Evaluation and in vitro verification of the prognostic value of palmitoylation-related genes in ovarian cancer

Evaluation and in vitro verification of the prognostic value of palmitoylation-related genes in ovarian cancer | 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 Article Evaluation and in vitro verification of the prognostic value of palmitoylation-related genes in ovarian cancer Qiang Huang, Min He, Ting Cao, Chunyan Chen, Peng Zhou, Zhongyan Liu, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8340423/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2026 Read the published version in Scientific Reports → Version 1 posted 20 You are reading this latest preprint version Abstract Background Ovarian cancer (OC) remains the most lethal malignancy within the spectrum of gynecological cancers globally. While protein S-palmitoylation has been extensively implicated in tumor progression, its specific functional contributions and molecular mechanisms in the context of OC pathogenesis remain to be fully elucidated. This article aims to explore the prognostic effect associated with palmitoylation in OC. Methods To begin with, we obtained transcriptomic data for ovarian cancer (OC) from publicly available genomic repositories. Using comparative analysis, we pinpointed two distinct sets of differentially expressed genes (DEGs): DEGs1, which are associated with OC, and DEGs2, which are linked to palmitoylation. Consequently, a prognostic risk model was constructed and validated. Following this, an independent survival analysis was executed, a nomogram was subsequently developed to establish a predictive model. Multiple analytical approaches were applied to stratified risk groups, including pathway enrichment assessment, immune microenvironment infiltration evaluation, immune checkpoint examination, potential drug screening, and genomic mutation profiling. The expression patterns of identified prognostic markers were subsequently validated by means of reverse transcription quantitative PCR (RT-qPCR). Results Through intersecting DEGs1 and DEGs2, we obtained 24 candidate biomarkers. Our investigation revealed that HSPG2, BRD4, RARRES1, and SCGB1D2 served as prognostic markers, which were utilized to develop a risk assessment model demonstrating excellent capability in evaluating OC patient prognosis via comprehensive analytical procedures. Risk scoring, ethnicity, and tumor staging emerged as independent predictive determinants for OC. The constructed prognostic nomogram exhibited robust predictive capacity for patient clinical outcomes. There were four variables, including age (> 45), race (white), stage 3, and histologic G3, with survival distinctions between two risk cohorts. Relevant pathways contained distinct ribosome-related and translation initiation activities. The prognostic genes were linked to seven immune cells, like Eosinophils, and notable distinctions were found in seven immune checkpoints, like CTLA4 and CD274, between the two risk cohorts. Finally, there was a notable distinction in IC 50 for all 131 drugs, like BMS.536924 and CGP.60474, and the TP53 gene showed a notable mutation rate in risk cohorts. Relative to the control cohort, the expression levels of HSPG2, SCGB1D2, and BRD4 showed a significant up-regulation in the OC cohort, while the expression of RARRES1 was notably up-regulated in the control cohort, consistent with its expression in GSE26712. Conclusion This study identified HSPG2, BRD4, RARRES1, and SCGB1D2, which were prognostic genes associated with palmitoylation, providing valuable insights that could lay the foundation for innovative therapeutic strategies. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Health sciences/Oncology Ovarian cancer Palmitoylation Related Genes Prognostic genes Independent prognostic factors Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 1. Introduction Worldwide, Ovarian cancer (OC) exhibits the highest incidence rate among gynecological malignant tumors and contributes substantially to global cancer-related mortality in females, holding an eighth-ranking position in terms of cancer-associated fatalities [ 1 , 2 ] . As reported in Hong Kong, China, the incidence rate was 11.5/100,000 in 2023 [ 3 ] . Clinically, OC manifests as a heterogeneous malignancy characterized by varied pathobiological behaviors and molecular subgroups that translate into distinct clinical trajectories. The majority of ovarian cancer pathologic types are of epithelial origin (90%), among which the high-grade serous subtype contributes to 70–80% of all mortality cases, while 70% of ovarian cancer is identified as advanced disease when diagnosed, with the lack of clinical manifestations. Following cytoreductive surgery combined with platinum-based chemotherapy, 70% of patients are still likely to develop metastasis within a 2–3 year period [ 4 ] . Despite the application of poly-ADP-ribose polymerase (PARP) inhibitors and angiogenesis blockers like bevacizumab as maintenance therapies, outcomes for advanced and recurrent cases remain disappointing. The clinical results have fallen short of expectations, leaving the long-term prognosis with much room for improvement. Given the pressing need to enhance clinical outcomes for ovarian cancer, discovering new therapeutic targets has become a critical priority. Palmitoylation, a critical lipid-mediated post-translational modification, exerts a pivotal function in modulating protein behavior, affecting membrane binding, intracellular localization, protein stability, and functional activity. This process holds substantial relevance in human disease, especially in the development and progression of various cancers [ 5 – 7 ] . S-palmitoylation constitutes the predominant modification subtype, characterized by covalent attachment of palmitic acid (16-carbon fatty acid) to specific cysteine residues via reversible thioester linkages [ 7 , 8 ] . This dynamic process is orchestrated by palmitoylating and depalmitoylating enzymes, which play pivotal roles in oncogenesis, tumor expansion, therapeutic response, and clinical outcomes. The zinc finger DHHC-type containing (ZDHHC) palmitoyl S-acyltransferase (PAT) family predominantly mediates protein palmitoylation reactions [ 9 ] . Mammalian systems express 23 zDHHC variants (zDHHC1-24, excluding zDHHC10). Growing evidence demonstrates that palmitoylation cycling directly influences protein functionality, consequently modifying cellular signaling networks and promoting malignant transformation. Furthermore, the palmitoylation state of key proteins (e.g., EGFR, RAS, and PD-1/PD-L1) exerts a critical influence on tumor progression and therapeutic responsiveness. This study employs an integrated multi-omics strategy To conduct a systematic investigation into the prognostic significance and molecular mechanisms of palmitoylation-related genes (PRGs) in OC. By analyzing transcriptomic datasets from GEO and TCGA-OV, We conducted differential gene expression profiling as well as survival prognostic analysis, and functional enrichment annotation to clarify the regulatory role of PRGs in OC pathogenesis. To assess the correlation between PRG activity and patient prognosis, single-sample gene set enrichment analysis (ssGSEA) was adopted, followed by the identification and functional annotation of OC-specific PRGs. The Cox proportional hazards model was utilized to establish a prognostic signature based on PRGs, and its predictive performance and clinical utility were thoroughly assessed. Furthermore, we explored PRG-mediated modulation of tumor microenvironment immune characteristics and validated the expression patterns of key PRGs through in vitro experiments. This study provides the first comprehensive characterization of the global palmitoylation regulatory network in OC, offering novel insights into disease mechanisms and laying the foundation for developing precision therapies targeting protein palmitoylation. 2. Materials and methods 2.1 Data collection The OC related transcriptome datasets (GSE26712, GSE51088) were acquired from the Gene Expression Omnibus (GEO) database ( https://www.ncbi.nlm.nih.gov/geo/ ). GSE26712, which uses the GPL96 platform, comprises 185 tumour tissue samples derived from OC patients and 10 control samples of ovarian surface epithelial tissue, among which 153 samples include survival-related information [ 10 ] . On the other hand, GSE51088 (GPL7264 platform) consisted of tumour tissue from 172 OC samples (containing 152 samples with survival information) [ 11 ] . Gene expression profiles, clinical information, additionally, survival records of The Cancer Genome Atlas (TCGA)-OV cohort were obtained from the University of California, Santa Cruz (UCSC) Xena platform (available at https://xenabrowser.net/ ) on May 17, 2024, encompassing 378 OC tumour tissue samples [ 12 ] . In addition, 23 palmitoylation-related genes (PRGs) were gained in published literature [ 13 ] . 2.2 Acquisition of candidate genes To identify genes associated with palmitoylation, we initially computed PRGs values in the GSE26712 dataset employing the ssGSEA methodology through the GSVA software package (v1.38.2) [ 14 ] . Then, we assessed the PRGs scores disparities between OC and control samples (P < 0.05) and employed the ggplot2 package (v 0.1.4) [ 15 ] for visualization. Subsequently, a total of 153 OC samples in GSE26712 that included survival information were classified into high-scoring and low-scoring cohorts according to the best ssGSEA cut-off value for differential PRGs (minprop = 0.2). The survival package (version 3.5-3) [ 16 ] , was used to assess survival differences between the two scoring groups; this was achieved by constructing Kaplan-Meier (K-M) survival curves and conducting Log-rank statistical tests (with a significance threshold of P < 0.05). Differentially expressed genes (DEGs1) identification was accomplished via the limma package (v 1.38.0) [ 17 ] by contrasting gene expression profiles of OC patients against control samples in the GSE26712 dataset, applying cutoff criteria of |log2Fold Change (FC)| > 0.5 and p-value < 0.05. Similarly, DEGs2 were detected in high-scoring and low-scoring groups by means of the limma package (version 1.38.0). The visualization of these DEGs1 and DEGs2 involved creating a heatmap and a volcano plot was generated using the ggplot2 package (version 0.1.4) [ 15 ] . A Venn diagram was created using the ggVenn package (v 1.2.2) [ 18 ] to determine common genes between DEGs1 and DEGs2 derived from the previous analyses, and these overlapping genes were termed candidate targets. 2.3 Biological enrichment and molecular interaction network analysis To investigate the biological processes and signaling networks that are implicated, we conducted a functional enrichment analysis on the target genes by applying both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) [ 19 – 21 ] approaches. This approach shed light on the molecular functions and key pathways involved, providing deeper insights into the genetic framework under investigation. For this in-depth analysis, we employed the ClusterProfiler package (version 4.4.4) with a statistical significance threshold of p < 0.05. Following this, protein-protein interaction networks were built by utilizing the STRING database (Search Tool for the Retrieval of Interacting Genes; https://string-db.org ) with a confidence threshold above 0.15 [ 22 ] . Network visualization and topological analysis were accomplished through Cytoscape software (v 3.1.1) [ 23 ] for the identification of hub genes and interaction patterns. 2.4 Construction and validation of risk model The TCGA-OV cohort (N = 378) functioned as the development dataset for risk model construction, whereas the GSE51088 cohort (N = 152) was employed for external validation of prognostic performance in OC patients. Initially, based on OC tumour samples from TCGA-OV, the survival package (v 3.5-3) cox.zph function was employed to conduct univariate Cox regression to pinpoint survival-associated genes (Hazard Ratio (HR) ≠ 1, P 0.05), and results were presented using the forestplot package (v 2.0.1) [ 24 ] . Subsequent to this, the glmnet package (version 4.1.4) [ 24 ] was used to perform least absolute selection and shrinkage operator (LASSO) analysis. The study's prognostic genes were determined at the point when lambda reached lambda.min. Thereafter, in TCGA-OV, for each ovarian cancer patient in the dataset, individualized risk scores were calculated by utilizing the relative expression levels of prognostic genes and the coefficients obtained from LASSO regression. The formula used was \(\:\text{R}\text{i}\text{s}\text{k}\text{s}\text{c}\text{o}\text{r}\text{e}\:=\:\sum\:_{\text{i}\:=\:1}^{\text{n}}\text{c}\text{o}\text{e}\text{f}\left({\text{g}\text{e}\text{n}\text{e}}_{\text{i}}\right)\ast\:\text{e}\text{x}\text{p}\text{r}\left({\text{g}\text{e}\text{n}\text{e}}_{\text{i}}\right)\) , wherein expr indicated the expression value of prognostic gene i, while coef represented the LASSO-derived coefficient for prognostic gene i. Patients were stratified into high-risk and low-risk subgroups using the optimal threshold. Risk distribution analysis was subsequently performed by generating scatter plots that illustrated risk score patterns and survival outcomes of OC patients. Additionally, the expression profiles of prognostic biomarkers were visualized, and Kaplan-Meier (K-M) survival analysis for overall survival (OS) between the two risk groups was performed using the survminer package (v 0.4.9) [ 25 ] (P < 0.05). Following this, receiver operating characteristic (ROC) analysis was conducted using the survivalROC package (v 1.0.3) [ 26 ] to calculate area under curve (AUC) values at 1, 2, and 3-year time points for assessing model performance. Additionally, model validation was conducted in an independent validation dataset. 2.5 Independent prognostic analysis To construct a risk stratification system for estimating patient survival probabilities in OC Firstly, integration of risk scores as well as clinical characteristics (including risk score, age, race, histological grade (G1/2 and G3), stage (stage1/2, stage3, stage4) of OC patients in TCGA-OV dataset were integrated, additionally, univariate Cox regression analysis (with P < 0.05 as the cutoff) was conducted together with multivariate Cox regression analysis to identify independent prognostic factors (using P < 0.05 as the significance threshold). Following this, a nomogram was built using the rms package (version 6.5-1) [ 27 ] , incorporating independent prognostic factors. The rms package (v 6.5-1), the same as before, allowed for the generation of calibration plots at 1, 2, and 3-year intervals, whereas ROC curve visualization was achieved through the timeROC package (v 0.4) [ 28 ] Clinical utility evaluation of the predictive model was conducted by generating decision curves using the ggDCA package (v 1.2) ( https://www.rdocumentation.org/packages/ggDCA/versions/1.1 ). 2.6 Correlation of risk score with clinical characteristics Through systematic evaluation involving multiple analytical approaches, The relationship between calculated risk scores and demographic as well as clinical features of OC study participants was assessed. Firstly, the distinctions in risk score among different clinical characteristics were compared. The survival package (v 3.5-3) and the survminer package (v 0.4.9) [ 29 ] were employed to construct KM curves, which assessed the distinctions in OS between the two risk cohorts under different subsets of clinical features. 2.7 Gene set enrichment analysis (GSEA) of high and low risk cohorts Using calculated risk assessment values, the TCGA-OV training dataset patients were categorized into separate risk groups. Then, all genes were sorted based on logFC between the two risk cohorts. Finally, to conduct GSEA on the two risk cohorts, the ClusterProfiler package was employed. The c2.cp.v2023.2.Hs.symbols.gmt gene set was sourced from MSigDB database ( https://www.gsea-msigdb.org/gsea/msigdb ) applying threshold parameters of false discovery rate (FDR) < 0.25 and P < 0.05. The enrichplot package (v 0.92) ( https://rdrr.io/cran/corrplot/ ) facilitated line plot generation, depicting the top 5 pathways ranked by significance from highest to lowest (P < 0.05). 2.8 Analysis of immune cell infiltration and assessment of immunotherapy response The CIBERSORT algorithm (v 1.03) [ 30 ] was used to perform immune cell composition analysis, quantifying 22 immune cell subtypes in the TCGA-OV cohort. Differential immune infiltration patterns were examined and visualized through box plots displaying immune cell abundance variations (P < 0.05). The psych package (v 2.4.3) [ 31 ] . Supported correlation analysis for examining relationships among differential immune cells and associations between prognostic genes and immune cell populations. Furthermore, to evaluate immunosuppressive traits and treatment response potential, immune checkpoint assessment across risk-stratified cohorts was carried out using the Tumor Immune Dysfunction and Exclusion (TIDE) platform ( http://tide.dfci.harvard.edu ). 2.9 Drug sensitivity analysis The information on chemical drugs for OC and their half maximal inhibitory concentration (IC 50 ) values were retrieved from the GDSC (Genomics of Drug Sensitivity in Cancer) databases ( http://cancerrxgene.org ). Pharmacogenomic sensitivity profiling was conducted using the pRRophetic algorithm (v 0.5) [ 32 ] to predict drug response metrics (IC50) for standard chemotherapeutics and targeted therapies in the TCGA-OV cohort. Subsequently, differential drug sensitivity analysis was performed to identify significant variations in IC50 values between risk-stratified patient subgroups (adjusted P-value < 0.05). ggplot2 (v 3.4.1) was used to visualize the most significantly altered agents, based on hierarchical ranking by statistical significance. 2.10 Mutation status of OC patients in high and low risk cohorts In order to better understand variations in driver genes between two risk cohorts, the maftools package (v 2.20.0) [ 33 ] was utilized to analyse gene mutations in two risk cohorts and display the top 20 high-frequency mutated genes in a tumor mutational burden (TMB) waterfall plot. 2.11 Prognostic gene expression levels To investigate the expression of prognostic genes in OC tumour tissue samples and control ovarian surface epithelial tissue samples in GSE26712, the ggplot2 package (v 0.1.4) was utilized to plot box-and-line diagrams for visualisation. RNAs from 5 pairs of tumour tissue—consisting of 5 OC samples and 5 corresponding adjacent samples—were collected. Soybean-sized OC tissue and corresponding adjacent tissue were obtained intraoperatively from each ovarian cancer patient respectively. For each specimen, the tumor tissue content (requiring tumor cell proportion ≥ 70%) was confirmed by a pathologist immediately after excision. Then, 3g of fresh tissue was aliquoted under sterile conditions, quickly frozen in liquid nitrogen, and subsequently transferred to a -80°C ultra-low temperature refrigerator for storage to ensure RNA integrity. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Second Affiliated Hospital of Army Medical University (approval number: 2024-311-02). Informed consent was obtained from the patients. RNA isolation from tissue samples adhered to the manufacturer's protocols. The SweScript First Strand cDNA synthesis kit was used for reverse transcription, with SYBR Green qPCR Master Mix employed in qPCR reactions. Primer details are provided in Supplementary Table 1 and patient clinical information is presented in Supplementary Table 2 . Expression levels of genes were normalized against H-GAPDH and quantified via the 2 −△△Ct approach, with statistical evaluation conducted via Graphpad Prism 5. 2.12 Statistical analysis R (v 4.2.2) was used to perform all computational analyses. The Wilcoxon test was applied to assess cohort differences, with P < 0.05 set as the threshold for statistical significance. 3. Results 3.1 Acquisition of 24 candidate genes Overall, 4,317 DEGs¹ were detected, with 2,114 being up-regulated and 2,203 being down-regulated genes in OC samples ( Fig. 1A, 1B ). Additional analysis evaluated the variations in PRGs scores between OC samples and control samples in GSE26712, revealing a notable distinction in PRGs scores between the two cohorts (P < 0.05) (Figure 1C). Ten PRGs showing notable distinctions in their scores between the OC and control cohorts were selected for subsequent analyses. Based on 153 OC samples containing survival information in GSE26712, using the optimal cut-off value of 1.57929 for the ssGSEA scores of the 10 PRGs, they were classified into high-score (n = 98) and low-score (n = 55) cohorts. Log-rank analysis demonstrated significant survival disparities between scoring groups (P = 0.041), with the low-scoring cohort exhibiting reduced survival outcomes ( Fig. 1D ). A total of 31 DEGs2 were identified, consisting of 7 upregulated and 24 downregulated genes when comparing high-score versus low-score groups ( Fig. 1E ). Venn diagram analysis between DEGs1 and DEGs2 yielded 24 overlapping genes designated as potential candidates ( Fig. 1F ). 3.2 Biological pathways and PPI analysis of candidate genes Gene Ontology (GO) enrichment analysis of the 24 prognostic genes identified 246 significantly enriched biological processes (BP), encompassing hydrogen peroxide degradation pathways and extracellular matrix organization, while 29 cellular components (CC) entries including complex of collagen trimers, collagen-containing extracellular matrix, haptoglobin-hemoglobin complex, and 31 molecular functions (MF) encompassing antioxidant activity and transcriptional coactivation (Fig. 2A). Moreover, KEGG pathway analysis revealed 7 enriched pathways, particularly focal adhesion, ATP-dependent chromatin remodeling, as well as proteoglycans in cancer, african trypanosomiasis, among others ( Fig. 2B ). PPI network consisting of 19 nodes and 42 edges; the top 3 proteins, COL4A2, HSPG2, and FLNA, were interacted most strongly with other proteins ( Fig. 2C ). 3.3 Recognition of HSPG2, BRD4, RARRES1, and SCGB1D2 as prognostic genes After conducting univariate Cox regression screening (P 0.05 to confirm validity), 4 survival-associated genes were chosen for further analysis (Fig. 3A, Table 1). To minimize overfitting and enhance model robustness, these 4 candidates underwent LASSO regression analysis. The optimal regularization resulted in the identification of 4 prognostic biomarkers—HSPG2, BRD4, RARRES1, and SCGB1D2—with the optimal lambda set at 0.002224881 (Fig. 3B,3C), indicating their collective contribution to survival prediction. 3.4 Development of a risk model with high accuracy Thus, a risk model was established based on the expression intensity and risk coefficients of 4 prognostic genes. The constructed risk model was as follows: RiskScore = HSPG2 × 0.09 + BRD4 × 0.14 + RARRES1 × 0.08 + SCGB1D2 × (-0.01). According to this model, patients in TCGA-OV were classified into high-risk (n = 213) and low-risk (n = 165) cohorts using optimal cutoff value (1.147367) for risk score ( Fig. 4A, 4B ), and the number of deaths increased as risk score in the sample increased, where patients in the high-risk cohort had a reduced survival rate (P = 0.00041) ( Fig. 4C ). ROC curve analysis suggested that risk model had impressive predictive capacity, with AUCs for 1, 2, and 3 years in TCGA-OV being 0.65, 0.65, and 0.61, respectively ( Fig. 4D ). Additionally, patients were divided into high-risk (n = 71) and low-risk (n = 74) cohorts according to the optimal risk score cutoff value of (-0.00153919). Furthermore, validation of the risk model in the GSE51088 dataset confirmed that it had some predictive accuracy, as evidenced by AUCs exceeding 0.6 for 1, 2, and 3 years, underscoring the model's consistent prognostic strength ( Fig. 4E, 4F, 4G, 4H ). These outcomes validated the robustness of the risk model for evaluating the prognostic risk of OC patients. 3.5 Independent prognostic value of risk score, age, and stage in TCGA-OV Risk scoring, ethnicity, and tumor staging emerged as independent predictive determinants for TCGA-OV patients (Fig. 5A, 5B) . Building upon these findings, we constructed a predictive nomogram incorporating these significant prognostic variables (Fig. 5C) . The resulting calibration curve reflected nomogram's high predictive precision for patient outcomes at 1, 2, and 3-year intervals ( Fig. 5D ). ROC curve analysis for 1-, 2-, and 3-year periods in TCGA-OV yielded values of 0.70, 0.67, and 0.65, respectively, which suggested that that predictive performance of nomogram plot was good ( Fig. 5E, 5F, 5G ). DCA suggested that this nomogram provided notable clinical utility, which was superior to the utility of independent prognostic factors when used individually ( Fig. 5H ). 3.6 Survival of patients in high and low-risk cohorts in different clinical characterization subgroups Within the subgroups of age (>45), race (white), stage 3, and histologic G3, a higher proportion of patients were distributed in the high-risk cohort than in the low-risk cohort ( Fig. 6A ). There were survival distinctions between two risk cohorts under different cohorts of age (>45), race (white), stage 3, and histologic G3 (P < 0.05) ( Fig. 6B ). 3.7 Biological pathway and mutation point analysis of risk cohorts in OC Comparative pathway analysis between prognostic risk-stratified cohorts in the TCGA-OV dataset revealed 429 statistically significant enriched pathways, with prominent representation of translational initiation (Medicus reference), ribosomal signaling, and eukaryotic translation elongation processes (Reactome database) (Fig.7A) .The process of identifying and categorizing genes based on the presence of mutations was conducted by analyzing mutational profiles from the TCGA-OV dataset. Within the high-risk cohort, the TP53 gene stood out with an exceptionally high mutation rate, hitting 95% ( Fig.7B ). On the other hand, in the low-risk cohort, the TP53 gene also showed a notable mutation rate, at 91% ( Fig.7B , 7C ). 3.8 Analysis of immune cell infiltration and assessment of immunotherapy response in risk cohorts The immune cells with notable distinctions were analyzed in two risk cohorts (P < 0.05), which showed 7 immune cells that were notably different between cohorts, namely B cell memory, Dendritic cells activated, Eosinophils, Macrophages M2, NK cells activated, Neutrophils, and T cells gamma delta. On the other hand, M2 macrophages showed a notable up-regulation in the high-risk cohort, and activated NK cells exhibited significant up-regulation in the low-risk cohort ( Fig. 8A, 8B ). These 7 immune cells showed a weak correlation with prognostic genes (|cor| < 0.3) ( Fig. 8C, 8D ). It might be that functional differences between immune cells with different roles in the immune response lead to a weak correlation. Immunotherapy response assessment was then performed in TCGA-OV, among a total of 8 immune checkpoints (CTLA4, PDCD1LG2, CD274, LAG3, HAVCR2, TIGIT, PDCD1, and SIGLEC15). Except for SIGLEC15, the other 7 immune checkpoints showed a notable distinction between high and low-risk cohorts (P < 0.05). Furthermore, results from TIDE scoring demonstrated significant disparities between the two risk cohorts, and the high-risk cohort exhibited a higher TIDE score with 7 immune checkpoints, indicating a high potential for immune escape from 7 immune checkpoints in high-risk cohort and potentially poorer efficacy of immune checkpoint inhibitory therapy (ICI) ( Fig. 8E, 8F ). 3.9 Identifying chemotherapeutics associated with the risk score in OC Systematic profiling of chemosensitivity parameters was conducted for standard chemotherapeutic agents based on predictions from the GDSC database. The IC 50 of 131 drugs, including BI.2536, BMS.509744, BMS.536924, and CGP.60474, were notably distinctive between the two risk cohorts. The top 5 drugs were presented according to their adjusted P value. Among them, the IC 50 of BI.2536, BMS.509744, BMS.536924, and CGP.60474 in low-risk cohort were higher. Furthermore, the IC 50 of GDC.0449 in high-risk cohort was higher (Padj<0.05) ( Fig. 9 ). 3.10 Prognostic gene expression levels In GSE26712, all 4 prognostic marker genes were significantly different between OC and control cohorts. The expression of HSPG2, SCGB1D2, and BRD4 were notably up-regulated in the OC cohort, while the expression of RARRES1 was notably up-regulated in the control cohort (P < 0.05) ( Fig 10A ). Furthermore, experimental validation using RT-qPCR was performed to determine the expression levels of the 4 identified prognostic genes. The results demonstrated significant overexpression of HSPG2, SCGB1D2, and BRD4 in OC samples compared with healthy tissue controls, while RARRES1 showed prominent upregulation in the control cohort (P < 0.05). This expression pattern aligns with the computational results from GSE26712 analysis (Fig 10 B-E) , providing experimental support for the bioinformatics predictions. 4. Discussion Ovarian cancer (OC) ranks among the most invasive and deadly malignant tumors affecting the female reproductive system, with over 300,000 new cases diagnosed and more than 200,000 deaths recorded worldwide annually [ 34 ] . The absence of distinctive early-stage symptoms and reliable screening methods results in approximately 70% of patients receiving their diagnosis during advanced disease phases (stages III or IV), leading to unfavorable clinical outcomes [ 4 ] . Despite substantial advancements in surgical interventions, chemotherapeutic strategies, and targeted treatment modalities, the overall five-year survival rate continues to remain under 45% [ 35 ] . Consequently, the identification of innovative molecular markers and elucidation of their underlying pathways represents a critical priority for enhancing early detection capabilities and developing more efficacious individualized treatment approaches for OC patients. Over the past few years, researchers have increasingly focused on how epigenetic alterations drive cancer initiation and progression. As a reversible lipid modification, S-palmitoylation modulates protein stability, subcellular localization, and protein–protein interactions, and is broadly involved in cell signaling, immune regulation, and tumor development [ 36 ] . Although several studies have reported associations between palmitoyltransferases—such as members of the ZDHHC family—and cancer prognosis, a systematic investigation of protein palmitoylation in OC remains lacking [ 37 ] . Within this study, we conducted a comprehensive characterization of the expression profile and prognostic significance of PRGs, subsequently establishing a reliable four-gene signature that incorporates HSPG2, BRD4, RARRES1, and SCGB1D2. The model demonstrated favorable predictive performance across multiple clinical subgroups, underscoring its potential value for clinical risk stratification and personalized treatment in ovarian cancer. BRD4 functions as a chromatin reader that recognizes acetylated histones and regulates transcriptional elongation, thereby regulating the expression of genes involved in the cell cycle, inflammation, and tumorigenesis. In breast cancer, the low-abundance short isoform (BRD4-S) exhibits oncogenic activity, while the predominant long isoform (BRD4-L) suppresses tumor formation and metastasis. Intriguingly, in ovarian cancer, BRD4-L similarly inhibits proliferation and migration [ 38 ] . In OC, BRD4 exhibits significant amplification, with elevated expression strongly associated with unfavorable prognosis. Overexpression of BRD4-S promotes ovarian cancer cell proliferation, accumulation of DNA damage, and G2/M phase arrest, leading to a more aggressive tumor phenotype and resistance to platinum-based chemotherapy [ 39 ] . Additionally, BRD4 acts as a crucial regulator of super-enhancer activity, prompting the initiation of cancer-promoting pathways like MYC and BCL2 [ 40 ] . BRD4 inhibitors, including JQ1 and AZD5153, have shown potent anti-tumor effects in vitro and are considered promising targeted agents for ovarian cancer [ 41 ] . RARRES1 (also known as TIG1, tazarotene-induced gene 1) is a retinoic acid–responsive tumor suppressor gene involved in cell differentiation and apoptosis. Although RARRES1 acts as a tumor suppressor in cancers, its role exhibits tissue-specific duality. For example, in glomerular diseases, RARRES1 promotes p53-mediated podocyte apoptosis, leading to podocytopenia and glomerulosclerosis [ 42 ] . Recent studies have identified RARRES1 as a tumor microenvironment (TME)-associated gene, and its overexpression has been shown to inhibit ovarian cancer cell proliferation, migration, and invasion—findings that suggest a tumor-suppressive role and its potential to serve as a predictive biomarker for immune checkpoint inhibitor (ICI) response [ 43 ] . Moreover, single-cell analyses have revealed that RARRES1 is primarily enriched in immune cell subsets, indicating that it may exert protective effects by modulating the immune microenvironment [ 44 ] . HSPG2 encodes Perlecan, a multifunctional heparan sulfate proteoglycan that serves as a structural scaffold in the extracellular matrix (ECM) while dynamically regulating tumor angiogenesis, cell adhesion, and metastatic dissemination. Increased expression of HSPG2 has been detected in multiple cancers and is tightly linked to enhanced tumor invasiveness and ECM remodeling [ 45 ] . Increased HSPG2 expression acts as an independent prognostic indicator in patients with acute myeloid leukemia (AML) and correlates with an unfavorable clinical prognosis [ 46 ] . Moreover, HSPG2 has been shown to bind multiple growth factors, such as VEGF and FGF2, enhancing their signaling activity and promoting tumor angiogenesis and progression [ 47 ] . SCGB1D2, a member of the secretoglobin protein family, is widely expressed in exocrine tissues and is implicated in modulating immune responses, suppressing cell migration, and regulating inflammation. It acts as a host defense factor found in skin, sweat, and other secretions, and plays a protective role against Borrelia burgdorferi infection, offering a promising therapeutic avenue for Lyme disease [ 48 ] . Although SCGB1D2 has not been extensively studied in ovarian cancer, its expression has been associated with benign tumors in breast cancer, and downregulation is often observed in more aggressive cancer subtypes [ 49 ] . In the present study, downregulation of SCGB1D2 was correlated with a high-risk score in patients with ovarian cancer, potentially reflecting an immunosuppressive tumor microenvironment phenotype. This study proposes a novel method for stratifying survival risk in ovarian cancer patients, using a prognostic risk model built from four palmitoylation-related genes: HSPG2, BRD4, RARRES1, and SCGB1D2. By integrating molecular characteristics with conventional clinical parameters such as tumor stage and histological grade, the model not only improves the accuracy of prognosis prediction but also offers a framework for understanding the biological mechanisms through which palmitoylation influences ovarian cancer progression [ 50 ] . The independent prognostic value of the risk score underscores its potential to serve as a tool for clinical decision-making, especially for detecting high-risk patients that could benefit from enhanced treatment or targeted intervention of the palmitoylation pathway. The robust performance of the nomogram (AUC > 0.7) indicates its practical utility in individualized survival prediction, effectively connecting basic research with clinical practice [ 51 ] . Further investigation is warranted to substantiate these results in prospective cohorts and to further investigate the functional mechanisms through which these genes mediate therapeutic resistance or immune escape. Subgroup analyses revealed significant survival disparities between risk groups stratified by age (> 45 years), race (White), stage (III), and histologic grade (G3). The results of this study align with earlier research indicating that age > 45 is an adverse prognostic factor for ovarian cancer, likely related to hormonal changes, immune decline, and delayed diagnosis [ 52 ] . Caucasian patients represent the majority of ovarian cancer cases and have been associated with poorer survival in some studies, possibly due to genetic and socioeconomic factors [ 53 ] . Stage III is the most common advanced stage, often accompanied by peritoneal dissemination and markedly poor outcomes [ 54 ] . These results not only validate the model’s predictive consistency across clinical subgroups but also demonstrate its ability to identify high-risk patients within traditionally defined risk strata, suggesting broad applicability and clinical potential. Dysregulation of protein translation has been implicated as a critical mechanism in ovarian cancer progression. We observed significant enrichment of translation initiation and elongation pathways in high-risk patients, implicating hyperactivated protein synthesis as a hallmark of aggressive OC. Intriguingly, palmitoylation may mediate this process, as prior studies show it regulates translation factor localization [ 55 ] . Previous research indicates that palmitoylation can directly affect the subcellular localization and activity of translation factors, supporting our findings [ 56 ] . Moreover, palmitoylation may promote tumor cells’ abnormal reliance on protein synthesis by influencing the mTOR signaling pathway and associated translational regulators [ 57 ] . The activation of pathways such as Medicus Reference Translation Initiation and Eukaryotic Translation Elongation in the high-risk group may partially explain their increased tumor metabolism and invasiveness. These enrichments suggest heightened protein synthesis, which may drive faster cell cycling and increased metastatic potential, which is consistent with the known role of translational dysregulation in cancer [ 58 ] . Key translational factors such as eIF4E, eEF2, and mTORC1 have already emerged as potential anticancer targets [ 59 ] . Taking into account the notable enrichment of translation-associated pathways in the high-risk group, small-molecule inhibitors targeting these factors or their networks may represent promising therapeutic strategies for high-risk ovarian cancer patients. The investigation identified substantial disparities in various characteristics across risk stratifications, comprising immune checkpoint expression (e.g., CTLA4, CD274), infiltrating immune cell levels, and therapeutic sensitivity patterns, demonstrating that palmitoylation could impact ovarian malignancy progression via alteration of the immune tumor microenvironment, consequently presenting viable targets for immune-based therapies. Research has shown that protein palmitoylation is closely linked to immune responses, and immune checkpoint molecules (e.g., CTLA4 and PD-L1) rely on lipid modifications, including palmitoylation, for membrane localization, stability, and function [ 60 , 61 ] . Our findings further demonstrated notable differences in the infiltration levels of immune cells (including CD8 + T cells, regulatory T cells, and M2 macrophages) between the groups. This aligns with previous research showing that lipid metabolic reprogramming of tumor-associated macrophages (TAMs), including palmitoylation, plays a crucial role in shaping their immune functions [ 62 ] . In vitro studies have demonstrated that inhibition of key palmitoyltransferases (e.g., ZDHHC9, ZDHHC3) can downregulate PD-L1 protein expression and enhance tumor sensitivity to immune checkpoint inhibitors (ICIs) [ 63 ] . Furthermore, we identified a markedly increased prevalence of TP53 genetic alterations among high-risk patients, implying that our risk classification may mirror fundamental genomic dysregulation. Within high-grade serous ovarian carcinoma (HGSOC), TP53 constitutes the most commonly altered gene, exhibiting mutations in 96% of patient samples [ 64 ] . Previous studies have reported that certain palmitoylation enzymes and substrate proteins participate in the DNA damage response (DDR) and chromatin regulation [ 39 ] . Palmitoylation may also indirectly disrupt genomic stability by affecting the localization and degradation of DNA repair proteins [ 56 ] . Therefore, we hypothesize that aberrant expression of palmitoylation-related genes involved in the risk model may promote TP53 mutation accumulation or exacerbate oncogenic processes following TP53 loss by altering DDR and chromatin states. 5. Conclusion This study systematically elucidated the prognostic characteristics of palmitoylation-related genes (PRGs) in ovarian cancer and successfully constructed a four-gene signature consisting of HSPG2, BRD4, RARRES1, and SCGB1D2. This signature not only reliably distinguishes patient risk subgroups but also reveals the underlying mechanisms by which PRGs promote tumor progression through dysregulation of protein translation and remodeling of the tumor immune microenvironment. Although multi-omics analyses provided clinically relevant findings, several limitations should be acknowledged: potential cohort selection bias arising from the retrospective nature of the data, and the requirement for experimental validation of PRG-mediated palmitoylation mechanisms. Future research should focus on three key directions: (1) Conducting multicenter prospective clinical trials to validate the utility of this model in guiding PARP inhibitor selection; (2) Employing organoid models to dissect the mechanism by which BRD4-S and HSPG2 palmitoylation synergistically activate the mTOR-mediated translation pathway; (3) Performing high-throughput screening for palmitoylation inhibitors targeting the ZDHHC-RARRES1 axis and exploring their combination with immunotherapy regimens. These translational efforts will facilitate the application of molecular discoveries in the field of precision medicine. Declarations Ethics approval and consent to participate The experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Xinqiao Hospital. Consent for publication All authors approved the final manuscript and submission to this journal. Availability of data and material The data and material used or analysed during the current study are available from the corresponding author on reasonable request. Competing interests The authors confirm that they have no conflicts of interest. Funding Supported by the Chongqing Natural Science Foundation General Project (CSTB2022NSCQ- MSX1014） Authors ’ contributions QH and RX put forward the ideas of this article. QH, MH and TC drafted the article. CC and PZ reviewed the article. PZ and ZL interpreted the data. All authors contributed to the article and approved the submitted version. References Lheureux, S., Braunstein, M. & Oza, A. M. Epithelial ovarian cancer: Evolution of management in the era of precision medicine. CA Cancer J. Clin. 69 (4), 280–304 (2019). Garcia, C., Yao, A., Camacho, F., Balkrishnan, R. & Cantrell, L. A. A SEER-Medicare analysis of the impact of metformin on overall survival in ovarian cancer. Gynecol. Oncol. 146 (2), 346–350 (2017). Chen, X., Du, J., Wang, X. & Wu, X. Trends and projections of ovarian cancer incidence in Hong Kong: A population-based study. Acta Obstet. Gynecol. Scand. 102 (7), 942–949 (2023). Torre, L. A. et al. Ovarian cancer statistics, 2018. CA Cancer J. Clin. 68 (4), 284–296 (2018). Jiang, H. et al. Protein Lipidation: Occurrence, Mechanisms, Biological Functions, and Enabling Technologies. Chem. Rev. 118 (3), 919–988 (2018). Ko, P. J. & Dixon, S. J. Protein palmitoylation and cancer. EMBO Rep. 19 (10). (2018). Resh, M. D. Palmitoylation of proteins in cancer. Biochem. Soc. Trans. 45 (2), 409–416 (2017). Malgapo, M. & Linder, M. E. Substrate recruitment by zDHHC protein acyltransferases. Open. Biol. 11 (4), 210026 (2021). Ali, A. et al. Fatty acid synthase mediates EGFR palmitoylation in EGFR mutated non-small cell lung cancer. EMBO Mol. Med. 10 (3). (2018). Wang, X., Zhou, L., Dong, Z. & Wang, G. Identification of iron metabolism-related predictive markers of endometriosis and endometriosis-relevant ovarian cancer. Med. (Baltim). 102 (15), e33478 (2023). Ji, J. et al. FBXO2 targets glycosylated SUN2 for ubiquitination and degradation to promote ovarian cancer development. Cell. Death Dis. 13 (5), 442 (2022). Yang, J. et al. A novel autophagy-related gene signature associated with prognosis and immune microenvironment in ovarian cancer. J. Ovarian Res. 16 (1), 86 (2023). Kong, Y. et al. Palmitoylation landscapes across human cancers reveal a role of palmitoylation in tumorigenesis. J. Transl Med. 21 (1), 826 (2023). Hänzelmann, S., Castelo, R. & Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinform. 14 , 7 (2013). Gustavsson, E. K., Zhang, D., Reynolds, R. H., Garcia-Ruiz, S. & Ryten, M. ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2. Bioinformatics 38 (15), 3844–3846 (2022). Lei, J. et al. Clinicopathological characteristics of pheochromocytoma/paraganglioma and screening of prognostic markers. J. Surg. Oncol. 128 (4), 510–518 (2023). Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 43 (7), e47 (2015). Zheng, Y. et al. Ferroptosis and Autophagy-Related Genes in the Pathogenesis of Ischemic Cardiomyopathy. Front. Cardiovasc. Med. 9 , 906753 (2022). Kanehisa, M. & Goto, S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28 (1), 27–30 (2000). Kanehisa, M. Toward understanding the origin and evolution of cellular organisms. Protein Sci. 28 (11), 1947–1951 (2019). Kanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. & Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. Nucleic Acids Res. 51 (D1), D587–D592 (2023). Yu, G., Wang, L. G., Han, Y. & He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS 16 (5), 284–287 (2012). Liu, P., Xu, H., Shi, Y., Deng, L. & Chen, X. Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology. Evid Based Complement Alternat Med. 2020: 3023127. (2020). Li, Y., Lu, F. & Yin, Y. Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease. Sci. Rep. 12 (1), 11340 (2022). Ramsay, I. S. et al. Model selection and prediction of outcomes in recent onset schizophrenia patients who undergo cognitive training. Schizophr Res. Cogn. 11 , 1–5 (2018). Heagerty, P. J., Lumley, T. & Pepe, M. S. Time-dependent ROC curves for censored survival data and a diagnostic marker. Biometrics 56 (2), 337–344 (2000). Sachs, M. C. plotROC: A Tool for Plotting ROC Curves. J. Stat. Softw. 79. (2017). Blanche, P., Dartigues, J. F. & Jacqmin-Gadda, H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. Stat. Med. 32 (30), 5381–5397 (2013). Cheng, Q., Chen, X., Wu, H. & Du, Y. Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis. J. Transl Med. 19 (1), 18 (2021). Chen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. & Alizadeh, A. A. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. Methods Mol. Biol. 1711 , 243–259 (2018). Orifjon, S. et al. Translation and Adaptation of the Adult Developmental Coordination Disorder/Dyspraxia Checklist (ADC) into Asian Uzbekistan. Sports (Basel). 11(7). (2023). Geeleher, P., Cox, N. & Huang, R. S. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One . 9 (9), e107468 (2014). Mayakonda, A., Lin, D. C., Assenov, Y., Plass, C. & Koeffler, H. P. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 28 (11), 1747–1756 (2018). Sung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J. Clin. 71 (3), 209–249 (2021). Lheureux, S., Gourley, C., Vergote, I. & Oza, A. M. Epithelial ovarian cancer. Lancet 393 (10177), 1240–1253 (2019). Salaun, C., Greaves, J. & Chamberlain, L. H. The intracellular dynamic of protein palmitoylation. J. Cell. Biol. 191 (7), 1229–1238 (2010). Zhou, B., Hao, Q., Liang, Y. & Kong, E. Protein palmitoylation in cancer: molecular functions and therapeutic potential. Mol. Oncol. 17 (1), 3–26 (2023). Wu, S. Y. et al. Opposing Functions of BRD4 Isoforms in Breast Cancer. Mol. Cell. 78 (6), 1114–1132e10 (2020). Drumond-Bock, A. L. & Bieniasz, M. The role of distinct BRD4 isoforms and their contribution to high-grade serous ovarian carcinoma pathogenesis. Mol. Cancer . 20 (1), 145 (2021). Lovén, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. Cell 153 (2), 320–334 (2013). Kurimchak, A. M. et al. Resistance to BET Bromodomain Inhibitors Is Mediated by Kinome Reprogramming in Ovarian Cancer. Cell. Rep. 16 (5), 1273–1286 (2016). Feng, Y. et al. Podocyte-derived soluble RARRES1 drives kidney disease progression through direct podocyte and proximal tubular injury. Kidney Int. 106 (1), 50–66 (2024). Yu, Z. et al. Integrative analysis identifies cancer cell-intrinsic RARRES1 as a predictor of prognosis and immune response in triple-negative breast cancer. Front. Genet. 15 , 1360507 (2024). Zhao, A. et al. Transcription factor networks and novel immune biomarkers reveal key prognostic and therapeutic insights in ovarian cancer. Discov Oncol. 16 (1), 309 (2025). Iozzo, R. V. & Sanderson, R. D. Proteoglycans in cancer biology, tumour microenvironment and angiogenesis. J. Cell. Mol. Med. 15 (5), 1013–1031 (2011). Zhou, X. et al. HSPG2 overexpression independently predicts poor survival in patients with acute myeloid leukemia. Cell. Death Dis. 11 (6), 492 (2020). Melrose, J. et al. Altered stress induced by partial transection of the infraspinatus tendon leads to perlecan (HSPG2) accumulation in an ovine model of tendinopathy. Tissue Cell. 45 (1), 77–82 (2013). Strausz, S. et al. SCGB1D2 inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease. Nat Commun. 15(1): 2041. (2024). Vrijmoeth, H. D. et al. Genome-wide analyses in Lyme borreliosis: identification of a genetic variant associated with disease susceptibility and its immunological implications. BMC Infect. Dis. 24 (1), 337 (2024). Balachandran, V. P., Gonen, M., Smith, J. J. & DeMatteo, R. P. Nomograms in oncology: more than meets the eye. Lancet Oncol. 16 (4), e173–e180 (2015). Sheehy, J., Gallanagh, M., Sullivan, C. & Lane, S. Clinical prediction models for febrile neutropenia and its outcomes: a systematic review. Support Care Cancer . 33 (7), 537 (2025). Yazaki, S. et al. Impact of germline variants on breast and ovarian cancer risk in Japanese women: an original cohort study and meta-analysis. EBioMedicine 116 , 105758 (2025). Milano, A. F. & Non-Hodgkin Lymphoma - Nodal and Extranodal: 20-Year Comparative Mortality, Survival & Biologic Behavior Analysis by Age, Sex, Race, Stage, Cell Morphology/Histology, Cohort Entry Time-Period and Disease Duration: A Systematic Review of 384,651 Total NHL Cases Including 261,144 Nodal and 123,507 Extranodal Cases for Diagnosis Years 1975–2016: (SEER*Stat 8.3.6). J. Insur Med. 50 (1), 1–35 (2023). Klar, M. et al. Prognostic factors in young ovarian cancer patients: An analysis of four prospective phase III intergroup trials of the AGO Study Group, GINECO and NSGO. Eur. J. Cancer . 66 , 114–124 (2016). Solis, G. P. et al. Local and substrate-specific S-palmitoylation determines subcellular localization of Gαo. Nat. Commun. 13 (1), 2072 (2022). Resh, M. D. Fatty acylation of proteins: The long and the short of it. Prog Lipid Res. 63 , 120–131 (2016). Thoreen, C. C. et al. An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1. J. Biol. Chem. 284 (12), 8023–8032 (2009). Meier, S. et al. Serum Glial Fibrillary Acidic Protein Compared With Neurofilament Light Chain as a Biomarker for Disease Progression in Multiple Sclerosis. JAMA Neurol. 80 (3), 287–297 (2023). Bhat, M. et al. Targeting the translation machinery in cancer. Nat. Rev. Drug Discov . 14 (4), 261–278 (2015). Feng, C., Zhang, L., Chang, X., Qin, D. & Zhang, T. Regulation of post-translational modification of PD-L1 and advances in tumor immunotherapy. Front. Immunol. 14 , 1230135 (2023). Su, Y. et al. [Regulation of PD-L1 posttranslational modification and its application progress in tumor immunotherapy]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. 38 (11): 1036–1043. (2022). Liu, P. S. et al. α-ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. Nat. Immunol. 18 (9), 985–994 (2017). Yao, H. et al. Inhibiting PD-L1 palmitoylation enhances T-cell immune responses against tumours. Nat. Biomed. Eng. 3 (4), 306–317 (2019). Network, C. G. A. R. Integrated genomic analyses of ovarian carcinoma. Nature 474 (7353), 609–615 (2011). Table Table 1 PH assumption test. Gene p HSPG2 0.686 BRD4 0.086 RARRES1 0.101 SCGB1D2 0.809 Additional Declarations No competing interests reported. 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16:34:38","extension":"html","order_by":25,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":163110,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/8daa4750e8e79e41a4b09e12.html"},{"id":99260265,"identity":"035a4ccf-9a8a-4215-9dd9-4b5f1f45c503","added_by":"auto","created_at":"2025-12-31 01:17:02","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":366487,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAcquisition of 24 candidate genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e: Volcano plot of DEGs1 in the GSE26712 dataset. Green dots indicate statistically significant downregulated genes and red dots indicate statistically significant upregulated genes. \u003cstrong\u003e(B)\u003c/strong\u003e: The heatmap of the 4,317 DEGs1 in GSE26712. \u003cstrong\u003e(C)\u003c/strong\u003e: There was a significant difference in the palmitoylation related genes score between the OC group and the control group. ****p \u0026lt; 0.0001. \u003cstrong\u003e(D)\u003c/strong\u003e: The overall survival of the two subgroups. Blue represents patients in low score group and red represents patients in high score group. The p-value is 0.041. \u003cstrong\u003e(E)\u003c/strong\u003e: Volcano plot of DEGs2 in the GSE26712 dataset. Green dots indicate statistically significant downregulated genes and red dots indicate statistically significant upregulated genes. \u003cstrong\u003e(F)\u003c/strong\u003e: Venn diagram of overlapping genes in DEGs1 and DEGs2.\u003c/p\u003e","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/7d59bb76a1c2f23bbb5a6071.png"},{"id":99319406,"identity":"8b36d167-d4c7-4e25-9679-05566a5b521c","added_by":"auto","created_at":"2025-12-31 16:37:10","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":118441,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiological pathways and PPI analysis of candidate genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A)\u0026nbsp; : Gene Ontology (GO) functional analysis showing enrichment of candidate genes. \u003cstrong\u003e(B)\u003c/strong\u003e: Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis of candidate genes. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway map was obtained from KEGG(https://www.kegg.,jp/). KEGG is a publicly available resource under the terms of the academic uselicense\u003csup\u003e[19-21]\u003c/sup\u003e. \u003cstrong\u003e(C)\u003c/strong\u003e: Protein interaction network. The network was constructed and visualized using Cytoscape software. The nodes represent proteins, the edges represent their interaction.\u003c/p\u003e","description":"","filename":"Onlinefloatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/979239c899edb93c18ef1dc8.png"},{"id":99260271,"identity":"c3926945-5128-4ed8-b7e3-4f81c296e9f2","added_by":"auto","created_at":"2025-12-31 01:17:02","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":56126,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eRecognition of HSPG2, BRD4, RARRES1, and SCGB1D2 as prognostic genes.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) : Top 4 genes associated with overall survival via univariate OC analysis. \u003cstrong\u003e(B)\u003c/strong\u003e: Coefficient curve. Different colors represent different genes. No zero values were selected as a penalty coefficient. \u003cstrong\u003e(C)\u003c/strong\u003e: The minimum lambda of the lasso model was selected via 10 folds of cross-validation. Lambda was determined when the partial likelihood deviance was smallest.\u003c/p\u003e","description":"","filename":"Onlinefloatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/07d8758f2048edd36d635eff.png"},{"id":99319401,"identity":"aeb8c59e-dfe5-4911-b515-23d54496cf44","added_by":"auto","created_at":"2025-12-31 16:37:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":165163,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDevelopment of a risk model with high accuracy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e: The x-axis represents the number of patients in the test dataset and the y-axis represents the risk score. Red represents patients in the high level and blue represents patients in the low level. \u003cstrong\u003e(B)\u003c/strong\u003e: The x-axis represents the number of patients and the y-axis represents the survival time of the patients in the TCGA dataset. \u003cstrong\u003e(C)\u003c/strong\u003e: Red represents high-risk patients and blue represents low-risk. The patients were divided according to their median risk score. \u003cstrong\u003e(D)\u003c/strong\u003e: ROC curves illustrated the predictive efficacy of the risk score for 1-, 2-, and 3-year survival in the GSE51088 cohort. \u003cstrong\u003e(E)\u003c/strong\u003e: The x-axis represents the number of patients in the test dataset and the y-axis represents the risk score. Red represents patients in the high level and blue represents patients in the low level. \u003cstrong\u003e(F)\u003c/strong\u003e: The x-axis represents the number of patients and the y-axis represents the survival time of the patients in the GSE51088 dataset. \u003cstrong\u003e(G)\u003c/strong\u003e: Red represents high-risk patients and blue represents low-risk. The patients were divided according to their median risk score. \u003cstrong\u003e(H)\u003c/strong\u003e: ROC curves illustrated the predictive efficacy of the risk score for 1-, 2-, and 3-year survival in the GSE51088 cohort.\u003c/p\u003e","description":"","filename":"Onlinefloatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/bb51e1f54f835c818b9245f1.png"},{"id":99319966,"identity":"8282f24d-de2c-4557-b6ae-ead31e31678b","added_by":"auto","created_at":"2025-12-31 16:38:03","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":119498,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIndependent prognostic value of risk score, age, and stage in TCGA-OV.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A-B)\u003c/strong\u003e: Univariate and multivariate analyses showed the prognostic value of the clinical features and risk scores. \u003cstrong\u003e(C)\u003c/strong\u003e: The construction of the nomogram. \u003cstrong\u003e(D)\u003c/strong\u003e: Calibration curves to evaluate the performance of the nomogram for 1-, 2-, and 3-year OS, respectively. \u003cstrong\u003e(E-G)\u003c/strong\u003e: The AUC of the nomograms compared for 1-, 2-, and 3-year OS, respectively. \u003cstrong\u003e(H)\u003c/strong\u003e: The DCA curves of the nomogram for 1-, 2-, and 3-year OS in TCGA-OV, respectively.\u003c/p\u003e","description":"","filename":"Onlinefloatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/72419a0dc35d56b5dce99b16.png"},{"id":99260284,"identity":"be2279fb-c93e-4e62-a058-741c896fdb90","added_by":"auto","created_at":"2025-12-31 01:17:02","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":136362,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSurvival of patients in high and low-risk cohorts in different clinical characterization subgroups.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e: Based on the distribution of different clinical characteristics between high-risk and low-risk groups. \u003cstrong\u003e(B)\u003c/strong\u003e: K-M survival curve analysis of risk scores for different clinical characteristics.\u003c/p\u003e","description":"","filename":"Onlinefloatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/d4b681b7497d410d9dd65034.png"},{"id":99260268,"identity":"f68a66b5-f4f2-427e-b0a9-c7edf110322b","added_by":"auto","created_at":"2025-12-31 01:17:02","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":148337,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBiological pathway and mutation point analysis of risk cohorts in OC.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e: Gene set enrichment analysis. \u003cstrong\u003e(B)\u003c/strong\u003e: TMB waterfall plot of the high-risk group. \u003cstrong\u003e(C)\u003c/strong\u003e: TMB waterfall plot of the low-risk group.\u003c/p\u003e","description":"","filename":"Onlinefloatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/3821dbfe91435e3c7d831899.png"},{"id":99260275,"identity":"4700be8a-c19d-47ae-abb4-c84d8966feb8","added_by":"auto","created_at":"2025-12-31 01:17:02","extension":"png","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":160806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of immune cell infiltration and assessment of immunotherapy response in risk cohorts.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e: Using the CIBERSORT algorithm, the bar plot provides an overview of the distribution of immune cells. \u003cstrong\u003e(B)\u003c/strong\u003e: The composition of the immune cell infiltrate in OC. \u003cstrong\u003e(C)\u003c/strong\u003e: Heatmap of the correlation between immune cell types. \u003cstrong\u003e(D)\u003c/strong\u003e: Correlation analysis of prognostic genes and different immune cell types. \u003cstrong\u003e(E)\u003c/strong\u003e: Differential expression analysis of common immune checkpoints between high-risk and low-risk groups. \u003cstrong\u003e(F)\u003c/strong\u003e: Difference in TIDE score between high-risk and low-risk groups. * p\u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Onlinefloatimage8.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/d93ded88500a443bec8993d2.png"},{"id":99320017,"identity":"c52c41a1-269f-489e-8014-02c2cb7e8157","added_by":"auto","created_at":"2025-12-31 16:38:05","extension":"png","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":129978,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eIdentifying chemotherapeutics associated with the risk score in OC. \u003c/strong\u003e*p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001\u003c/p\u003e","description":"","filename":"Onlinefloatimage9.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/136e3360cac1180c5e4f4d3d.png"},{"id":99318187,"identity":"753c46ef-bc84-4a4a-add8-12711de289b3","added_by":"auto","created_at":"2025-12-31 16:31:51","extension":"png","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":77690,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003ePrognostic gene expression levels.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e: Expression difference of prognostic genes. \u003cstrong\u003e(B-E)\u003c/strong\u003e: RT-PCR analysis. *p \u0026lt; 0.05, **p \u0026lt; 0.01, ***p \u0026lt; 0.001, ****p \u0026lt; 0.0001, and ns, no significance.\u003c/p\u003e","description":"","filename":"Onlinefloatimage10.png","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/a64527fe0aa32d6042487704.png"},{"id":108495229,"identity":"2f52b266-d09d-498b-94fa-82749183e2e6","added_by":"auto","created_at":"2026-05-05 10:09:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2553113,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/d45df287-8cc9-44da-966f-b9e164754ae7.pdf"},{"id":99260264,"identity":"12e3df73-79b1-4212-98fe-2055ee3740c3","added_by":"auto","created_at":"2025-12-31 01:17:02","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16674,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTables.docx","url":"https://assets-eu.researchsquare.com/files/rs-8340423/v1/7e46dd17dadf8d95aa0c95b7.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Evaluation and in vitro verification of the prognostic value of palmitoylation-related genes in ovarian cancer","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eWorldwide, Ovarian cancer (OC) exhibits the highest incidence rate among gynecological malignant tumors and contributes substantially to global cancer-related mortality in females, holding an eighth-ranking position in terms of cancer-associated fatalities\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/sup\u003e. As reported in Hong Kong, China, the incidence rate was 11.5/100,000 in 2023\u003csup\u003e[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. Clinically, OC manifests as a heterogeneous malignancy characterized by varied pathobiological behaviors and molecular subgroups that translate into distinct clinical trajectories. The majority of ovarian cancer pathologic types are of epithelial origin (90%), among which the high-grade serous subtype contributes to 70\u0026ndash;80% of all mortality cases, while 70% of ovarian cancer is identified as advanced disease when diagnosed, with the lack of clinical manifestations. Following cytoreductive surgery combined with platinum-based chemotherapy, 70% of patients are still likely to develop metastasis within a 2\u0026ndash;3 year period\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Despite the application of poly-ADP-ribose polymerase (PARP) inhibitors and angiogenesis blockers like bevacizumab as maintenance therapies, outcomes for advanced and recurrent cases remain disappointing. The clinical results have fallen short of expectations, leaving the long-term prognosis with much room for improvement. Given the pressing need to enhance clinical outcomes for ovarian cancer, discovering new therapeutic targets has become a critical priority.\u003c/p\u003e \u003cp\u003ePalmitoylation, a critical lipid-mediated post-translational modification, exerts a pivotal function in modulating protein behavior, affecting membrane binding, intracellular localization, protein stability, and functional activity. This process holds substantial relevance in human disease, especially in the development and progression of various cancers\u003csup\u003e[\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e. S-palmitoylation constitutes the predominant modification subtype, characterized by covalent attachment of palmitic acid (16-carbon fatty acid) to specific cysteine residues via reversible thioester linkages \u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]\u003c/sup\u003e. This dynamic process is orchestrated by palmitoylating and depalmitoylating enzymes, which play pivotal roles in oncogenesis, tumor expansion, therapeutic response, and clinical outcomes. The zinc finger DHHC-type containing (ZDHHC) palmitoyl S-acyltransferase (PAT) family predominantly mediates protein palmitoylation reactions \u003csup\u003e[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]\u003c/sup\u003e. Mammalian systems express 23 zDHHC variants (zDHHC1-24, excluding zDHHC10). Growing evidence demonstrates that palmitoylation cycling directly influences protein functionality, consequently modifying cellular signaling networks and promoting malignant transformation. Furthermore, the palmitoylation state of key proteins (e.g., EGFR, RAS, and PD-1/PD-L1) exerts a critical influence on tumor progression and therapeutic responsiveness.\u003c/p\u003e \u003cp\u003eThis study employs an integrated multi-omics strategy To conduct a systematic investigation into the prognostic significance and molecular mechanisms of palmitoylation-related genes (PRGs) in OC. By analyzing transcriptomic datasets from GEO and TCGA-OV, We conducted differential gene expression profiling as well as survival prognostic analysis, and functional enrichment annotation to clarify the regulatory role of PRGs in OC pathogenesis. To assess the correlation between PRG activity and patient prognosis, single-sample gene set enrichment analysis (ssGSEA) was adopted, followed by the identification and functional annotation of OC-specific PRGs. The Cox proportional hazards model was utilized to establish a prognostic signature based on PRGs, and its predictive performance and clinical utility were thoroughly assessed. Furthermore, we explored PRG-mediated modulation of tumor microenvironment immune characteristics and validated the expression patterns of key PRGs through in vitro experiments. This study provides the first comprehensive characterization of the global palmitoylation regulatory network in OC, offering novel insights into disease mechanisms and laying the foundation for developing precision therapies targeting protein palmitoylation.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data collection\u003c/h2\u003e \u003cp\u003eThe OC related transcriptome datasets (GSE26712, GSE51088) were acquired from the Gene Expression Omnibus (GEO) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/geo/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/geo/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). GSE26712, which uses the GPL96 platform, comprises 185 tumour tissue samples derived from OC patients and 10 control samples of ovarian surface epithelial tissue, among which 153 samples include survival-related information\u003csup\u003e[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e. On the other hand, GSE51088 (GPL7264 platform) consisted of tumour tissue from 172 OC samples (containing 152 samples with survival information)\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]\u003c/sup\u003e. Gene expression profiles, clinical information, additionally, survival records of The Cancer Genome Atlas (TCGA)-OV cohort were obtained from the University of California, Santa Cruz (UCSC) Xena platform (available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://xenabrowser.net/\u003c/span\u003e\u003cspan address=\"https://xenabrowser.net/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) on May 17, 2024, encompassing 378 OC tumour tissue samples\u003csup\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. In addition, 23 palmitoylation-related genes (PRGs) were gained in published literature\u003csup\u003e[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Acquisition of candidate genes\u003c/h2\u003e \u003cp\u003eTo identify genes associated with palmitoylation, we initially computed PRGs values in the GSE26712 dataset employing the ssGSEA methodology through the GSVA software package (v1.38.2)\u003csup\u003e[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/sup\u003e. Then, we assessed the PRGs scores disparities between OC and control samples (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and employed the ggplot2 package (v 0.1.4)\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e for visualization. Subsequently, a total of 153 OC samples in GSE26712 that included survival information were classified into high-scoring and low-scoring cohorts according to the best ssGSEA cut-off value for differential PRGs (minprop\u0026thinsp;=\u0026thinsp;0.2). The survival package (version 3.5-3)\u003csup\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/sup\u003e, was used to assess survival differences between the two scoring groups; this was achieved by constructing Kaplan-Meier (K-M) survival curves and conducting Log-rank statistical tests (with a significance threshold of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Differentially expressed genes (DEGs1) identification was accomplished via the limma package (v 1.38.0) \u003csup\u003e[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/sup\u003e by contrasting gene expression profiles of OC patients against control samples in the GSE26712 dataset, applying cutoff criteria of |log2Fold Change (FC)| \u0026gt; 0.5 and p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Similarly, DEGs2 were detected in high-scoring and low-scoring groups by means of the limma package (version 1.38.0). The visualization of these DEGs1 and DEGs2 involved creating a heatmap and a volcano plot was generated using the ggplot2 package (version 0.1.4) \u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e. A Venn diagram was created using the ggVenn package (v 1.2.2)\u003csup\u003e[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/sup\u003e to determine common genes between DEGs1 and DEGs2 derived from the previous analyses, and these overlapping genes were termed candidate targets.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Biological enrichment and molecular interaction network analysis\u003c/h2\u003e \u003cp\u003eTo investigate the biological processes and signaling networks that are implicated, we conducted a functional enrichment analysis on the target genes by applying both Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG)\u003csup\u003e[\u003cspan additionalcitationids=\"CR20\" citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003eapproaches. This approach shed light on the molecular functions and key pathways involved, providing deeper insights into the genetic framework under investigation. For this in-depth analysis, we employed the ClusterProfiler package (version 4.4.4) with a statistical significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Following this, protein-protein interaction networks were built by utilizing the STRING database (Search Tool for the Retrieval of Interacting Genes; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://string-db.org\u003c/span\u003e\u003cspan address=\"https://string-db.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with a confidence threshold above 0.15\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]\u003c/sup\u003e. Network visualization and topological analysis were accomplished through Cytoscape software (v 3.1.1) \u003csup\u003e[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e for the identification of hub genes and interaction patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Construction and validation of risk model\u003c/h2\u003e \u003cp\u003eThe TCGA-OV cohort (N\u0026thinsp;=\u0026thinsp;378) functioned as the development dataset for risk model construction, whereas the GSE51088 cohort (N\u0026thinsp;=\u0026thinsp;152) was employed for external validation of prognostic performance in OC patients. Initially, based on OC tumour samples from TCGA-OV, the survival package (v 3.5-3) cox.zph function was employed to conduct univariate Cox regression to pinpoint survival-associated genes (Hazard Ratio (HR)\u0026thinsp;\u0026ne;\u0026thinsp;1, P\u0026thinsp;\u0026lt;\u0026thinsp;0.1). Proportional hazards (PH) assumption testing was performed (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and results were presented using the forestplot package (v 2.0.1) \u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. Subsequent to this, the glmnet package (version 4.1.4)\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e was used to perform least absolute selection and shrinkage operator (LASSO) analysis. The study's prognostic genes were determined at the point when lambda reached lambda.min. Thereafter, in TCGA-OV, for each ovarian cancer patient in the dataset, individualized risk scores were calculated by utilizing the relative expression levels of prognostic genes and the coefficients obtained from LASSO regression. The formula used was \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{R}\\text{i}\\text{s}\\text{k}\\text{s}\\text{c}\\text{o}\\text{r}\\text{e}\\:=\\:\\sum\\:_{\\text{i}\\:=\\:1}^{\\text{n}}\\text{c}\\text{o}\\text{e}\\text{f}\\left({\\text{g}\\text{e}\\text{n}\\text{e}}_{\\text{i}}\\right)\\ast\\:\\text{e}\\text{x}\\text{p}\\text{r}\\left({\\text{g}\\text{e}\\text{n}\\text{e}}_{\\text{i}}\\right)\\)\u003c/span\u003e\u003c/span\u003e, wherein expr indicated the expression value of prognostic gene i, while coef represented the LASSO-derived coefficient for prognostic gene i. Patients were stratified into high-risk and low-risk subgroups using the optimal threshold. Risk distribution analysis was subsequently performed by generating scatter plots that illustrated risk score patterns and survival outcomes of OC patients. Additionally, the expression profiles of prognostic biomarkers were visualized, and Kaplan-Meier (K-M) survival analysis for overall survival (OS) between the two risk groups was performed using the survminer package (v 0.4.9) \u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e (P \u0026lt; 0.05). Following this, receiver operating characteristic (ROC) analysis was conducted using the survivalROC package (v 1.0.3) \u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e to calculate area under curve (AUC) values at 1, 2, and 3-year time points for assessing model performance. Additionally, model validation was conducted in an independent validation dataset.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Independent prognostic analysis\u003c/h2\u003e \u003cp\u003eTo construct a risk stratification system for estimating patient survival probabilities in OC Firstly, integration of risk scores as well as clinical characteristics (including risk score, age, race, histological grade (G1/2 and G3), stage (stage1/2, stage3, stage4) of OC patients in TCGA-OV dataset were integrated, additionally, univariate Cox regression analysis (with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the cutoff) was conducted together with multivariate Cox regression analysis to identify independent prognostic factors (using P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as the significance threshold). Following this, a nomogram was built using the rms package (version 6.5-1) \u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e, incorporating independent prognostic factors. The rms package (v 6.5-1), the same as before, allowed for the generation of calibration plots at 1, 2, and 3-year intervals, whereas ROC curve visualization was achieved through the timeROC package (v 0.4) \u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e Clinical utility evaluation of the predictive model was conducted by generating decision curves using the ggDCA package (v 1.2) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.rdocumentation.org/packages/ggDCA/versions/1.1\u003c/span\u003e\u003cspan address=\"https://www.rdocumentation.org/packages/ggDCA/versions/1.1\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Correlation of risk score with clinical characteristics\u003c/h2\u003e \u003cp\u003eThrough systematic evaluation involving multiple analytical approaches, The relationship between calculated risk scores and demographic as well as clinical features of OC study participants was assessed. Firstly, the distinctions in risk score among different clinical characteristics were compared. The survival package (v 3.5-3) and the survminer package (v 0.4.9)\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e were employed to construct KM curves, which assessed the distinctions in OS between the two risk cohorts under different subsets of clinical features.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Gene set enrichment analysis (GSEA) of high and low risk cohorts\u003c/h2\u003e \u003cp\u003eUsing calculated risk assessment values, the TCGA-OV training dataset patients were categorized into separate risk groups. Then, all genes were sorted based on logFC between the two risk cohorts. Finally, to conduct GSEA on the two risk cohorts, the ClusterProfiler package was employed. The c2.cp.v2023.2.Hs.symbols.gmt gene set was sourced from MSigDB database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gsea-msigdb.org/gsea/msigdb\u003c/span\u003e\u003cspan address=\"https://www.gsea-msigdb.org/gsea/msigdb\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) applying threshold parameters of false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.25 and P\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The enrichplot package (v 0.92) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://rdrr.io/cran/corrplot/\u003c/span\u003e\u003cspan address=\"https://rdrr.io/cran/corrplot/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) facilitated line plot generation, depicting the top 5 pathways ranked by significance from highest to lowest (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Analysis of immune cell infiltration and assessment of immunotherapy response\u003c/h2\u003e \u003cp\u003eThe CIBERSORT algorithm (v 1.03) \u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/sup\u003e was used to perform immune cell composition analysis, quantifying 22 immune cell subtypes in the TCGA-OV cohort. Differential immune infiltration patterns were examined and visualized through box plots displaying immune cell abundance variations (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The psych package (v 2.4.3) \u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. Supported correlation analysis for examining relationships among differential immune cells and associations between prognostic genes and immune cell populations. Furthermore, to evaluate immunosuppressive traits and treatment response potential, immune checkpoint assessment across risk-stratified cohorts was carried out using the Tumor Immune Dysfunction and Exclusion (TIDE) platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://tide.dfci.harvard.edu\u003c/span\u003e\u003cspan address=\"http://tide.dfci.harvard.edu\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Drug sensitivity analysis\u003c/h2\u003e \u003cp\u003eThe information on chemical drugs for OC and their half maximal inhibitory concentration (IC\u003csub\u003e50\u003c/sub\u003e) values were retrieved from the GDSC (Genomics of Drug Sensitivity in Cancer) databases (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://cancerrxgene.org\u003c/span\u003e\u003cspan address=\"http://cancerrxgene.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Pharmacogenomic sensitivity profiling was conducted using the pRRophetic algorithm (v 0.5) \u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e to predict drug response metrics (IC50) for standard chemotherapeutics and targeted therapies in the TCGA-OV cohort. Subsequently, differential drug sensitivity analysis was performed to identify significant variations in IC50 values between risk-stratified patient subgroups (adjusted P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ggplot2 (v 3.4.1) was used to visualize the most significantly altered agents, based on hierarchical ranking by statistical significance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.10 Mutation status of OC patients in high and low risk cohorts\u003c/h2\u003e \u003cp\u003eIn order to better understand variations in driver genes between two risk cohorts, the maftools package (v 2.20.0)\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e was utilized to analyse gene mutations in two risk cohorts and display the top 20 high-frequency mutated genes in a tumor mutational burden (TMB) waterfall plot.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.11 Prognostic gene expression levels\u003c/h2\u003e \u003cp\u003eTo investigate the expression of prognostic genes in OC tumour tissue samples and control ovarian surface epithelial tissue samples in GSE26712, the ggplot2 package (v 0.1.4) was utilized to plot box-and-line diagrams for visualisation. RNAs from 5 pairs of tumour tissue\u0026mdash;consisting of 5 OC samples and 5 corresponding adjacent samples\u0026mdash;were collected. Soybean-sized OC tissue and corresponding adjacent tissue were obtained intraoperatively from each ovarian cancer patient respectively. For each specimen, the tumor tissue content (requiring tumor cell proportion\u0026thinsp;\u0026ge;\u0026thinsp;70%) was confirmed by a pathologist immediately after excision. Then, 3g of fresh tissue was aliquoted under sterile conditions, quickly frozen in liquid nitrogen, and subsequently transferred to a -80\u0026deg;C ultra-low temperature refrigerator for storage to ensure RNA integrity. This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of the Second Affiliated Hospital of Army Medical University (approval number: 2024-311-02). Informed consent was obtained from the patients. RNA isolation from tissue samples adhered to the manufacturer's protocols. The SweScript First Strand cDNA synthesis kit was used for reverse transcription, with SYBR Green qPCR Master Mix employed in qPCR reactions. Primer details are provided in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e and patient clinical information is presented in \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e. Expression levels of genes were normalized against H-GAPDH and quantified via the 2\u003csup\u003e\u0026minus;△△Ct\u003c/sup\u003e approach, with statistical evaluation conducted via Graphpad Prism 5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e\u003cb\u003e2.12 Statistical analysis\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eR (v 4.2.2) was used to perform all computational analyses. The Wilcoxon test was applied to assess cohort differences, with P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 set as the threshold for statistical significance.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Acquisition of 24 candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOverall, 4,317 DEGs¹\u0026nbsp;were detected, with 2,114 being up-regulated and 2,203 being down-regulated genes in OC samples (\u003cstrong\u003eFig. 1A, 1B\u003c/strong\u003e).\u0026nbsp;Additional analysis evaluated the variations in PRGs scores between OC samples and control samples in GSE26712, revealing a notable distinction in PRGs scores between the two cohorts (P \u0026lt; 0.05) (Figure 1C). Ten PRGs showing notable distinctions in their scores between the OC and control cohorts were selected for subsequent analyses. Based on 153 OC samples containing survival information in GSE26712, using the optimal cut-off value of 1.57929 for the ssGSEA scores of the 10 PRGs, they were classified into high-score (n = 98) and low-score (n = 55) cohorts. Log-rank analysis demonstrated significant survival disparities between scoring groups (P = 0.041), with the low-scoring cohort exhibiting reduced survival outcomes (\u003cstrong\u003eFig. 1D\u003c/strong\u003e). A total of 31 DEGs2 were identified, consisting of 7 upregulated and 24 downregulated genes when comparing high-score versus low-score groups (\u003cstrong\u003eFig. 1E\u003c/strong\u003e). Venn diagram analysis between DEGs1 and DEGs2 yielded 24 overlapping genes designated as potential candidates (\u003cstrong\u003eFig. 1F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Biological pathways and PPI analysis of candidate genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Ontology (GO) enrichment analysis of the 24 prognostic genes identified 246 significantly enriched biological processes (BP), encompassing hydrogen peroxide degradation pathways and extracellular matrix organization, while 29 cellular components (CC) entries including complex of collagen trimers, collagen-containing extracellular matrix, haptoglobin-hemoglobin complex, and 31 molecular functions (MF) encompassing antioxidant activity and transcriptional coactivation \u003cstrong\u003e(Fig. 2A).\u003c/strong\u003e Moreover, KEGG pathway analysis revealed 7 enriched pathways, particularly focal adhesion, ATP-dependent chromatin remodeling, as well as proteoglycans in cancer, african trypanosomiasis, among others (\u003cstrong\u003eFig. 2B\u003c/strong\u003e). PPI network consisting of 19 nodes and 42 edges; the top 3 proteins, COL4A2, HSPG2, and FLNA, were interacted most strongly with other proteins (\u003cstrong\u003eFig. 2C\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Recognition of HSPG2, BRD4, RARRES1, and SCGB1D2 as prognostic genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter conducting univariate Cox regression screening (P \u0026lt; 0.1) and validating the proportional hazards (PH) assumption (P \u0026gt; 0.05 to confirm validity), 4 survival-associated genes were chosen for further analysis (Fig. 3A, Table 1). To minimize overfitting and enhance model robustness, these 4 candidates underwent LASSO regression analysis. The optimal regularization resulted in the identification of 4 prognostic biomarkers—HSPG2, BRD4, RARRES1, and SCGB1D2—with the optimal lambda set at 0.002224881 (Fig. 3B,3C), indicating their collective contribution to survival prediction.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Development of a risk model with high accuracy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThus, a risk model was established based on the expression intensity and risk coefficients of 4 prognostic genes. The constructed risk model was as follows: RiskScore = HSPG2 × 0.09 + BRD4 × 0.14 + RARRES1 × 0.08 + SCGB1D2 × (-0.01). According to this model, patients in TCGA-OV were classified into high-risk (n = 213) and low-risk (n = 165) cohorts using optimal cutoff value (1.147367) for risk score (\u003cstrong\u003eFig. 4A, 4B\u003c/strong\u003e), and the number of deaths increased as risk score in the sample increased, where patients in the high-risk cohort had a reduced survival rate (P = 0.00041) (\u003cstrong\u003eFig. 4C\u003c/strong\u003e). ROC curve analysis suggested that risk model had impressive predictive capacity, with AUCs for 1, 2, and 3 years in TCGA-OV being 0.65, 0.65, and 0.61, respectively (\u003cstrong\u003eFig. 4D\u003c/strong\u003e). Additionally, patients were divided into high-risk (n = 71) and low-risk (n = 74) cohorts according to the optimal risk score cutoff value of (-0.00153919). Furthermore, validation of the risk model in the GSE51088 dataset confirmed that it had some predictive accuracy, as evidenced by AUCs exceeding 0.6 for 1, 2, and 3 years, underscoring the model's consistent prognostic strength (\u003cstrong\u003eFig. 4E, 4F, 4G, 4H\u003c/strong\u003e). These outcomes validated the robustness of the risk model for evaluating the prognostic risk of OC patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.5 Independent prognostic value of risk score, age, and stage in\u003c/strong\u003e \u003cstrong\u003eTCGA-OV\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRisk scoring, ethnicity, and tumor staging emerged as independent predictive determinants for TCGA-OV patients\u0026nbsp;\u003cstrong\u003e(Fig. 5A, 5B)\u003c/strong\u003e. Building upon these findings, we constructed a predictive nomogram incorporating these significant prognostic variables\u0026nbsp;\u003cstrong\u003e(Fig. 5C)\u003c/strong\u003e. The resulting calibration curve reflected nomogram's high predictive precision for patient outcomes at 1, 2, and 3-year intervals\u0026nbsp;(\u003cstrong\u003eFig. 5D\u003c/strong\u003e).\u0026nbsp;ROC curve analysis for 1-, 2-, and 3-year periods in TCGA-OV yielded values of 0.70, 0.67, and 0.65, respectively, which suggested that that predictive performance of nomogram plot was good\u0026nbsp;(\u003cstrong\u003eFig. 5E, 5F, 5G\u003c/strong\u003e). DCA suggested that this nomogram provided notable clinical utility, which was superior to the utility of independent prognostic factors when used individually (\u003cstrong\u003eFig. 5H\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.6 Survival of patients in high and low-risk cohorts in different clinical characterization subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWithin the subgroups of age (\u0026gt;45), race (white), stage 3, and histologic G3, a higher proportion of patients were distributed in the high-risk cohort than in the low-risk cohort (\u003cstrong\u003eFig. 6A\u003c/strong\u003e). There were survival\u0026nbsp;distinctions between two risk cohorts under different cohorts of age (\u0026gt;45), race (white), stage 3, and histologic G3\u0026nbsp;(P \u0026lt;\u0026nbsp;0.05)\u0026nbsp;(\u003cstrong\u003eFig. 6B\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.7\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eBiological pathway and mutation point analysis of risk cohorts\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in OC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eComparative pathway analysis between prognostic risk-stratified cohorts in the TCGA-OV dataset revealed 429 statistically significant enriched pathways, with prominent representation of translational initiation (Medicus reference), ribosomal signaling, and eukaryotic translation elongation processes (Reactome database) \u003cstrong\u003e(Fig.7A)\u003c/strong\u003e.The process of identifying and categorizing genes based on the presence of mutations was conducted by analyzing mutational profiles from the TCGA-OV dataset. Within the high-risk cohort, the TP53 gene stood out with an exceptionally high mutation rate, hitting 95% (\u003cstrong\u003eFig.7B\u003c/strong\u003e). On the other hand, in the low-risk cohort, the TP53 gene also showed a notable mutation rate, at 91% (\u003cstrong\u003eFig.7B\u003c/strong\u003e\u003cstrong\u003e, 7C\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.8\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eAnalysis of\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eimmune cell infiltration and assessment of immunotherapy response\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;in risk cohorts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe immune cells with notable distinctions were analyzed in two risk cohorts (P \u0026lt; 0.05), which showed 7 immune cells that were notably different between cohorts, namely B cell memory, Dendritic cells activated,\u0026nbsp;Eosinophils, Macrophages M2, NK cells activated, Neutrophils, and T cells gamma delta. On the other hand,\u0026nbsp;M2 macrophages showed a notable up-regulation in the high-risk cohort, and activated NK cells exhibited significant up-regulation in the low-risk cohort\u0026nbsp;(\u003cstrong\u003eFig. 8A, 8B\u003c/strong\u003e). These 7 immune cells showed a weak correlation with prognostic genes (|cor|\u0026nbsp;\u0026lt;\u0026nbsp;0.3)\u0026nbsp;(\u003cstrong\u003eFig. 8C, 8D\u003c/strong\u003e). It might be that functional differences between immune cells with\u0026nbsp;different roles in the immune response lead to a weak correlation. Immunotherapy response assessment was then performed in TCGA-OV, among a total of 8 immune checkpoints (CTLA4, PDCD1LG2, CD274, LAG3, HAVCR2, TIGIT, PDCD1, and SIGLEC15). Except for SIGLEC15, the other 7 immune checkpoints showed a notable\u0026nbsp;distinction between high and low-risk cohorts (P\u0026nbsp;\u0026lt; 0.05). Furthermore, results from TIDE scoring demonstrated significant disparities between the two risk cohorts, and the high-risk cohort exhibited a higher TIDE score with 7 immune checkpoints, indicating a high potential for immune escape from 7 immune checkpoints in high-risk cohort and potentially poorer efficacy of immune checkpoint inhibitory therapy (ICI) (\u003cstrong\u003eFig. 8E, 8F\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.9\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003eIdentifying chemotherapeutics associated with the risk score in OC\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSystematic profiling of chemosensitivity parameters was conducted for standard chemotherapeutic agents based on predictions from the GDSC database.\u0026nbsp;The IC\u003csub\u003e50\u003c/sub\u003e of\u0026nbsp;131 drugs, including BI.2536, BMS.509744,\u0026nbsp;BMS.536924, and CGP.60474,\u0026nbsp;were notably distinctive between the two risk cohorts. The top 5 drugs were presented according to their adjusted P value. Among them, the IC\u003csub\u003e50\u003c/sub\u003e of BI.2536, BMS.509744, BMS.536924, and CGP.60474\u0026nbsp;in low-risk cohort were higher. Furthermore, the IC\u003csub\u003e50\u003c/sub\u003e of\u0026nbsp;GDC.0449\u0026nbsp;in high-risk cohort was higher (Padj\u0026lt;0.05)\u0026nbsp;(\u003cstrong\u003eFig. 9\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.10 Prognostic gene expression levels\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn GSE26712, all 4 prognostic marker genes were significantly different between OC and control cohorts. The expression of HSPG2, SCGB1D2, and BRD4 were notably up-regulated in the OC cohort, while the expression of RARRES1 was notably up-regulated in the control cohort\u0026nbsp;(P \u0026lt; 0.05)\u0026nbsp;(\u003cstrong\u003eFig 10A\u003c/strong\u003e). Furthermore, experimental validation using RT-qPCR was performed to determine the expression levels of the 4 identified prognostic genes. The results demonstrated significant overexpression of HSPG2, SCGB1D2, and BRD4 in OC samples compared with healthy tissue controls, while RARRES1 showed prominent upregulation in the control cohort (P \u0026lt; 0.05). This expression pattern aligns with the computational results from GSE26712 analysis\u0026nbsp;\u003cstrong\u003e(Fig 10 B-E)\u003c/strong\u003e, providing experimental support for the bioinformatics predictions.\u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eOvarian cancer (OC) ranks among the most invasive and deadly malignant tumors affecting the female reproductive system, with over 300,000 new cases diagnosed and more than 200,000 deaths recorded worldwide annually\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The absence of distinctive early-stage symptoms and reliable screening methods results in approximately 70% of patients receiving their diagnosis during advanced disease phases (stages III or IV), leading to unfavorable clinical outcomes\u003csup\u003e[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]\u003c/sup\u003e. Despite substantial advancements in surgical interventions, chemotherapeutic strategies, and targeted treatment modalities, the overall five-year survival rate continues to remain under 45%\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e. Consequently, the identification of innovative molecular markers and elucidation of their underlying pathways represents a critical priority for enhancing early detection capabilities and developing more efficacious individualized treatment approaches for OC patients.\u003c/p\u003e \u003cp\u003eOver the past few years, researchers have increasingly focused on how epigenetic alterations drive cancer initiation and progression. As a reversible lipid modification, S-palmitoylation modulates protein stability, subcellular localization, and protein\u0026ndash;protein interactions, and is broadly involved in cell signaling, immune regulation, and tumor development\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. Although several studies have reported associations between palmitoyltransferases\u0026mdash;such as members of the ZDHHC family\u0026mdash;and cancer prognosis, a systematic investigation of protein palmitoylation in OC remains lacking\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. Within this study, we conducted a comprehensive characterization of the expression profile and prognostic significance of PRGs, subsequently establishing a reliable four-gene signature that incorporates HSPG2, BRD4, RARRES1, and SCGB1D2. The model demonstrated favorable predictive performance across multiple clinical subgroups, underscoring its potential value for clinical risk stratification and personalized treatment in ovarian cancer.\u003c/p\u003e \u003cp\u003eBRD4 functions as a chromatin reader that recognizes acetylated histones and regulates transcriptional elongation, thereby regulating the expression of genes involved in the cell cycle, inflammation, and tumorigenesis. In breast cancer, the low-abundance short isoform (BRD4-S) exhibits oncogenic activity, while the predominant long isoform (BRD4-L) suppresses tumor formation and metastasis. Intriguingly, in ovarian cancer, BRD4-L similarly inhibits proliferation and migration\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/sup\u003e. In OC, BRD4 exhibits significant amplification, with elevated expression strongly associated with unfavorable prognosis. Overexpression of BRD4-S promotes ovarian cancer cell proliferation, accumulation of DNA damage, and G2/M phase arrest, leading to a more aggressive tumor phenotype and resistance to platinum-based chemotherapy\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Additionally, BRD4 acts as a crucial regulator of super-enhancer activity, prompting the initiation of cancer-promoting pathways like MYC and BCL2\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/sup\u003e. BRD4 inhibitors, including JQ1 and AZD5153, have shown potent anti-tumor effects in vitro and are considered promising targeted agents for ovarian cancer\u003csup\u003e[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRARRES1 (also known as TIG1, tazarotene-induced gene 1) is a retinoic acid\u0026ndash;responsive tumor suppressor gene involved in cell differentiation and apoptosis. Although RARRES1 acts as a tumor suppressor in cancers, its role exhibits tissue-specific duality. For example, in glomerular diseases, RARRES1 promotes p53-mediated podocyte apoptosis, leading to podocytopenia and glomerulosclerosis \u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Recent studies have identified RARRES1 as a tumor microenvironment (TME)-associated gene, and its overexpression has been shown to inhibit ovarian cancer cell proliferation, migration, and invasion\u0026mdash;findings that suggest a tumor-suppressive role and its potential to serve as a predictive biomarker for immune checkpoint inhibitor (ICI) response\u003csup\u003e[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/sup\u003e. Moreover, single-cell analyses have revealed that RARRES1 is primarily enriched in immune cell subsets, indicating that it may exert protective effects by modulating the immune microenvironment\u003csup\u003e[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHSPG2 encodes Perlecan, a multifunctional heparan sulfate proteoglycan that serves as a structural scaffold in the extracellular matrix (ECM) while dynamically regulating tumor angiogenesis, cell adhesion, and metastatic dissemination. Increased expression of HSPG2 has been detected in multiple cancers and is tightly linked to enhanced tumor invasiveness and ECM remodeling\u003csup\u003e[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/sup\u003e. Increased HSPG2 expression acts as an independent prognostic indicator in patients with acute myeloid leukemia (AML) and correlates with an unfavorable clinical prognosis\u003csup\u003e[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e. Moreover, HSPG2 has been shown to bind multiple growth factors, such as VEGF and FGF2, enhancing their signaling activity and promoting tumor angiogenesis and progression\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSCGB1D2, a member of the secretoglobin protein family, is widely expressed in exocrine tissues and is implicated in modulating immune responses, suppressing cell migration, and regulating inflammation. It acts as a host defense factor found in skin, sweat, and other secretions, and plays a protective role against Borrelia burgdorferi infection, offering a promising therapeutic avenue for Lyme disease\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]\u003c/sup\u003e. Although SCGB1D2 has not been extensively studied in ovarian cancer, its expression has been associated with benign tumors in breast cancer, and downregulation is often observed in more aggressive cancer subtypes\u003csup\u003e[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. In the present study, downregulation of SCGB1D2 was correlated with a high-risk score in patients with ovarian cancer, potentially reflecting an immunosuppressive tumor microenvironment phenotype.\u003c/p\u003e \u003cp\u003eThis study proposes a novel method for stratifying survival risk in ovarian cancer patients, using a prognostic risk model built from four palmitoylation-related genes: HSPG2, BRD4, RARRES1, and SCGB1D2. By integrating molecular characteristics with conventional clinical parameters such as tumor stage and histological grade, the model not only improves the accuracy of prognosis prediction but also offers a framework for understanding the biological mechanisms through which palmitoylation influences ovarian cancer progression\u003csup\u003e[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]\u003c/sup\u003e. The independent prognostic value of the risk score underscores its potential to serve as a tool for clinical decision-making, especially for detecting high-risk patients that could benefit from enhanced treatment or targeted intervention of the palmitoylation pathway. The robust performance of the nomogram (AUC\u0026thinsp;\u0026gt;\u0026thinsp;0.7) indicates its practical utility in individualized survival prediction, effectively connecting basic research with clinical practice\u003csup\u003e[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]\u003c/sup\u003e. Further investigation is warranted to substantiate these results in prospective cohorts and to further investigate the functional mechanisms through which these genes mediate therapeutic resistance or immune escape.\u003c/p\u003e \u003cp\u003eSubgroup analyses revealed significant survival disparities between risk groups stratified by age (\u0026gt;\u0026thinsp;45 years), race (White), stage (III), and histologic grade (G3). The results of this study align with earlier research indicating that age\u0026thinsp;\u0026gt;\u0026thinsp;45 is an adverse prognostic factor for ovarian cancer, likely related to hormonal changes, immune decline, and delayed diagnosis\u003csup\u003e[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. Caucasian patients represent the majority of ovarian cancer cases and have been associated with poorer survival in some studies, possibly due to genetic and socioeconomic factors\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]\u003c/sup\u003e. Stage III is the most common advanced stage, often accompanied by peritoneal dissemination and markedly poor outcomes\u003csup\u003e[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. These results not only validate the model\u0026rsquo;s predictive consistency across clinical subgroups but also demonstrate its ability to identify high-risk patients within traditionally defined risk strata, suggesting broad applicability and clinical potential.\u003c/p\u003e \u003cp\u003eDysregulation of protein translation has been implicated as a critical mechanism in ovarian cancer progression. We observed significant enrichment of translation initiation and elongation pathways in high-risk patients, implicating hyperactivated protein synthesis as a hallmark of aggressive OC. Intriguingly, palmitoylation may mediate this process, as prior studies show it regulates translation factor localization\u003csup\u003e[\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]\u003c/sup\u003e. Previous research indicates that palmitoylation can directly affect the subcellular localization and activity of translation factors, supporting our findings\u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Moreover, palmitoylation may promote tumor cells\u0026rsquo; abnormal reliance on protein synthesis by influencing the mTOR signaling pathway and associated translational regulators\u003csup\u003e[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. The activation of pathways such as Medicus Reference Translation Initiation and Eukaryotic Translation Elongation in the high-risk group may partially explain their increased tumor metabolism and invasiveness. These enrichments suggest heightened protein synthesis, which may drive faster cell cycling and increased metastatic potential, which is consistent with the known role of translational dysregulation in cancer\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e. Key translational factors such as eIF4E, eEF2, and mTORC1 have already emerged as potential anticancer targets\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]\u003c/sup\u003e. Taking into account the notable enrichment of translation-associated pathways in the high-risk group, small-molecule inhibitors targeting these factors or their networks may represent promising therapeutic strategies for high-risk ovarian cancer patients.\u003c/p\u003e \u003cp\u003eThe investigation identified substantial disparities in various characteristics across risk stratifications, comprising immune checkpoint expression (e.g., CTLA4, CD274), infiltrating immune cell levels, and therapeutic sensitivity patterns, demonstrating that palmitoylation could impact ovarian malignancy progression via alteration of the immune tumor microenvironment, consequently presenting viable targets for immune-based therapies. Research has shown that protein palmitoylation is closely linked to immune responses, and immune checkpoint molecules (e.g., CTLA4 and PD-L1) rely on lipid modifications, including palmitoylation, for membrane localization, stability, and function\u003csup\u003e[\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]\u003c/sup\u003e. Our findings further demonstrated notable differences in the infiltration levels of immune cells (including CD8\u0026thinsp;+\u0026thinsp;T cells, regulatory T cells, and M2 macrophages) between the groups. This aligns with previous research showing that lipid metabolic reprogramming of tumor-associated macrophages (TAMs), including palmitoylation, plays a crucial role in shaping their immune functions\u003csup\u003e[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e. In vitro studies have demonstrated that inhibition of key palmitoyltransferases (e.g., ZDHHC9, ZDHHC3) can downregulate PD-L1 protein expression and enhance tumor sensitivity to immune checkpoint inhibitors (ICIs)\u003csup\u003e[\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, we identified a markedly increased prevalence of TP53 genetic alterations among high-risk patients, implying that our risk classification may mirror fundamental genomic dysregulation. Within high-grade serous ovarian carcinoma (HGSOC), TP53 constitutes the most commonly altered gene, exhibiting mutations in 96% of patient samples\u003csup\u003e[\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]\u003c/sup\u003e. Previous studies have reported that certain palmitoylation enzymes and substrate proteins participate in the DNA damage response (DDR) and chromatin regulation\u003csup\u003e[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e. Palmitoylation may also indirectly disrupt genomic stability by affecting the localization and degradation of DNA repair proteins \u003csup\u003e[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]\u003c/sup\u003e. Therefore, we hypothesize that aberrant expression of palmitoylation-related genes involved in the risk model may promote TP53 mutation accumulation or exacerbate oncogenic processes following TP53 loss by altering DDR and chromatin states.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study systematically elucidated the prognostic characteristics of palmitoylation-related genes (PRGs) in ovarian cancer and successfully constructed a four-gene signature consisting of HSPG2, BRD4, RARRES1, and SCGB1D2. This signature not only reliably distinguishes patient risk subgroups but also reveals the underlying mechanisms by which PRGs promote tumor progression through dysregulation of protein translation and remodeling of the tumor immune microenvironment. Although multi-omics analyses provided clinically relevant findings, several limitations should be acknowledged: potential cohort selection bias arising from the retrospective nature of the data, and the requirement for experimental validation of PRG-mediated palmitoylation mechanisms.\u003c/p\u003e \u003cp\u003eFuture research should focus on three key directions: (1) Conducting multicenter prospective clinical trials to validate the utility of this model in guiding PARP inhibitor selection; (2) Employing organoid models to dissect the mechanism by which BRD4-S and HSPG2 palmitoylation synergistically activate the mTOR-mediated translation pathway; (3) Performing high-throughput screening for palmitoylation inhibitors targeting the ZDHHC-RARRES1 axis and exploring their combination with immunotherapy regimens. These translational efforts will facilitate the application of molecular discoveries in the field of precision medicine.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe experimental protocol was established, according to the ethical guidelines of the Helsinki Declaration and was approved by the Human Ethics Committee of Xinqiao Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors approved the final manuscript and submission to this journal.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and material used or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors confirm that they have no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSupported by the Chongqing Natural Science Foundation General Project (CSTB2022NSCQ- MSX1014）\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u003c/strong\u003e\u003cstrong\u003e’\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQH and RX put forward the ideas of this article. QH, MH and TC drafted the article. CC and PZ reviewed the article. PZ and ZL interpreted the data. All authors contributed to the article and approved the submitted version.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eLheureux, S., Braunstein, M. \u0026amp; Oza, A. M. Epithelial ovarian cancer: Evolution of management in the era of precision medicine. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e69\u003c/b\u003e (4), 280\u0026ndash;304 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia, C., Yao, A., Camacho, F., Balkrishnan, R. \u0026amp; Cantrell, L. A. A SEER-Medicare analysis of the impact of metformin on overall survival in ovarian cancer. \u003cem\u003eGynecol. Oncol.\u003c/em\u003e \u003cb\u003e146\u003c/b\u003e (2), 346\u0026ndash;350 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, X., Du, J., Wang, X. \u0026amp; Wu, X. Trends and projections of ovarian cancer incidence in Hong Kong: A population-based study. \u003cem\u003eActa Obstet. Gynecol. Scand.\u003c/em\u003e \u003cb\u003e102\u003c/b\u003e (7), 942\u0026ndash;949 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTorre, L. A. et al. Ovarian cancer statistics, 2018. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e68\u003c/b\u003e (4), 284\u0026ndash;296 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang, H. et al. Protein Lipidation: Occurrence, Mechanisms, Biological Functions, and Enabling Technologies. \u003cem\u003eChem. Rev.\u003c/em\u003e \u003cb\u003e118\u003c/b\u003e (3), 919\u0026ndash;988 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKo, P. J. \u0026amp; Dixon, S. J. Protein palmitoylation and cancer. \u003cem\u003eEMBO Rep.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e(10). (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResh, M. D. Palmitoylation of proteins in cancer. \u003cem\u003eBiochem. Soc. Trans.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e (2), 409\u0026ndash;416 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMalgapo, M. \u0026amp; Linder, M. E. Substrate recruitment by zDHHC protein acyltransferases. \u003cem\u003eOpen. Biol.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (4), 210026 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAli, A. et al. Fatty acid synthase mediates EGFR palmitoylation in EGFR mutated non-small cell lung cancer. \u003cem\u003eEMBO Mol. Med.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e(3). (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang, X., Zhou, L., Dong, Z. \u0026amp; Wang, G. Identification of iron metabolism-related predictive markers of endometriosis and endometriosis-relevant ovarian cancer. \u003cem\u003eMed. (Baltim).\u003c/em\u003e \u003cb\u003e102\u003c/b\u003e (15), e33478 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJi, J. et al. FBXO2 targets glycosylated SUN2 for ubiquitination and degradation to promote ovarian cancer development. \u003cem\u003eCell. Death Dis.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (5), 442 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang, J. et al. A novel autophagy-related gene signature associated with prognosis and immune microenvironment in ovarian cancer. \u003cem\u003eJ. Ovarian Res.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (1), 86 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKong, Y. et al. Palmitoylation landscapes across human cancers reveal a role of palmitoylation in tumorigenesis. \u003cem\u003eJ. Transl Med.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e (1), 826 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eH\u0026auml;nzelmann, S., Castelo, R. \u0026amp; Guinney, J. GSVA: gene set variation analysis for microarray and RNA-seq data. \u003cem\u003eBMC Bioinform.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 7 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGustavsson, E. K., Zhang, D., Reynolds, R. H., Garcia-Ruiz, S. \u0026amp; Ryten, M. ggtranscript: an R package for the visualization and interpretation of transcript isoforms using ggplot2. \u003cem\u003eBioinformatics\u003c/em\u003e \u003cb\u003e38\u003c/b\u003e (15), 3844\u0026ndash;3846 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLei, J. et al. Clinicopathological characteristics of pheochromocytoma/paraganglioma and screening of prognostic markers. \u003cem\u003eJ. Surg. Oncol.\u003c/em\u003e \u003cb\u003e128\u003c/b\u003e (4), 510\u0026ndash;518 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e43\u003c/b\u003e (7), e47 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZheng, Y. et al. Ferroptosis and Autophagy-Related Genes in the Pathogenesis of Ischemic Cardiomyopathy. \u003cem\u003eFront. Cardiovasc. Med.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, 906753 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M. \u0026amp; Goto, S. KEGG: kyoto encyclopedia of genes and genomes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (1), 27\u0026ndash;30 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M. Toward understanding the origin and evolution of cellular organisms. \u003cem\u003eProtein Sci.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (11), 1947\u0026ndash;1951 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKanehisa, M., Furumichi, M., Sato, Y., Kawashima, M. \u0026amp; Ishiguro-Watanabe, M. KEGG for taxonomy-based analysis of pathways and genomes. \u003cem\u003eNucleic Acids Res.\u003c/em\u003e \u003cb\u003e51\u003c/b\u003e (D1), D587\u0026ndash;D592 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, G., Wang, L. G., Han, Y. \u0026amp; He, Q. Y. clusterProfiler: an R package for comparing biological themes among gene clusters. \u003cem\u003eOMICS\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (5), 284\u0026ndash;287 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, P., Xu, H., Shi, Y., Deng, L. \u0026amp; Chen, X. Potential Molecular Mechanisms of Plantain in the Treatment of Gout and Hyperuricemia Based on Network Pharmacology. Evid Based Complement Alternat Med. 2020: 3023127. (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi, Y., Lu, F. \u0026amp; Yin, Y. Applying logistic LASSO regression for the diagnosis of atypical Crohn's disease. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e (1), 11340 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRamsay, I. S. et al. Model selection and prediction of outcomes in recent onset schizophrenia patients who undergo cognitive training. \u003cem\u003eSchizophr Res. Cogn.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e, 1\u0026ndash;5 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHeagerty, P. J., Lumley, T. \u0026amp; Pepe, M. S. Time-dependent ROC curves for censored survival data and a diagnostic marker. \u003cem\u003eBiometrics\u003c/em\u003e \u003cb\u003e56\u003c/b\u003e (2), 337\u0026ndash;344 (2000).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSachs, M. C. plotROC: A Tool for Plotting ROC Curves. \u003cem\u003eJ. Stat. Softw.\u003c/em\u003e 79. (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlanche, P., Dartigues, J. F. \u0026amp; Jacqmin-Gadda, H. Estimating and comparing time-dependent areas under receiver operating characteristic curves for censored event times with competing risks. \u003cem\u003eStat. Med.\u003c/em\u003e \u003cb\u003e32\u003c/b\u003e (30), 5381\u0026ndash;5397 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng, Q., Chen, X., Wu, H. \u0026amp; Du, Y. Three hematologic/immune system-specific expressed genes are considered as the potential biomarkers for the diagnosis of early rheumatoid arthritis through bioinformatics analysis. \u003cem\u003eJ. Transl Med.\u003c/em\u003e \u003cb\u003e19\u003c/b\u003e (1), 18 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen, B., Khodadoust, M. S., Liu, C. L., Newman, A. M. \u0026amp; Alizadeh, A. A. Profiling Tumor Infiltrating Immune Cells with CIBERSORT. \u003cem\u003eMethods Mol. Biol.\u003c/em\u003e \u003cb\u003e1711\u003c/b\u003e, 243\u0026ndash;259 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrifjon, S. et al. Translation and Adaptation of the Adult Developmental Coordination Disorder/Dyspraxia Checklist (ADC) into Asian Uzbekistan. Sports (Basel). 11(7). (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGeeleher, P., Cox, N. \u0026amp; Huang, R. S. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. \u003cem\u003ePLoS One\u003c/em\u003e. \u003cb\u003e9\u003c/b\u003e (9), e107468 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMayakonda, A., Lin, D. C., Assenov, Y., Plass, C. \u0026amp; Koeffler, H. P. Maftools: efficient and comprehensive analysis of somatic variants in cancer. \u003cem\u003eGenome Res.\u003c/em\u003e \u003cb\u003e28\u003c/b\u003e (11), 1747\u0026ndash;1756 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSung, H. et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. \u003cem\u003eCA Cancer J. Clin.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e (3), 209\u0026ndash;249 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLheureux, S., Gourley, C., Vergote, I. \u0026amp; Oza, A. M. Epithelial ovarian cancer. \u003cem\u003eLancet\u003c/em\u003e \u003cb\u003e393\u003c/b\u003e (10177), 1240\u0026ndash;1253 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalaun, C., Greaves, J. \u0026amp; Chamberlain, L. H. The intracellular dynamic of protein palmitoylation. \u003cem\u003eJ. Cell. Biol.\u003c/em\u003e \u003cb\u003e191\u003c/b\u003e (7), 1229\u0026ndash;1238 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, B., Hao, Q., Liang, Y. \u0026amp; Kong, E. Protein palmitoylation in cancer: molecular functions and therapeutic potential. \u003cem\u003eMol. Oncol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e (1), 3\u0026ndash;26 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWu, S. Y. et al. Opposing Functions of BRD4 Isoforms in Breast Cancer. \u003cem\u003eMol. Cell.\u003c/em\u003e \u003cb\u003e78\u003c/b\u003e (6), 1114\u0026ndash;1132e10 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDrumond-Bock, A. L. \u0026amp; Bieniasz, M. The role of distinct BRD4 isoforms and their contribution to high-grade serous ovarian carcinoma pathogenesis. \u003cem\u003eMol. Cancer\u003c/em\u003e. \u003cb\u003e20\u003c/b\u003e (1), 145 (2021).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLov\u0026eacute;n, J. et al. Selective inhibition of tumor oncogenes by disruption of super-enhancers. \u003cem\u003eCell\u003c/em\u003e \u003cb\u003e153\u003c/b\u003e (2), 320\u0026ndash;334 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKurimchak, A. M. et al. Resistance to BET Bromodomain Inhibitors Is Mediated by Kinome Reprogramming in Ovarian Cancer. \u003cem\u003eCell. Rep.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (5), 1273\u0026ndash;1286 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng, Y. et al. Podocyte-derived soluble RARRES1 drives kidney disease progression through direct podocyte and proximal tubular injury. \u003cem\u003eKidney Int.\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e (1), 50\u0026ndash;66 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu, Z. et al. Integrative analysis identifies cancer cell-intrinsic RARRES1 as a predictor of prognosis and immune response in triple-negative breast cancer. \u003cem\u003eFront. Genet.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 1360507 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhao, A. et al. Transcription factor networks and novel immune biomarkers reveal key prognostic and therapeutic insights in ovarian cancer. \u003cem\u003eDiscov Oncol.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (1), 309 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIozzo, R. V. \u0026amp; Sanderson, R. D. Proteoglycans in cancer biology, tumour microenvironment and angiogenesis. \u003cem\u003eJ. Cell. Mol. Med.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e (5), 1013\u0026ndash;1031 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhou, X. et al. HSPG2 overexpression independently predicts poor survival in patients with acute myeloid leukemia. \u003cem\u003eCell. Death Dis.\u003c/em\u003e \u003cb\u003e11\u003c/b\u003e (6), 492 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMelrose, J. et al. Altered stress induced by partial transection of the infraspinatus tendon leads to perlecan (HSPG2) accumulation in an ovine model of tendinopathy. \u003cem\u003eTissue Cell.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e (1), 77\u0026ndash;82 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStrausz, S. et al. SCGB1D2 inhibits growth of Borrelia burgdorferi and affects susceptibility to Lyme disease. Nat Commun. 15(1): 2041. (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVrijmoeth, H. D. et al. Genome-wide analyses in Lyme borreliosis: identification of a genetic variant associated with disease susceptibility and its immunological implications. \u003cem\u003eBMC Infect. Dis.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e (1), 337 (2024).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalachandran, V. P., Gonen, M., Smith, J. J. \u0026amp; DeMatteo, R. P. Nomograms in oncology: more than meets the eye. \u003cem\u003eLancet Oncol.\u003c/em\u003e \u003cb\u003e16\u003c/b\u003e (4), e173\u0026ndash;e180 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSheehy, J., Gallanagh, M., Sullivan, C. \u0026amp; Lane, S. Clinical prediction models for febrile neutropenia and its outcomes: a systematic review. \u003cem\u003eSupport Care Cancer\u003c/em\u003e. \u003cb\u003e33\u003c/b\u003e (7), 537 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYazaki, S. et al. Impact of germline variants on breast and ovarian cancer risk in Japanese women: an original cohort study and meta-analysis. \u003cem\u003eEBioMedicine\u003c/em\u003e \u003cb\u003e116\u003c/b\u003e, 105758 (2025).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMilano, A. F. \u0026amp; Non-Hodgkin Lymphoma - Nodal and Extranodal: 20-Year Comparative Mortality, Survival \u0026amp; Biologic Behavior Analysis by Age, Sex, Race, Stage, Cell Morphology/Histology, Cohort Entry Time-Period and Disease Duration: A Systematic Review of 384,651 Total NHL Cases Including 261,144 Nodal and 123,507 Extranodal Cases for Diagnosis Years 1975\u0026ndash;2016: (SEER*Stat 8.3.6). \u003cem\u003eJ. Insur Med.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e (1), 1\u0026ndash;35 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlar, M. et al. Prognostic factors in young ovarian cancer patients: An analysis of four prospective phase III intergroup trials of the AGO Study Group, GINECO and NSGO. \u003cem\u003eEur. J. Cancer\u003c/em\u003e. \u003cb\u003e66\u003c/b\u003e, 114\u0026ndash;124 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSolis, G. P. et al. Local and substrate-specific S-palmitoylation determines subcellular localization of Gαo. \u003cem\u003eNat. Commun.\u003c/em\u003e \u003cb\u003e13\u003c/b\u003e (1), 2072 (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eResh, M. D. Fatty acylation of proteins: The long and the short of it. \u003cem\u003eProg Lipid Res.\u003c/em\u003e \u003cb\u003e63\u003c/b\u003e, 120\u0026ndash;131 (2016).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThoreen, C. C. et al. An ATP-competitive mammalian target of rapamycin inhibitor reveals rapamycin-resistant functions of mTORC1. \u003cem\u003eJ. Biol. Chem.\u003c/em\u003e \u003cb\u003e284\u003c/b\u003e (12), 8023\u0026ndash;8032 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeier, S. et al. Serum Glial Fibrillary Acidic Protein Compared With Neurofilament Light Chain as a Biomarker for Disease Progression in Multiple Sclerosis. \u003cem\u003eJAMA Neurol.\u003c/em\u003e \u003cb\u003e80\u003c/b\u003e (3), 287\u0026ndash;297 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBhat, M. et al. Targeting the translation machinery in cancer. \u003cem\u003eNat. Rev. Drug Discov\u003c/em\u003e. \u003cb\u003e14\u003c/b\u003e (4), 261\u0026ndash;278 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFeng, C., Zhang, L., Chang, X., Qin, D. \u0026amp; Zhang, T. Regulation of post-translational modification of PD-L1 and advances in tumor immunotherapy. \u003cem\u003eFront. Immunol.\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 1230135 (2023).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSu, Y. et al. [Regulation of PD-L1 posttranslational modification and its application progress in tumor immunotherapy]. Xi Bao Yu Fen Zi Mian Yi Xue Za Zhi. \u003cb\u003e38\u003c/b\u003e(11): 1036\u0026ndash;1043. (2022).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu, P. S. et al. α-ketoglutarate orchestrates macrophage activation through metabolic and epigenetic reprogramming. \u003cem\u003eNat. Immunol.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e (9), 985\u0026ndash;994 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYao, H. et al. Inhibiting PD-L1 palmitoylation enhances T-cell immune responses against tumours. \u003cem\u003eNat. Biomed. Eng.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e (4), 306\u0026ndash;317 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNetwork, C. G. A. R. Integrated genomic analyses of ovarian carcinoma. \u003cem\u003eNature\u003c/em\u003e \u003cb\u003e474\u003c/b\u003e (7353), 609\u0026ndash;615 (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Table","content":"\u003cp\u003e\u003cstrong\u003eTable 1 PH assumption test.\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"173\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eGene\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 101px;\"\u003e\n \u003cp\u003ep\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eHSPG2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.686\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eBRD4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eRARRES1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.101\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd\u003e\n \u003cp\u003eSCGB1D2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd\u003e\n \u003cp\u003e0.809\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ovarian cancer, Palmitoylation Related Genes, Prognostic genes, Independent prognostic factors","lastPublishedDoi":"10.21203/rs.3.rs-8340423/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8340423/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOvarian cancer (OC) remains the most lethal malignancy within the spectrum of gynecological cancers globally. While protein S-palmitoylation has been extensively implicated in tumor progression, its specific functional contributions and molecular mechanisms in the context of OC pathogenesis remain to be fully elucidated. This article aims to explore the prognostic effect associated with palmitoylation in OC.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eTo begin with, we obtained transcriptomic data for ovarian cancer (OC) from publicly available genomic repositories. Using comparative analysis, we pinpointed two distinct sets of differentially expressed genes (DEGs): DEGs1, which are associated with OC, and DEGs2, which are linked to palmitoylation. Consequently, a prognostic risk model was constructed and validated. Following this, an independent survival analysis was executed, a nomogram was subsequently developed to establish a predictive model. Multiple analytical approaches were applied to stratified risk groups, including pathway enrichment assessment, immune microenvironment infiltration evaluation, immune checkpoint examination, potential drug screening, and genomic mutation profiling. The expression patterns of identified prognostic markers were subsequently validated by means of reverse transcription quantitative PCR (RT-qPCR).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThrough intersecting DEGs1 and DEGs2, we obtained 24 candidate biomarkers. Our investigation revealed that HSPG2, BRD4, RARRES1, and SCGB1D2 served as prognostic markers, which were utilized to develop a risk assessment model demonstrating excellent capability in evaluating OC patient prognosis via comprehensive analytical procedures. Risk scoring, ethnicity, and tumor staging emerged as independent predictive determinants for OC. The constructed prognostic nomogram exhibited robust predictive capacity for patient clinical outcomes. There were four variables, including age (\u0026gt;\u0026thinsp;45), race (white), stage 3, and histologic G3, with survival distinctions between two risk cohorts. Relevant pathways contained distinct ribosome-related and translation initiation activities. The prognostic genes were linked to seven immune cells, like Eosinophils, and notable distinctions were found in seven immune checkpoints, like CTLA4 and CD274, between the two risk cohorts. Finally, there was a notable distinction in IC\u003csub\u003e50\u003c/sub\u003e for all 131 drugs, like BMS.536924 and CGP.60474, and the TP53 gene showed a notable mutation rate in risk cohorts. Relative to the control cohort, the expression levels of HSPG2, SCGB1D2, and BRD4 showed a significant up-regulation in the OC cohort, while the expression of RARRES1 was notably up-regulated in the control cohort, consistent with its expression in GSE26712.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThis study identified HSPG2, BRD4, RARRES1, and SCGB1D2, which were prognostic genes associated with palmitoylation, providing valuable insights that could lay the foundation for innovative therapeutic strategies.\u003c/p\u003e","manuscriptTitle":"Evaluation and in vitro verification of the prognostic value of palmitoylation-related genes in ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-31 01:16:57","doi":"10.21203/rs.3.rs-8340423/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision 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