Transcriptomic Profiling of Cumulus Cells Reveals Dysregulated Genes and Pathways in PCOS-Related Infertility

Transcriptomic Profiling of Cumulus Cells Reveals Dysregulated Genes and Pathways in PCOS-Related Infertility | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Transcriptomic Profiling of Cumulus Cells Reveals Dysregulated Genes and Pathways in PCOS-Related Infertility Akeem Babatunde Sikiru, Nurulfiza Mat Isa, Kasim Sakran Abass, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6487439/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Background: Polycystic ovarian syndrome (PCOS) is a leading cause of infertility and metabolic dysfunction in women, characterized by hyperandrogenism, anovulation, and insulin resistance. Cumulus cells play a crucial role in folliculogenesis and oocyte maturation, necessitating a deeper understanding of their molecular alterations impact in PCOS. Method: This study investigates transcriptomic differences in cumulus cells between PCOS and non-PCOS women using high-throughput RNA sequencing data obtained from the NCBI Gene Expression Omnibus (GEO) database (Accession Number: GSE277906). The RNA sequencing data from 23 PCOS and 17 non-PCOS women were analysed to identify differentially expressed genes (DEGs) using R-based computational pipelines. Results: Differential gene expression analysis identified 3,245 significantly dysregulated genes, comprising 1,723 up regulated and 1,522 downregulated genes in PCOS samples. Functional enrichment analysis revealed that key DEGs (CDH5, CLEC4D, and GNAT1) were associated with follicular development, insulin signaling, and immune response. Gene Set Enrichment Analysis (GSEA) further identified dysregulation in metabolic and reproductive pathways, including ribonucleoprotein complex biogenesis and vascular endothelial growth factor (VEGF) signaling. Conclusion: Findings from this study suggest that altered gene expression in cumulus cells may impair oocyte competence, potentially influencing fertility outcomes in PCOS patients. Additionally, the study identifies GNAT1, previously linked to diabetes, as a novel candidate in PCOS pathophysiology. Future studies should validate these findings through functional experiments and explore targeted therapeutic interventions to mitigate the reproductive consequences of PCOS. Polycystic ovarian syndrome (PCOS) Cumulus cells Guanine nucleotide-binding protein G(t) subunit alpha-1 (GNAT1) Oocyte competence infertility metabolic dysfunction Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 BACKGROUND Polycystic ovarian syndrome (PCOS) is one of the most prevalent endocrine disorders affecting reproductive-aged women, with an estimated global prevalence of 6–20% [ 1 , 2 ]. It is a multifactorial disorder characterized by anovulation, hyperandrogenism, and polycystic ovarian morphology, frequently accompanied by metabolic conditions such as insulin resistance and type 2 diabetes mellitus [ 3 , 4 ]. The complexity of PCOS extends beyond reproductive dysfunction, affecting systemic metabolism, inflammation, and cardiovascular health, making its management particularly challenging given the fact that it has a strong genetic complexity [ 2 ]. Cumulus cells, which surround the oocyte, are essential for folliculogenesis, oocyte maturation, and fertilization [ 5 ]. They play a vital role in metabolic support and the regulation of local endocrine signaling, which in turn affects oocyte quality and overall reproductive success [ 6 , 7 ]. However, while there are reviews and other non-empirical data establishing cumulus cells as critical components of ovarian physiology, their molecular alterations in relation to progression of PCOS remain poorly characterized. Hence, understanding how the disruptions in pattern of gene expression within cumulus cells may impair follicular development and poor oocyte competence, affecting fertility outcomes in women with PCOS. Meanwhile, previous studies have suggested a strong association between PCOS and dysregulation of insulin signaling, oxidative stress, and chronic low-grade inflammation, all of which may contribute to abnormal gene expression in cumulus cells [ 2 ]. However, the specific transcriptional changes and their functional implications remain unexplored. Therefore, this study was conducted to fill this knowledge gap by comparing gene expression profiles in cumulus cells from PCOS and non-PCOS women. By identifying differentially expressed genes (DEGs) and enriched biological pathways, this study to elucidate the molecular mechanisms underlying PCOS pathophysiology. This was undertaken because understanding these transcriptional changes could pave the way for novel biomarkers and therapeutic strategies aimed at improving ovarian function and fertility outcomes in PCOS patients. This study investigates differences in mRNA expression in cumulus cells between women with polycystic ovary syndrome (PCOS) and non-PCOS women. METHODS Source data and sequencing overview RNA sequencing data of cumulus cells collected from PCOS and non-PCOS women was obtained from the NCBI Gene Expression Omnibus (GEO) database (accession number: GSE277906). The dataset was generated using the Illumina NovaSeq 6000 platform and includes RNA samples pooled from 23 PCOS and 17 non-PCOS women attending a fertility clinic in China [ 8 ]. These data were studied to elucidate metabolic alterations in follicular environments that influence reproductive function in PCOS. RNA-Seq data processing The Fragments Per Kilobase of transcript per Million mapped reads (FPKM) RNA-Seq data was processed using RStudio (version 2024.12.1 + 563 for Windows). The steps included data import whereby the FPKM data file was loaded using the readxl package. Data structuring was also conducted whereby the gene identifiers were set as row names, and sample identifiers were used as column names. The quality control followed handling of missing values using the na.rm = TRUE argument, and overall distribution was assessed for downstream analysis [ 9 ]. Differential genes expression analysis and their statistics Differentially expressed genes (DEGs) were identified by first programmatically categorizing the samples into "Control" and "PCOS" groups. The mean expression of each gene across the 28,001 genes was calculated for both groups. Fold change (FC) and log2 fold change (log2FC) values were computed using the dplyr package in R software to determine the magnitude of expression differences [ 10 ]. A Student’s t-test was then applied on a per-gene basis to compare expression levels between the PCOS and control groups, generating p-values for statistical significance. The genes were classified as up regulated (FC ≥ 1, p < 0.05) or downregulated (FC < 1, p < 0.05). To account for multiple testing and reduce false positives, a false discovery rate (FDR) correction was applied using the Benjamini-Hochberg method. The classification of genes into significant and non-significant categories, along with the associated parameters, was utilized as a data processing strategy to enhance our understanding of the functional roles of these genes within each regulatory category [ 11 ]. Statistical analysis Basic descriptive statistics were calculated for the 28,001 genes to provide an overall summary of gene expression patterns. These included measures such as the mean, median, and standard error of the mean (SEM) to assess central tendency and variability. Additionally, the minimum, third quartile (Q3), and maximum values were determined to capture the range and distribution of expression levels across samples. All these computations were performed using built-in R functions, including mean(), median(), quantile(), and max(), ensuring that missing values were managed appropriately by setting the na.rm argument to TRUE [ 12 ]. This statistical analysis provided a foundation for further interpretation of gene expression differences between PCOS and control samples. Visualization of differentially and variedly expressed genes To enhance the interpretability of the gene expression patterns, several visualization techniques were employed. Boxplots were generated using ggplot2 in R and Seaborn in Python to display the expression levels of the top 10 differentially expressed genes, providing a clear comparison between the PCOS and control groups [ 13 , 14 ]. Additionally, a heatmap was created using the ComplexHeatmap package in R to visualize the expression patterns of the 50 most variable genes, highlighting clustering patterns that distinguish the two groups [ 15 ]. To examine the overall distribution of log2 fold change (log2FC) values, a histogram was constructed with ggplot2, offering insights into the skewness and spread of differential expression. Lastly, a volcano plot was generated to illustrate the relationship between the magnitude of gene expression changes and their statistical significance, allowing for the identification of key up regulated and down regulated genes. These visualization techniques were conducted to collectively provide a comprehensive overview of the gene expression dynamics in the PCOS group. Gene Set Enrichment Analysis (GSEA) Gene Set Enrichment Analysis (GSEA) was conducted to identify biological processes associated with the differentially expressed genes (DEGs) in PCOS using the clusterProfiler package [ 16 ]. To perform the analysis, genes were first sorted based on their log2 fold change (log2FC) values, prioritizing those with the most significant expression differences. The gseGO function was then applied to determine enriched Gene Ontology (GO) biological processes, allowing for a deeper understanding of functional pathways influenced by dysregulated genes. Additionally, pathway enrichment analysis was performed using g:Profiler [ 17 ], using the top 10 significantly differentially up regulated genes including CDH5, CLEC4D, GNAT1, GPR25, KRTAP7-1, LINC00398, LINC02269, LOC101928295, RGS7BP and RUNX3-AS1 in the PCOS group for ensuring that the most relevant pathways were highlighted. The complete list of Differentially Expressed Genes (DEGs) was saved including list of the significantly up regulated genes in a plain text file (DEG_Gene_List.txt) for further analysis. The scripts used for preprocessing, statistical analysis, and visualization were written in R and Python and are available as supplementary materials. RESULTS Differential Expressed Genes (DEGs) A total of 28,001 genes were analysed to identify differences in gene expression between PCOS and control samples. Out of these, 3,245 genes were found to be significantly differentially expressed based on an adjusted p-value threshold of 0.05 (FDR-adjusted). Among these differentially expressed genes, 1,723 were up regulated in PCOS samples, while 1,522 genes were down regulated. The distribution of gene expression across all samples showed an average expression value of 3.21 FPKM, a median of 2.45 FPKM, and a standard error of the mean (SEM) of 0.89 FPKM (Table 1 ). These findings highlight significant transcriptional alterations in cumulus cells, suggesting potential molecular pathways that may be dysregulated in PCOS. Table 1 Summary of Differential Gene Expression Analysis in PCOS and Control Samples Parameters Value Total number of genes analysed 28,001 Significantly differentially expressed genes (FDR < 0.05) 3,245 Significantly upregulated genes in PCOS 1,723 Significantly downregulated genes in PCOS 1,522 Average expression value (FPKM) 3.21 Median expression value (FPKM) 2.45 Standard error of the mean (SEM) 0.89 This table summarizes the key findings from the differential gene expression analysis comparing PCOS and control samples. It highlights total number of genes, significantly differentially expressed genes, and their regulation patterns. Expression values are reported in fragments per kilobase of transcript per million mapped reads (FPKM). False discovery rate (FDR) correction was applied to determine statistical significance. PCOS: Polycystic Ovary Syndrome; FDR: False Discovery Rate; FPKM: Fragments Per Kilobase of transcript per Million mapped reads. Visualization of the gene expression patterns To further explore the differences in gene expression between PCOS and control samples, several visualization techniques were employed. Boxplots were generated to examine the expression levels of the top 10 differentially expressed genes, confirming distinct alterations in gene regulation between the two groups (Fig. 1 ). Apart from the top differentially expressed gene, the volcano plot showed overall pattern of the gene expressions (Fig. 2 ). The heatmap provided a comprehensive view of clustering patterns among highly variable genes (n = 50), demonstrating distinct gene expression profiles in the PCOS samples compared to the controls (Fig. 3 ). Additionally, histogram of log2 fold change (log2FC) values revealed a skewed distribution, indicating that a subset of genes exhibited substantial expression changes (Fig. 4 ). These visualizations showed relationships between the magnitude of gene expression changes and their statistical significance providing insights into the molecular alterations associated with PCOS phenotype. Gene set enrichment and functional pathway analysis Gene Set Enrichment Analysis (GSEA) was performed to identify biological processes and molecular pathways enriched among the differentially expressed genes. The analysis revealed several key pathways significantly associated with PCOS. Notably, follicular development was enriched, highlighting disruptions in ovarian function that may impact oocyte quality. Additionally, the insulin signaling pathway was significantly enriched, supporting previous evidence linking PCOS to insulin resistance and metabolic dysfunction. Another important pathway, ribonucleoprotein complex biogenesis, was identified, indicating alterations in hormone production that may contribute to the pathophysiology of PCOS (Fig. 5 ). Further pathway analysis using g:Profiler confirmed enrichment in metabolic and reproductive pathways, reinforcing the role of dysregulated gene expression in the molecular mechanisms underlying PCOS (Table 2 ). These findings suggest potential targets for therapeutic intervention and provide a deeper understanding of the biological processes involved in the disorder. Table 2 Functional Enrichment Analysis of Differentially Expressed Genes in PCOS Source Term name Term id intersections GO:MF acyl binding GO:0000035 GNAT1 GO:MF fibrinogen binding GO:0070051 CDH5 GO:MF vascular endothelial growth factor receptor 2 binding GO:0043184 CDH5 GO:MF BMP receptor binding GO:0070700 CDH5 GO:MF vascular endothelial growth factor receptor binding GO:0005172 CDH5 GO:BP positive regulation of myeloid dendritic cell activation GO:0030887 CLEC4D GO:BP neural tissue regeneration GO:0097719 GNAT1 GO:BP negative regulation of cyclic-nucleotide phosphodiesterase activity GO:0051344 GNAT1 GO:BP regulation of myeloid dendritic cell activation GO:0030885 CLEC4D GO:BP protein localization to bicellular tight junction GO:1902396 CDH5 GO:CC extrinsic component of plasma membrane GO:0019897 CDH5,GNAT1 GO:CC side of membrane GO:0098552 CDH5,CLEC4D,GNAT1 GO:CC dendritic spine head GO:0044327 RGS7BP GO:CC external side of plasma membrane GO:0009897 CDH5,CLEC4D KEGG Phototransduction KEGG:04744 GNAT1 REAC Activation of the phototransduction cascade REAC:R-HSA-2485179 GNAT1 WP Purinergic signaling WP:WP4900 GNAT1 TF Factor: KLF8; motif: NGGGGTGYGG TF:M08818 CDH5,GNAT1, GPR25,RGS7BP TF Factor: NR4A2; motif: AGGTCANNNNNTGACCT TF:M04478 GNAT1 CORUM PAR-6-VE-cadherin complex, endothelial CORUM:829 CDH5 CORUM VEcad-VEGFR complex CORUM:6586 CDH5 CORUM ZO1-(beta)cadherin-(VE)cadherin-VEGFR2 complex CORUM:5772 CDH5 HP Electronegative electroretinogram HP:0007984 GNAT1 HP Congenital stationary night blindness with abnormal fundus HP:0030639 GNAT1 GO:MF peptide hormone binding GO:0017046 INSR,FSHR GO:MF hormone binding GO:0042562 INSR,FSHR GO:MF follicle-stimulating hormone receptor activity GO:0004963 FSHR GO:BP female gonad development GO:0008585 ESR1,INSR,FSHR GO:BP development of primary female sexual characteristics GO:0046545 ESR1,INSR,FSHR GO:BP female sex differentiation GO:0046660 ESR1,INSR,FSHR GO:BP developmental growth GO:0048589 ESR1,LEPR,INSR,FSHR GO:BP development of primary sexual characteristics GO:0045137 ESR1,INSR,FSHR GO:BP uterus development GO:0060065 ESR1,FSHR GO:BP sex differentiation GO:0007548 ESR1,INSR,FSHR GO:BP reproductive structure development GO:0048608 ESR1,INSR,FSHR GO:BP reproductive system development GO:0061458 ESR1,INSR,FSHR GO:BP response to endogenous stimulus GO:0009719 ESR1,LEPR,INSR,FSHR GO:BP reproductive process GO:0022414 ESR1,LEPR,INSR,FSHR GO:BP ovulation cycle process GO:0022602 ESR1,FSHR GO:BP estrogen receptor signaling pathway GO:0030520 ESR1,FSHR GO:BP ovarian follicle development GO:0001541 ESR1,FSHR GO:BP ovulation cycle GO:0042698 ESR1,FSHR KEGG Ovarian steroidogenesis KEGG:04913 INSR,FSHR HP Delayed puberty HP:0000823 ESR1,LEPR,INSR,FSHR HP Abnormality of the ovary HP:0000137 ESR1,LEPR,INSR,FSHR HP Abnormal serum estradiol HP:0025133 ESR1,LEPR,FSHR HP Abnormal circulating estrogen level HP:0025132 ESR1,LEPR,FSHR HP Increased serum testosterone level HP:0030088 INSR,FSHR HP Ovarian cyst HP:0000138 ESR1,INSR,FSHR HP Enlarged polycystic ovaries HP:0008675 ESR1,FSHR This table presents the functional enrichment analysis results the 10 set of differentially expressed genes (DEGs) identified in Polycystic Ovary Syndrome (PCOS). The table lists the enriched terms, their corresponding identifiers, and the genes that contribute to the enrichment. The terms are categorized by their ontology or database, including Gene Ontology Molecular Function (GO:MF), Gene Ontology Biological Process (GO:BP), Gene Ontology Cellular Component (GO:CC), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome (REAC), WikiPathways (WP), Transcription Factor (TF), Comprehensive Resource of Mammalian protein complexes (CORUM), and Human Phenotype Ontology (HP). The "intersections" column indicates the specific genes from the input list that are associated with each term. This analysis aims to provide biological context and potential functional implications of the observed gene expression changes in PCOS. DISCUSSION This study identified significant transcriptional changes in cumulus cells of women with PCOS, revealing differentially expressed genes (DEGs) up regulated in the PCOS women compared to non-PCOS controls, where they were down regulated. The visualization highlighted key clusters of differentially expressed genes, reflecting distinct gene expression profiles in PCOS. Notably, CDH5, CLEC4D, and GNAT1 emerged as significant DEGs, with their involvement in processes such as ovarian follicle development, immune regulation, and hormone receptor signaling. The Gene Set Enrichment Analysis (GSEA) further revealed enrichment in biological pathways, including follicular development, insulin signaling, and ribonucleoprotein complex biogenesis. These findings suggest that the altered molecular environment in cumulus cells could impact oocyte competence, influencing fertility outcomes in PCOS patients [ 8 ]. Moreover, the interplay between these differentially expressed genes and metabolic pathways signifies the multifactorial nature of PCOS. The dysregulation of key genes involved in metabolic homeostasis and cellular signaling may contribute to the compromised microenvironment surrounding the oocyte and consequentially could cause infertility in PCOS patients [ 18 ]. Notably, the aberrant expression of genes linked to oxidative stress and inflammatory responses may further exacerbate follicular dysfunction [ 2 ]. This highlights the potential role of targeted therapeutic strategies aimed at modulating these molecular pathways to improve reproductive outcomes in PCOS-affected individuals. These findings align with the longstanding relationship between metabolic and hormonal dysregulation and the pathogenesis of PCOS and can be linked to the enrichment of genes involved in insulin signaling, which supports the well-established association between PCOS and insulin resistance. This was reported several other prior studies that highlighted insulin receptor (INSR) dysregulation in PCOS granulosa and cumulus cells, contributing to aberrant follicular development and hyperandrogenism [ 19 , 20 ]. Similarly, CDH5, which was significantly differentially expressed in this present study, has been implicated in vascular endothelial growth factor (VEGF) signaling, a pathway known to regulate follicular angiogenesis and ovarian function [ 21 ]. The overexpression of CLEC4D, involved in immune signaling, suggests a potential inflammatory component in PCOS, consistent with existing literature indicating chronic low-grade inflammation as a characteristic feature of the syndrome [ 22 ]. Interestingly, GNAT1, a gene associated with phototransduction, was found to be significantly dysregulated in this study. While GNAT1 has not been previously linked to PCOS, emerging evidence suggests that the gene is associated with diabetes [ 23 ], which is a known comorbidity with PCOS. This could indicate a role for G-protein signaling pathways in ovarian follicle maturation, warranting further investigation. While many findings in this study are consistent with established literature, the differential regulation of specific genes such as GNAT1 introduces novel insights that merit further validation. The altered gene expression in cumulus cells suggests disruptions in key biological processes critical for oocyte competence and follicular health [ 24 – 26 ]. However, the enrichment of ribonucleoprotein (RNP) complex biogenesis pathways indicates an alteration in RNA processing and protein translation in cumulus cells, which may contribute to defective oocyte maturation. The RNP has been reported to be crucial for vital cellular functions like transcription, translation, and gene regulation, impacting development, cell function, and disease development, therefore its understanding in relation to PCOS could be critical [ 27 ]. The identification of differentially expressed genes involved in follicle-stimulating hormone receptor (FSHR) activity suggests potential dysregulation of gonadotropin signaling, a key factor in follicular development and ovulation [ 28 , 29 ]. Additionally, the enrichment of genes involved in VEGF receptor binding and endothelial cell adhesion suggests that vascular dysfunction may contribute to abnormal folliculogenesis in PCOS. Clinically, these findings could have significant implications in the use of the identified DEGs as potential biomarkers for early PCOS diagnosis or therapeutic targets for improving ovarian function in affected women. For example, modulating VEGF signaling could enhance follicular angiogenesis, potentially improving oocyte quality and fertility outcomes in PCOS patients. Although, this study leveraged high-throughput RNA sequencing to provide an in-depth transcriptomic profile of cumulus cells in PCOS, offering novel insights into molecular mechanisms underlying the disorder. Also, the use of stringent statistical analyses including FDR correction enhances the reliability of the findings. Additionally, the study incorporated multiple visualization techniques to improve the interpretability of gene expression patterns. However, the limitations such as the derivation of the dataset from a publicly available repository may introduce batch effects and variability in sample collection or processing methods. Additionally, while gene expression data suggest potential regulatory mechanisms that are reported through the functional enrichment analysis, the validation of these pathways through in vitro or in vivo models is necessary to establish causality of the identified pathways and genes regulations. Further the samples were obtained in a Chinese population, future studies with larger and ethnically diverse cohorts may provide a more comprehensive understanding of the transcriptomic landscape in PCOS. There is also a need for future research could focus on validation of the top identified DEGs and pathways through functional studies. Experimental approaches such as CRISPR-based gene editing or siRNA knockdown studies in cumulus cell cultures could help determine the mechanistic role of key genes such as CDH5 and GNAT1 in follicular development. Additionally, integrating proteomics and metabolomics with transcriptomics findings could provide a more holistic understanding of how transcriptomic changes translate into functional protein and metabolic alterations in PCOS. Furthermore, expanding similar study to include a broader patient cohort with varying PCOS phenotypes such as hyperandrogenic vs. normoandrogenic PCOS group may also help elucidate phenotype-specific molecular mechanisms. Lastly, exploring therapeutic interventions targeting dysregulated pathways, such as modulating VEGF or insulin signaling, could pave the way for novel treatment strategies aimed at improving reproductive outcomes in PCOS patients. Conclusion This study provides novel insights into the molecular mechanisms underlying PCOS by identifying key transcriptional changes in cumulus cells. The differential expression of 3,245 genes, including CDH5, CLEC4D, and GNAT1, highlights significant alterations in pathways associated with follicular development, insulin signaling, and immune regulation. The findings suggest that these dysregulated pathways may impair oocyte competence, contributing to the infertility challenges faced by PCOS patients. The identification of GNAT1, previously linked to metabolic disorders, as a novel PCOS-associated gene further underscores the intersection between reproductive and metabolic dysfunction in this condition. Beyond advancing the understanding of PCOS pathophysiology, these results have potential clinical implications for both diagnosis and treatment. The identified DEGs could serve as biomarkers for early detection and disease monitoring, while dysregulated pathways such as VEGF and insulin signaling offer possible therapeutic targets. Future studies are warranted to concentrate on confirming these results within clinical environments, examining specific interventions like gene therapy, small-molecule inhibitors, or hormonal modulation, and investigating personalized treatment approaches that are informed by the molecular profiling of cumulus cells. Abbreviations Polycystic Ovary Syndrome PCOS Declarations Ethics approval and consent to participate Not Applicable Consent for publication Not Applicable Availability of data and material Not Applicable Competing interests Authors declare no competing interests Funding There is no funding support for this work Authors' contributions ABS: Conceptualization, Data Processing, Computational and Statistical Analysis, Writing of Original Draft. NMI: Formal Analysis, Validation, Writing of Original Draft, Writing, Review and Editing. KSA: Formal Analysis, Validation, Methodology. MAA: Writing, Review, Editing, and Validation. SSAE: Supervision, Validation, Writing, Review and Editing. KA: Writing, Review, and Editing. Acknowledgements Not Applicable Authors' information Affiliations 1 Molecular, Cellular and Integrative Physiology Laboratory (MCIP), Department of Animal Science, Federal University of Agriculture Zuru, 872101, Kebbi State, Nigeria. ORCID: 0000-0003-4956-7094. Email: [email protected] 2 Laboratory of Vaccines and Biomolecules (VacBio), Institute of Biosciences, Universiti Putra Malaysia, Serdang, 43400 Seri Kembangan, Selangor, Malaysia. ORCID: 0000-0002-4726-7728. Email: [email protected] 3 Department of Physiology, Biochemistry, and Pharmacology; College of Veterinary Medicine; University of Kirkuk; Kirkuk 36001, Iraq. ORCID: 0000-0002-5796-7170. Email: [email protected] 4 Department of Obstetrics and Gynaecology, College of Clinical Sciences, Faculty of Health Sciences, Ladoke Akintola University of Technology, Ogbomoso, 210101, Nigeria. ORCID: 0000-0002-1985-3789. Email: [email protected] 5 Department of Animal Production, School of Food and Agriculture Technology, Federal University of Technology Minna, Nigeria. ORCID: 0000-0002-1673-5103. Email: [email protected] 6 Department of Public Health, Fountain University Osogbo, Nigeria. Corresponding Author Akeem Babatunde Sikiru [email protected] References B. 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Paradiso, S. D’Alessandro, K. Zaręba, M. Simoni, L. Casarini, Endocrine disruption of the follicle-stimulating hormone receptor signaling during the human antral follicle growth, Front Endocrinol (Lausanne) 12 (2021) 791763. Additional Declarations No competing interests reported. Supplementary Files DEGGeneList.txt Pythoncodes.txt GSE277906FPKManno.txt.gz Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 24 Jun, 2025 Reviews received at journal 20 Jun, 2025 Reviews received at journal 20 Jun, 2025 Reviews received at journal 18 Jun, 2025 Reviewers agreed at journal 17 Jun, 2025 Reviewers agreed at journal 12 Jun, 2025 Reviewers agreed at journal 11 Jun, 2025 Reviewers invited by journal 11 Jun, 2025 Editor assigned by journal 02 May, 2025 Submission checks completed at journal 21 Apr, 2025 First submitted to journal 20 Apr, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6487439","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470591763,"identity":"ae442775-fd53-4c59-b75a-9c4095225543","order_by":0,"name":"Akeem Babatunde Sikiru","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAtklEQVRIiWNgGAWjYDACHhBRAWFLkKDlDMlaGNtI0cLfc/jgo5vzDufpNjAfvM3DsC2xgZAWibNtyca52w4Xmx1gS7bmYbhNWAvDeR4zaaCWxG0HgAyitMif5//+O3cOSAv/N+K0GJztYWPObQDbwkacFsMzx4ylc46lF5sdZjO2nGNw25igFrkzyQ8/59RY55kdb354403FbVmCWmAggYEZ7E4GRxK0QIE9sTpGwSgYBaNg5AAA0/g/vlrpMywAAAAASUVORK5CYII=","orcid":"","institution":"Federal University of Agriculture Zuru","correspondingAuthor":true,"prefix":"","firstName":"Akeem","middleName":"Babatunde","lastName":"Sikiru","suffix":""},{"id":470591765,"identity":"b60d0f2f-8e0d-4005-a902-8380d4f2a4ea","order_by":1,"name":"Nurulfiza Mat Isa","email":"","orcid":"","institution":"Universiti Putra Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Nurulfiza","middleName":"Mat","lastName":"Isa","suffix":""},{"id":470591767,"identity":"b5084dd2-b20e-4328-bd58-108e5fa5dcb5","order_by":2,"name":"Kasim Sakran Abass","email":"","orcid":"","institution":"University of Kirkuk","correspondingAuthor":false,"prefix":"","firstName":"Kasim","middleName":"Sakran","lastName":"Abass","suffix":""},{"id":470591768,"identity":"828dc072-30b9-4295-be46-b8a24fe2670c","order_by":3,"name":"Muibat Adeniran","email":"","orcid":"","institution":"Ladoke Akintola University of Technology","correspondingAuthor":false,"prefix":"","firstName":"Muibat","middleName":"","lastName":"Adeniran","suffix":""},{"id":470591769,"identity":"4b6037c5-ba7b-4edd-b804-8bf5344a5f56","order_by":4,"name":"Stephen Sunday Acheneje Egena","email":"","orcid":"","institution":"Federal University of Technology Minna","correspondingAuthor":false,"prefix":"","firstName":"Stephen","middleName":"Sunday Acheneje","lastName":"Egena","suffix":""},{"id":470591770,"identity":"9258bd85-40d2-4d51-b2b7-43547dd39478","order_by":5,"name":"Karimot Akinola","email":"","orcid":"","institution":"Fountain University Osogbo","correspondingAuthor":false,"prefix":"","firstName":"Karimot","middleName":"","lastName":"Akinola","suffix":""}],"badges":[],"createdAt":"2025-04-20 04:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6487439/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6487439/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84776493,"identity":"77442998-4d72-44af-9761-6d4589b8a017","added_by":"auto","created_at":"2025-06-17 09:03:34","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":70509,"visible":true,"origin":"","legend":"\u003cp\u003eExpression Levels of Top 10 Differentially Expressed Genes (DEGs) in PCOS versus the Control. This plot displays the expression levels (measured as FPKM - Fragments Per Kilobase of transcript per Million mapped reads) of the top 10 differentially expressed genes (DEGs) between the PCOS and Control groups. The genes are ranked by their significance of differential expression, with the 5 most upregulated genes and the 5 most downregulated genes shown. The x-axis represents the gene names. The y-axis represents the FPKM expression level. The data is presented as box plots, where each box represents the interquartile range (IQR) of the data, the line inside the box represents the median, and the whiskers extend to 1.5 times the IQR. \u003csup\u003e\u0026nbsp;\u003c/sup\u003eIndividual data points outside this range are shown as diamonds. The bars are coloured to distinguish between the two groups: blue for the Control group and red for the PCOS group. This visualization allows for a direct comparison of the expression levels of these key genes between the two groups, highlighting the magnitude and direction of differential expression. \u0026nbsp;\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/ea124581b87e3f716d9a2bdd.png"},{"id":84778092,"identity":"b200a32d-8e5d-4857-bd6b-62e409b0c1b3","added_by":"auto","created_at":"2025-06-17 09:11:34","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":159533,"visible":true,"origin":"","legend":"\u003cp\u003eVolcano Plot of Differential Gene Expression. This volcano plot displays the results of the differential gene expression analysis, showing the relationship between the magnitude of gene expression change (log2 fold change) and its statistical significance (-log10(p-value)). Each point represents a gene; the genes that are not significantly regulated are shown in gray colour, while genes that are significantly regulated are shown in red colour. The horizontal dashed line indicates the significance threshold (typically -log10(p-value) = 1.3, corresponding to p = 0.05), the genes above this line are considered statistically significant. The x-axis represents the log2 fold change, where values greater than zero indicate upregulation and values less than zero indicate downregulation. The further a point is from the centre along the x-axis, the greater the magnitude of the change in gene expression. This plot helps to visualize which genes have both a large magnitude of change and high statistical significance in expression.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/d3a0a5848928ec2969b25b3c.png"},{"id":84776496,"identity":"064ec7f8-4d37-48fc-9237-8f18d00b1106","added_by":"auto","created_at":"2025-06-17 09:03:34","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":104088,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap of Top 50 most variable genes across samples. This heatmap visualizes the expression levels of the 50 most variable genes across different samples. The rows represent the genes, and each column represents a sample. The colour gradient, indicated by the legend, represents the gene expression level, with red indicating higher expression and blue indicating lower expression. The samples are grouped into two categories as shown by the colour bar at the top. The dendrogram on the left shows the hierarchical clustering of the genes based on their expression patterns, revealing groups of co-expressed genes. The sample names are displayed at the bottom of the heatmap, allowing for easy identification of individual samples. This visualization helps to identify gene expression patterns and differences between the two sample groups.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/5990cf6b2694d2462ed06f83.png"},{"id":84776499,"identity":"062a5c22-b90f-4fda-9723-73cb5e3fe896","added_by":"auto","created_at":"2025-06-17 09:03:34","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":79475,"visible":true,"origin":"","legend":"\u003cp\u003eDistribution of Log2 Fold Change (log2FC). This plot displays the distribution of log2 fold change (log2FC) values observed in the differential gene expression analysis. The x-axis represents the log2FC, indicating the magnitude and direction of gene expression change between the control and the PCOS conditions. The log2FC value 0 indicates no change, positive values indicate upregulation, and negative values indicate downregulation. The y-axis represents the frequency, showing the number of genes falling within each log2FC bin. The histogram bars represent the actual frequency counts for each bin, while the overlaid curve represents a kernel density estimation, providing a smoothed representation of the data distribution. The plot highlights the overall spread and central tendency of log2FC values, revealing the extent of gene expression changes and potential biases or trends in the data.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/c4be151ef60738139c97ad28.png"},{"id":84776497,"identity":"562b3aee-21b8-4c19-93c0-a8fc86c3d039","added_by":"auto","created_at":"2025-06-17 09:03:34","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":53624,"visible":true,"origin":"","legend":"\u003cp\u003eThe GSEA plot for ribonucleoprotein complex biogenesis in the context of Polycystic Ovary Syndrome (PCOS). Top Panel (Green Line): Shows the running enrichment score (ES) as the analysis walks down the ranked list of genes, ordered by their correlation with the PCOS phenotype. The ES reflects the degree to which genes in the ribonucleoprotein complex biogenesis gene set are overrepresented at the top or bottom of the ranked list. A positive ES indicates enrichment at the top of the list (genes in the set are positively correlated with PCOS), while a negative ES indicates enrichment at the bottom (genes in the set are negatively correlated with PCOS). Middle Panel (Black Tick Marks): Represents the position of the genes in the ribonucleoprotein complex biogenesis gene set within the ranked list of genes. The clustering of ticks towards the left or right indicates enrichment at the top or bottom of the list, respectively. The colour gradient below the ticks indicates the correlation of the genes with the PCOS phenotype, with red representing positive correlation and blue representing negative correlation\u003cstrong\u003e. \u003c/strong\u003eBottom Panel (Gray Bars): Shows the ranked list metric, which is typically the correlation score of each gene with the PCOS phenotype. This provides context for the gene ranking used in the GSEA, highlighting genes that are strongly associated with PCOS. The plot collectively illustrates the enrichment of the ribonucleoprotein complex biogenesis gene set within the ranked list of genes associated with PCOS, suggesting a potential biological relevance of this gene set to the development or progression of PCOS.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/822f6544a4e4fcced903042d.png"},{"id":84780729,"identity":"1ea0238e-be11-4794-ae1e-5c041a40028a","added_by":"auto","created_at":"2025-06-17 09:27:35","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1267254,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/fe43f36f-c1f7-4afb-b295-9625a1993361.pdf"},{"id":84776494,"identity":"cf820a26-4312-4558-925f-e463d292b3c9","added_by":"auto","created_at":"2025-06-17 09:03:34","extension":"txt","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2296,"visible":true,"origin":"","legend":"","description":"","filename":"DEGGeneList.txt","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/5e00dc06fedc6c3d773b9274.txt"},{"id":84776501,"identity":"89140b10-4e46-4820-85c7-e487a6756f49","added_by":"auto","created_at":"2025-06-17 09:03:34","extension":"txt","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2071,"visible":true,"origin":"","legend":"","description":"","filename":"Pythoncodes.txt","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/f0952d30f90f4ec0cd872a2d.txt"},{"id":84776520,"identity":"c4a5e893-516d-4be1-ab57-a00cb3be48bf","added_by":"auto","created_at":"2025-06-17 09:03:34","extension":"gz","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":11721192,"visible":true,"origin":"","legend":"","description":"","filename":"GSE277906FPKManno.txt.gz","url":"https://assets-eu.researchsquare.com/files/rs-6487439/v1/935015acfb9d22a8913b4855.gz"}],"financialInterests":"No competing interests reported.","formattedTitle":"Transcriptomic Profiling of Cumulus Cells Reveals Dysregulated Genes and Pathways in PCOS-Related Infertility","fulltext":[{"header":"BACKGROUND","content":"\u003cp\u003ePolycystic ovarian syndrome (PCOS) is one of the most prevalent endocrine disorders affecting reproductive-aged women, with an estimated global prevalence of 6\u0026ndash;20% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. It is a multifactorial disorder characterized by anovulation, hyperandrogenism, and polycystic ovarian morphology, frequently accompanied by metabolic conditions such as insulin resistance and type 2 diabetes mellitus [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The complexity of PCOS extends beyond reproductive dysfunction, affecting systemic metabolism, inflammation, and cardiovascular health, making its management particularly challenging given the fact that it has a strong genetic complexity [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCumulus cells, which surround the oocyte, are essential for folliculogenesis, oocyte maturation, and fertilization [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. They play a vital role in metabolic support and the regulation of local endocrine signaling, which in turn affects oocyte quality and overall reproductive success [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, while there are reviews and other non-empirical data establishing cumulus cells as critical components of ovarian physiology, their molecular alterations in relation to progression of PCOS remain poorly characterized. Hence, understanding how the disruptions in pattern of gene expression within cumulus cells may impair follicular development and poor oocyte competence, affecting fertility outcomes in women with PCOS. Meanwhile, previous studies have suggested a strong association between PCOS and dysregulation of insulin signaling, oxidative stress, and chronic low-grade inflammation, all of which may contribute to abnormal gene expression in cumulus cells [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, the specific transcriptional changes and their functional implications remain unexplored.\u003c/p\u003e \u003cp\u003eTherefore, this study was conducted to fill this knowledge gap by comparing gene expression profiles in cumulus cells from PCOS and non-PCOS women. By identifying differentially expressed genes (DEGs) and enriched biological pathways, this study to elucidate the molecular mechanisms underlying PCOS pathophysiology. This was undertaken because understanding these transcriptional changes could pave the way for novel biomarkers and therapeutic strategies aimed at improving ovarian function and fertility outcomes in PCOS patients. This study investigates differences in mRNA expression in cumulus cells between women with polycystic ovary syndrome (PCOS) and non-PCOS women.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSource data and sequencing overview\u003c/h2\u003e \u003cp\u003eRNA sequencing data of cumulus cells collected from PCOS and non-PCOS women was obtained from the NCBI Gene Expression Omnibus (GEO) database (accession number: GSE277906). The dataset was generated using the Illumina NovaSeq 6000 platform and includes RNA samples pooled from 23 PCOS and 17 non-PCOS women attending a fertility clinic in China [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These data were studied to elucidate metabolic alterations in follicular environments that influence reproductive function in PCOS.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eRNA-Seq data processing\u003c/h3\u003e\n\u003cp\u003eThe Fragments Per Kilobase of transcript per Million mapped reads (FPKM) RNA-Seq data was processed using RStudio (version 2024.12.1\u0026thinsp;+\u0026thinsp;563 for Windows). The steps included data import whereby the FPKM data file was loaded using the readxl package. Data structuring was also conducted whereby the gene identifiers were set as row names, and sample identifiers were used as column names. The quality control followed handling of missing values using the na.rm\u0026thinsp;=\u0026thinsp;TRUE argument, and overall distribution was assessed for downstream analysis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eDifferential genes expression analysis and their statistics\u003c/h3\u003e\n\u003cp\u003eDifferentially expressed genes (DEGs) were identified by first programmatically categorizing the samples into \"Control\" and \"PCOS\" groups. The mean expression of each gene across the 28,001 genes was calculated for both groups. Fold change (FC) and log2 fold change (log2FC) values were computed using the dplyr package in R software to determine the magnitude of expression differences [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. A Student\u0026rsquo;s t-test was then applied on a per-gene basis to compare expression levels between the PCOS and control groups, generating p-values for statistical significance. The genes were classified as up regulated (FC\u0026thinsp;\u0026ge;\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) or downregulated (FC\u0026thinsp;\u0026lt;\u0026thinsp;1, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). To account for multiple testing and reduce false positives, a false discovery rate (FDR) correction was applied using the Benjamini-Hochberg method. The classification of genes into significant and non-significant categories, along with the associated parameters, was utilized as a data processing strategy to enhance our understanding of the functional roles of these genes within each regulatory category [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eBasic descriptive statistics were calculated for the 28,001 genes to provide an overall summary of gene expression patterns. These included measures such as the mean, median, and standard error of the mean (SEM) to assess central tendency and variability. Additionally, the minimum, third quartile (Q3), and maximum values were determined to capture the range and distribution of expression levels across samples. All these computations were performed using built-in R functions, including mean(), median(), quantile(), and max(), ensuring that missing values were managed appropriately by setting the na.rm argument to TRUE [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This statistical analysis provided a foundation for further interpretation of gene expression differences between PCOS and control samples.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eVisualization of differentially and variedly expressed genes\u003c/h3\u003e\n\u003cp\u003eTo enhance the interpretability of the gene expression patterns, several visualization techniques were employed. Boxplots were generated using ggplot2 in R and Seaborn in Python to display the expression levels of the top 10 differentially expressed genes, providing a clear comparison between the PCOS and control groups [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Additionally, a heatmap was created using the ComplexHeatmap package in R to visualize the expression patterns of the 50 most variable genes, highlighting clustering patterns that distinguish the two groups [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. To examine the overall distribution of log2 fold change (log2FC) values, a histogram was constructed with ggplot2, offering insights into the skewness and spread of differential expression. Lastly, a volcano plot was generated to illustrate the relationship between the magnitude of gene expression changes and their statistical significance, allowing for the identification of key up regulated and down regulated genes. These visualization techniques were conducted to collectively provide a comprehensive overview of the gene expression dynamics in the PCOS group.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eGene Set Enrichment Analysis (GSEA)\u003c/h2\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) was conducted to identify biological processes associated with the differentially expressed genes (DEGs) in PCOS using the clusterProfiler package [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. To perform the analysis, genes were first sorted based on their log2 fold change (log2FC) values, prioritizing those with the most significant expression differences. The gseGO function was then applied to determine enriched Gene Ontology (GO) biological processes, allowing for a deeper understanding of functional pathways influenced by dysregulated genes. Additionally, pathway enrichment analysis was performed using g:Profiler [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], using the top 10 significantly differentially up regulated genes including CDH5, CLEC4D, GNAT1, GPR25, KRTAP7-1, LINC00398, LINC02269, LOC101928295, RGS7BP and RUNX3-AS1 in the PCOS group for ensuring that the most relevant pathways were highlighted. The complete list of Differentially Expressed Genes (DEGs) was saved including list of the significantly up regulated genes in a plain text file (DEG_Gene_List.txt) for further analysis. The scripts used for preprocessing, statistical analysis, and visualization were written in R and Python and are available as supplementary materials.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDifferential Expressed Genes (DEGs)\u003c/h2\u003e \u003cp\u003eA total of 28,001 genes were analysed to identify differences in gene expression between PCOS and control samples. Out of these, 3,245 genes were found to be significantly differentially expressed based on an adjusted p-value threshold of 0.05 (FDR-adjusted). Among these differentially expressed genes, 1,723 were up regulated in PCOS samples, while 1,522 genes were down regulated. The distribution of gene expression across all samples showed an average expression value of 3.21 FPKM, a median of 2.45 FPKM, and a standard error of the mean (SEM) of 0.89 FPKM (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). These findings highlight significant transcriptional alterations in cumulus cells, suggesting potential molecular pathways that may be dysregulated in PCOS.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary of Differential Gene Expression Analysis in PCOS and Control Samples\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eParameters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eValue\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal number of genes analysed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28,001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificantly differentially expressed genes (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3,245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificantly upregulated genes in PCOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,723\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSignificantly downregulated genes in PCOS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1,522\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage expression value (FPKM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedian expression value (FPKM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard error of the mean (SEM)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis table summarizes the key findings from the differential gene expression analysis comparing PCOS and control samples. It highlights total number of genes, significantly differentially expressed genes, and their regulation patterns. Expression values are reported in fragments per kilobase of transcript per million mapped reads (FPKM). False discovery rate (FDR) correction was applied to determine statistical significance. PCOS: Polycystic Ovary Syndrome; FDR: False Discovery Rate; FPKM: Fragments Per Kilobase of transcript per Million mapped reads.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eVisualization of the gene expression patterns\u003c/h2\u003e \u003cp\u003eTo further explore the differences in gene expression between PCOS and control samples, several visualization techniques were employed. Boxplots were generated to examine the expression levels of the top 10 differentially expressed genes, confirming distinct alterations in gene regulation between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Apart from the top differentially expressed gene, the volcano plot showed overall pattern of the gene expressions (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The heatmap provided a comprehensive view of clustering patterns among highly variable genes (n\u0026thinsp;=\u0026thinsp;50), demonstrating distinct gene expression profiles in the PCOS samples compared to the controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Additionally, histogram of log2 fold change (log2FC) values revealed a skewed distribution, indicating that a subset of genes exhibited substantial expression changes (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These visualizations showed relationships between the magnitude of gene expression changes and their statistical significance providing insights into the molecular alterations associated with PCOS phenotype.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eGene set enrichment and functional pathway analysis\u003c/h2\u003e \u003cp\u003eGene Set Enrichment Analysis (GSEA) was performed to identify biological processes and molecular pathways enriched among the differentially expressed genes. The analysis revealed several key pathways significantly associated with PCOS. Notably, follicular development was enriched, highlighting disruptions in ovarian function that may impact oocyte quality. Additionally, the insulin signaling pathway was significantly enriched, supporting previous evidence linking PCOS to insulin resistance and metabolic dysfunction. Another important pathway, ribonucleoprotein complex biogenesis, was identified, indicating alterations in hormone production that may contribute to the pathophysiology of PCOS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Further pathway analysis using g:Profiler confirmed enrichment in metabolic and reproductive pathways, reinforcing the role of dysregulated gene expression in the molecular mechanisms underlying PCOS (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These findings suggest potential targets for therapeutic intervention and provide a deeper understanding of the biological processes involved in the disorder.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFunctional Enrichment Analysis of Differentially Expressed Genes in PCOS\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTerm name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTerm id\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eintersections\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:MF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eacyl binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0000035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:MF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efibrinogen binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0070051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:MF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evascular endothelial growth factor receptor 2 binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0043184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:MF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBMP receptor binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0070700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:MF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003evascular endothelial growth factor receptor binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0005172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epositive regulation of myeloid dendritic cell activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0030887\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCLEC4D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eneural tissue regeneration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0097719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003enegative regulation of cyclic-nucleotide phosphodiesterase activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0051344\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eregulation of myeloid dendritic cell activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0030885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCLEC4D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eprotein localization to bicellular tight junction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:1902396\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eextrinsic component of plasma membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0019897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5,GNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eside of membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0098552\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5,CLEC4D,GNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edendritic spine head\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0044327\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRGS7BP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:CC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eexternal side of plasma membrane\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0009897\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5,CLEC4D\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePhototransduction\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEGG:04744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREAC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eActivation of the phototransduction cascade\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eREAC:R-HSA-2485179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePurinergic signaling\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWP:WP4900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor: KLF8; motif: NGGGGTGYGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTF:M08818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5,GNAT1,\u003c/p\u003e \u003cp\u003eGPR25,RGS7BP\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactor: NR4A2; motif: AGGTCANNNNNTGACCT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTF:M04478\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCORUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAR-6-VE-cadherin complex, endothelial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCORUM:829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCORUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVEcad-VEGFR complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCORUM:6586\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCORUM\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eZO1-(beta)cadherin-(VE)cadherin-VEGFR2 complex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCORUM:5772\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCDH5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eElectronegative electroretinogram\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0007984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCongenital stationary night blindness with abnormal fundus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0030639\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGNAT1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:MF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003epeptide hormone binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0017046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:MF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ehormone binding\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0042562\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:MF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efollicle-stimulating hormone receptor activity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0004963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale gonad development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0008585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edevelopment of primary female sexual characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0046545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003efemale sex differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0046660\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edevelopmental growth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0048589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,LEPR,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003edevelopment of primary sexual characteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0045137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003euterus development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0060065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003esex differentiation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0007548\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereproductive structure development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0048608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereproductive system development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0061458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eresponse to endogenous stimulus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0009719\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,LEPR,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ereproductive process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0022414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,LEPR,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eovulation cycle process\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0022602\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eestrogen receptor signaling pathway\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0030520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eovarian follicle development\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0001541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGO:BP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eovulation cycle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGO:0042698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eKEGG\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvarian steroidogenesis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKEGG:04913\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDelayed puberty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0000823\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,LEPR,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormality of the ovary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0000137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,LEPR,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal serum estradiol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0025133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,LEPR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbnormal circulating estrogen level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0025132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,LEPR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIncreased serum testosterone level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0030088\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eINSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOvarian cyst\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0000138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,INSR,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnlarged polycystic ovaries\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHP:0008675\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eESR1,FSHR\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThis table presents the functional enrichment analysis results the 10 set of differentially expressed genes (DEGs) identified in Polycystic Ovary Syndrome (PCOS). The table lists the enriched terms, their corresponding identifiers, and the genes that contribute to the enrichment. The terms are categorized by their ontology or database, including Gene Ontology Molecular Function (GO:MF), Gene Ontology Biological Process (GO:BP), Gene Ontology Cellular Component (GO:CC), Kyoto Encyclopedia of Genes and Genomes (KEGG), Reactome (REAC), WikiPathways (WP), Transcription Factor (TF), Comprehensive Resource of Mammalian protein complexes (CORUM), and Human Phenotype Ontology (HP). The \"intersections\" column indicates the specific genes from the input list that are associated with each term. This analysis aims to provide biological context and potential functional implications of the observed gene expression changes in PCOS.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eThis study identified significant transcriptional changes in cumulus cells of women with PCOS, revealing differentially expressed genes (DEGs) up regulated in the PCOS women compared to non-PCOS controls, where they were down regulated. The visualization highlighted key clusters of differentially expressed genes, reflecting distinct gene expression profiles in PCOS. Notably, CDH5, CLEC4D, and GNAT1 emerged as significant DEGs, with their involvement in processes such as ovarian follicle development, immune regulation, and hormone receptor signaling. The Gene Set Enrichment Analysis (GSEA) further revealed enrichment in biological pathways, including follicular development, insulin signaling, and ribonucleoprotein complex biogenesis. These findings suggest that the altered molecular environment in cumulus cells could impact oocyte competence, influencing fertility outcomes in PCOS patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, the interplay between these differentially expressed genes and metabolic pathways signifies the multifactorial nature of PCOS. The dysregulation of key genes involved in metabolic homeostasis and cellular signaling may contribute to the compromised microenvironment surrounding the oocyte and consequentially could cause infertility in PCOS patients [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Notably, the aberrant expression of genes linked to oxidative stress and inflammatory responses may further exacerbate follicular dysfunction [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This highlights the potential role of targeted therapeutic strategies aimed at modulating these molecular pathways to improve reproductive outcomes in PCOS-affected individuals.\u003c/p\u003e \u003cp\u003eThese findings align with the longstanding relationship between metabolic and hormonal dysregulation and the pathogenesis of PCOS and can be linked to the enrichment of genes involved in insulin signaling, which supports the well-established association between PCOS and insulin resistance. This was reported several other prior studies that highlighted insulin receptor (INSR) dysregulation in PCOS granulosa and cumulus cells, contributing to aberrant follicular development and hyperandrogenism [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Similarly, CDH5, which was significantly differentially expressed in this present study, has been implicated in vascular endothelial growth factor (VEGF) signaling, a pathway known to regulate follicular angiogenesis and ovarian function [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe overexpression of CLEC4D, involved in immune signaling, suggests a potential inflammatory component in PCOS, consistent with existing literature indicating chronic low-grade inflammation as a characteristic feature of the syndrome [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Interestingly, GNAT1, a gene associated with phototransduction, was found to be significantly dysregulated in this study. While GNAT1 has not been previously linked to PCOS, emerging evidence suggests that the gene is associated with diabetes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which is a known comorbidity with PCOS. This could indicate a role for G-protein signaling pathways in ovarian follicle maturation, warranting further investigation. While many findings in this study are consistent with established literature, the differential regulation of specific genes such as GNAT1 introduces novel insights that merit further validation.\u003c/p\u003e \u003cp\u003eThe altered gene expression in cumulus cells suggests disruptions in key biological processes critical for oocyte competence and follicular health [\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. However, the enrichment of ribonucleoprotein (RNP) complex biogenesis pathways indicates an alteration in RNA processing and protein translation in cumulus cells, which may contribute to defective oocyte maturation. The RNP has been reported to be crucial for vital cellular functions like transcription, translation, and gene regulation, impacting development, cell function, and disease development, therefore its understanding in relation to PCOS could be critical [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. The identification of differentially expressed genes involved in follicle-stimulating hormone receptor (FSHR) activity suggests potential dysregulation of gonadotropin signaling, a key factor in follicular development and ovulation [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Additionally, the enrichment of genes involved in VEGF receptor binding and endothelial cell adhesion suggests that vascular dysfunction may contribute to abnormal folliculogenesis in PCOS. Clinically, these findings could have significant implications in the use of the identified DEGs as potential biomarkers for early PCOS diagnosis or therapeutic targets for improving ovarian function in affected women. For example, modulating VEGF signaling could enhance follicular angiogenesis, potentially improving oocyte quality and fertility outcomes in PCOS patients.\u003c/p\u003e \u003cp\u003eAlthough, this study leveraged high-throughput RNA sequencing to provide an in-depth transcriptomic profile of cumulus cells in PCOS, offering novel insights into molecular mechanisms underlying the disorder. Also, the use of stringent statistical analyses including FDR correction enhances the reliability of the findings. Additionally, the study incorporated multiple visualization techniques to improve the interpretability of gene expression patterns. However, the limitations such as the derivation of the dataset from a publicly available repository may introduce batch effects and variability in sample collection or processing methods. Additionally, while gene expression data suggest potential regulatory mechanisms that are reported through the functional enrichment analysis, the validation of these pathways through in vitro or in vivo models is necessary to establish causality of the identified pathways and genes regulations. Further the samples were obtained in a Chinese population, future studies with larger and ethnically diverse cohorts may provide a more comprehensive understanding of the transcriptomic landscape in PCOS.\u003c/p\u003e \u003cp\u003eThere is also a need for future research could focus on validation of the top identified DEGs and pathways through functional studies. Experimental approaches such as CRISPR-based gene editing or siRNA knockdown studies in cumulus cell cultures could help determine the mechanistic role of key genes such as CDH5 and GNAT1 in follicular development. Additionally, integrating proteomics and metabolomics with transcriptomics findings could provide a more holistic understanding of how transcriptomic changes translate into functional protein and metabolic alterations in PCOS. Furthermore, expanding similar study to include a broader patient cohort with varying PCOS phenotypes such as hyperandrogenic vs. normoandrogenic PCOS group may also help elucidate phenotype-specific molecular mechanisms. Lastly, exploring therapeutic interventions targeting dysregulated pathways, such as modulating VEGF or insulin signaling, could pave the way for novel treatment strategies aimed at improving reproductive outcomes in PCOS patients.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study provides novel insights into the molecular mechanisms underlying PCOS by identifying key transcriptional changes in cumulus cells. The differential expression of 3,245 genes, including CDH5, CLEC4D, and GNAT1, highlights significant alterations in pathways associated with follicular development, insulin signaling, and immune regulation. The findings suggest that these dysregulated pathways may impair oocyte competence, contributing to the infertility challenges faced by PCOS patients. The identification of GNAT1, previously linked to metabolic disorders, as a novel PCOS-associated gene further underscores the intersection between reproductive and metabolic dysfunction in this condition. Beyond advancing the understanding of PCOS pathophysiology, these results have potential clinical implications for both diagnosis and treatment. The identified DEGs could serve as biomarkers for early detection and disease monitoring, while dysregulated pathways such as VEGF and insulin signaling offer possible therapeutic targets. Future studies are warranted to concentrate on confirming these results within clinical environments, examining specific interventions like gene therapy, small-molecule inhibitors, or hormonal modulation, and investigating personalized treatment approaches that are informed by the molecular profiling of cumulus cells.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePolycystic Ovary Syndrome\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePCOS\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003cbr\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003cbr\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003cbr\u003e\u003cstrong\u003eCompeting interests\u003cbr\u003e \u003c/strong\u003eAuthors declare no competing interests\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere is no funding support for this work\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eABS:\u003c/strong\u003e Conceptualization, Data Processing, Computational and Statistical Analysis, Writing of Original Draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNMI:\u003c/strong\u003e Formal Analysis, Validation, Writing of Original Draft, Writing, Review and Editing.\u003cbr\u003e\u003cstrong\u003eKSA:\u003c/strong\u003e Formal Analysis, Validation, Methodology.\u003cbr\u003e\u003cstrong\u003eMAA:\u003c/strong\u003e Writing, Review, Editing, and Validation.\u003cbr\u003e\u003cstrong\u003eSSAE:\u003c/strong\u003e Supervision, Validation, Writing, Review and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eKA:\u003c/strong\u003e Writing, Review, and Editing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot Applicable\u003cbr\u003e \u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAffiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eMolecular, Cellular and Integrative Physiology Laboratory (MCIP), Department of Animal Science, Federal University of Agriculture Zuru, 872101, Kebbi State, Nigeria. ORCID: 0000-0003-4956-7094. Email: [email protected] \u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eLaboratory of Vaccines and Biomolecules (VacBio), Institute of Biosciences, Universiti Putra Malaysia, Serdang, 43400 Seri Kembangan, Selangor, Malaysia.\u003c/p\u003e\n\u003cp\u003eORCID: 0000-0002-4726-7728. Email: [email protected] \u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Physiology, Biochemistry, and Pharmacology; College of Veterinary Medicine; University of Kirkuk; Kirkuk 36001, Iraq. ORCID: 0000-0002-5796-7170. Email: [email protected] \u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003eDepartment of Obstetrics and Gynaecology, College of Clinical Sciences, Faculty of Health Sciences, Ladoke Akintola University of Technology, Ogbomoso, 210101, Nigeria. ORCID: 0000-0002-1985-3789. Email: [email protected] \u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eDepartment of Animal Production, School of Food and Agriculture Technology, Federal University of Technology Minna, Nigeria. ORCID: 0000-0002-1673-5103. Email: [email protected] \u003c/p\u003e\n\u003cp\u003e\u003csup\u003e6\u003c/sup\u003eDepartment of Public Health, Fountain University Osogbo, Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding Author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAkeem Babatunde Sikiru\u003cbr\u003e [email protected] \u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eB. Jiang, The Global Burden of Polycystic Ovary Syndrome in Women of Reproductive Age: Findings from the GBD 2019 Study, Int J Womens Health (2025) 153\u0026ndash;165.\u003c/li\u003e\n \u003cli\u003eA.B. Sikiru, M.A. Adeniran, K. Akinola, H. Behera, G. Kalaignazhal, S.S.A. 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Funahashi, Oocyte-cumulus cells crosstalk: New comparative insights, Theriogenology 205 (2023) 87\u0026ndash;93.\u003c/li\u003e\n \u003cli\u003eM.G. Da Broi, V.S.I. Giorgi, F. Wang, D.L. Keefe, D. Albertini, P.A. Navarro, Influence of follicular fluid and cumulus cells on oocyte quality: clinical implications, J Assist Reprod Genet 35 (2018) 735\u0026ndash;751.\u003c/li\u003e\n \u003cli\u003eY. Chen, M. Xie, S. Wu, Z. Deng, Y. Tang, Y. Guan, Y. Ye, Q. He, L. Li, Multi-omics approach to reveal follicular metabolic changes and their effects on oocyte competence in PCOS patients., Front Endocrinol (Lausanne) 15 (2024) 1426517. https://doi.org/10.3389/fendo.2024.1426517.\u003c/li\u003e\n \u003cli\u003eH. Wickham, J. Bryan, M. Kalicinski, K. Valery, C. Leitienne, B. Colbert, D. Hoerl, E. Miller, M.J. Bryan, Package \u0026lsquo;readxl,\u0026rsquo; Version 13 (2019) 1.\u003c/li\u003e\n \u003cli\u003eW. Yarberry, W. Yarberry, Dplyr, CRAN Recipes: DPLYR, Stringr, Lubridate, and Regex in R (2021) 1\u0026ndash;58.\u003c/li\u003e\n \u003cli\u003eF. Finotello, B. Di Camillo, Measuring differential gene expression with RNA-seq: challenges and strategies for data analysis, Brief Funct Genomics 14 (2015) 130\u0026ndash;142.\u003c/li\u003e\n \u003cli\u003eD. Luo, X. Wan, J. Liu, T. Tong, Optimally estimating the sample mean from the sample size, median, mid-range, and/or mid-quartile range, Stat Methods Med Res 27 (2018) 1785\u0026ndash;1805.\u003c/li\u003e\n \u003cli\u003eH. Wickham, ggplot2, Wiley Interdiscip Rev Comput Stat 3 (2011) 180\u0026ndash;185.\u003c/li\u003e\n \u003cli\u003eM.K. Ranjan, K. Barot, V. Khairnar, V. Rawal, A. Pimpalgaonkar, S. Saxena, A.M. Sattar, Python: Empowering Data Science Applications and Research, Journal of Operating Systems Development \u0026amp; Trends 10 (2023) 27\u0026ndash;33.\u003c/li\u003e\n \u003cli\u003eZ. Gu, R. Eils, M. Schlesner, Complex heatmaps reveal patterns and correlations in multidimensional genomic data, Bioinformatics 32 (2016) 2847\u0026ndash;2849.\u003c/li\u003e\n \u003cli\u003eS.E. Innis, K. Reinaltt, M. Civelek, W.D. Anderson, GSEAplot: a package for customizing gene set enrichment analysis in R, Journal of Computational Biology 28 (2021) 629\u0026ndash;631.\u003c/li\u003e\n \u003cli\u003eL. Kolberg, U. Raudvere, I. Kuzmin, P. Adler, J. Vilo, H. Peterson, g:Profiler\u0026mdash;interoperable web service for functional enrichment analysis and gene identifier mapping (2023 update), Nucleic Acids Res 51 (2023) W207\u0026ndash;W212. https://doi.org/10.1093/nar/gkad347.\u003c/li\u003e\n \u003cli\u003eM. Anazawa, S. Ashibe, Y. Nagao, Gene expression levels in cumulus cells are correlated with developmental competence of bovine oocytes, Theriogenology 231 (2025) 11\u0026ndash;20.\u003c/li\u003e\n \u003cli\u003eL.A. Owens, S.G. Kristensen, A. Lerner, G. Christopoulos, S. Lavery, A.C. Hanyaloglu, K. Hardy, C. Yding Andersen, S. Franks, Gene expression in granulosa cells from small antral follicles from women with or without polycystic ovaries, J Clin Endocrinol Metab 104 (2019) 6182\u0026ndash;6192.\u003c/li\u003e\n \u003cli\u003eM. Khatun, K. Lundin, F. Naillat, L. Loog, U. Saarela, T. Tuuri, A. Salumets, T.T. Piltonen, J.S. Tapanainen, Induced pluripotent stem cells as a possible approach for exploring the pathophysiology of Polycystic Ovary Syndrome (PCOS), Stem Cell Rev Rep 20 (2024) 67\u0026ndash;87.\u003c/li\u003e\n \u003cli\u003eA. Ranjbaran, H.R. Nejabati, T. Ghasemnejad, Z. Latifi, K. Hamdi, H. Hajipour, N. Raffel, Z. Bahrami-Asl, P. Hakimi, A. Mihanfar, Follicular fluid levels of adrenomedullin 2, vascular endothelial growth factor and its soluble receptors are associated with ovarian response during ART cycles, Geburtshilfe Frauenheilkd 79 (2019) 86\u0026ndash;93.\u003c/li\u003e\n \u003cli\u003eQ. Xie, W. Hong, Y. Li, S. Ling, Z. Zhou, Y. Dai, W. Wu, R. Weng, Z. Zhong, J. Tan, Chitosan oligosaccharide improves ovarian granulosa cells inflammation and oxidative stress in patients with polycystic ovary syndrome, Front Immunol 14 (2023) 1086232.\u003c/li\u003e\n \u003cli\u003eH. Liu, J. Tang, Y. Du, A. Saadane, I. Samuels, A. Veenstra, J.Z. Kiser, K. Palczewski, T.S. Kern, Transducin1, phototransduction and the development of early diabetic retinopathy, Invest Ophthalmol Vis Sci 60 (2019) 1538\u0026ndash;1546.\u003c/li\u003e\n \u003cli\u003eM.G. Da Broi, V.S.I. Giorgi, F. Wang, D.L. Keefe, D. Albertini, P.A. Navarro, Influence of follicular fluid and cumulus cells on oocyte quality: clinical implications, J Assist Reprod Genet 35 (2018) 735\u0026ndash;751.\u003c/li\u003e\n \u003cli\u003eN. Sayutti, M.A. Abu, M.F. Ahmad, PCOS and role of cumulus gene expression in assessing oocytes quality, Front Endocrinol (Lausanne) 13 (2022) 843867.\u003c/li\u003e\n \u003cli\u003eZ. Huang, D. Wells, The human oocyte and cumulus cells relationship: new insights from the cumulus cell transcriptome, Mol Hum Reprod 16 (2010) 715\u0026ndash;725.\u003c/li\u003e\n \u003cli\u003eM.K. Mihailovic, A. Chen, J.C. Gonzalez-Rivera, L.M. Contreras, Defective ribonucleoproteins, mistakes in RNA processing, and diseases, Biochemistry 56 (2017) 1367\u0026ndash;1382.\u003c/li\u003e\n \u003cli\u003eV.P. Chakravarthi, A. Ratri, S. Masumi, S. Borosha, S. Ghosh, L.K. Christenson, K.F. Roby, M.W. Wolfe, M.A.K. Rumi, Granulosa cell genes that regulate ovarian follicle development beyond the antral stage: The role of estrogen receptor \u0026beta;, Mol Cell Endocrinol 528 (2021) 111212.\u003c/li\u003e\n \u003cli\u003eN. Roy, E. Mascolo, C. Lazzaretti, E. Paradiso, S. D\u0026rsquo;Alessandro, K. Zaręba, M. Simoni, L. Casarini, Endocrine disruption of the follicle-stimulating hormone receptor signaling during the human antral follicle growth, Front Endocrinol (Lausanne) 12 (2021) 791763.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"middle-east-fertility-society-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mefj","sideBox":"Learn more about [High Temperature Corrosion of Materials](https://www.springer.com/journal/43043)","snPcode":"43043","submissionUrl":"https://submission.nature.com/new-submission/43043/3","title":"Middle East Fertility Society Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Polycystic ovarian syndrome (PCOS), Cumulus cells, Guanine nucleotide-binding protein G(t) subunit alpha-1 (GNAT1), Oocyte competence, infertility, metabolic dysfunction","lastPublishedDoi":"10.21203/rs.3.rs-6487439/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6487439/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003ePolycystic ovarian syndrome (PCOS) is a leading cause of infertility and metabolic dysfunction in women, characterized by hyperandrogenism, anovulation, and insulin resistance. Cumulus cells play a crucial role in folliculogenesis and oocyte maturation, necessitating a deeper understanding of their molecular alterations impact in PCOS.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethod: \u003c/strong\u003eThis study investigates transcriptomic differences in cumulus cells between PCOS and non-PCOS women using high-throughput RNA sequencing data obtained from the NCBI Gene Expression Omnibus (GEO) database (Accession Number: GSE277906). The RNA sequencing data from 23 PCOS and 17 non-PCOS women were analysed to identify differentially expressed genes (DEGs) using R-based computational pipelines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eDifferential gene expression analysis identified 3,245 significantly dysregulated genes, comprising 1,723 up regulated and 1,522 downregulated genes in PCOS samples. Functional enrichment analysis revealed that key DEGs (CDH5, CLEC4D, and GNAT1) were associated with follicular development, insulin signaling, and immune response. Gene Set Enrichment Analysis (GSEA) further identified dysregulation in metabolic and reproductive pathways, including ribonucleoprotein complex biogenesis and vascular endothelial growth factor (VEGF) signaling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Findings from this study suggest that altered gene expression in cumulus cells may impair oocyte competence, potentially influencing fertility outcomes in PCOS patients. Additionally, the study identifies GNAT1, previously linked to diabetes, as a novel candidate in PCOS pathophysiology. Future studies should validate these findings through functional experiments and explore targeted therapeutic interventions to mitigate the reproductive consequences of PCOS.\u003c/p\u003e","manuscriptTitle":"Transcriptomic Profiling of Cumulus Cells Reveals Dysregulated Genes and Pathways in PCOS-Related Infertility","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 09:03:29","doi":"10.21203/rs.3.rs-6487439/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-06-24T07:31:54+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T11:06:35+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-20T08:32:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-18T14:36:41+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"305000066983848140662883656580263434776","date":"2025-06-17T05:08:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"179363328318355182717607251229940257756","date":"2025-06-12T08:44:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"98298615987691675766127460666823422690","date":"2025-06-11T18:08:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-06-11T17:15:26+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-05-02T08:13:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-21T14:21:56+00:00","index":"","fulltext":""},{"type":"submitted","content":"Middle East Fertility Society Journal","date":"2025-04-20T04:47:43+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"middle-east-fertility-society-journal","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"mefj","sideBox":"Learn more about [High Temperature Corrosion of Materials](https://www.springer.com/journal/43043)","snPcode":"43043","submissionUrl":"https://submission.nature.com/new-submission/43043/3","title":"Middle East Fertility Society Journal","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Springer Open","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"95f5c8d1-d944-488a-9acc-81e3581e0009","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-07-26T05:53:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 09:03:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6487439","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6487439","identity":"rs-6487439","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}