNA H19 is associated with polycystic ovary syndrome in Chinese women: a preliminary study,” Endocrine Journal, vol. 66, no. 7, pp. 587–595, 2019.View at: Publisher Site | Google Scholar
M. Liu, H. Zhu, Y. Li, J. Zhuang, T. Cao, and Y. Wang, “Expression of serum lncRNA-Xist in patients with polycystic ovary syndrome and its relationship with pregnancy outcome,” Taiwanese Journal of Obstetrics & Gynecology, vol. 59, no. 3, pp. 372–376, 2020.View at: Publisher Site | Google Scholar
J. S. Mattick and I. V. Makunin, “Non-coding RNA,” Human Molecular Genetics, vol. 15, Supplement 1, pp. R17–R29, 2006.View at: Publisher Site | Google Scholar
Y. Zhang, Y. W. Dang, X. Wang et al., “Comprehensive analysis of long non-coding RNA PVT1 gene interaction regulatory network in hepatocellular carcinoma using gene microarray and bioinformatics,” American Journal of Translational Research, vol. 9, no. 9, pp. 3904–3917, 2017.View at: Google Scholar
D. Fu, Y. Shi, J. B. Liu et al., “Targeting long non-coding RNA to therapeutically regulate gene expression in cancer,” Mol Ther Nucleic Acids, vol. 21, pp. 712–724, 2020.View at: Publisher Site | Google Scholar
X. Luan and Y. Wang, “lncRNA XLOC_006390 facilitates cervical cancer tumorigenesis and metastasis as a ceRNA against miR-331-3p and miR-338-3p,” Journal of Gynecologic Oncology, vol. 29, no. 6, article e95, 2018.View at: Publisher Site | Google Scholar
G. Zhang, S. Li, J. Lu et al., “lncRNA MT1JP functions as a ceRNA in regulating FBXW7 through competitively binding to miR-92a-3p in gastric cancer,” Molecular Cancer, vol. 17, no. 1, p. 87, 2018.View at: Publisher Site | Google Scholar
T. L. Setji, N. D. Holland, L. L. Sanders, K. C. Pereira, A. M. Diehl, and A. J. Brown, “Nonalcoholic steatohepatitis and nonalcoholic fatty liver disease in young women with polycystic ovary syndrome,” The Journal of Clinical Endocrinology and Metabolism, vol. 91, no. 5, pp. 1741–1747, 2006.View at: Publisher Site | Google Scholar
R. A. Wild, E. Carmina, E. Diamanti-Kandarakis et al., “Assessment of cardiovascular risk and prevention of cardiovascular disease in women with the polycystic ovary syndrome: a consensus statement by the Androgen Excess and Polycystic Ovary Syndrome (AE-PCOS) Society,” The Journal of Clinical Endocrinology and Metabolism, vol. 95, no. 5, pp. 2038–2049, 2010.View at: Publisher Site | Google Scholar
A. Gupta, D. Jakubowicz, and J. E. Nestler, “Pioglitazone therapy increases insulin-stimulated release of d-chiro-inositol-containing inositolphosphoglycan mediator in women with polycystic ovary syndrome,” Metabolic Syndrome and Related Disorders, vol. 14, no. 8, pp. 391–396, 2016.View at: Publisher Site | Google Scholar
J. Rojas, M. Chávez, L. Olivar et al., “Polycystic ovary syndrome, insulin resistance, and obesity: navigating the pathophysiologic labyrinth,” International Journal of Reproductive Medicine, vol. 2014, Article ID 719050, 17 pages, 2014.View at: Publisher Site | Google Scholar
S. B. Kjøtrød, A. Sunde, V. von Düring, and S. M. Carlsen, “Possible metformin effect on adrenal androgens during pretreatment and IVF cycle in women with polycystic ovary syndrome,” Fertility and Sterility, vol. 91, no. 2, pp. 500–508, 2009.View at: Publisher Site | Google Scholar
A. Cagnacci, A. Tirelli, A. Renzi, A. M. Paoletti, and A. Volpe, “Effects of two different oral contraceptives on homocysteine metabolism in women with polycystic ovary syndrome,” Contraception, vol. 73, no. 4, pp. 348–351, 2006.View at: Publisher Site | Google Scholar
Q. Zhang and K. T. Jeang, “Long non-coding RNAs (lncRNAs) and viral infections,” Biomedicine & Pharmacotherapy, vol. 3, no. 1, pp. 34–42, 2013.View at: Publisher Site | Google Scholar
H. Luo, C. Xu, W. le, B. Ge, and T. Wang, “lncRNA CASC11 promotes cancer cell proliferation in bladder cancer through miRNA-150,” Journal of Cellular Biochemistry, vol. 120, no. 8, pp. 13487–13493, 2019.View at: Publisher Site | Google Scholar
S. J. Wang, Y. J. Li, B. Gao, X. L. Li, Y. T. Li, and H. Y. He, “Long non-coding RNA 00152 slicing represses the growth and aggressiveness of hemangioma cell by modulating miR-139-5p,” Biomedicine & Pharmacotherapy, vol. 120, p. 109385, 2019.View at: Publisher Site | Google Scholar
Q. Wu, L. Guo, F. Jiang, L. Li, Z. Li, and F. Chen, “Analysis of the miRNA-mRNA-lncRNA networks in ER+ and ER- breast cancer cell lines,” Journal of Cellular and Molecular Medicine, vol. 19, no. 12, pp. 2874–2887, 2015.View at: Publisher Site | Google Scholar
S. Ye, L. Yang, X. Zhao, W. Song, W. Wang, and S. Zheng, “Bioinformatics method to predict two regulation mechanism: TF-miRNA-mRNA and lncRNA-miRNA-mRNA in pancreatic cancer,” Cell Biochemistry and Biophysics, vol. 70, no. 3, pp. 1849–1858, 2014.View at: Publisher Site | Google Scholar
Y. Wu, X. Lu, B. Shen, and Y. Zeng, “The therapeutic potential and role of miRNA, lncRNA, and circRNA in osteoarthritis,” Current Gene Therapy, vol. 19, no. 4, pp. 255–263, 2019.View at: Publisher Site | Google Scholar
L. Salmena, L. Poliseno, Y. Tay, L. Kats, and P. P. Pandolfi, “A ceRNA hypothesis: the Rosetta Stone of a hidden RNA language?” Cell, vol. 146, no. 3, pp. 353–358, 2011.View at: Publisher Site | Google Scholar
A. Eulalio, E. Huntzinger, and E. Izaurralde, “Getting to the root of miRNA-mediated gene silencing,” Cell, vol. 132, no. 1, pp. 9–14, 2008.View at: Publisher Site | Google Scholar
Y. An, K. L. Furber, and S. Ji, “Pseudogenes regulate parental gene expression via ceRNA network,” Journal of Cellular and Molecular Medicine, vol. 21, no. 1, pp. 185–192, 2017.View at: Publisher Site | Google Scholar
W. Xu, S. Yu, J. Xiong, J. Long, Y. Zheng, and X. Sang, “CeRNA regulatory network-based analysis to study the roles of noncoding RNAs in the pathogenesis of intrahepatic cholangiocellular carcinoma,” Aging (Albany NY), vol. 12, no. 2, pp. 1047–1086, 2020.View at: Publisher Site | Google Scholar
S. D. Rosenzweig and S. M. Holland, “Defects in the interferon-gamma and interleukin-12 pathways,” Immunological Reviews, vol. 203, no. 1, pp. 38–47, 2005.View at: Publisher Site | Google Scholar
R. Zhang, J. Shu, L. Zhao, and C. Cai, “Analysis of co-segregation of methylation pattern and gene ontology among pedigrees affected with neural tube defects,” Zhonghua Yi Xue Yi Chuan Xue Za Zhi, vol. 36, no. 8, pp. 769–772, 2019.View at: Publisher Site | Google Scholar
N. N. Kulkarni, T. Takahashi, J. A. Sanford et al., “Innate immune dysfunction in Rosacea promotes photosensitivity and vascular adhesion molecule expression,” The Journal of Investigative Dermatology, vol. 140, no. 3, pp. 645–655.e6, 2020, e6.View at: Publisher Site | Google Scholar
E. A. Bridenbaugh, W. Wang, M. Srimushnam et al., “An immunological fingerprint differentiates muscular lymphatics from arteries and veins,” Lymphatic Research and Biology, vol. 11, no. 3, pp. 155–171, 2013.View at: Publisher Site | Google Scholar
K. Okada, S. Hakata, J. Terashima, T. Gamou, W. Habano, and S. Ozawa, “Combination of the histone deacetylase inhibitor depsipeptide and 5-fluorouracil upregulates major histocompatibility complex class II and p 21 genes and activates caspase-3/7 in human colon cancer HCT-116 cells,” Oncology Reports, vol. 36, no. 4, pp. 1875–1885, 2016.View at: Publisher Site | Google Scholar
D. Basu, R. Lettan, K. Damodaran, S. Strellec, M. Reyes-Mugica, and A. Rebbaa, “Identification, mechanism of action, and antitumor activity of a small molecule inhibitor of hippo, TGF-β, and Wnt signaling pathways,” Molecular Cancer Therapeutics, vol. 13, no. 6, pp. 1457–1467, 2014.View at: Publisher Site | Google Scholar
H. Ihara, Y. Mitsuishi, M. Kato et al., “Nintedanib inhibits epithelial-mesenchymal transition in A549 alveolar epithelial cells through regulation of the TGF-β/Smad pathway,” Respiratory Investigation, vol. 58, no. 4, pp. 275–284, 2020.View at: Publisher Site | Google Scholar
X. Liu, D. Tang, F. Zheng et al., “Single-cell sequencing reveals the relationship between phenotypes and genotypes of Klinefelter syndrome,” Cytogenetic and Genome Research, vol. 159, no. 2, pp. 55–65, 2019.View at: Publisher Site | Google Scholar
E. Samper, L. Morgado, J. C. Estrada et al., “Increase in mitochondrial biogenesis, oxidative stress, and glycolysis in murine lymphomas,” Free Radical Biology & Medicine, vol. 46, no. 3, pp. 387–396, 2009.View at: Publisher Site | Google Scholar
P. W. Caton, N. K. Nayuni, J. Kieswich, N. Q. Khan, M. M. Yaqoob, and R. Corder, “Metformin suppresses hepatic gluconeogenesis through induction of SIRT1 and GCN5,” The Journal of Endocrinology, vol. 205, no. 1, pp. 97–106, 2010.View at: Publisher Site | Google Scholar
A. Malik, E. J. Lee, A. T. Jan et al., “Network analysis for the identification of differentially expressed hub genes using myogenin knock-down muscle satellite cells,” PLoS One, vol. 10, no. 7, article e0133597, 2015.View at: Publisher Site | Google Scholar
J. He, X. Li, Y. Zhang, Q. Zhang, and L. Li, “Comprehensive analysis of ceRNA regulation network involved in the development of coronary artery disease,” BioMed Research International, vol. 2021, Article ID 6658115, 14 pages, 2021.View at: Publisher Site | Google Scholar
K. Le, H. Guo, Q. Zhang et al., “Gene and lncRNA co-expression network analysis reveals novel ceRNA network for triple-negative breast cancer,” Scientific Reports, vol. 9, no. 1, article 15122, 2019.View at: Publisher Site | Google Scholar
Y. Hou, Y. Wang, S. Xu, G. Qi, and X. Wu, “Bioinformatics identification of microRNAs involved in polycystic ovary syndrome based on microarray data,” Molecular Medicine Reports, vol. 20, no. 1, pp. 281–291, 2019.View at: Publisher Site | Google Scholar
Y. Wang, S. Xu, Y. Wang et al., “Identification and potential value of candidate microRNAs in granulosa cells of polycystic ovary syndrome,” Technology and Health Care, vol. 27, no. 6, pp. 579–587, 2019.View at: Publisher Site | Google Scholar