Next Article in Journal
The Role of Curcumin in Modulating Vascular Function and Structure during Menopause: A Systematic Review
Previous Article in Journal
SMYD2 Promotes Calcium Oxalate-Induced Glycolysis in Renal Tubular Epithelial Cells via PTEN Methylation
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 12
Issue 10
10.3390/biomedicines12102280
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Communication

Polycystic Ovary Syndrome and Ferroptosis: Following Ariadne’s Thread

by
Styliani Geronikolou
1,2,*,
Athanasia Pavlopoulou
3,4,
Ioannis Koutelekos
5,
Dimitrios Kalogirou
6,
Flora Bacopoulou
2 and
Dennis V. Cokkinos
1
1
Clinical, Translational and Experimental Surgery Research Center, Biomedical Research Foundation of the Academy of Athens, 11527 Athens, Greece
2
Center for Adolescent Medicine and UNESCO Chair in Adolescent Health Care, First Department of Pediatrics, School of Medicine, National and Kapodistrian University of Athens, 11527 Athens, Greece
3
Izmir Biomedicine and Genome Center (IBG), 35340 Izmir, Turkey
4
Izmir International Biomedicine and Genome Institute, Dokuz Eylül University, 35340 Izmir, Turkey
5
Department of Nursing, School of Health and Care Sciences, University of West Attica, 12243 Athens, Greece
6
Department of Public and Community Health, School of Public Health, University of West Attica, 11521 Athens, Greece
*
Author to whom correspondence should be addressed.
Biomedicines 2024, 12(10), 2280; https://doi.org/10.3390/biomedicines12102280
Submission received: 3 August 2024 / Revised: 23 September 2024 / Accepted: 4 October 2024 / Published: 8 October 2024
(This article belongs to the Special Issue Advances in Polycystic Ovary Syndrome Research: From Molecular Mechanisms to Therapeutic Strategies—2nd Edition)
Browse Figures
Versions Notes

Abstract

:
Background: Recent literature suggests that ferroptosis (FPT) may be a key player in polycystic ovary syndrome (PCOS) pathogenesis, but the underlying mechanism(s) remain(s) unclear. Aim: Therefore, herein, we made an effort to reproduce the molecular signature of the syndrome by including FPT and exploring novel drug targets for PCOS. Methods: (a) Our previously constructed PCOS interactions molecular network was extended with the addition of FPT–associated genes (interaction score above 0.7) and (b) gene set enrichment analysis was performed so as to detect over-represented KEGG pathways. Results: The updated interactome includes 140 molecules, 20 of which are predicted/novel, with an interaction score of 7.3, and 12 major hubs. Moreover, we identified 16 over-represented KEGG pathways, with FPT being the most overexpressed pathway. The FPT subnetwork is connected with the PCOS network through KDM1A. Conclusions: FPT cell death is involved in PCOS development, as its major hub TP53 was shown to be the most important hub in the whole PCOS interactome, hence representing a prioritized drug target.

1. Introduction

Polycystic ovary syndrome (PCOS) is one of the chronic composites and difficult-to-treat disorders with genetic, epigenetic, and environmental facets. Its clinical image is compound, involving hirsutism, uneven menstruation, acne, and occasionally, infertility [1]. Four clinical phenotypes have been suggested by the Rotterdam criteria with regard to the clinical, metabolic, and hormonal profile (Table 1) [2,3]. Its prevalence depends on the diagnostic tools adopted, varying from 6 to 20% [4]. The geographic and ethnicity variation has been attributed to the different diagnostic criteria adopted, but the recent literature raises concerns about racial, genetic, and cultural implications [5]. Its treatment is directed towards relieving distressing symptoms, including changes in lifestyle, weight loss, and menstrual cycle regulation by administrating contraceptives and/or anti-diabetic drugs [6]. In a previous work of ours, we had revealed that metabolic disturbances lie upstream from the reproductive ones, linking together through insulin [6].
Iron metabolism has been suggested as a contributing factor in endocrine pathology including PCOS [7,8]. Ferroptosis (FPT) is a distinct form of programmed cell death, originating from the iron-dependent aggregation of lipid hydroperoxides [9,10].
The mechanism involved especially in PCOS pathology is poorly understood, thus meriting further research. So far, in silico and in vivo investigations have generated limited information. Animal models and human studies have focused on illuminating the phenomenon; however, this target is both costly and time-consuming. We opted to study the mechanism in silico so as to save time and costs, and direct further in vivo research targets.

2. Materials and Methods

The overall methodology followed in this study is illustrated in Figure 1.

2.1. Molecular Network

PCOS-related and FPT–PCOS-associated genes/proteins were retrieved through an extensive literature search of the bibliographic database PubMed/MEDLINE up to the current date. Given that the proteins implicated in the same disease tend to interact, either physically or functionally, with each other [11], the interactions among these gene products were investigated through STRING v12.0 [12], a database of both known and predicted associations among genes/proteins. A high confidence interaction score of above 0.7 was chosen. To avoid false positives (i.e., erroneous associations), only those interactions derived from experiments, text mining of published studies, and curated knowledge bases of protein complexes and pathways were included. Moreover, genes/gene products not previously reported to be associated with PCOS or FPT–PCOS, and therefore not included in the initial set of molecules in STRING, are referred to as “novel” throughout the manuscript; the latter were identified through iterative searches for the minimum number of nodes interacting with the existing nodes.

2.2. Functional Enrichment Analysis

To interpret in a biologically meaningful way the components of the PCOS interactome, gene set enrichment analysis (GSEA) was performed to identify relevant statistically significant KEGG (Kyoto Encyclopedia of Genes and Genomes) [13] pathways over-represented in those genes coding for the proteins that comprise the PCOS interaction network. GSEA was conducted with WebGestalt (WEB-based GEne SeT AnaLysis Toolkit) 2024 [14], an online tool used for the identification of significantly enriched terms in given gene sets. The default advanced parameters were selected, and the KEGG pathways with false discovery rate (FDR)-adjusted p-value less than 0.05 were considered in the analysis. The weighted set cover algorithm was used for clustering the terms by selecting a subset of representative terms.

3. Results

3.1. Molecular Network Construction

Collectively, 82 PCOS-related and 38 FPT–PCOS-associated genes/proteins were retrieved. Moreover, 20 “novel” genes/proteins connecting the “unconnected” input nodes (i.e., not joined to the core PCOS network) were predicted. A total of 140 nodes formed a highly interconnected network (Figure 2), suggesting physical or functional associations within the context of PCOS. The average node degree was calculated to be 7.3. All molecules included in the network are listed in alphabetical order in Table 2. The nodes associated with FPT are marked with purple and the predicted ones in green in Table 2. The major hubs are described in Table 3.

3.2. KEGG Analysis Results

The over-represented KEGG pathways in the PCOS-relevant genes are described in Table 4; the major hubs of the herein presented updated interactome are underlined. Accordingly, in Figure 3, the enrichment analysis results of KEGG pathways are illustrated. The color code adopted in Figure 3 is similar to the one adopted in the pathway highlighted in Figure 2.

3.3. FPT Related to the PCOS Molecular Subnetwork

The herein created PCOS-related FPT subnetwork (Figure 4) includes the FPT-associated molecules reported in the literature (marked in purple in Table 2) plus the KEGG FPT over-represented pathway (marked in red in Figure 3), as well as the mediating nodes, which are shown in white.

4. Discussion

The AKR1C3 enzyme reduces androstenedione (A4) to testosterone (T) in the ovaries, adrenal glands, and adipose tissues, where testosterone is mainly produced in women. [5,15].

4.1. Pathways

In the present study, we deciphered sixteen over-represented KEGG pathways in the PCOS-relevant genes, described in Table 4 and Figure 3 in color code.

Major Hubs

The major hubs of this updated PCOS-specific molecular interactions network (Figure 2) are listed in Table 3, and according to the KEGG pathway analysis, the following can be deduced:
  • TP53 is implicated in cancer, FPT, apoptosis, cellular senescence, and endocrine resistance pathways. In fact, it is the main link between FPT and the syndrome, as it consists of the main hub in both networks.
  • ESR1 is embroiled in prolactin signaling in cancer and endocrine resistance pathways.
  • TNF is involved in non-alcoholic fatty liver, apoptosis, diabetes and its complications (AGE-RAGE signaling), insulin resistance, apoptosis, and TGF beta signaling pathways.
  • INS is included in diabetes, insulin resistance, aldosterone-regulated sodium reabsorption, prolactin signaling, ovarian steroeidogenesis, FoxO signaling, and non-alcoholic fatty liver pathways.
  • TGFB1 is involved in cancer, FoxO, non-alcoholic fatty liver, AGE-RAGE, and TGF beta signaling pathways.
  • EP300 is implicated in cancer, FoxO, and TGF beta signaling pathways.
  • ACTB crosses only the apoptosis pathway.
  • IL6 is included in cancer, FoxO, non-alcoholic fatty liver, AGE-RAGE, insulin resistance, and cellular senescence pathways.
  • IGF1 entangles with endocrine resistance, aldosterone-regulated sodium reabsorption, ovarian steroeidogenesis, cancer, and FoxO pathways.
  • IL1B is embroiled in non-alcoholic fatty liver and AGE-RAGE signaling pathways.
  • PPRAG is implicated in the cancer (but in the literature, in atherosclerosis, obesity, etc.) pathway.
Seven major hubs (EP300, NFKB, ESR1, PPRAG, IGF1, IL6, and TP53) appear to entangle with cancer pathways. Shetty et al. [16] reviewed ten articles studying the risk of developing cancer in those with PCOS, concluding that the controversial evidence published originates in the complexity of the syndrome pathophysiology. Hyperandrogenism, hyperinsulinemia, dyslipidemia, chronic inflammation, and unopposed estrogen action seem to play key roles in this direction. Shetty and colleagues [16] concluded that women with PCOS are at higher risk of developing endometrial cancer but not ovarian or breast cancer. Moreover, a recent meta-analysis showed that women with a family history of PCOS have a lower risk of developing ovarian cancer [17].

4.2. Ferroptosis

FPT was described two decades ago as a non-apoptotic cell death mechanism featured by iron-dependent reactive oxygen species (ROS) [18]. Iron is proviso for a variety of cellular functions including homeostasis [19]; thus, FPT seems to be implicated in the pathophysiology of plenty of morbid entities accordingly (i.e., cardiomyopathy, diabetes mellitus, Parkinson’s disease, renal failure, cancer) [20,21]. The FPT–related genes can be classified into drivers, suppressors, markers, inducers, inhibitors, and diseases [22]. The heavy (H) and light (L) chain subunits of ferritin (FTH and FTL, respectively) are responsible for intracellular iron storage and thus are established markers of FPT [23]. Iron ingestion activates NOX1 signaling (through transferrin receptor (TFRC), promoting mitochondrial damage [24,25]. This finding drove Bennett et al. to suggest that folliculogenesis inhibition by TFRC/NOX1 signaling might be a potential drug target of PCOS [26].
The PHD finger protein 21A (PHF21A)-mediating mechanism explaining the FPT involvement in PCOS pathophysiology has not been studied or reported so far [27].
We have created a subnetwork, illustrated in Figure 4, where we reveal all the FPT-related interactions extracted from the KEGG analysis, as well as all those sparse results reported in the literature. In this subnetwork, we untangle the FPT–PCOS-related full interactions network for the first time.
Our interactions molecular network unraveled that the FPT subnetwork connects with the whole network through lysine demethylase 1A (KDM1A) (Figure 2 and Figure 4). KDM1A, in turn, interacts with TP53 and TET1, TRIM28, AR, MUC1, ESR1, and SETDB1 directly (Figure 2). NCOR1 regulates B lymphocyte development, thus determining immunity to health or morbidity directions [28]. This remark ascertains that the roles of PHF21A and NCOR1 merit further elucidation with in vivo or in vitro experiments (bed and bedside research).
It has been shown that TRIM28 is involved in DNA damage repair, although the means are uncertain, possibly by triggering cell cycle arrest [29].
The histone methyltransferase SETDB1, a common ovarian gene, has served as a marker of PCOS efficacy treatment [30].
Mutations in ESR1/2 and enhanced androgenic (AR) activity are established causes of PCOS. Yet, according to Sagvekar and collaborators’ (2022) investigation, epigenetic peripheral DNA methylation changes in CGCs of women with PCOS may arise partly due to intrinsic alterations in the transcriptional regulation of TET1 and DNMT3A [31].
TP53 is the major hub of the FPT subnetwork, apart from the whole PCOS interactome. It is a known regulator of cell cycle metabolism and apoptosis [32,33]. TP53 mutation influences energy metabolism facets in many ways [34,35]. Although it represents an established cancer marker (suppressor) [33,36], it is implicated in many pathways linking many nodes and enhancing PCOS complexity. In our interactome, it is implicated in insulin resistance, FPT, and prolactin signaling non-alcoholic fatty liver (NAFL) pathways. More importantly, it connects directly with INS—the node that connects metabolic—related genes (KISS1, IGF1, CCK, AGT, AVP, FTO, GCG, PPRAG, SERPINE1, LEP, GRHL, GHSR, cytokines, and interleukins) to reproduction-related ones (AR, GNRH1, SHBG, and LHCGR). Indeed, KEGG pathway analysis proved that INS is implicated in ovarian steroidogenesis, FOXO1 signaling, and aldosterone-regulated sodium reabsorption pathways apart from IR, NAFL, DM2, and prolactin signaling pathways.
Insulin resistance (IR) is an established component of NAFL according to the “second” and the “multiple strike” (lipotoxicity, mitochondrial dysfunction, activation of the inflammatory pathway, and an imbalance of the intestinal microbiota) theories [37]. Accordingly, the literature evidence supports that IR represents a major hazard for NAFL in PCOS as well [38,39].
GOT1 is an established key regulator of glutamate levels, the main excitatory neurotransmitter of the vertebrate central nervous system. It acts as a scavenger of glutamate in brain neuroprotection. It is associated with NAFL and cholocystitis, as well as siderosis (deposition of iron) [6,40].
GPT has been linked to NAFL in children and adolescents [41]. It has been established that androgens may lead to mitochondrial β-oxidation imbalance and de novo lipogenesis through PPARs and can exacerbate liver inflammatory damage by upregulating the expressions of cytokines such as IL-6, TNF-α, and IL-1β [42]. So far, the proposed treatment of NAFL in PCOS includes life style changes, GLP1 receptor agonists, spironolactone, thiazolidinedione (in non-obese people with PCOS), metformin, and nutritional supplements containing 1000 mg omega-3 fatty acids (containing 400 mg of α-linolenic acid) and 400 IU vitamin E [43].
Moreover, HIF1A has also been identified as a mediator of the FPT-activated angiogenesis in sleep apnea [44].
Although (following the Rotterdam criteria) hyperprolactinaemia (HPRL) is an exclusion criterion for PCOS diagnosis, in our interactome, HIF1A has been included in the prolactin pathway. HPRL and PCOS are the major causative factors of anovulation in women. For 70 years, a notion linking these two pathological entities exists, but the mechanism is still unknown (although many hypotheses have been suggested) [45,46]. The recent literature provides evidence that in PCOS, the HPRL is either temporal or macroprolactinemia-related [46].
Oxygen homeostasis is regulated and/or expressed by HIF1A, as it plays a dual role (regulator and transcription factor); ROS/HIF1A promotes oxidative stress, increasing inflammation, whilst inflammation enhances HIF1A expression accelerating oxidative stress, thus promoting FPT [25], resulting in diminished or non-existent mitochondrial cristae and ruptured and contracted outer mitochondrial membranes. Therefore, our analysis unravels a novel mechanism that actually links the two pathophysiologies, contrary to the belief of Delcour et al., who stated that this is a “myth” [46].
ATM is involved in insulin resistance, TGF-beta, and FOXO1 pathways in the constructed interactome.
The FOXO1 pathway has been reported as significantly elevated in the cumulus cells of PCOS women compared to the ones obtained from non-PCOS individuals, through participating in gluconeogenesis, oxidative stress, cell proliferation, and cell apoptosis [47]. It has been associated with obesity as well [6,48,49].

4.3. Predictions—Novel Connectors

The predicted novel connectors (20) from our analysis are the following: ACTB, APOE, BYSL, CAPN1, DCN, DDX58, DLG2, ESR1, FTSJ1, KDM1A, MGLL, MUC1/7, PTEN, RPS9, SMAD2, TROAP, ZNF197/41, and ZSCAN20 (Table 2).
Of those, only ESR1 and ACTB are highly connected hubs (Table 3).
ACTB regulates gene transcription and motility, as well as DNA damage repair [50]. It is known to be associated with thrombocytopenia, deafness, and congenital diseases (i.e., congenital blepharoptosis and juvenile dystonia onset) [51]. Through pathway enrichment analysis and hub gene miRNA networks, Heidarzadehpilehrood et al. highlighted ACTB, KRAS, JUNE, PTEN, and MAPK1 as potential therapeutic targets for PCOS treatment [52].
BYSL is linked to ectopic pregnancy, while CAPN1 (to T cell receptor signaling pathway) and DDX58 are associated with immune response [53,54,55].
DCN is a proteoglycan of the extracellular matrix involved in the differentiation of retinal ganglion cells, exocrine gland phenotype, and metabolic homeostasis, and it affects ovarian function in women [56,57].
The FTSJ1 enzyme modifies rRNA [58].
KDM1A coactivates androgen receptor (AR)-dependent transcription by being recruited to AR target genes and mediating the demethylation of H3K9me, a specific tag for epigenetic transcriptional repression. It might be the one of the links between the syndrome manifestation and the epigenetic lifestyle-related effects [59].
MUC1/7 appear to be components of gene expression alterations that potentially contribute to endometrial insufficiency in people with PCOS and lipid metabolism, accordingly [60,61]. RPS9 is a ribosomal gene and SMAD modulates ovarian steroidogenesis; its defect may lead to hyperandrogenism, becoming a potential drug target for PCOS treatment [62].
APOE alleles have been found to be higher in PCOS patients than their non-PCOS counterparts in various clinical trials [63,64,65,66,67], but the parallel increase in cardiovascular risk has been questioned [66]. The APOE increase in PCOS has been associated with neurodegenerative diseases, such as dementia [65,67].
MGLL encodes monoacylglycerol lipase that catalyzes the monoesters of fatty acids, as well as the endocannabinoid 2-arachidonoglycerol [68,69,70]. To our knowledge, this is the first study that depicts its implication in PCOS. In our interactome, it mediates CNR1 (cannabinoid receptor) with the FPT-associated ACSL4.
Although TROAP’s role in PCOS is unknown, our network unraveled that it links BYSL with MAPRE1, the role of which in PCOS is also undetermined but has been correlated to cell functions and autophagosomes in general [62].
Of those, in our wild-type gene interactions network, only JUNE is absent. ACTB and PTEN are predicted nodes, while PTEN is not a hub for the protein–protein, gene–protein, and gene–gene interaction network.
Estrogen receptor (ESR1) ligand mutations have been linked to endocrine resistance, osteoporosis, and breast cancer [71]. It is an emerging predictive biomarker and chemotherapy guide for breast cancer patients [72].
The “novel” ZSCAN20 interacts physically with ZKSCAN5 based on affinity chromatography and co-immunoprecipitation assays. It is also connected through the “novel” interactor ZNF197 to TRIM28. ZSCAN20 has been associated with atrial tachyarrhythmia and angiosarcoma, whereas ZKSCAN5 is a poor prognostic factor of breast cancer; yet, both cancer types are not associated with the syndrome [16,65].

5. Conclusions

Our analysis contributed to a deeper insight into the PCOS physiology and untangled the unknown or contested interactions. We identified the FPT-related subnetwork to the PCOS interactions. More importantly, our interactome a) proposed FPT (via HIF1A-ATM) as the link between HPRL and PCOS and b) unraveled the implication of TROAP and MGLL to PCOS. Furthermore, it provided novel perspectives in this complex disease treatment by shedding light on the significance of already proposed drug targets and unraveled new ones, limiting them to KDM1A, TP53, and RPS9; as TP53 is the major hub of the whole network, it should be granted priority.

Author Contributions

Conceptualization, S.G. and D.V.C.; methodology, S.G. and A.P.; software, A.P.; validation, S.G., A.P., I.K., D.K., F.B. and D.V.C.; formal analysis, S.G. and A.P.; investigation, S.G. and A.P.; resources, S.G. and A.P.; data curation, S.G. and A.P.; visualization, S.G., A.P., I.K., D.K., F.B. and D.V.C.; writing—original draft preparation, S.G. and A.P.; writing—review and editing, S.G., A.P., I.K., D.K., F.B. and D.V.C.; supervision, S.G., F.B. and D.V.C.; project administration, S.G. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

All data and analysis methodologies are contained in the manuscript. Any additional data requests can be addressed to the corresponding author.

Conflicts of Interest

The authors have declared that no competing interests exist.

References

  1. Visser, J.A. The importance of metabolic dysfunction in polycystic ovary syndrome. Nat. Rev. Endocrinol. 2021, 17, 77–78. [Google Scholar] [CrossRef] [PubMed]
  2. Sachdeva, G.; Gainder, S.; Suri, V.; Sachdeva, N.; Chopra, S. Comparison of the Different PCOS Phenotypes Based on Clinical Metabolic, and Hormonal Profile, and their Response to Clomiphene. Indian J. Endocrinol. Metab. 2019, 23, 326–331. [Google Scholar] [CrossRef]
  3. Lizneva, D.; Suturina, L.; Walker, W.; Brakta, S.; Gavrilova-Jordan, L.; Azziz, R. Criteria, prevalence, and phenotypes of polycystic ovary syndrome. Fertil. Steril. 2016, 106, 6–15. [Google Scholar] [CrossRef] [PubMed]
  4. Escobar-Morreale, H.F. Polycystic ovary syndrome: Definition, aetiology, diagnosis and treatment. Nat. Rev. Endocrinol. 2018, 14, 270–284. [Google Scholar] [CrossRef] [PubMed]
  5. VanHise, K.; Wang, E.T.; Norris, K.; Azziz, R.; Pisarska, M.D.; Chan, J.L. Racial and ethnic disparities in polycystic ovary syndrome. Fertil. Steril. 2023, 119, 348–354. [Google Scholar] [CrossRef]
  6. Geronikolou, S.A.; Pavlopoulou, A.; Cokkinos, D.V.; Bacopoulou, F.; Chrousos, G.P. Polycystic οvary syndrome revisited: An interactions network approach. Eur. J. Clin. Investig. 2021, 51, e13578. [Google Scholar] [CrossRef]
  7. Stancic, A.; Velickovic, K.; Markelic, M.; Grigorov, I.; Saksida, T.; Savic, N.; Vucetic, M.; Martinovic, V.; Ivanovic, A.; Otasevic, V. Involvement of Ferroptosis in Diabetes-Induced Liver Pathology. Int. J. Mol. Sci. 2022, 23, 9309. [Google Scholar] [CrossRef]
  8. Yin, J.; Hong, X.; Ma, J.; Bu, Y.; Liu, R. Serum Trace Elements in Patients With Polycystic Ovary Syndrome: A Systematic Review and Meta-Analysis. Front. Endocrinol. 2020, 11, 572384. [Google Scholar] [CrossRef]
  9. Tan, S.; Schubert, D.; Maher, P. Oxytosis: A novel form of programmed cell death. Curr. Top. Med. Chem. 2001, 1, 497–506. [Google Scholar] [CrossRef]
  10. Yang, W.S.; Stockwell, B.R. Ferroptosis: Death by Lipid Peroxidation. Trends Cell Biol. 2016, 26, 165–176. [Google Scholar] [CrossRef]
  11. Kontou, P.I.; Pavlopoulou, A.; Dimou, N.L.; Pavlopoulos, G.A.; Bagos, P.G. Network analysis of genes and their association with diseases. Gene 2016, 590, 68–78. [Google Scholar] [CrossRef] [PubMed]
  12. Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; et al. STRING v11: Protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 2019, 47, D607–D613. [Google Scholar] [CrossRef] [PubMed]
  13. Kanehisa, M.; Furumichi, M.; Sato, Y.; Kawashima, M.; Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 2023, 51, D587–D592. [Google Scholar] [CrossRef] [PubMed]
  14. Elizarraras, J.M.; Liao, Y.; Shi, Z.; Zhu, Q.; Pico, A.R.; Zhang, B. WebGestalt 2024: Faster gene set analysis and new support for metabolomics and multi-omics. Nucleic Acids Res. 2024, 52, W415–W421. [Google Scholar] [CrossRef] [PubMed]
  15. Goodarzi, M.O.; Jones, M.R.; Antoine, H.J.; Pall, M.; Chen, Y.D.; Azziz, R. Nonreplication of the type 5 17beta-hydroxysteroid dehydrogenase gene association with polycystic ovary syndrome. J. Clin. Endocrinol. Metab. 2008, 93, 300–303. [Google Scholar] [CrossRef]
  16. Shetty, C.; Rizvi, S.; Sharaf, J.; Williams, K.D.; Tariq, M.; Acharekar, M.V.; Guerrero Saldivia, S.E.; Unnikrishnan, S.N.; Chavarria, Y.Y.; Akindele, A.O.; et al. Risk of Gynecological Cancers in Women With Polycystic Ovary Syndrome and the Pathophysiology of Association. Cureus 2023, 15, e37266. [Google Scholar] [CrossRef]
  17. Harris, H.R.; Cushing-Haugen, K.L.; Webb, P.M.; Nagle, C.M.; Jordan, S.J.; Group, A.O.C.S.; Risch, H.A.; Rossing, M.A.; Doherty, J.A.; Goodman, M.T.; et al. Association between genetically predicted polycystic ovary syndrome and ovarian cancer: A Mendelian randomization study. Int. J. Epidemiol. 2019, 48, 822–830. [Google Scholar] [CrossRef]
  18. Dixon, S.J.; Lemberg, K.M.; Lamprecht, M.R.; Skouta, R.; Zaitsev, E.M.; Gleason, C.E.; Patel, D.N.; Bauer, A.J.; Cantley, A.M.; Yang, W.S.; et al. Ferroptosis: An iron-dependent form of nonapoptotic cell death. Cell 2012, 149, 1060–1072. [Google Scholar] [CrossRef]
  19. Yan, H.F.; Zou, T.; Tuo, Q.Z.; Xu, S.; Li, H.; Belaidi, A.A.; Lei, P. Ferroptosis: Mechanisms and links with diseases. Signal Transduct. Target. Ther. 2021, 6, 49. [Google Scholar] [CrossRef]
  20. Mahoney-Sánchez, L.; Bouchaoui, H.; Ayton, S.; Devos, D.; Duce, J.A.; Devedjian, J.C. Ferroptosis and its potential role in the physiopathology of Parkinson’s Disease. Prog. Neurobiol. 2021, 196, 101890. [Google Scholar] [CrossRef]
  21. Mou, Y.; Wang, J.; Wu, J.; He, D.; Zhang, C.; Duan, C.; Li, B. Ferroptosis, a new form of cell death: Opportunities and challenges in cancer. J. Hematol. Oncol. 2019, 12, 34. [Google Scholar] [CrossRef] [PubMed]
  22. Ye, L.F.; Chaudhary, K.R.; Zandkarimi, F.; Harken, A.D.; Kinslow, C.J.; Upadhyayula, P.S.; Dovas, A.; Higgins, D.M.; Tan, H.; Zhang, Y.; et al. Radiation-Induced Lipid Peroxidation Triggers Ferroptosis and Synergizes with Ferroptosis Inducers. ACS Chem. Biol. 2020, 15, 469–484. [Google Scholar] [CrossRef] [PubMed]
  23. Honarmand Ebrahimi, K.; Hagedoorn, P.L.; Hagen, W.R. Unity in the biochemistry of the iron-storage proteins ferritin and bacterioferritin. Chem. Rev. 2015, 115, 295–326. [Google Scholar] [CrossRef] [PubMed]
  24. Zhang, L.; Wang, F.; Li, D.; Yan, Y.; Wang, H. Transferrin receptor-mediated reactive oxygen species promotes ferroptosis of KGN cells via regulating NADPH oxidase 1/PTEN induced kinase 1/acyl-CoA synthetase long chain family member 4 signaling. Bioengineered 2021, 12, 4983–4994. [Google Scholar] [CrossRef]
  25. Park, E.; Chung, S.W. ROS-mediated autophagy increases intracellular iron levels and ferroptosis by ferritin and transferrin receptor regulation. Cell Death Dis. 2019, 10, 822. [Google Scholar] [CrossRef]
  26. Bennett, E.P.; Mandel, U.; Clausen, H.; Gerken, T.A.; Fritz, T.A.; Tabak, L.A. Control of mucin-type O-glycosylation: A classification of the polypeptide GalNAc-transferase gene family. Glycobiology 2012, 22, 736–756. [Google Scholar] [CrossRef]
  27. Lin, S.; Jin, X.; Gu, H.; Bi, F. Relationships of ferroptosis-related genes with the pathogenesis in polycystic ovary syndrome. Front. Med. 2023, 10, 1120693. [Google Scholar] [CrossRef]
  28. Althwaiqeb, S.A.; Bordoni, B. Histology, B Cell Lymphocyte; StatPearls Publishing: Treasure Island, FL, USA, 2024. [Google Scholar]
  29. Iyengar, S.; Farnham, P.J. KAP1 protein: An enigmatic master regulator of the genome. J. Biol. Chem. 2011, 286, 26267–26276. [Google Scholar] [CrossRef]
  30. Zhang, Y.; Lin, Y.; Li, G.; Yuan, Y.; Wang, X.; Li, N.; Xiong, C.; Yang, Y.; Ma, Y.; Zhang, Z.; et al. Glucagon-like peptide-1 receptor agonists decrease hyperinsulinemia and hyperandrogenemia in dehydroepiandrosterone-induced polycystic ovary syndrome mice and are associated with mitigating inflammation and inducing browning of white adipose tissue. Biol. Reprod. 2023, 108, 945–959. [Google Scholar] [CrossRef]
  31. Sagvekar, P.; Shinde, G.; Mangoli, V.; Desai, S.K.; Mukherjee, S. Evidence for TET-mediated DNA demethylation as an epigenetic alteration in cumulus granulosa cells of women with polycystic ovary syndrome. Mol. Hum. Reprod. 2022, 28, gaac019. [Google Scholar] [CrossRef]
  32. Biglari-Zadeh, G.; Sargazi, S.; Mohammadi, M.; Ghasemi, M.; Majidpour, M.; Saravani, R.; Mirinejad, S. Relationship Between Genetic Polymorphisms in Cell Cycle Regulatory Gene TP53 and Polycystic Ovarian Syndrome: A Case-Control Study and In Silico Analyses. Biochem. Genet. 2023, 61, 1827–1849. [Google Scholar] [CrossRef] [PubMed]
  33. Liu, Y.; Su, Z.; Tavana, O.; Gu, W. Understanding the complexity of p53 in a new era of tumor suppression. Cancer Cell 2024, 42, 946–967. [Google Scholar] [CrossRef] [PubMed]
  34. Zanjirband, M.; Hodayi, R.; Safaeinejad, Z.; Nasr-Esfahani, M.H.; Ghaedi-Heydari, R. Evaluation of the p53 pathway in polycystic ovarian syndrome pathogenesis and apoptosis enhancement in human granulosa cells through transcriptome data analysis. Sci. Rep. 2023, 13, 11648. [Google Scholar] [CrossRef] [PubMed]
  35. Harami-Papp, H.; Pongor, L.S.; Munkácsy, G.; Horváth, G.; Nagy, Á.M.; Ambrus, A.; Hauser, P.; Szabó, A.; Tretter, L.; Győrffy, B. TP53 mutation hits energy metabolism and increases glycolysis in breast cancer. Oncotarget 2016, 7, 67183–67195. [Google Scholar] [CrossRef]
  36. Yumiceba, V.; López-Cortés, A.; Pérez-Villa, A.; Yumiseba, I.; Guerrero, S.; García-Cárdenas, J.M.; Armendáriz-Castillo, I.; Guevara-Ramírez, P.; Leone, P.E.; Zambrano, A.K.; et al. Oncology and Pharmacogenomics Insights in Polycystic Ovary Syndrome: An Integrative Analysis. Front. Endocrinol. 2020, 11, 585130. [Google Scholar] [CrossRef]
  37. Buzzetti, E.; Pinzani, M.; Tsochatzis, E.A. The multiple-hit pathogenesis of non-alcoholic fatty liver disease (NAFLD). Metabolism 2016, 65, 1038–1048. [Google Scholar] [CrossRef]
  38. Petta, S.; Ciresi, A.; Bianco, J.; Geraci, V.; Boemi, R.; Galvano, L.; Magliozzo, F.; Merlino, G.; Craxì, A.; Giordano, C. Insulin resistance and hyperandrogenism drive steatosis and fibrosis risk in young females with PCOS. PLoS ONE 2017, 12, e0186136. [Google Scholar] [CrossRef]
  39. Harsha Varma, S.; Tirupati, S.; Pradeep, T.V.S.; Sarathi, V.; Kumar, D. Insulin resistance and hyperandrogenemia independently predict nonalcoholic fatty liver disease in women with polycystic ovary syndrome. Diabetes Metab. Syndr. 2019, 13, 1065–1069. [Google Scholar] [CrossRef]
  40. Sookoian, S.; Castaño, G.O.; Scian, R.; Fernández Gianotti, T.; Dopazo, H.; Rohr, C.; Gaj, G.; San Martino, J.; Sevic, I.; Flichman, D.; et al. Serum aminotransferases in nonalcoholic fatty liver disease are a signature of liver metabolic perturbations at the amino acid and Krebs cycle level12. Am. J. Clin. Nutr. 2016, 103, 422–434. [Google Scholar] [CrossRef]
  41. Guijarro de Armas, M.G.; Monereo Megías, S.; Navea Aguilera, C.; Merino Viveros, M.; Vega Piñero, M.B. Non-alcoholic fatty liver in children and adolescents with excess weight and obesity. Med. Clin. 2015, 144, 55–58. [Google Scholar] [CrossRef]
  42. Zhang, Y.; Meng, F.; Sun, X.; Sun, X.; Hu, M.; Cui, P.; Vestin, E.; Li, X.; Li, W.; Wu, X.K.; et al. Hyperandrogenism and insulin resistance contribute to hepatic steatosis and inflammation in female rat liver. Oncotarget 2018, 9, 18180–18197. [Google Scholar] [CrossRef] [PubMed]
  43. Wang, D.; He, B. Current Perspectives on Nonalcoholic Fatty Liver Disease in Women with Polycystic Ovary Syndrome. Diabetes Metab. Syndr. Obes. 2022, 15, 1281–1291. [Google Scholar] [CrossRef] [PubMed]
  44. Liu, P.; Zhao, D.; Pan, Z.; Tang, W.; Chen, H.; Hu, K. Identification and validation of ferroptosis-related hub genes in obstructive sleep apnea syndrome. Front. Neurol. 2023, 14, 1130378. [Google Scholar] [CrossRef] [PubMed]
  45. Duignan, N.M. Polycystic ovarian disease. Br. J. Obstet. Gynaecol. 1976, 83, 593–602. [Google Scholar] [CrossRef]
  46. Delcour, C.; Robin, G.; Young, J.; Dewailly, D. PCOS and Hyperprolactinemia: What do we know in 2019? Clin. Med. Insights Reprod. Health 2019, 13, 1179558119871921. [Google Scholar] [CrossRef]
  47. Xu, R.; Wang, Z. Involvement of Transcription Factor FoxO1 in the Pathogenesis of Polycystic Ovary Syndrome. Front. Physiol. 2021, 12, 649295. [Google Scholar] [CrossRef]
  48. Geronikolou, S.A.; Pavlopoulou, A.; Uça Apaydin, M.; Albanopoulos, K.; Cokkinos, D.V.; Chrousos, G. Non-Hereditary Obesity Type Networks and New Drug Targets: An In Silico Approach. Int. J. Mol. Sci. 2024, 25, 7684. [Google Scholar] [CrossRef]
  49. Geronikolou, S.A.; Bacopoulou, F.; Cokkinos, D. Bioimpedance Measurements in Adolescents with Polycystic Ovary Syndrome: A Pilot Study. Adv. Exp. Med. Biol. 2017, 987, 291–299. [Google Scholar] [CrossRef]
  50. Schrank, B.R.; Aparicio, T.; Li, Y.; Chang, W.; Chait, B.T.; Gundersen, G.G.; Gottesman, M.E.; Gautier, J. Nuclear ARP2/3 drives DNA break clustering for homology-directed repair. Nature 2018, 559, 61–66. [Google Scholar] [CrossRef]
  51. Sandestig, A.; Green, A.; Jonasson, J.; Vogt, H.; Wahlström, J.; Pepler, A.; Ellnebo, K.; Biskup, S.; Stefanova, M. Could Dissimilar Phenotypic Effects of ACTB Missense Mutations Reflect the Actin Conformational Change? Two Novel Mutations and Literature Review. Mol. Syndromol. 2019, 9, 259–265. [Google Scholar] [CrossRef]
  52. Heidarzadehpilehrood, R.; Pirhoushiaran, M.; Binti Osman, M.; Ling, K.H.; Abdul Hamid, H. Unveiling Key Biomarkers and Therapeutic Drugs in Polycystic Ovary Syndrome (PCOS) Through Pathway Enrichment Analysis and Hub Gene-miRNA Networks. Iran. J. Pharm. Res. 2023, 22, e139985. [Google Scholar] [CrossRef] [PubMed]
  53. Miyoshi, M.; Okajima, T.; Matsuda, T.; Fukuda, M.N.; Nadano, D. Bystin in human cancer cells: Intracellular localization and function in ribosome biogenesis. Biochem. J. 2007, 404, 373–381. [Google Scholar] [CrossRef] [PubMed]
  54. Anastasia, K.; Koika, V.; Roupas, N.D.; Armeni, A.; Marioli, D.; Panidis, D.; George, A.; Georgopoulos, N.A. Association of Calpain (CAPN) 10 (UCSNP-43, rs3792267) gene polymorphism with elevated serum androgens in young women with the most severe phenotype of polycystic ovary syndrome (PCOS). Gynecol. Endocrinol. 2015, 31, 630–634. [Google Scholar] [CrossRef] [PubMed]
  55. Xiong, Y.; Chen, C.; He, C.; Yang, X.; Cheng, W. Identification of shared gene signatures and biological mechanisms between preeclampsia and polycystic ovary syndrome. Heliyon 2024, 10, e29225. [Google Scholar] [CrossRef]
  56. Daghestani, M.H.; Alqahtani, H.A.; AlBakheet, A.; Al Deery, M.; Awartani, K.A.; Daghestani, M.H.; Kaya, N.; Warsy, A.; Coskun, S.; Colak, D. Global Transcriptional Profiling of Granulosa Cells from Polycystic Ovary Syndrome Patients: Comparative Analyses of Patients with or without History of Ovarian Hyperstimulation Syndrome Reveals Distinct Biomarkers and Pathways. J. Clin. Med. 2022, 11, 6941. [Google Scholar] [CrossRef]
  57. Messini, C.I.; Vasilaki, A.; Korona, E.; Anifandis, G.; Georgoulias, P.; Dafopoulos, K.; Garas, A.; Daponte, A.; Messinis, I.E. Effect of resistin on estradiol and progesterone secretion from human luteinized granulosa cells in culture. Syst. Biol. Reprod. Med. 2019, 65, 350–356. [Google Scholar] [CrossRef]
  58. Ramser, J.; Winnepenninckx, B.; Lenski, C.; Errijgers, V.; Platzer, M.; Schwartz, C.E.; Meindl, A.; Kooy, R.F. A splice site mutation in the methyltransferase gene FTSJ1 in Xp11.23 is associated with non-syndromic mental retardation in a large Belgian family (MRX9). J. Med. Genet. 2004, 41, 679–683. [Google Scholar] [CrossRef]
  59. Metzler, V.M.; de Brot, S.; Haigh, D.B.; Woodcock, C.L.; Lothion-Roy, J.; Harris, A.E.; Nilsson, E.M.; Ntekim, A.; Persson, J.L.; Robinson, B.D.; et al. The KDM5B and KDM1A lysine demethylases cooperate in regulating androgen receptor expression and signalling in prostate cancer. Front. Cell Dev. Biol. 2023, 11, 1116424. [Google Scholar] [CrossRef]
  60. Margarit, L.; Taylor, A.; Roberts, M.H.; Hopkins, L.; Davies, C.; Brenton, A.G.; Conlan, R.S.; Bunkheila, A.; Joels, L.; White, J.O.; et al. MUC1 as a discriminator between endometrium from fertile and infertile patients with PCOS and endometriosis. J. Clin. Endocrinol. Metab. 2010, 95, 5320–5329. [Google Scholar] [CrossRef]
  61. Humaidan, P.; Van Vaerenbergh, I.; Bourgain, C.; Alsbjerg, B.; Blockeel, C.; Schuit, F.; Van Lommel, L.; Devroey, P.; Fatemi, H. Endometrial gene expression in the early luteal phase is impacted by mode of triggering final oocyte maturation in recFSH stimulated and GnRH antagonist co-treated IVF cycles. Hum. Reprod. 2012, 27, 3259–3272. [Google Scholar] [CrossRef]
  62. Liu, Y.; Du, S.Y.; Ding, M.; Dou, X.; Zhang, F.F.; Wu, Z.Y.; Qian, S.W.; Zhang, W.; Tang, Q.Q.; Xu, C.J. The BMP4-Smad signaling pathway regulates hyperandrogenism development in a female mouse model. J. Biol. Chem. 2017, 292, 11740–11750. [Google Scholar] [CrossRef] [PubMed]
  63. Butler, A.E.; Moin, A.S.M.; Reiner, Ž.; Sathyapalan, T.; Jamialahmadi, T.; Sahebkar, A.; Atkin, S.L. HDL-Associated Proteins in Subjects with Polycystic Ovary Syndrome: A Proteomic Study. Cells 2023, 12, 855. [Google Scholar] [CrossRef] [PubMed]
  64. Fan, P.; Liu, H.; Wang, Y.; Zhang, F.; Bai, H. Apolipoprotein E-containing HDL-associated platelet-activating factor acetylhydrolase activities and malondialdehyde concentrations in patients with PCOS. Reprod. Biomed. Online 2012, 24, 197–205. [Google Scholar] [CrossRef] [PubMed]
  65. Liu, H.W.; Zhang, F.; Fan, P.; Bai, H.; Zhang, J.X.; Wang, Y. Effects of apolipoprotein E genotypes on metabolic profile and oxidative stress in southwest Chinese women with polycystic ovary syndrome. Eur. J. Obstet. Gynecol. Reprod. Biol. 2013, 170, 146–151. [Google Scholar] [CrossRef]
  66. Cetinkalp, S.; Karadeniz, M.; Erdogan, M.; Zengi, A.; Cetintas, V.; Tetik, A.; Eroglu, Z.; Kosova, B.; Ozgen, A.G.; Saygili, F.; et al. Apolipoprotein E gene polymorphism and polycystic ovary syndrome patients in Western Anatolia, Turkey. J. Assist. Reprod. Genet. 2009, 26, 1–6. [Google Scholar] [CrossRef]
  67. Butler, A.E.; Moin, A.S.M.; Sathyapalan, T.; Atkin, S.L. A Cross-Sectional Study of Protein Changes Associated with Dementia in Non-Obese Weight Matched Women with and without Polycystic Ovary Syndrome. Int. J. Mol. Sci. 2024, 25, 2409. [Google Scholar] [CrossRef]
  68. Karlsson, M.; Reue, K.; Xia, Y.R.; Lusis, A.J.; Langin, D.; Tornqvist, H.; Holm, C. Exon-intron organization and chromosomal localization of the mouse monoglyceride lipase gene. Gene 2001, 272, 11–18. [Google Scholar] [CrossRef]
  69. Dinh, T.P.; Carpenter, D.; Leslie, F.M.; Freund, T.F.; Katona, I.; Sensi, S.L.; Kathuria, S.; Piomelli, D. Brain monoglyceride lipase participating in endocannabinoid inactivation. Proc. Natl. Acad. Sci. USA 2002, 99, 10819–10824. [Google Scholar] [CrossRef]
  70. Makara, J.K.; Mor, M.; Fegley, D.; Szabó, S.I.; Kathuria, S.; Astarita, G.; Duranti, A.; Tontini, A.; Tarzia, G.; Rivara, S.; et al. Selective inhibition of 2-AG hydrolysis enhances endocannabinoid signaling in hippocampus. Nat. Neurosci. 2005, 8, 1139–1141. [Google Scholar] [CrossRef]
  71. Grinshpun, A.; Chen, V.; Sandusky, Z.M.; Fanning, S.W.; Jeselsohn, R. ESR1 activating mutations: From structure to clinical application. Biochim. Biophys. Acta Rev. Cancer 2023, 1878, 188830. [Google Scholar] [CrossRef]
  72. Betz, M.; Massard, V.; Gilson, P.; Witz, A.; Dardare, J.; Harlé, A.; Merlin, J.L. ESR1 Gene Mutations and Liquid Biopsy in ER-Positive Breast Cancers: A Small Step Forward, a Giant Leap for Personalization of Endocrine Therapy? Cancers 2023, 15, 5169. [Google Scholar] [CrossRef]
Biomedicines 12 02280 g001
Figure 1. Flow chart describing the overall methodology followed herewith.
Figure 1. Flow chart describing the overall methodology followed herewith.
Biomedicines 12 02280 g001
Biomedicines 12 02280 g002
Figure 2. The updated PCOS network: network depicting the associations (connecting lines) of PCOS-relevant genes/gene products (nodes). Over-represented KEGG pathways: FPT (red), ovarian steroidogenesis (blue), AGE-RAGE signaling pathway in diabetic complications (light green), aldosterone-regulated sodium reabsorption (mauve), prolactin signaling pathway (dark gray), type II diabetes mellitus (yellow), insulin resistance (magenta), FoxO signaling pathway (olive green), TGF-beta signaling pathway (pastel pink), cellular senescence (turquoise), non-alcoholic fatty liver disease (pale green), endocrine resistance (pale orange), apoptosis (light buff), GnRH secretion (light gray), pathways in cancer (brown), and neuroactive ligand–receptor interaction (blue-gray). The nodes that are not over-represented are marked in white.
Figure 2. The updated PCOS network: network depicting the associations (connecting lines) of PCOS-relevant genes/gene products (nodes). Over-represented KEGG pathways: FPT (red), ovarian steroidogenesis (blue), AGE-RAGE signaling pathway in diabetic complications (light green), aldosterone-regulated sodium reabsorption (mauve), prolactin signaling pathway (dark gray), type II diabetes mellitus (yellow), insulin resistance (magenta), FoxO signaling pathway (olive green), TGF-beta signaling pathway (pastel pink), cellular senescence (turquoise), non-alcoholic fatty liver disease (pale green), endocrine resistance (pale orange), apoptosis (light buff), GnRH secretion (light gray), pathways in cancer (brown), and neuroactive ligand–receptor interaction (blue-gray). The nodes that are not over-represented are marked in white.
Biomedicines 12 02280 g002
Biomedicines 12 02280 g003
Figure 3. The x-axis represents the enrichment ratio of the number of observed genes to the number of expected genes from each KEGG category in the input gene set.
Figure 3. The x-axis represents the enrichment ratio of the number of observed genes to the number of expected genes from each KEGG category in the input gene set.
Biomedicines 12 02280 g003
Biomedicines 12 02280 g004
Figure 4. PCOS-related FPT subnetwork; the KEGG FPT enriched pathway nodes are marked in red (as in Figure 3), and the mediating nodes are shown in white.
Figure 4. PCOS-related FPT subnetwork; the KEGG FPT enriched pathway nodes are marked in red (as in Figure 3), and the mediating nodes are shown in white.
Biomedicines 12 02280 g004
Table 1. Rotterdam criteria for PCOS phenotype distribution.
Table 1. Rotterdam criteria for PCOS phenotype distribution.
PCOS PhenotypeCharacterizationClinical Image
AClassicHA + OD + PCOM
BNon-PCOM PCOSHA + OD
COvulatory PCOSHA + PCOM
DNon-hyperandrogenic PCOSOD + PCOM
HA, hyperandrogenism; OD, ovulatory dysfunction; PCOM, polycystic ovarian morphology.
Table 2. Genes/gene products included in the PCOS network.
Table 2. Genes/gene products included in the PCOS network.
SymbolName
ACEangiotensin I converting enzyme
ACE2angiotensin converting enzyme 2
ACSL4acyl-CoA synthetase long chain family member 4
ACTBactin beta
ACVR1Bactivin A receptor type 1B
AGTangiotensinogen
AKR1C3aldo-keto reductase family 1 member C3
AMHanti-Mullerian hormone
AMHR2anti-Mullerian hormone receptor type 2
APOEapolipoprotein E
ARandrogen receptor
ARL14EPADP ribosylation factor-like GTPase 14 effector protein
ARPC1Aactin-related protein 2/3 complex subunit 1A
ASCL4achaete-scute family bHLH transcription factor 4
ATMATM serine/threonine kinase
AVParginine vasopressin
BCL2BCL2 apoptosis regulator
BCL2L11BCL2-like 11
BMP15bone morphogenetic protein 15
BMPR1Bbone morphogenetic protein receptor type 1B
BYSLbystin-like
CAPN1calpain 1
CAPN10calpain 10
CASTcalpastatin
CCKcholecystokinin
CGAglycoprotein hormones, alpha polypeptide
CNR1cannabinoid receptor 1
CREB1cAMP responsive element binding protein 1
CXCL8C-X-C motif chemokine ligand 8
CYP17A1cytochrome P450 family 17 subfamily A member 1
CYP19A1cytochrome P450 family 19 subfamily A member 1
CYP21A2cytochrome P450 family 21 subfamily A member 2
DCNdecorin
DCTN1dynactin subunit 1
DDX58DExD/H-Box Helicase 58
DENND1ADENN domain containing 1A
DLG2disks large MAGUK scaffold protein 2
DPP4dipeptidyl peptidase 4
EP300E1A binding protein p300
ERBB4erb-b2 receptor tyrosine kinase 4
ESR1estrogen receptor 1
ESR2estrogen receptor 2
FANCCFA complementation group C
FBN3fibrillin 3
FGFR2fibroblast growth factor receptor 2
FSHBfollicle stimulating hormone subunit beta
FTLferritin light chain
FTOFTO alpha-ketoglutarate dependent dioxygenase
FTSJ1FtsJ RNA 2’-O-methyltransferase 1
G6PCglucose-6-phosphatase catalytic subunit 1
G6PDglucose-6-phosphate dehydrogenase
GALNT14polypeptide N-acetylgalactosaminyltransferase 14
GATA4GATA binding protein 4
GCGglucagon
GDF9growth differentiation factor 9
GHSRgrowth hormone secretagogue receptor
GLP1glucagon-like peptide 1 receptor
GLP2glucagon-like peptide 2 receptor
GNRH1gonadotropin releasing hormone 1
GNRHRgonadotropin releasing hormone receptor
GOT1glutamic oxaloacetic transaminase 1
GPTglutamic pyruvic transaminase
GPX4glutathione peroxidase 4
GSTA1glutathione S-transferase alpha 1
GSTA2glutathione S-transferase alpha 2
HAMPhepcidin antimicrobial peptide
HSPA5heat shock protein family A (Hsp70) member 5
HHEXhematopoietically expressed homeobox
HIF1Ahypoxia inducible factor 1 subunit alpha
IGF1insulin-like growth factor 1
IGF2insulin-like growth factor 2
IL1Ainterleukin 1 alpha
IL1Binterleukin 1 beta
IL6interleukin 6
INSinsulin
INSRinsulin receptor
IRF1interferon regulatory factor 1
IRS1insulin receptor substrate 1
IRS2insulin receptor substrate 2
ITGB1integrin subunit beta 1
KCNA4potassium voltage-gated channel subfamily A member 4
KDM1Alysine demethylase 1A
KISS1KiSS-1 metastasis suppressor
KISS1RKISS1 receptor
KRR1KRR1 small subunit processome component homolog
LDLRlow-density lipoprotein receptor
LHCGRluteinizing hormone/choriogonadotropin receptor
MAPK14mitogen-activated protein kinase 14
MAPKmitogen-activated protein kinase 8
MAPRE1microtubule-associated protein RP/EB family member 1
MGLLmonoglyceride lipase
MUC1mucin 1, cell surface associated
MUC7mucin 7, secreted
NCOR1nuclear receptor corepressor 1
NEDD4LNEDD4-like E3 ubiquitin protein ligase
NEIL2nei-like DNA glycosylase 2
NFE2L2NFE2-like bZIP transcription factor 2
NFKB1nuclear factor kappa B subunit 1
NOX1NADPH oxidase 1
PCBP1poly(rC) binding protein 1
PCBP2poly(rC) binding protein 2
PHF21APHD finger protein 21A
PINK1PTEN induced kinase 1
PPARGperoxisome proliferator-activated receptor gamma
PRDM2PR/SET domain 2
PTENphosphatase and tensin homolog
PTPN11protein tyrosine phosphatase non-receptor type 11
RAD50RAD50 double strand break repair protein
RPS9ribosomal protein S9
SERPINE1serpin family E member 1
SETDB1SET domain bifurcated histone lysine methyltransferase 1
SHBGsex hormone binding globulin
SIRT3sirtuin 3
SLC11A2solute carrier family 11 member 2
SLC7A11solute carrier family 7 member 11
SMAD2SMAD family member 2
SOD2superoxide dismutase 2
SULT2A1sulfotransferase family 2A member 1
TCF7L2transcription factor 7-like 2
TET1tet methylcytosine dioxygenase 1
TET2tet methylcytosine dioxygenase 2
TFRCtransferrin receptor
TGFB1transforming growth factor beta 1
THADATHADA armadillo repeat containing
TLR4Toll-like receptor 4
TNFtumor necrosis factor
TOX3TOX high-mobility group box family member 3
TP53tumor protein p53
TRIM28tripartite motif containing 28
TRIM4tripartite motif containing 4
TROAPtrophinin-associated protein
UNC5Cunc-5 netrin receptor C
WWTR1WW domain containing transcription regulator 1
XRCC1X-ray repair cross complementing 1
YAP1Yes1-associated transcriptional regulator
ZBTB16zinc finger and BTB domain containing 16
ZKSCAN5zinc finger with KRAB and SCAN domains 5
ZNF197zinc finger protein 197
ZNF41zinc finger protein 41
ZSCAN20zinc finger and SCAN domain containing 20
FPT–PCOS-associated (in purple);Novel Connectors (in green).
Table 3. Major hubs of the PCOS network.
Table 3. Major hubs of the PCOS network.
HubConnections
TP5341
INS34
IL633
ESR129
IL1B29
IGF127
TNF26
ACTB24
EP30023
NFKB121
PPARG21
TLR420
Table 4. Over-represented KEGG pathways in the PCOS-relevant genes. The major hubs of the herein presented updated interactome are underlined.
Table 4. Over-represented KEGG pathways in the PCOS-relevant genes. The major hubs of the herein presented updated interactome are underlined.
KEGG PatwhayGenes
AGE-RAGE signaling pathway in diabetic complicationsAGT BCL2 CXCL8 IL1A IL1B IL6 MAPK14 MAPK8 NFKB1 NOX1 SERPINE1 SMAD2 TGFB1 TNF
Ovarian steroidogenesisAKR1C3 BMP15 CYP17A1 CYP19A1 FSHB IGF1 INS INSR LDLR LHCGR
FoxO signaling pathwayATM BCL2L11 EP300 IGF1 G6PC IL6 INS INSR IRS1 IRS2 MAPK14 MAPK8 PTEN SOD2 TGFB1
Non-alcoholic fatty liver diseaseBCL2L11 CXCL8 GPT GOT1 IL1A IL1B IL6 INS INSR IRS1 IRS2 MAPK8 NFKB1 TGFB1 TNF
FerroptosisACSL4 FTL GPX4 PCBP1 PCBP2 SLC11A2 SLC7A11 TFRC TP53
Pathways in cancerAGT AR BCL2 BCL2L11 CXCL8 EP300 ESR1 ESR2 FGFR2 GSTA1 GSTA2 HIF1A IGF1 IGF2 IL6 ITGB1 MAPK8 NFE2L2 NFKB1 PPARG PTEN SMAD2 TCF7L2 TGFB1 TP53 ZBTB16
Cellular senescenceATM CAPN1 CXCL8 GATA4 IL1A IL6 MAPK14 NFKB1 PTEN RAD50 SERPINE1 SMAD2 TGFB1 TP53
TGF-beta signaling pathwayAMH AMHR2 BMPR1B DCN EP300 HAMP SMAD2 TGFB1 TNF
Insulin resistanceAGT CREB1 G6PC IL6 INS INSR IRS1 IRS2 MAPK8 NFKB1 PTEN PTPN11 TNF
Prolactin signaling pathwayCYP17A1 ESR1 ESR2 INS IRF1 LHCGR MAPK14 MAPK8 NFKB1 HIF1A
Type II diabetes mellitusINS INSR IRS1 IRS2 MAPK8 TNF
Endocrine resistanceBCL2 ESR1 ESR2 IGF1 MAPK14 MAPK8 NCOR1 TP53
ApoptosisACTB ATM BCL2 BCL2L11 CAPN1 MAPK8 NFKB1 TNF TP53
Neuroactive ligand–receptor interactionAGT AVP CCK CNR1 FSHB GCG GHSR GLP1R GLP2R GNRH1 GNRHR KISS1 KISS1R LHCGR
Aldosterone-regulated sodium reabsorptionIGF1 INS INSR IRS1 NEDD4L
GnRH secretionESR2 GNRH1 KISS1 KISS1R
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Geronikolou, S.; Pavlopoulou, A.; Koutelekos, I.; Kalogirou, D.; Bacopoulou, F.; Cokkinos, D.V. Polycystic Ovary Syndrome and Ferroptosis: Following Ariadne’s Thread. Biomedicines 2024, 12, 2280. https://doi.org/10.3390/biomedicines12102280

AMA Style

Geronikolou S, Pavlopoulou A, Koutelekos I, Kalogirou D, Bacopoulou F, Cokkinos DV. Polycystic Ovary Syndrome and Ferroptosis: Following Ariadne’s Thread. Biomedicines. 2024; 12(10):2280. https://doi.org/10.3390/biomedicines12102280

Chicago/Turabian Style

Geronikolou, Styliani, Athanasia Pavlopoulou, Ioannis Koutelekos, Dimitrios Kalogirou, Flora Bacopoulou, and Dennis V. Cokkinos. 2024. "Polycystic Ovary Syndrome and Ferroptosis: Following Ariadne’s Thread" Biomedicines 12, no. 10: 2280. https://doi.org/10.3390/biomedicines12102280

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop