Constructing an Ovarian Cancer Prognostic Model Based on Shared m6A Modifications and Cellular Senescence-Related Genes with Polycystic Ovary Syndrome 

Constructing an Ovarian Cancer Prognostic Model Based on Shared m6A Modifications and Cellular Senescence-Related Genes with Polycystic Ovary Syndrome | 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 Constructing an Ovarian Cancer Prognostic Model Based on Shared m6A Modifications and Cellular Senescence-Related Genes with Polycystic Ovary Syndrome Yang Liu, Yujie Gengxiao, Yanzhi Wu, Xi Hu, Yingying Ma, Chunyi Sun This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4034917/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Polycystic ovary syndrome (PCOS) and ovarian cancer (OV) are significant women's health concerns. This study aims to identify genes related to m6A modification and cellular senescence that are shared by PCOS and OV and to develop a prognostic model for OV outcomes. Methods: Transcriptomic datasets from GEO and TCGA were collected to identify differentially expressed genes (DEGs) associated with m6A modifications and cellular senescence in both PCOS and OV. We identified hub genes through WGCNA and assessed their prognostic significance. An OV prognostic model was developed and validated using multiple datasets, and correlations between risk scores and tumor immune microenvironment were evaluated. Drug sensitivity was predicted based on risk scores. qRT-PCR was performed to verify model gene alterations in OV cell lines. Results: We identified 73 DEGs that were common to both PCOS and OV, with eight genes associated with m6A modification and cellular senescence. WGCNA revealed 242 DEhub genes. A 19-gene prognostic model was established, exhibiting impressive efficacy with AUCs exceeding 0.7 for predicting OV patient survival. High-risk scores correlated with increased M0 macrophages and monocytes and decreased M1, CD8+ T cells, and T follicular helper cells. Drug sensitivity varied based on risk scores. Model gene alterations were verified in external OV datasets and OV cell lines. In external datasets, CXCR4 emerged as a key target gene of m6A modification. Conclusions: The derived 19-gene prognostic model offers potential avenues for risk assessment and personalized therapy in OV patients. Polycystic Ovary Syndrome Ovarian Neoplasms m6A RNA Modification Cellular Senescence Prognosis Tumor Microenvironment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Introduction Polycystic Ovary Syndrome (PCOS) is an endocrine disorder that affects 5-20% of reproductive-age women worldwide[1]. Characterized by hormonal imbalances, irregular menstrual cycles, and ovarian cysts, PCOS has been intimately associated with a myriad of health issues[2-4]. Of these, one of the most concerning is the potential link between PCOS and ovarian cancer (OV)[5], a lethal gynecological malignancy that accounted for 4.2% of all cancer-related deaths worldwide in 2020[6]. The frequency of both disorders and their potential connection has led to increasing concerns about the progression from PCOS to OV[7, 8]. Unraveling this relationship is crucial, not only for understanding disease etiology but also for the development of timely interventions. Recent studies have demonstrated the significant involvement of N6-methyladenosine (m6A) RNA modification in OV. Elevated expression of the methyltransferase-like-3 (METTL3) protein in OV has been associated with tumor growth and invasion[9], along with other m6A-related proteins such as YTH domain family protein 1 (YTHDF1), YTHDF2, and obesity-associated protein (FTO)[10-12]. Interestingly, these genes have also been found to be upregulated in PCOS[13]. Furthermore, m6A-related osteoglycin has been identified as a risk marker linking PCOS to OV[14]. Abnormal m6A modification has been linked to cellular senescence[15-17], which is a process where cells lose their ability to divide and contribute to age-related diseases, including ovarian cancer. It plays a multifaceted role in OV pathobiology, including the disease-promoting activity of normal peritoneal mesothelial cells, the effect of drugs on the senescence of normal cells, and spontaneous senescence in OV cells[18]. PCOS patients with irregular menstruation have been shown to share mRNA expression profiles similar to those observed in OV, including the alterations in cellular senescence-related genes[8]. However, the roles of the shared genes in OV development and prognosis remain largely unknown. In this study, we aimed to investigate the shared molecular mechanisms between PCOS and OV to uncover a potential link from PCOS to OV. We employed a comprehensive approach, including transcriptomic data analysis, co-expression network analysis, and the development of a prognostic model for OV patients. Our methods involved the integration of multiple datasets, identification of differentially expressed genes (DEGs), and the exploration of gene modules associated with m6A modification and cellular senescence. Additionally, we constructed a prognostic risk signature and validated its effectiveness across different OV cohorts. Our results may identify novel biomarkers and therapeutic avenues for OV, ultimately improving patient care and outcomes. Methods Data retrieval and processing The schematic overview of the study is shown in Fig. S1. Transcriptomic data were obtained from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/), including PCOS datasets GSE34526, GSE137684, GSE80432, GSE114419, and GSE102293, as well as OV datasets GSE18520, GSE27651, and GSE140082 (Table 1). The PCOS datasets were merged for DEG identification, comprising 28 PCOS granulocyte samples and 22 normal granulocyte samples. The GSE18520 OV dataset was used for the identification of OV-related DEGs, consisting of 53 OV cases and 10 normal controls. An external dataset, GSE27651, was combined with GSE18520 to explore model gene expression in OV. GSE18520 and the external dataset GSE140082 were used to validate the efficacy of the prognostic risk model, comprising 380 cancer tissue samples with prognosis data. GEO data processing involved matching probes to gene names, removing empty probes, and calculating the median expression value for genes with multiple corresponding probes. Batch-effect correction of the merged datasets was carried out using the "ComBat" function from the "sva" package. Transcriptomic data of The Cancer Genome Atlas (TCGA) OV dataset were retrieved from the UCSC Xena Browser (https://xenabrowser.net/) to develop a prognostic model for OV patients. FPKM values were converted into TPM using a formula: and were subsequently subjected to a log2(x + 1) transformation for subsequent analysis. m6A target genes in humans were obtained from m6A2Target (http://rm2target.canceromics.org/#/home). A total of 701 m6A target genes were selected as they were validated m6A targets reported in the literature for the human genome (hg38). A total of 866 genes related to cellular senescence were sourced from the CellAge database (https://genomics.senescence.info/cells/). Identification of DEGs Differential gene expression analysis between the disease and control groups was performed using the R package "limma." In the integrated PCOS dataset, DEGs were identified with a significance threshold of P 1 and an adjusted P-value < 0.05. ssGSEA analysis of DEGs in PCOS and OV for m6A and cellular senescence The DEGs that consistently exhibited altered expression in both PCOS and OV were combined. Subsequently, these genes were intersected with m6A target genes and cellular senescence genes, resulting in the identification of m6A_CellAgeRGs. The m6A_CellAgeRG score was then computed using the "GSVA" package to evaluate m6A and cell aging-related metrics. Weighted Gene Co-Expression Network Analysis (WGCNA) A WGCNA network was constructed using the "WGCNA" R package. In this analysis, the m6A_CellAgeRG score was used to identify gene modules associated with this score. For each cohort (PCOS and OV), disease samples from patients were selected. The top 50% of genes with the highest variance were used as input data. Outliers were removed, and the optimal soft threshold was determined based on the scale-free topology criterion. Subsequently, the weighted adjacency matrix and topological overlap matrix transformation were constructed. Modules containing more than 30 genes were identified using the hierarchical clustering tree method, with a merged similarity threshold set at 0.25. Each module was visually distinguished by a unique color. Furthermore, modules underwent additional scrutiny based on their correlation with the m6A_CellAgeRG score. Hub genes within these modules were identified using dual criteria: |Module Membership (MM)| > 0.6 and Gene Significance (GS) > 0.5. Those belonging to OV-related DEGs (DEhub) were extracted for subsequent analysis. Functional enrichment analysis Functional enrichment analysis of the DEhub genes was conducted using the R package "clusterProfiler." Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed. To enhance the robustness of the results, P-values were corrected using the Benjamini-Hochberg method. Enrichment results associated with the corrected P-values were reported. Construction of a prognostic risk signature for OV A prognostic risk signature for OV was established using TCGA OV cohort data. Prognosis-related DEhub genes were identified through univariate Cox analysis based on a significance threshold of P-value < 0.05. Subsequently, TCGA OV patients were randomly divided into training and test sets in a 7:3 ratio. LASSO COX regression analysis was conducted within the training set using the "glmnet" R package. The optimal penalty parameter (λ) was determined, and nineteen genes with non-zero coefficients were selected as significant contributors to the model. To calculate the risk score, weight coefficients from the model were combined with gene expression levels using the following formula: Construction of protein-protein interaction (PPI) network To explore the interactions between model genes and their functionally similar counterparts, a PPI was constructed using GeneMANIA (www.genemania.org) Construction and evaluation of a prognostic nomogram Univariate and multivariate Cox analyses were conducted to assess the prognostic value of the risk score and clinical variables, including age, stage, and grade. The R package "forestplot" was utilized to visualize the results of the Cox analyses in the form of forest plots. A nomogram was constructed using the R package “regplot”. The performance, which measures the agreement between predicted and observed outcomes, of the nomogram was evaluated using calibration curves generated by the calibrate function of the R package "rms". The clinical decision-making performance was assessed using decision curve analysis (DCA) curves generated by the R package "ggDCA". Immune response prediction The Tumor Immune Dysfunction and Exclusion (TIDE) tool (http://tide.dfci.harvard.edu) was used to predict the response of patients within the TCGA cohort to immunotherapy. The TIDE scores were compared among patients with different risk levels. Immune cell infiltration assessment The "CIBERSORT" algorithm was used to assess the infiltration levels of 22 different immune cell types within the TCGA OV cohort. To ensure reliable analysis, immune cell types with an abundance of 0 in more than half of the samples were removed. The remaining immune cells were evaluated for their infiltration patterns in patients with different risk scores. The R package "estimate" was utilized to evaluate the immune score, tumor purity, and stromal score in patients at varying risk levels. Drug Sensitivity Prediction Drug sensitivity was predicted in the training set of the TCGA OV cohort using the "calcPhenotype" function of the "oncoPredict" package, utilizing data from the Genomics of Drug Sensitivity in Cancer (GDSC2) and Cancer Therapeutics Response Portal (CTRP V2) databases. qRT-PCR qRT-PCR was conducted to determine mRNA expression of model genes in SKOV3 and A2780 OV cell lines compared to human ovarian surface epithelial cells (HOSEpiC). Total RNA was isolated using Trizol reagent (Ambion, Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer's instructions, followed by cDNA synthesis using Hifair ® III 1st Strand cDNA Synthesis SuperMix (Yeasen Biotechnology, China). The cDNA amplification was performed using Hieff UNICON ® Universal Blue qPCR SYBR Green Master Mix (Yeasen Biotechnology) following the manufacturer’s protocol. GAPDH was used as the internal reference, and relative expression was calculated using the 2 -ΔΔ Ct method. The experiment was repeated three times. The primer sequences are summarized in Table S4. Statistical analysis Statistical analysis was carried out using R (v4.3.0). Survival analysis and visualization were performed using the R packages "survival" and "survminer". Heat maps were generated using the "pheatmap" package. Time-dependent Receiver Operating Characteristic analysis (ROC) was conducted using the "timeROC" package. Venn diagrams were generated using the "ggvenn" package. Other results were visualized using ggplot2 or plot functions. Correlation analysis was performed using the Pearson method. The significance of differences between two groups was assessed using the Wilcox test. A P-value of less than 0.05 was considered statistically significant. Results Identification of DEGs in PCOS and OV To identify shared DEGs between PCOS and OV, we analyzed gene expression data from public datasets. The synthesis of five PCOS datasets yielded data from 28 PCOS and 22 normal granulocyte samples. The TCGA OV dataset GSE18520 provided 53 cancer and 10 normal tissue samples. Our analysis revealed 1179 PCOS-associated DEGs (520 upregulated and 659 downregulated; Fig. 1A) and 2092 OV-related DEGs (1237 upregulated and 855 downregulated; Fig. 1B). Remarkably, there was an overlap of 28 upregulated and 45 downregulated DEGs between PCOS and OV (Fig. 1C). Association of DEGs with m6A modification and cellular senescence To identify DEGs associated with both m6A modification and cellular senescence (m6A_CellAgeRG), we aligned the DEGs from both PCOS and OV with known m6A target genes and cellular senescence genes. This intersection produced 8 significant m6A_CellAgeRGs, including LMNB1, SNAI2, NOTCH1, DDIT3, MEIS2, STAT5A, TACC3, and VCAN (Fig. 1D). A subsequent m6A_CellAgeRG score was derived using the ssGSEA algorithm, which revealed elevated scores in PCOS and OV patients compared to controls (Fig. 1E and 1F). This finding suggests that m6A modification and cellular senescence may play a pivotal role in the pathogenesis of both PCOS and OV. Identification of core modules and OV-related hub genes To identify core modules related to the m6A_CellAgeRG score, we conducted WGCNA on PCOS and TCGA OV datasets. After initial sample clustering, we detected and removed one outlier in the PCOS dataset (Fig. 2A). To establish an optimal scale-free topology and connectivity, we set the soft threshold β to 12 (Fig. 2B). Utilizing hierarchical clustering, we categorized the genes into 10 distinct modules (Fig. 2C). Notably, the blue module exhibited the highest correlation (R = 0.8) with the m6A_CellAgeRG score (Fig. 2D). Similarly, in the TCGA OV dataset, we identified and removed 13 outliers (Fig. 2E), selected a soft threshold β of 8 (Fig. 2F), and organized the genes into 19 modules (Fig. 2G), with the brown module showing the highest correlation (R = 0.74) with the m6A_CellAgeRG score (Fig. 2H). By applying the criteria GS > 0.5 and |MM| > 0.6, we identified 1246 hub genes within the blue module in the PCOS cohort (Fig. 3A) and 308 hub genes within the brown module in the OV cohort (Fig. 3B). Out of these hub genes, 242 were also among the 2092 DEGs associated with OV (termed as DEhub genes) (Fig. 3C). These data suggest the involvement of these hub genes in OV development and progression. Functional enrichment and pathway analysis of DEhub genes To investigate the biological functions and pathways of the DEhub genes, we performed GO and KEGG enrichment analyses. For functional annotation, GO analysis revealed that the DEhub genes were significantly enriched in 2627 biological processes, including cell-substrate adhesion, positive regulation of angiogenesis, and positive regulation of vasculature development. They were also associated with 18 cellular components, such as the collagen-containing extracellular matrix, external side of the plasma membrane, and membrane raft, as well as 11 molecular functions, including extracellular matrix structural constituent, coreceptor activity, and cytokine binding (Fig. 3D, Table S1). Furthermore, through KEGG pathway enrichment analysis, we identified 11 significant pathways, including Adherens junction, Fc gamma R-mediated phagocytosis, and the AGE-RAGE signaling pathway in diabetic complications (Fig. 3E, Table S2). These results suggest that these pathways and processes may be potential therapeutic targets for OV. Development and validation of an OV prognostic model To identify hub genes related to the prognosis of OV, we conducted a univariate Cox analysis to assess the correlation between the expression levels of 242 DEhub genes and patient prognosis in the TCGA OV cohort. Using a significance threshold of P < 0.05, we identified 31 DEhub genes significantly associated with patient prognosis (Table S3). To address potential issues such as variable collinearity and overfitting, we performed LASSO Cox analysis on these 31 DEhub genes in the training set (Fig. 4A), selecting the minimum λ value from the model (Fig. 4B). This process led us to identify 19 genes (APBB2, C5AR1, CCDC80, CFI, CXCR2, CXCR4, DOCK11, FNIP1, GBP5, KATNAL1, KCTD1, LILRA2, MRC1, P2RX1, P2RY14, PI3, PYGB, TMOD2, and ZBP1) with corresponding weight coefficients shown in Fig. 4C. Subsequently, these 19 genes underwent multivariate Cox analysis (Fig. 4D), and their functional interactions with closely related proteins were explored (Fig. 4E). Using the expression levels and regression coefficients of these 19 DEhub genes, we calculated risk scores for each TCGA sample. Patients were then divided into high-risk and low-risk groups based on the median risk score, and the risk score formula was as follows: risk score = (-0.102) × APBB2 + 0.016 × C5AR1 + 0.13 × CCDC80 + (-0.066) × CFI + 0.485 × CXCR2 + (-0.133) × CXCR4 + 0.073 × DOCK11 + 0.067 × FNIP1 + (-0.088) × GBP5 + 0.108 × KATNAL1 + (-0.041) × KCTD1 + 0.165 × LILRA2 + 0.05 × MRC1 + (-0.192) × P2RX1 + (-0.678) × P2RY14 + 0.089 × PI3 + 0.177 × PYGB + 0.073 × TMOD2 + (-0.13) × ZBP1. A heatmap depicted the expression of these 19 DEhub genes in each TCGA OV sample (Fig. 4F). Efficacy of prognostic risk score model across OV cohorts To explore the prognostic implications of the risk score, we performed Kaplan-Meier (KM) survival analysis on the TCGA OV cohort. The results showed diminished OS for high-risk patients in both the training and test sets (Fig. S2A and S2B). The ROC curves indicated AUC values of 0.72, 0.77, and 0.74 for 1-year, 2-year, and 3-year OS in the training set and 0.61, 0.67, and 0.64 in the test set (Fig. S2D and 2E). When evaluating the model's reliability on an external OV cohort, GSE140082, a significant decrease in OS for the high-risk group was observed (Fig. S2C). The corresponding ROC curves showed AUC values of 0.56, 0.63, and 0.64 for 1-year, 2-year, and 3-year survival (Fig. S2F). Upon analyzing the correlation between risk scores and OS among the TCGA OV patients, we categorized these patients based on their age, tumor stage, and grade. The data showed that high-risk patients generally had lower OS rates compared to their low-risk counterparts across the overall patient population (P < 0.0001; Fig. S2G) and when differentiated by age (P < 0.0001; Fig. S2H and S2I). The difference in OS was notably significant among patients with high-grade (G3+G4, P < 0.0001; Fig. S2J) and advanced-stage tumors (III+IV, P < 0.0001; Fig. S2K). However, for those diagnosed with early-stage (I+II, P = 0.25; Fig. S2L) or low-grade tumors (G1+G2, P = 0.46; Fig. S2M), there wasn't a significant difference in OS between the high and low-risk groups. The results suggest that the risk score is a reliable prognostic indicator for OS in OV, particularly among high-grade and advanced-stage tumors. Construction and validation of the prognostic nomogram To explore the clinical utility of the prognostic model, we identified independent prognostic clinical factors through univariate and multivariate Cox analyses. Both analyses unveiled significant associations between OS and the risk score (both HR = 2.7, P < 0.001; Fig. 5A and 5B). Subsequently, we constructed a nomogram by integrating the risk score with tumor stage, grade, and age, aiming to forecast 1-year, 2-year, and 3-year survival probabilities (Fig. 5A). The calibration plot showed good agreement between the observed and predicted survival rates spanning 1, 2, and 3 years (Fig. 4D-F). The DCA for these durations further underscored the clinical utility of this nomogram (Fig. 4G-I). These data suggest that the constructed nomogram provides a valuable tool for predicting survival outcomes in OV patients. Association of risk score with immune cell infiltration To explore the potential interplay between the risk score and the tumor's immune microenvironment, we used the CIBERSORT algorithm to quantify the infiltration of immune cells within the tumor and compared these measurements across risk groups. The results revealed significant differences in the abundance of seven immune cell types between the risk groups (Fig. 6A and 6B). In particular, the high-risk group was characterized by elevated counts of M0 macrophages and monocytes, coupled with a subtle rise in M2 macrophages. Conversely, counts of M1, CD8+ T cells, and T follicular helper (Tfh) cells decreased significantly in this group. By employing the ESTIMATE algorithm, we found that the ESTIMATEScore and stromal score were noticeably higher in the high-risk group compared to the low-risk group (P = 0.014 and 0.00011, respectively; Fig. 6C and 6E), while tumor purity reduced significantly in the high-risk group (P = 0.014; Fig. 6D). However, no marked difference was observed in the immune score across the groups (Fig. 6F). Subsequent TIDE analyses to measure the potential for immunotherapy response revealed that interferon gamma (IFNG), CD8, microsatellite instability (MSI), and myeloid-derived suppressor cell (MDSC) levels decreased in the high-risk group, while T cell dysfunction, exclusion, and cancer-associated fibroblasts (CAF) increased (Fig. 6G). These observations suggest an intricate relationship between the risk score and the immune landscape of the tumor. Drug sensitivity analysis in different risk categories To investigate how the risk score reflects therapeutic responsiveness, we projected drug sensitivity in patients. Differences in drug sensitivity between risk groups were determined based on the prediction scores (Fig. 7A). The lower the prediction score, the higher the sensitivity of the corresponding patients to the drug. Three drugs, namely Sabutoclax_1849, AGI-6780_1634, and BMS-536924_1091, emerged as the most correlated with the risk scores (Fig. 7B). However, the prediction scores of Sabutoclax_1849 showed no difference between the two groups. Thus, we only display the Pearson correlation coefficient (r) and P-value between the risk score and the prediction scores of AGI-6780_1634 and BMS-536924_1091 (Fig. 7C and 7D). BMS-536924_1091 response was negatively correlated with the risk score (r = -0.22, p = 1.5e-05), while AGI-6780_1634 response was positively correlated with the risk score (r = 0.25, p = 1.4e-06). These patterns suggest that risk scores could potentially serve as informative indicators for therapeutic strategies in OV. Model gene expression in external OV datasets and OV cell lines. Lastly, we examined the alterations in model gene expression in external OV datasets and OV cell lines. The expression patterns of these model genes in GSE18520 and GSE27651 datasets (Fig. 8A) are summarized in Table 2. Notably, our analysis revealed that CXCR4 emerged as the only m6A target gene, subject to regulation by m6A-associated genes FTO, METTL14, ELAVL1, and YTHDF2 (Fig. 8B). This finding suggests a complicated regulatory network governing CXCR4 in OV. Furthermore, in SKOV-3 and A2780 OC cell lines, qRT-PCR analysis revealed notable upregulation of C5AR1, CFI, CXCR2, CXCR4, KCTD1, PI3, PYGB, ZBP1, and LILRA2 (only in SKOV3 cells) compared to HOSEpiC. Conversely, CCDC80, FNIP1, GBP5 (only in SKOV3 cells), KATNAL1, MRC1, P2RX1, and TMOD2 (only in A2780 cells) showed significant downregulation (all P 0.05; Fig. 9). These findings are generally consistent with the gene expression alterations observed in OV datasets (Table 2). Discussion In this study, shared DEGs were identified between PCOS and OV using multiple datasets. Eight DEGs were associated with both m6A modification and cellular senescence, suggesting their role in both conditions. A 19-gene prognostic model was developed and validated for OV, demonstrating its effectiveness in predicting patient outcomes and revealing associations with immune cell infiltration and drug sensitivity. Furthermore, qRT-PCR analysis confirmed notable upregulation or downregulation of the model genes in SKOV-3 and A2780 cell lines compared to HOSEpiC, consistent with the gene expression patterns observed in OV datasets. The developed 19-gene prognostic model for OV offers potential clinical significance by providing a valuable tool for predicting patient outcomes and guiding personalized therapeutic approaches. We identified eight significant m6A_CellAgeRGs, including LMNB1, SNAI2, NOTCH1, DDIT3, MEIS2, STAT5A, TACC3, and VCAN, which are common between PCOS and OV. This overlap suggests potential shared molecular mechanisms or pathways that might underlie the progression from PCOS to OV. LMNB1, from the lamin family, is pivotal for maintaining the structure and function of the cell nucleus[19]. Notably, its related protein, LMNB2, has been identified as a potential biomarker for ovarian cancer risk in women with PCOS[20]. Moreover, LMNB1 is one of the top hub genes in the PPI network of PCOS, playing a central role in the core network of PCOS-related genes[21]. This connection suggests that changes in the nuclear lamina could bridge PCOS and OV. The association between irregular menstrual cycles and an elevated risk of OV further strengthens the link between PCOS and OV[22]. SNAI2 exhibits differential expression in ovarian tissue from PCOS patients with irregular menstruation compared to those with regular cycles[8]. This implies that hormonal imbalances or irregular menstrual cycles, common in PCOS, might set the stage for cellular changes that predispose to OV. DDIT3's association with both PCOS and OV is also intriguing. While it correlates positively with tumor purity in OV, its role as a core ferroptosis-related gene in PCOS suggests that cellular stress and death pathways might be a bridge connecting these two conditions[23]. Moreover, the enrichment of DEhub genes in pathways like the AGE-RAGE signaling pathway in OV, offers another potential connection between PCOS and OV, given its role in chronic inflammation[24], a hallmark of PCOS[25-27]. In OV, the AGE-RAGE pathway is associated with tumor growth, angiogenesis, and metastasis[28, 29]. These results suggest that the shared DEGs and signaling pathways might collectively drive the progression from PCOS to OV. In this study, we observed that the high-risk group exhibited increased levels of M0 macrophages and monocytes, accompanied by a slight elevation in M2 macrophages. Conversely, we observed a significant reduction in the counts of M1 macrophages, CD8+ T cells, and TFH cells within this group. These findings collectively suggest a distinct immunological profile in the high-risk individuals, potentially indicating an immune microenvironment conducive to OV progression. Elevated levels of M0 macrophages and monocytes indicate a potential pro-tumorigenic environment, as these cells can promote tumor growth and suppress anti-tumor immune responses[30]. Malignant OV cells can release M2-like cytokines, including IL-10, CCL2/3/4/5/7/8, CXCL12, VEGF, and PDGF, as part of their strategy to attract and recruit additional monocytes and M0 macrophages to the tumor site, subsequently inducing their transformation into the M2 phenotype[31]. The subtle increase in M2 macrophages further supports a tumor-promoting milieu, as M2 macrophages are known for their immunosuppressive functions in OV[32]. Conversely, the significant decrease in M1 macrophages, CD8+ T cells, and TFH cells suggests impaired anti-tumor immunity, as these cells play crucial roles in mounting effective immune responses against OV[33]. High-grade serous OV tumors with higher estimated proportions of TFH and M1 macrophages were significantly and independently associated with long-term survival[34]. This distorted immune profile in the high-risk group may contribute to disease progression and highlights the importance of therapeutic strategies aimed at restoring a balanced and robust anti-tumor immune response in OV patients at risk. TIDE analyses in the high-risk group indicated a decrease in key immune factors such as IFNG, CD8 T cells, MSI, and MDSC, coupled with an increase in indicators of T cell dysfunction, immune exclusion, and the presence of CAF. In OV immunotherapy, these findings suggest that the high-risk group may face challenges in responding effectively to immunotherapeutic interventions. The decrease in CD8 T cells, which are crucial for anti-tumor immune responses, and the rise in immunosuppressive MDSCs can compromise the immune system's ability to target and control OV[35, 36]. Additionally, elevated T cell dysfunction and immune exclusion markers indicate a potentially hostile tumor microenvironment that hinders immune cell infiltration and function in OV[37, 38]. The presence of CAFs, known for their role in promoting tumor growth and immune evasion, further complicates the therapeutic landscape[39, 40]. These results highlight the need for personalized immunotherapeutic strategies that address the specific immune challenges faced by high-risk OV patients. The limitations of this study include the reliance on bioinformatics and data analysis, which requires further experimental validation to confirm the functional roles of identified genes and their clinical significance. Additionally, the study's findings are based on retrospective data, and prospective clinical studies are needed to validate the prognostic model's effectiveness in real-world clinical settings. Lastly, the study focuses on associations and does not establish causation, warranting future mechanistic investigations. Conclusion In conclusion, our study identified shared dDEGs between PCOS and OV, highlighting the potential involvement of m6A modification and cellular senescence in both conditions. We developed a prognostic model for OV based on 19 hub genes, offering promise for risk assessment in OV patients. This model demonstrated efficacy across multiple OV cohorts and revealed associations with the tumor immune microenvironment and drug sensitivity. Further research and clinical validation are needed to fully realize the clinical significance of these findings and their potential impact on OV patient care. Abbreviations Polycystic ovary syndrome (PCOS); ovarian cancer (OV); differentially expressed genes (DEGs); ovarian cancer (OV); N6-methyladenosine (m6A); methyltransferase-like-3 (METTL3); YTH domain family protein 1 (YTHDF1); Weighted Gene Co-Expression Network Analysis (WGCNA); Gene Ontology (GO); Genomics of Drug Sensitivity in Cancer (GDSC2); Cancer Therapeutics Response Portal (CTRP V2); Receiver Operating Characteristic analysis (ROC); Kaplan-Meier (KM); T follicular helper (Tfh); interferon gamma (IFNG); microsatellite instability (MSI); myeloid-derived suppressor cell (MDSC); cancer-associated fibroblasts (CAF) Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data generated or analysed during this study are included in this published article. Competing interests The authors declare that they have no competing interests. Funding The study was supported by the Famous Doctor Project of Xingdian Talent Support Program in Yunnan Province [XDYC-MY-2022-0057]; Kunming Medical UniversityYoung and Middle-aged Discipline Leaders and Reserve Candidates -"Riding the Wind* Talent Cultivation Programme [2023(108)]; External Cooperation Research Project of the Second Affiliated Hospital of Kunming Medical University - Mechanism of histone lactate regulation of METTL3 in PCOS affecting the proliferation of ovarian granulosus cells [2022dwhz03]. Authors' contributions YL and CYS carried out the studies, participated in collecting data, and drafted the manuscript. YJGX and YZW performed the statistical analysis and participated in its design. YYM and XH participated in acquisition, analysis, or interpretation of data and draft the manuscript. All authors read and approved the final manuscript. Acknowledgements Not applicable. References Kh MM, Boboev K. 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Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, et al. Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: a cancer journal for clinicians. 2021;71:209-49. Tanha K, Mottaghi A, Nojomi M, Moradi M, Rajabzadeh R, Lotfi S, et al. Investigation on factors associated with ovarian cancer: An umbrella review of systematic review and meta-analyses. Journal of ovarian research. 2021;14:1-17. Jiao J, Sagnelli M, Shi B, Fang Y, Shen Z, Tang T, et al. Genetic and epigenetic characteristics in ovarian tissues from polycystic ovary syndrome patients with irregular menstruation resemble those of ovarian cancer. BMC Endocrine Disorders. 2019;19:1-10. Hua W, Zhao Y, Jin X, Yu D, He J, Xie D, et al. METTL3 promotes ovarian carcinoma growth and invasion through the regulation of AXL translation and epithelial to mesenchymal transition. Gynecologic oncology. 2018;151:356-65. Hao L, Wang J-M, Liu B-Q, Yan J, Li C, Jiang J-Y, et al. m6A-YTHDF1-mediated TRIM29 upregulation facilitates the stem cell-like phenotype of cisplatin-resistant ovarian cancer cells. Biochimica et Biophysica Acta (BBA)-Molecular Cell Research. 2021;1868:118878. Xu F, Li J, Ni M, Cheng J, Zhao H, Wang S, et al. FBW7 suppresses ovarian cancer development by targeting the N 6-methyladenosine binding protein YTHDF2. Molecular cancer. 2021;20:1-16. Huang H, Wang Y, Kandpal M, Zhao G, Cardenas H, Ji Y, et al. FTO-dependent N 6-methyladenosine modifications inhibit ovarian cancer stem cell self-renewal by blocking cAMP signaling. Cancer research. 2020;80:3200-14. Zhang S, Deng W, Liu Q, Wang P, Yang W, Ni W. Altered m6A modification is involved in up‐regulated expression of FOXO3 in luteinized granulosa cells of non‐obese polycystic ovary syndrome patients. Journal of cellular and molecular medicine. 2020;24:11874-82. Zou J, Li Y, Liao N, Liu J, Zhang Q, Luo M, et al. Identification of key genes associated with polycystic ovary syndrome (PCOS) and ovarian cancer using an integrated bioinformatics analysis. Journal of Ovarian Research. 2022;15:1-16. Shafik AM, Zhang F, Guo Z, Dai Q, Pajdzik K, Li Y, et al. N6-methyladenosine dynamics in neurodevelopment and aging, and its potential role in Alzheimer’s disease. Genome biology. 2021;22:1-19. Chen X, Gong W, Shao X, Shi T, Zhang L, Dong J, et al. METTL3-mediated m6A modification of ATG7 regulates autophagy-GATA4 axis to promote cellular senescence and osteoarthritis progression. Annals of the rheumatic diseases. 2022;81:85-97. Liu P, Li F, Lin J, Fukumoto T, Nacarelli T, Hao X, et al. m6A-independent genome-wide METTL3 and METTL14 redistribution drives the senescence-associated secretory phenotype. Nature cell biology. 2021;23:355-65. Książek K, editor Where does cellular senescence belong in the pathophysiology of ovarian cancer? Seminars in Cancer Biology; 2022: Elsevier. Lin F, Worman HJ. Structural organization of the human gene encoding nuclear lamin A and nuclear lamin C. Journal of Biological Chemistry. 1993;268:16321-6. Galazis N, Olaleye O, Haoula Z, Layfield R, Atiomo W. Proteomic biomarkers for ovarian cancer risk in women with polycystic ovary syndrome: a systematic review and biomarker database integration. Fertility and sterility. 2012;98:1590-601. e1. Zhou F, Xing Y, Cheng T, Yang L, Ma H. Exploration of hub genes involved in PCOS using biological informatics methods. Medicine. 2022;101. Cirillo PM, Wang ET, Cedars MI, Chen Lm, Cohn BA. Irregular menses predicts ovarian cancer: Prospective evidence from the Child Health and Development Studies. International journal of cancer. 2016;139:1009-17. Zhang Y, Zhao T, Hu L, Xue J. Integrative Analysis of Core Genes and Biological Process Involved in Polycystic Ovary Syndrome. Reproductive Sciences. 2023:1-16. Xu W, Tang M, Wang J, Wang L. Identification of the active constituents and significant pathways of Cangfu Daotan decoction for the treatment of PCOS based on network pharmacology. Evidence-Based Complementary and Alternative Medicine. 2020;2020. Xie Q, Xiong X, Xiao N, He K, Chen M, Peng J, et al. Mesenchymal stem cells alleviate DHEA-induced polycystic ovary syndrome (PCOS) by inhibiting inflammation in mice. Stem cells international. 2019;2019. Ozcaka O, Buduneli N, Ceyhan BO, Akcali A, Hannah V, Nile C, et al. Is interleukin-17 involved in the interaction between polycystic ovary syndrome and gingival inflammation? J Periodontol. 2013;84:1827-37. Foroozanfard F, Soleimani A, Arbab E, Samimi M, Tamadon MR. Relationship between IL-17 serum level and ambulatory blood pressure in women with polycystic ovary syndrome. J Nephropathol. 2017;6:15-24. Wang Y, Li BX, Li X. Identification and validation of angiogenesis-related gene expression for predicting prognosis in patients with ovarian cancer. Frontiers in Oncology. 2022;11:783666. Rahimi F, Karimi J, Goodarzi MT, Saidijam M, Khodadadi I, Razavi ANE, et al. Overexpression of receptor for advanced glycation end products (RAGE) in ovarian cancer. Cancer Biomarkers. 2017;18:61-8. Boutilier AJ, Elsawa SF. Macrophage polarization states in the tumor microenvironment. International journal of molecular sciences. 2021;22:6995. Zhang M, He Y, Sun X, Li Q, Wang W, Zhao A, et al. A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients. Journal of ovarian research. 2014;7:1-16. Nowak M, Klink M. The role of tumor-associated macrophages in the progression and chemoresistance of ovarian cancer. Cells. 2020;9:1299. Gao Y, Chen L, Cai G, Xiong X, Wu Y, Ma D, et al. Heterogeneity of immune microenvironment in ovarian cancer and its clinical significance: a retrospective study. Oncoimmunology. 2020;9:1760067. Berry L, Kelly M, Miller L. Tumor immunogenicity status in high-grade serous ovarian cancer. Gynecologic Oncology. 2021;162:S319. Wu JW, Dand S, Doig L, Papenfuss AT, Scott CL, Ho G, et al. T-Cell receptor therapy in the treatment of ovarian cancer: A mini review. Frontiers in immunology. 2021;12:672502. Mabuchi S, Sasano T, Komura N. Targeting myeloid-derived suppressor cells in ovarian cancer. Cells. 2021;10:329. Desbois M, Udyavar AR, Ryner L, Kozlowski C, Guan Y, Dürrbaum M, et al. Integrated digital pathology and transcriptome analysis identifies molecular mediators of T-cell exclusion in ovarian cancer. Nature communications. 2020;11:5583. Jiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nature medicine. 2018;24:1550-8. Gao Q, Yang Z, Xu S, Li X, Yang X, Jin P, et al. Heterotypic CAF-tumor spheroids promote early peritoneal metastasis of ovarian cancer. Journal of Experimental Medicine. 2019;216:688-703. Zhang M, Chen Z, Wang Y, Zhao H, Du Y. The role of cancer-associated fibroblasts in ovarian cancer. Cancers. 2022;14:2637. Tables Table 1 Information of public datasets ID Platform Sample type Sample size Data type Disease GSE34526 GPL570 Normal or PCOS granulocytes 10 mRNA array PCOS GSE137684 GPL17077 Normal or PCOS granulocytes 12 mRNA array PCOS GSE80432 GPL6244 Normal or PCOS granulocytes 16 mRNA array PCOS GSE114419 GPL17586 Normal or PCOS granulocytes 6 mRNA array PCOS GSE102293 GPL570 Normal or PCOS granulocytes 6 mRNA array PCOS GSE18520 GPL570 Normal or cancer tissue 63 mRNA array OV GSE27651 GPL570 Normal or cancer tissue 49 mRNA array OV GSE140082 GPL14951 Cancer tissue with prognosis data 380 mRNA array OV TCGA-OV Cancer tissue with prognosis data 373 RNASeq OV Table 2 Model gene expression in external OV datasets Gene OV_GSE18520 OV_GSE27651 PCOS_DEGs APBB2 Up Down Down C5AR1 Down - Up CCDC80 Down Down - CFI Down - - CXCR2 Down - Up CXCR4 Up - - DOCK11 Down Down Up FNIP1 Down - - GBP5 Up - Up KATNAL1 Down Down - KCTD1 Up Up - LILRA2 Down Down Up MRC1 Down Down Up P2RX1 Up Down - P2RY14 Down Down - PI3 Up - - PYGB Up - - TMOD2 Down Down - ZBP1 Up - - Additional Declarations No competing interests reported. Supplementary Files Supplementarymaterial.docx TableS1.xlsx TableS2.csv TableS3.csv TableS4.docx Cite Share Download PDF Status: Posted Version 1 posted 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. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4034917","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":277567333,"identity":"f1934f07-66fc-4469-bd0b-beac867a48d0","order_by":0,"name":"Yang Liu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIie3PvWrDMBDA8TMCTSJeFQjJKwgylEx9lRMFT2kIdPFgiKClGfqR1X2LjB1PGDQpe7a4FLomXkKmUu8tkbN10G+6QX/uBBBF/xBPVzU138Xi2pCuMS/CSU/6xJbcoUroU9XehZMhTFklOEPF7Ff/44F1OAw2ZEvBZ1e8ynJtOKTLJzyfsFekgxzcTZ5dttXvA5B+sw5sIWVLxROz9W3iOSh5G0pQVQJZYnb741w/si7JtE2I6TVRBt0S6dC+GTfuG7qR6J0I/mW0uq8OjSmGKZBuTnk7LF/OJ7+Iy55HURRFf/oBS6VUuRubY6gAAAAASUVORK5CYII=","orcid":"","institution":"Department of Reproductive Medicine, The Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":true,"prefix":"","firstName":"Yang","middleName":"","lastName":"Liu","suffix":""},{"id":277567335,"identity":"d76e2677-787a-403a-b76f-ceba17e1adb4","order_by":1,"name":"Yujie Gengxiao","email":"","orcid":"","institution":"Department of Reproductive Medicine, The Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Gengxiao","suffix":""},{"id":277567337,"identity":"3ed2dc99-d85c-442a-be4d-d651ed15ee35","order_by":2,"name":"Yanzhi Wu","email":"","orcid":"","institution":"Department of Reproductive Medicine, The Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yanzhi","middleName":"","lastName":"Wu","suffix":""},{"id":277567339,"identity":"c628c174-c6f0-46b1-bfc3-9c88115ae7db","order_by":3,"name":"Xi Hu","email":"","orcid":"","institution":"Department of Reproductive Medicine, The Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xi","middleName":"","lastName":"Hu","suffix":""},{"id":277567340,"identity":"125a5c05-95bb-4a76-944c-a62e4ae91994","order_by":4,"name":"Yingying Ma","email":"","orcid":"","institution":"Department of Reproductive Medicine, The Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yingying","middleName":"","lastName":"Ma","suffix":""},{"id":277567342,"identity":"3a5484eb-d8ca-413c-b92c-1585eec647eb","order_by":5,"name":"Chunyi Sun","email":"","orcid":"","institution":"Department of gynecology, The Second Affiliated Hospital of Kunming Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chunyi","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2024-03-08 02:52:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4034917/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4034917/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":52542769,"identity":"651681f3-2855-4c99-bc3c-11658da6b8af","added_by":"auto","created_at":"2024-03-12 17:46:10","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1539096,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of differentially expressed genes (DEGs) associated with both polycystic ovary syndrome (PCOS) and ovarian cancer (OV).\u003c/strong\u003e Five PCOS datasets (GSE34526, GSE137684, GSE80432, GSE114419, and GSE102293) were merged, including 28 PCOS granulocyte samples and 22 normal granulocyte samples. The TCGA OV dataset GSE18520 contained 53 cancer tissue samples and 10 normal control tissue samples. (A, B) Volcano plots illustrate DEGs associated with PCOS (A) and OV (B), where red represents upregulated genes, blue represents downregulated genes, and gray represents non-significant changes. (C) Venn diagrams depict the intersection of upregulated (left) and downregulated (right) DEGs in both PCOS and OV cohorts. (D) Venn diagram depicting the intersection of PCOS and OV-related DEGs with m6A target genes and cellular senescence genes (m6A_CellAgeRG). (E and F) Comparison of the m6A_CellAgeRG scores in merged PCOS dataset (E) and TCGA OV dataset GSE18520 (F) between disease and control groups.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/20dbe4c5dbceef5353b93dbd.png"},{"id":52542774,"identity":"6183c769-1cd7-45b1-af4f-fd2faedc0b5c","added_by":"auto","created_at":"2024-03-12 17:46:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":6925607,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of hub genes in PCOS and OV cohorts.\u003c/strong\u003e (A, E) Sample clustering with the removal of outliers in PCOS (A) and TCGA OV (E) cohorts. (B, F) Soft threshold β selection. (C, G) Co-expression module clustering. Different colors represent various co-expression modules. (D, H) Correlation between modules and m6A_CellAgeRG Score.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/d49267fa1b6808b4dd136ff4.png"},{"id":52542776,"identity":"7e7f6aac-8bd4-4291-97f1-21e8bfcfd95c","added_by":"auto","created_at":"2024-03-12 17:46:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2941051,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of hub genes.\u003c/strong\u003e (A, B) Hub gene selection within the blue module of the PCOS cohort (A) and the brown module of the OV cohort (B). (C) Intersection of the hub genes with the DEGs associated with OV. (D, E) GO (D) and KEGG (E) enrichment analyses on intersected hub genes.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/b450cd6343237d457632ce49.png"},{"id":52542771,"identity":"86de3604-eeac-4315-8e83-c1597501b498","added_by":"auto","created_at":"2024-03-12 17:46:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4209911,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentification of prognostic-related hub genes in OV. \u003c/strong\u003e(A) Distribution of LASSO coefficients of 31 prognosis-related DEhub genes. Coefficients are plotted against log lambda (λ), depicting the trend of approaching zero as λ increases. (B) λ selection by 10-fold cross-validation. The vertical dot lines represent the minimum λ and optimal λ values for the model. (C) Bar plot showing the weight coefficients of the 19 selected candidate genes in the model. (D) Forest plot of the multivariate Cox regression analysis for the model genes. (E) Protein interaction network illustrating the functional interactions of the model genes with proteins that have similar functions. (F) Heatmap displaying the expression patterns of the model genes in each TCGA OV sample.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/be5b2ba2aa4ada96470a12e0.png"},{"id":52542778,"identity":"4b184b3b-b81b-4881-8aa4-c5817a7ff08b","added_by":"auto","created_at":"2024-03-12 17:46:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1524203,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConstruction and evaluation of the nomogram. \u003c/strong\u003e(A, B) Forest plots showing the results of univariate and multivariate Cox analyses for the risk score and clinical factors. (C) The nomogram constructed based on the risk score, age, stage, and grade. (D-F) Calibration plots of the nomogram for predicting 1-year, 2-year, and 3-year survival rates. (G-I) Clinical decision curve analysis (DCA) curves evaluating the predictive performance of the model for 1-year, 2-year, and 3-year survival rates.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/8ff1032a4cf2f721f7a1c1a9.png"},{"id":52542773,"identity":"420ca9c6-f7be-43a1-b132-fe39614cdc6f","added_by":"auto","created_at":"2024-03-12 17:46:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3036612,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCorrelation between risk score and immune cell infiltration. \u003c/strong\u003e(A) Heatmap illustrating the differential abundance of immune cells in different risk groups. (B)Box plots showing the abundance of immune cells in different risk groups. (C-F) Box plots depicting the ESTIMATEScore, tumor purity, stromal score, and immune score in different risk groups. (G) Violin plot demonstrating the interferon gamma (IFNG) response, T cell dysfunction scores, T cell exclusion scores, CD8 levels, MSI expression signature, CAF score, and MDSC score in different risk groups.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/b5a87c6881f12ad3d3a7f2f8.png"},{"id":52542781,"identity":"d4b4a652-4ffa-47f2-bdcf-c1509dd4e303","added_by":"auto","created_at":"2024-03-12 17:46:12","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":1467286,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePredicting drug sensitivity in patients from different risk groups.\u003c/strong\u003e(A) Comparison of drug prediction scores between different risk groups. (B) Correlation between drug sensitivity and risk score. (C, D) the Pearson correlation coefficient (r) and p-value (p) between the risk score and the prediction scores of BMS-536924_1091 (C) and AGI-6780_1634 (D).\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/e64761fd56136fc2e9fa2549.png"},{"id":52542780,"identity":"1addc1bc-22b2-4ca8-a262-48f0c0ecfc5e","added_by":"auto","created_at":"2024-03-12 17:46:12","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":1036853,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel gene expression in external OV datasets.\u003c/strong\u003e (A) Box plot of model gene expression in GSE27651 dataset. (B) CXCR4 is regulated by FTO, METTL14, ELAVL1, and YTHDF2.\u003c/p\u003e","description":"","filename":"Figure8.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/ec3822f07f6805efc2c04b1a.png"},{"id":52542782,"identity":"e865c257-5242-4208-94c2-22115e3d049c","added_by":"auto","created_at":"2024-03-12 17:46:12","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":807474,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eModel gene expression in OV cell lines. \u003c/strong\u003eqRT-PCR was conducted to determine mRNA levels of model genes in SKOV3 and A2780 cells compared to human ovarian surface epithelial cells (HOSEpiC). Data are expressed as mean ± standard deviation. *P \u0026lt; 0.05, **P \u0026lt; 0.01, ****P \u0026lt; 0.0001, ns, non-significant; n = 3.\u003c/p\u003e","description":"","filename":"Figure9.png","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/8c36046073932a602f2f047e.png"},{"id":53800470,"identity":"199c0b82-908d-4f2d-a17d-6dbc6349deda","added_by":"auto","created_at":"2024-03-31 06:52:36","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4015800,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/ce7bee34-be8a-4814-aec4-fb8276535ece.pdf"},{"id":52543374,"identity":"08a18837-72fd-48f8-9efb-46d019a756dc","added_by":"auto","created_at":"2024-03-12 17:54:11","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":815135,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/50a827a569722e896b708c07.docx"},{"id":52542775,"identity":"81bde2d9-8e27-48a0-a492-e29c48de21fc","added_by":"auto","created_at":"2024-03-12 17:46:11","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":385357,"visible":true,"origin":"","legend":"","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/974fc54ea67d4a3658cb8d9b.xlsx"},{"id":52543375,"identity":"0f07f29c-40be-4a54-b4ff-c435c140b9fd","added_by":"auto","created_at":"2024-03-12 17:54:11","extension":"csv","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":29398,"visible":true,"origin":"","legend":"","description":"","filename":"TableS2.csv","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/35b4dcbd3085876cb2774e57.csv"},{"id":52542770,"identity":"8e96d143-2148-47e4-9fbb-37f9e3bf447d","added_by":"auto","created_at":"2024-03-12 17:46:11","extension":"csv","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":9522,"visible":true,"origin":"","legend":"","description":"","filename":"TableS3.csv","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/8024a4e7595a6105dc33178d.csv"},{"id":52543376,"identity":"0553b6aa-fc64-4109-a593-c9bb10752299","added_by":"auto","created_at":"2024-03-12 17:54:11","extension":"docx","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":16321,"visible":true,"origin":"","legend":"","description":"","filename":"TableS4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4034917/v1/2cd911e0dcee56c6fbbe7607.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Constructing an Ovarian Cancer Prognostic Model Based on Shared m6A Modifications and Cellular Senescence-Related Genes with Polycystic Ovary Syndrome ","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePolycystic Ovary Syndrome (PCOS) is an endocrine disorder that affects 5-20% of reproductive-age women worldwide[1]. Characterized by hormonal imbalances, irregular menstrual cycles, and ovarian cysts, PCOS has been intimately associated with a myriad of health issues[2-4]. Of these, one of the most concerning is the potential link between PCOS and ovarian cancer (OV)[5], a lethal gynecological malignancy that accounted for 4.2% of all cancer-related deaths worldwide in 2020[6]. The frequency of both disorders and their potential connection has led to increasing concerns about the progression from PCOS to OV[7, 8]. Unraveling this relationship is crucial, not only for understanding disease etiology but also for the development of timely interventions.\u003c/p\u003e\n\u003cp\u003eRecent studies have demonstrated the significant involvement of N6-methyladenosine (m6A) RNA modification in OV. Elevated expression of the methyltransferase-like-3 (METTL3) protein in OV has been associated with tumor growth and invasion[9], along with other m6A-related proteins such as YTH domain family protein 1 (YTHDF1), YTHDF2, and obesity-associated protein (FTO)[10-12]. Interestingly, these genes have also been found to be upregulated in PCOS[13]. Furthermore, m6A-related osteoglycin has been identified as a risk marker linking PCOS to OV[14]. Abnormal m6A modification has been linked to cellular senescence[15-17], which is a process where cells lose their ability to divide and contribute to age-related diseases, including ovarian cancer. It plays a multifaceted role in OV pathobiology, including the disease-promoting activity of normal peritoneal mesothelial cells, the effect of drugs on the senescence of normal cells, and spontaneous senescence in OV cells[18]. PCOS patients with irregular menstruation have been shown to share mRNA expression profiles similar to those observed in OV, including the alterations in cellular senescence-related \u0026nbsp;genes[8]. However, the roles of the shared genes in OV development and prognosis remain largely unknown.\u003c/p\u003e\n\u003cp\u003eIn this study, we aimed to investigate the shared molecular mechanisms between PCOS and OV to uncover a potential link from PCOS to OV. We employed a comprehensive approach, including transcriptomic data analysis, co-expression network analysis, and the development of a prognostic model for OV patients. Our methods involved the integration of multiple datasets, identification of differentially expressed genes (DEGs), and the exploration of gene modules associated with m6A modification and cellular senescence. Additionally, we constructed a prognostic risk signature and validated its effectiveness across different OV cohorts. Our results may identify novel biomarkers and therapeutic avenues for OV, ultimately improving patient care and outcomes.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eData retrieval and processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe schematic overview of the study is shown in Fig. S1. Transcriptomic data were obtained from the Gene Expression Omnibus database (GEO, https://www.ncbi.nlm.nih.gov/geo/), including PCOS datasets GSE34526, GSE137684, GSE80432, GSE114419, and GSE102293, as well as OV datasets GSE18520, GSE27651, and GSE140082 (Table 1). The PCOS datasets were merged for DEG identification, comprising 28 PCOS granulocyte samples and 22 normal granulocyte samples. The GSE18520 OV dataset was used for the identification of OV-related DEGs, consisting of 53 OV cases and 10 normal controls. An external dataset, GSE27651, was combined with GSE18520 to explore model gene expression in OV. GSE18520 and the external dataset GSE140082 were used to validate the efficacy of the prognostic risk model, comprising 380 cancer tissue samples with prognosis data. GEO data processing involved matching probes to gene names, removing empty probes, and calculating the median expression value for genes with multiple corresponding probes. Batch-effect correction of the merged datasets was carried out using the \u0026quot;ComBat\u0026quot; function from the \u0026quot;sva\u0026quot; package.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTranscriptomic data of The Cancer Genome Atlas (TCGA) OV dataset were retrieved from the UCSC Xena Browser (https://xenabrowser.net/) to develop a prognostic model for OV patients. FPKM values were converted into TPM using a formula: \u0026nbsp; \u0026nbsp;and were subsequently subjected to a log2(x + 1) transformation for subsequent analysis.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003em6A target genes in humans were obtained from m6A2Target (http://rm2target.canceromics.org/#/home).\u0026nbsp;A total of 701 m6A target genes were selected as they were validated m6A targets reported in the literature for the human genome (hg38). A total of 866 genes related to cellular senescence were sourced from the CellAge database (https://genomics.senescence.info/cells/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of DEGs\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential gene expression analysis between the disease and control groups was performed using the R package \u0026quot;limma.\u0026quot; In the integrated PCOS dataset, DEGs were identified with a significance threshold of P \u0026lt; 0.05. In the OV dataset GSE18520, DEGs were determined based on the criteria of |logFC| \u0026gt; 1 and an adjusted P-value \u0026lt; 0.05.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003essGSEA analysis of DEGs in PCOS and OV for m6A and cellular senescence\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DEGs that consistently exhibited altered expression in both PCOS and OV were combined. Subsequently, these genes were intersected with m6A target genes and cellular senescence genes, resulting in the identification of m6A_CellAgeRGs. The m6A_CellAgeRG score was then computed using the \u0026quot;GSVA\u0026quot; package to evaluate m6A and cell aging-related metrics.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWeighted Gene Co-Expression Network Analysis (WGCNA)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA WGCNA network was constructed using the \u0026quot;WGCNA\u0026quot; R package. In this analysis, the m6A_CellAgeRG score was used to identify gene modules associated with this score. For each cohort (PCOS and OV), disease samples from patients were selected. The top 50% of genes with the highest variance were used as input data. Outliers were removed, and the optimal soft threshold was determined based on the scale-free topology criterion. Subsequently, the weighted adjacency matrix and topological overlap matrix transformation were constructed. Modules containing more than 30 genes were identified using the hierarchical clustering tree method, with a merged similarity threshold set at 0.25. Each module was visually distinguished by a unique color. Furthermore, modules underwent additional scrutiny based on their correlation with the m6A_CellAgeRG score. Hub genes within these modules were identified using dual criteria: |Module Membership (MM)| \u0026gt; 0.6 and Gene Significance (GS) \u0026gt; 0.5. Those belonging to OV-related DEGs (DEhub) were extracted for subsequent analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFunctional enrichment analysis of the DEhub genes was conducted using the R package \u0026quot;clusterProfiler.\u0026quot; Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analyses were performed. To enhance the robustness of the results, P-values were corrected using the Benjamini-Hochberg method. Enrichment results associated with the corrected P-values were reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of a prognostic risk signature for OV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA prognostic risk signature for OV was established using TCGA OV cohort data. Prognosis-related DEhub genes were identified through univariate Cox analysis based on a significance threshold of P-value \u0026lt; 0.05. Subsequently, TCGA OV patients were randomly divided into training and test sets in a 7:3 ratio. LASSO COX regression analysis was conducted within the training set using the \u0026quot;glmnet\u0026quot; R package. The optimal penalty parameter (\u0026lambda;) was determined, and nineteen genes with non-zero coefficients were selected as significant contributors to the model. To calculate the risk score, weight coefficients from the model were combined with gene expression levels using the following formula:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cimg src=\"data:image/png;base64,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width=\"345\" height=\"71\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction of protein-protein interaction (PPI) network\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the interactions between model genes and their functionally similar counterparts, a PPI was constructed using GeneMANIA (www.genemania.org)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and evaluation of a prognostic nomogram\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eUnivariate and multivariate Cox analyses were conducted to assess the prognostic value of the risk score and clinical variables, including age, stage, and grade. The R package \u0026quot;forestplot\u0026quot; was utilized to visualize the results of the Cox analyses in the form of forest plots. A nomogram was constructed using the R package \u0026ldquo;regplot\u0026rdquo;. The performance, which measures the agreement between predicted and observed outcomes, of the nomogram was evaluated using calibration curves generated by the calibrate function of the R package \u0026quot;rms\u0026quot;. The clinical decision-making performance was assessed using decision curve analysis (DCA) curves generated by the R package \u0026quot;ggDCA\u0026quot;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune response prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Tumor Immune Dysfunction and Exclusion (TIDE) tool (http://tide.dfci.harvard.edu) was used to predict the response of patients within the TCGA cohort to immunotherapy. The TIDE scores were compared among patients with different risk levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmune cell infiltration assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe \u0026quot;CIBERSORT\u0026quot; algorithm was used to assess the infiltration levels of 22 different immune cell types within the TCGA OV cohort. To ensure reliable analysis, immune cell types with an abundance of 0 in more than half of the samples were removed. The remaining immune cells were evaluated for their infiltration patterns in patients with different risk scores. The R package \u0026quot;estimate\u0026quot; was utilized to evaluate the immune score, tumor purity, and stromal score in patients at varying risk levels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug Sensitivity Prediction\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDrug sensitivity was predicted in the training set of the TCGA OV cohort using the \u0026quot;calcPhenotype\u0026quot; function of the \u0026quot;oncoPredict\u0026quot; package, utilizing data from the Genomics of Drug Sensitivity in Cancer (GDSC2) and Cancer Therapeutics Response Portal (CTRP V2) databases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eqRT-PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eqRT-PCR was conducted to determine mRNA expression of model genes in SKOV3 and A2780 OV cell lines compared to human ovarian surface epithelial cells (HOSEpiC). Total RNA was isolated using Trizol reagent (Ambion, Thermo Fisher Scientific, Waltham, MA, USA) following the manufacturer\u0026apos;s instructions, followed by cDNA synthesis using Hifair\u003csup\u003e\u0026reg;\u003c/sup\u003e III 1st Strand cDNA Synthesis SuperMix (Yeasen Biotechnology, China). The cDNA amplification was performed using Hieff UNICON\u003csup\u003e\u0026reg;\u003c/sup\u003e Universal Blue qPCR SYBR Green Master Mix (Yeasen Biotechnology) following the manufacturer\u0026rsquo;s protocol. GAPDH was used as the internal reference, and relative expression was calculated using the 2\u003csup\u003e-\u0026Delta;\u0026Delta;\u003c/sup\u003eCt method. The experiment was repeated three times. The primer sequences are summarized in Table S4.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis was carried out using R (v4.3.0). Survival analysis and visualization were performed using the R packages \u0026quot;survival\u0026quot; and \u0026quot;survminer\u0026quot;. Heat maps were generated using the \u0026quot;pheatmap\u0026quot; package. Time-dependent Receiver Operating Characteristic analysis (ROC) was conducted using the \u0026quot;timeROC\u0026quot; package. Venn diagrams were generated using the \u0026quot;ggvenn\u0026quot; package. Other results were visualized using ggplot2 or plot functions. Correlation analysis was performed using the Pearson method. The significance of differences between two groups was assessed using the Wilcox test. A P-value of less than 0.05 was considered statistically significant.\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eIdentification of DEGs in PCOS and OV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify shared DEGs between PCOS and OV, we analyzed gene expression data from public datasets. The synthesis of five PCOS datasets yielded data from 28 PCOS and 22 normal granulocyte samples. The TCGA OV dataset GSE18520 provided 53 cancer and 10 normal tissue samples. Our analysis revealed 1179 PCOS-associated DEGs (520 upregulated and 659 downregulated; Fig. 1A) and 2092 OV-related DEGs (1237 upregulated and 855 downregulated; Fig. 1B). Remarkably, there was an overlap of 28 upregulated and 45 downregulated DEGs between PCOS and OV (Fig. 1C).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of DEGs with m6A modification and cellular senescence\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify DEGs associated with both m6A modification and cellular senescence (m6A_CellAgeRG), we aligned the DEGs from both PCOS and OV with known m6A target genes and cellular senescence genes. This intersection produced 8 significant m6A_CellAgeRGs, including LMNB1, SNAI2, NOTCH1, DDIT3, MEIS2, STAT5A, TACC3, and VCAN (Fig. 1D). A subsequent m6A_CellAgeRG score was derived using the ssGSEA algorithm, which revealed elevated scores in PCOS and OV patients compared to controls (Fig. 1E and 1F). This finding suggests that m6A modification and cellular senescence may play a pivotal role in the pathogenesis of both PCOS and OV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of core modules and OV-related hub genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify core modules related to the m6A_CellAgeRG score, we conducted WGCNA on PCOS and TCGA OV datasets. After initial sample clustering, we detected and removed one outlier in the PCOS dataset (Fig. 2A). To establish an optimal scale-free topology and connectivity, we set the soft threshold \u0026beta; to 12 (Fig. 2B). Utilizing hierarchical clustering, we categorized the genes into 10 distinct modules (Fig. 2C). Notably, the blue module exhibited the highest correlation (R = 0.8) with the m6A_CellAgeRG score (Fig. 2D). Similarly, in the TCGA OV dataset, we identified and removed 13 outliers (Fig. 2E), selected a soft threshold \u0026beta; of 8 (Fig. 2F), and organized the genes into 19 modules (Fig. 2G), with the brown module showing the highest correlation (R = 0.74) with the m6A_CellAgeRG score (Fig. 2H). By applying the criteria GS \u0026gt; 0.5 and |MM| \u0026gt; 0.6, we identified 1246 hub genes within the blue module in the PCOS cohort (Fig. 3A) and 308 hub genes within the brown module in the OV cohort (Fig. 3B). Out of these hub genes, 242 were also among the 2092 DEGs associated with OV (termed as DEhub genes) (Fig. 3C). These data suggest the involvement of these hub genes in OV development and progression.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunctional enrichment and pathway analysis of DEhub genes\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate the biological functions and pathways of the DEhub genes, we performed GO and KEGG enrichment analyses.\u0026nbsp;For functional annotation, GO analysis revealed that the DEhub genes were significantly enriched in 2627 biological processes, including cell-substrate adhesion, positive regulation of angiogenesis, and positive regulation of vasculature development. They were also associated with 18 cellular components, such as the collagen-containing extracellular matrix, external side of the plasma membrane, and membrane raft, as well as 11 molecular functions, including extracellular matrix structural constituent, coreceptor activity, and cytokine binding (Fig. 3D, Table S1). Furthermore, through KEGG pathway enrichment analysis, we identified 11 significant pathways, including Adherens junction, Fc gamma R-mediated phagocytosis, and the AGE-RAGE signaling pathway in diabetic complications (Fig. 3E, Table S2). These results suggest that these pathways and processes may be potential therapeutic targets for OV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDevelopment and validation of an OV prognostic model\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo identify hub genes related to the prognosis of OV, we conducted a univariate Cox analysis to assess the correlation between the expression levels of 242 DEhub genes and patient prognosis in the TCGA OV cohort. Using a significance threshold of P \u0026lt; 0.05, we identified 31 DEhub genes significantly associated with patient prognosis (Table S3). To address potential issues such as variable collinearity and overfitting, we performed LASSO Cox analysis on these 31 DEhub genes in the training set (Fig. 4A), selecting the minimum \u0026lambda; value from the model (Fig. 4B). This process led us to identify 19 genes (APBB2, C5AR1, CCDC80, CFI, CXCR2, CXCR4, DOCK11, FNIP1, GBP5, KATNAL1, KCTD1, LILRA2, MRC1, P2RX1, P2RY14, PI3, PYGB, TMOD2, and ZBP1) with corresponding weight coefficients shown in Fig. 4C. Subsequently, these 19 genes underwent multivariate Cox analysis (Fig. 4D), and their functional interactions with closely related proteins were explored (Fig. 4E). Using the expression levels and regression coefficients of these 19 DEhub genes, we calculated risk scores for each TCGA sample. Patients were then divided into high-risk and low-risk groups based on the median risk score, and the risk score formula was as follows: risk score = (-0.102) \u0026times; APBB2 + 0.016 \u0026times; C5AR1 + 0.13 \u0026times; CCDC80 + (-0.066) \u0026times; CFI + 0.485 \u0026times; CXCR2 + (-0.133) \u0026times; CXCR4 + 0.073 \u0026times; DOCK11 + 0.067 \u0026times; FNIP1 + (-0.088) \u0026times; GBP5 + 0.108 \u0026times; KATNAL1 + (-0.041) \u0026times; KCTD1 + 0.165 \u0026times; LILRA2 + 0.05 \u0026times; MRC1 + (-0.192) \u0026times; P2RX1 + (-0.678) \u0026times; P2RY14 + 0.089 \u0026times; PI3 + 0.177 \u0026times; PYGB + 0.073 \u0026times; TMOD2 + (-0.13) \u0026times; ZBP1. A heatmap depicted the expression of these 19 DEhub genes in each TCGA OV sample (Fig. 4F).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEfficacy of prognostic risk score model\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eacross OV cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the prognostic implications of the risk score, we performed Kaplan-Meier (KM) survival analysis on the TCGA OV cohort. The results\u0026nbsp;showed diminished OS for high-risk patients in both the training and test sets (Fig. S2A and S2B). The ROC curves indicated AUC values of 0.72, 0.77, and 0.74 for 1-year, 2-year, and 3-year OS in the training set and 0.61, 0.67, and 0.64 in the test set (Fig. S2D and 2E). When evaluating the model\u0026apos;s reliability on an external OV cohort, GSE140082, a significant decrease in OS for the high-risk group was observed (Fig. S2C). The corresponding ROC curves showed AUC values of 0.56, 0.63, and 0.64 for 1-year, 2-year, and 3-year survival (Fig. S2F). Upon analyzing the correlation between risk scores and OS among the TCGA OV patients, we categorized these patients based on their age, tumor stage, and grade. The data showed that high-risk patients generally had lower OS rates compared to their low-risk counterparts across the overall patient population (P \u0026lt; 0.0001; Fig. S2G) and when differentiated by age (P \u0026lt; 0.0001; Fig. S2H and S2I). The difference in OS was notably significant among patients with high-grade (G3+G4, P \u0026lt; 0.0001; Fig. S2J) and advanced-stage tumors (III+IV, P \u0026lt; 0.0001; Fig. S2K). However, for those diagnosed with early-stage (I+II, P = 0.25; Fig. S2L) or low-grade tumors (G1+G2, P = 0.46; Fig. S2M), there wasn\u0026apos;t a significant difference in OS between the high and low-risk groups. The results suggest that the risk score is a reliable prognostic indicator for OS in OV, particularly among high-grade and advanced-stage tumors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConstruction and validation of the prognostic nomogram\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the clinical utility of the prognostic model, we identified independent prognostic clinical factors through univariate and multivariate Cox analyses. Both analyses unveiled significant associations between OS and the risk score (both HR = 2.7, P \u0026lt; 0.001; Fig. 5A and 5B). Subsequently, we constructed a nomogram by integrating the risk score with tumor stage, grade, and age, aiming to forecast 1-year, 2-year, and 3-year survival probabilities (Fig. 5A). The calibration plot showed good agreement between the observed and predicted survival rates spanning 1, 2, and 3 years (Fig. 4D-F). The DCA for these durations further underscored the clinical utility of this nomogram (Fig. 4G-I). These data suggest that the constructed nomogram provides a valuable tool for predicting survival outcomes in OV patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation of risk score with immune cell infiltration\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the potential interplay between the risk score and the tumor\u0026apos;s immune microenvironment, we used the CIBERSORT algorithm to quantify the infiltration of immune cells within the tumor and compared these measurements across risk groups. The results revealed significant differences in the abundance of seven immune cell types between the risk groups (Fig. 6A and 6B). In particular, the high-risk group was characterized by elevated counts of M0 macrophages and monocytes, coupled with a subtle rise in M2 macrophages. Conversely, counts of M1, CD8+ T cells, and T follicular helper (Tfh) cells decreased significantly in this group. By employing the ESTIMATE algorithm, we found that the ESTIMATEScore and stromal score were noticeably higher in the high-risk group compared to the low-risk group (P = 0.014 and 0.00011, respectively; Fig. 6C and 6E), while tumor purity reduced significantly in the high-risk group (P = 0.014; Fig. 6D). However, no marked difference was observed in the immune score across the groups (Fig. 6F). Subsequent TIDE analyses to measure the potential for immunotherapy response revealed that interferon gamma (IFNG), CD8, microsatellite instability (MSI), and myeloid-derived suppressor cell (MDSC) levels decreased in the high-risk group, while T cell dysfunction, exclusion, and cancer-associated fibroblasts (CAF) increased (Fig. 6G). These observations suggest an intricate relationship between the risk score and the immune landscape of the tumor.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug sensitivity analysis in different risk categories\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate how the risk score reflects therapeutic responsiveness, we projected drug sensitivity in patients. Differences in drug sensitivity between risk groups were determined based on the prediction scores (Fig. 7A). The lower the prediction score, the higher the sensitivity of the corresponding patients to the drug. Three drugs, namely Sabutoclax_1849, AGI-6780_1634, and BMS-536924_1091, emerged as the most correlated with the risk scores (Fig. 7B). However, the prediction scores of Sabutoclax_1849 showed no difference between the two groups. Thus, we only display the Pearson correlation coefficient (r) and P-value between the risk score and the prediction scores of AGI-6780_1634 and BMS-536924_1091 (Fig. 7C and 7D). BMS-536924_1091 response was negatively correlated with the risk score (r = -0.22, p = 1.5e-05), while AGI-6780_1634 response was positively correlated with the risk score (r = 0.25, p = 1.4e-06). These patterns suggest that risk scores could potentially serve as informative indicators for therapeutic strategies in OV.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eModel gene expression in external OV datasets and OV cell lines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLastly, we examined the alterations in model gene expression in external OV datasets and OV cell lines. The expression patterns of these model genes in GSE18520 and GSE27651 datasets (Fig. 8A) are summarized in Table 2. Notably, our analysis revealed that CXCR4 emerged as the only m6A target gene, subject to regulation by m6A-associated genes FTO, METTL14, ELAVL1, and YTHDF2 (Fig. 8B). This finding suggests a complicated regulatory network governing CXCR4 in OV. Furthermore, in SKOV-3 and A2780 OC cell lines, qRT-PCR analysis revealed notable upregulation of C5AR1, CFI, CXCR2, CXCR4, \u0026nbsp;KCTD1, PI3, PYGB, ZBP1, and LILRA2 (only in SKOV3 cells) compared to HOSEpiC. Conversely, CCDC80, FNIP1, GBP5 (only in SKOV3 cells), KATNAL1, \u0026nbsp;MRC1, P2RX1, and TMOD2 (only in A2780 cells) showed significant downregulation (all P \u0026lt; 0.05). The other genes did not exhibit significant changes (all P \u0026gt; 0.05; Fig. 9). These findings are generally consistent with the gene expression alterations observed in OV datasets (Table 2). \u0026nbsp;\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, shared DEGs were identified between PCOS and OV using multiple datasets. Eight DEGs were associated with both m6A modification and cellular senescence, suggesting their role in both conditions. A 19-gene prognostic model was developed and validated for OV, demonstrating its effectiveness in predicting patient outcomes and revealing associations with immune cell infiltration and drug sensitivity. Furthermore, qRT-PCR analysis confirmed notable upregulation or downregulation of the model genes in SKOV-3 and A2780 cell lines compared to HOSEpiC, consistent with the gene expression patterns observed in OV datasets. The developed 19-gene prognostic model for OV offers potential clinical significance by providing a valuable tool for predicting patient outcomes and guiding personalized therapeutic approaches.\u003c/p\u003e\n\u003cp\u003eWe identified eight significant m6A_CellAgeRGs, including LMNB1, SNAI2, NOTCH1, DDIT3, MEIS2, STAT5A, TACC3, and VCAN, which are common between PCOS and OV. This overlap suggests potential shared molecular mechanisms or pathways that might underlie the progression from PCOS to OV. LMNB1, from the lamin family, is pivotal for maintaining the structure and function of the cell nucleus[19]. Notably, its related protein, LMNB2, has been identified as a potential biomarker for ovarian cancer risk in women with PCOS[20]. Moreover, LMNB1 is one of the top hub genes in the PPI network of PCOS, playing a central role in the core network of PCOS-related genes[21]. This connection suggests that changes in the nuclear lamina could bridge PCOS and OV. The association between irregular menstrual cycles and an elevated risk of OV further strengthens the link between PCOS and OV[22]. SNAI2 exhibits differential expression in ovarian tissue from PCOS patients with irregular menstruation compared to those with regular cycles[8]. This implies that hormonal imbalances or irregular menstrual cycles, common in PCOS, might set the stage for cellular changes that predispose to OV. DDIT3\u0026apos;s association with both PCOS and OV is also intriguing. While it correlates positively with tumor purity in OV, its role as a core ferroptosis-related gene in PCOS suggests that cellular stress and death pathways might be a bridge connecting these two conditions[23]. Moreover, the enrichment of DEhub genes in pathways like the AGE-RAGE signaling pathway in OV, offers another potential connection between PCOS and OV, given its role in chronic inflammation[24], a hallmark of PCOS[25-27]. In OV, the AGE-RAGE pathway is associated with tumor growth, angiogenesis, and metastasis[28, 29]. These results suggest that the shared DEGs and signaling pathways might collectively drive the progression from PCOS to OV.\u003c/p\u003e\n\u003cp\u003eIn this study, we observed that the high-risk group exhibited increased levels of M0 macrophages and monocytes, accompanied by a slight elevation in M2 macrophages. Conversely, we observed a significant reduction in the counts of M1 macrophages, CD8+ T cells, and TFH cells within this group. These findings collectively suggest a distinct immunological profile in the high-risk individuals, potentially indicating an immune microenvironment conducive to OV progression. Elevated levels of M0 macrophages and monocytes indicate a potential pro-tumorigenic environment, as these cells can promote tumor growth and suppress anti-tumor immune responses[30]. Malignant OV cells can release M2-like cytokines, including IL-10, CCL2/3/4/5/7/8, CXCL12, VEGF, and PDGF, as part of their strategy to attract and recruit additional monocytes and M0 macrophages to the tumor site, subsequently inducing their transformation into the M2 phenotype[31]. The subtle increase in M2 macrophages further supports a tumor-promoting milieu, as M2 macrophages are known for their immunosuppressive functions in OV[32]. Conversely, the significant decrease in M1 macrophages, CD8+ T cells, and TFH cells suggests impaired anti-tumor immunity, as these cells play crucial roles in mounting effective immune responses against OV[33]. High-grade serous OV tumors with higher estimated proportions of TFH and M1 macrophages were significantly and independently associated with long-term survival[34]. This distorted immune profile in the high-risk group may contribute to disease progression and highlights the importance of therapeutic strategies aimed at restoring a balanced and robust anti-tumor immune response in OV patients at risk.\u003c/p\u003e\n\u003cp\u003eTIDE analyses in the high-risk group indicated a decrease in key immune factors such as IFNG, CD8 T cells, MSI, and MDSC, coupled with an increase in indicators of T cell dysfunction, immune exclusion, and the presence of CAF. In OV immunotherapy, these findings suggest that the high-risk group may face challenges in responding effectively to immunotherapeutic interventions. The decrease in CD8 T cells, which are crucial for anti-tumor immune responses, and the rise in immunosuppressive MDSCs can compromise the immune system\u0026apos;s ability to target and control OV[35, 36]. Additionally, elevated T cell dysfunction and immune exclusion markers indicate a potentially hostile tumor microenvironment that hinders immune cell infiltration and function in OV[37, 38]. The presence of CAFs, known for their role in promoting tumor growth and immune evasion, further complicates the therapeutic landscape[39, 40]. These results highlight the need for personalized immunotherapeutic strategies that address the specific immune challenges faced by high-risk OV patients.\u003c/p\u003e\n\u003cp\u003eThe limitations of this study include the reliance on bioinformatics and data analysis, which requires further experimental validation to confirm the functional roles of identified genes and their clinical significance. Additionally, the study\u0026apos;s findings are based on retrospective data, and prospective clinical studies are needed to validate the prognostic model\u0026apos;s effectiveness in real-world clinical settings. Lastly, the study focuses on associations and does not establish causation, warranting future mechanistic investigations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, our study identified shared dDEGs between PCOS and OV, highlighting the potential involvement of m6A modification and cellular senescence in both conditions. We developed a prognostic model for OV based on 19 hub genes, offering promise for risk assessment in OV patients. This model demonstrated efficacy across multiple OV cohorts and revealed associations with the tumor immune microenvironment and drug sensitivity. Further research and clinical validation are needed to fully realize the clinical significance of these findings and their potential impact on OV patient care.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003ePolycystic ovary syndrome (PCOS); ovarian cancer (OV); differentially expressed genes (DEGs); ovarian cancer (OV); N6-methyladenosine (m6A); methyltransferase-like-3 (METTL3); YTH domain family protein 1 (YTHDF1); Weighted Gene Co-Expression Network Analysis (WGCNA); Gene Ontology (GO); Genomics of Drug Sensitivity in Cancer (GDSC2); Cancer Therapeutics Response Portal (CTRP V2); Receiver Operating Characteristic analysis (ROC); Kaplan-Meier (KM); T follicular helper (Tfh); interferon gamma (IFNG); microsatellite instability (MSI); myeloid-derived suppressor cell (MDSC); cancer-associated fibroblasts (CAF)\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data generated or analysed during this study are included in this published article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was supported by the Famous Doctor Project of Xingdian Talent Support Program in Yunnan Province [XDYC-MY-2022-0057]; Kunming Medical UniversityYoung and Middle-aged Discipline Leaders and Reserve Candidates -\u0026quot;Riding the Wind* Talent Cultivation Programme [2023(108)]; External Cooperation Research Project of the Second Affiliated Hospital of Kunming Medical University - Mechanism of histone lactate regulation of METTL3 in PCOS affecting the proliferation of ovarian granulosus cells [2022dwhz03].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYL and CYS carried out the studies, participated in collecting data, and drafted the manuscript. YJGX and YZW performed the statistical analysis and participated in its design. YYM and XH participated in acquisition, analysis, or interpretation of data and draft the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eKh MM, Boboev K. Modern aspects of etiology, diagnosis, and treatment of polycystic ovarian syndrome. European journal of modern medicine and practice. 2022;2:86-93.\u003c/li\u003e\n\u003cli\u003eGuan C, Zahid S, Minhas AS, Ouyang P, Vaught A, Baker VL, et al. Polycystic ovary syndrome: a \u0026ldquo;risk-enhancing\u0026rdquo; factor for cardiovascular disease. Fertility and sterility. 2022;117:924-35.\u003c/li\u003e\n\u003cli\u003eMaldonado SS, Grab J, Wang CW, Huddleston H, Cedars M, Sarkar M. 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Is interleukin-17 involved in the interaction between polycystic ovary syndrome and gingival inflammation? J Periodontol. 2013;84:1827-37.\u003c/li\u003e\n\u003cli\u003eForoozanfard F, Soleimani A, Arbab E, Samimi M, Tamadon MR. Relationship between IL-17 serum level and ambulatory blood pressure in women with polycystic ovary syndrome. J Nephropathol. 2017;6:15-24.\u003c/li\u003e\n\u003cli\u003eWang Y, Li BX, Li X. Identification and validation of angiogenesis-related gene expression for predicting prognosis in patients with ovarian cancer. Frontiers in Oncology. 2022;11:783666.\u003c/li\u003e\n\u003cli\u003eRahimi F, Karimi J, Goodarzi MT, Saidijam M, Khodadadi I, Razavi ANE, et al. Overexpression of receptor for advanced glycation end products (RAGE) in ovarian cancer. Cancer Biomarkers. 2017;18:61-8.\u003c/li\u003e\n\u003cli\u003eBoutilier AJ, Elsawa SF. Macrophage polarization states in the tumor microenvironment. International journal of molecular sciences. 2021;22:6995.\u003c/li\u003e\n\u003cli\u003eZhang M, He Y, Sun X, Li Q, Wang W, Zhao A, et al. A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients. Journal of ovarian research. 2014;7:1-16.\u003c/li\u003e\n\u003cli\u003eNowak M, Klink M. The role of tumor-associated macrophages in the progression and chemoresistance of ovarian cancer. Cells. 2020;9:1299.\u003c/li\u003e\n\u003cli\u003eGao Y, Chen L, Cai G, Xiong X, Wu Y, Ma D, et al. Heterogeneity of immune microenvironment in ovarian cancer and its clinical significance: a retrospective study. Oncoimmunology. 2020;9:1760067.\u003c/li\u003e\n\u003cli\u003eBerry L, Kelly M, Miller L. Tumor immunogenicity status in high-grade serous ovarian cancer. Gynecologic Oncology. 2021;162:S319.\u003c/li\u003e\n\u003cli\u003eWu JW, Dand S, Doig L, Papenfuss AT, Scott CL, Ho G, et al. T-Cell receptor therapy in the treatment of ovarian cancer: A mini review. Frontiers in immunology. 2021;12:672502.\u003c/li\u003e\n\u003cli\u003eMabuchi S, Sasano T, Komura N. Targeting myeloid-derived suppressor cells in ovarian cancer. Cells. 2021;10:329.\u003c/li\u003e\n\u003cli\u003eDesbois M, Udyavar AR, Ryner L, Kozlowski C, Guan Y, D\u0026uuml;rrbaum M, et al. Integrated digital pathology and transcriptome analysis identifies molecular mediators of T-cell exclusion in ovarian cancer. Nature communications. 2020;11:5583.\u003c/li\u003e\n\u003cli\u003eJiang P, Gu S, Pan D, Fu J, Sahu A, Hu X, et al. Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response. Nature medicine. 2018;24:1550-8.\u003c/li\u003e\n\u003cli\u003eGao Q, Yang Z, Xu S, Li X, Yang X, Jin P, et al. Heterotypic CAF-tumor spheroids promote early peritoneal metastasis of ovarian cancer. Journal of Experimental Medicine. 2019;216:688-703.\u003c/li\u003e\n\u003cli\u003eZhang M, Chen Z, Wang Y, Zhao H, Du Y. The role of cancer-associated fibroblasts in ovarian cancer. Cancers. 2022;14:2637.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1 Information of public datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003e\u003cstrong\u003eID\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePlatform\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSample size\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003e\u003cstrong\u003eData type\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eDisease\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGSE34526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eGPL570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNormal or PCOS granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003emRNA array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003ePCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGSE137684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eGPL17077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNormal or PCOS granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003emRNA array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003ePCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGSE80432\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eGPL6244\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNormal or PCOS granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003emRNA array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003ePCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGSE114419\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eGPL17586\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNormal or PCOS granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003emRNA array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003ePCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGSE102293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eGPL570\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNormal or PCOS granulocytes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003emRNA array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003ePCOS\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGSE18520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eGPL570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNormal or cancer tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003emRNA array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eOV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGSE27651\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eGPL570\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eNormal or cancer tissue\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003emRNA array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eOV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eGSE140082\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003eGPL14951\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eCancer tissue with prognosis data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003emRNA array\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eOV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"15.306122448979592%\"\u003e\n \u003cp\u003eTCGA-OV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.224489795918368%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"28.571428571428573%\"\u003e\n \u003cp\u003eCancer tissue with prognosis data\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.183673469387756%\"\u003e\n \u003cp\u003e373\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.285714285714286%\"\u003e\n \u003cp\u003eRNASeq\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eOV\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2 Model gene expression in external OV datasets\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv align=\"\"\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003e\u003cstrong\u003eGene\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOV_GSE18520\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003eOV_GSE27651\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePCOS_DEGs\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eAPBB2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eC5AR1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eCCDC80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eCFI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eCXCR2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eCXCR4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eDOCK11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eFNIP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eGBP5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n 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width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003ePYGB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eTMOD2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003eDown\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.489795918367346%\"\u003e\n \u003cp\u003eZBP1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"32.6530612244898%\"\u003e\n \u003cp\u003eUp\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"21.428571428571427%\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Polycystic Ovary Syndrome, Ovarian Neoplasms, m6A RNA Modification, Cellular Senescence, Prognosis, Tumor Microenvironment","lastPublishedDoi":"10.21203/rs.3.rs-4034917/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4034917/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Polycystic ovary syndrome (PCOS) and ovarian cancer (OV) are significant women's health concerns. This study aims to identify genes related to m6A modification and cellular senescence that are shared by PCOS and OV and to develop a prognostic model for OV outcomes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Transcriptomic datasets from GEO and TCGA were collected to identify differentially expressed genes (DEGs) associated with m6A modifications and cellular senescence in both PCOS and OV. We identified hub genes through WGCNA and assessed their prognostic significance. An OV prognostic model was developed and validated using multiple datasets, and correlations between risk scores and tumor immune microenvironment were evaluated. Drug sensitivity was predicted based on risk scores. qRT-PCR was performed to verify model gene alterations in OV cell lines.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e We identified 73 DEGs that were common to both PCOS and OV, with eight genes associated with m6A modification and cellular senescence. WGCNA revealed 242 DEhub genes. A 19-gene prognostic model was established, exhibiting impressive efficacy with AUCs exceeding 0.7 for predicting OV patient survival. High-risk scores correlated with increased M0 macrophages and monocytes and decreased M1, CD8+ T cells, and T follicular helper cells. Drug sensitivity varied based on risk scores. Model gene alterations were verified in external OV datasets and OV cell lines. In external datasets, CXCR4 emerged as a key target gene of m6A modification.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions: \u003c/strong\u003eThe derived 19-gene prognostic model offers potential avenues for risk assessment and personalized therapy in OV patients.\u003c/p\u003e","manuscriptTitle":"Constructing an Ovarian Cancer Prognostic Model Based on Shared m6A Modifications and Cellular Senescence-Related Genes with Polycystic Ovary Syndrome ","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-03-12 17:45:59","doi":"10.21203/rs.3.rs-4034917/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"062908db-377e-4895-b1d5-bf0f3bf74926","owner":[],"postedDate":"March 12th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-31T06:44:27+00:00","versionOfRecord":[],"versionCreatedAt":"2024-03-12 17:45:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4034917","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4034917","identity":"rs-4034917","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}