Integration of scRNA-Seq and bulk RNA-Seq to Establish a Macrophage-related Prognostic Model in Ovarian Cancer

Integration of scRNA-Seq and bulk RNA-Seq to Establish a Macrophage-related Prognostic Model 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 Integration of scRNA-Seq and bulk RNA-Seq to Establish a Macrophage-related Prognostic Model in Ovarian Cancer Tao Yu, Guangji Yang, Dongyan Ren, Yantao Li, Chong Yue, Qin Yang, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5259146/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The immunosuppressive tumor microenvironment (TME) poses challenges to effective immunotherapy in ovarian cancer (OC). Tumor-associated macrophages play a crucial role in the TME and are closely linked to OC prognosis. While many studies have used bulk RNA-seq for prognostic biomarker exploration, its limitation is in discerning gene expression differences between individual cells. Methods This study integrates single-cell RNA sequencing (scRNA-seq) with bulk RNA-seq data to accurately investigate the relationship between macrophage molecular characteristics and prognosis. RNA-seq data and prognostic information were obtained from the GEO and TCGA datasets. Using the R package "Seurat," this study annotates cell types and visualizes single-cell data. Differentially expressed genes in macrophages are identified using the FindAllMarkers function and further analyzed with the limma package. The resulting genes, combined with survival data, undergo single-factor COX regression and LASSO regression to construct a prognosis model. Results Integrating scRNA-seq and bulk RNA-seq data, this study established a prognosis model comprising 19 macrophage-related genes. Validation confirms the risk score as an independent prognostic factor for overall survival (OS) in patients with OC. The area under the ROC curve (AUC) for 1-year, 3-year, and 5-year survival periods were 0.71, 0.68, and 0.73, respectively. Subsequent immune analysis revealed distinct TMEs between high- and low-risk groups. The high-risk group shows distinct TMEs. The high-risk group exhibited higher immune infiltration, increased M1 macrophage infiltration, elevated M2 macrophage infiltration, and reduced sensitivity to immunotherapy but enhanced sensitivity to anti-angiogenic drugs compared to that in the low-risk group. Conclusion The study analyzed differentially expressed genes related to macrophages in OC and constructed a prognosis model. Moreover, it revealed the risk score as a prognostic factor in OC, with implications for patient sensitivity to immunotherapy. Biological sciences/Cancer Biological sciences/Immunology ovarian cancer macrophage-related genes prognostic model immune landscape scRNA-seq Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Introduction Ovarian cancer (OC) ranks third in incidence among common malignant tumors of the female reproductive system. Due to the lack of effective early detection and diagnostic techniques, 60–70% of patients with OC are diagnosed at an advanced stage.(Schem, Bauerschlag et al. 2007) While most patients recover following primary cytoreductive surgery (PCS) and standard first-line chemotherapy, approximately 70% of advanced-stage patients relapse within 2–3 years, eventually developing resistance with a 5-year survival rate of less than 40%(Torre, Trabert et al. 2018). In recent years, immunotherapy has emerged as a potential treatment strategy for multiple treatment-refractory cancers. However, immunotherapies, including PD-1 (PD-L1) checkpoint inhibitors (ICIs), have shown limited efficacy in OC (Maiorano, Maiorano et al. 2021). Even in combination with first-line chemotherapy, it fails to show meaningful improvement in progression-free survival (PFS) (Konstantinopoulos and Cannistra 2021). This low response rate primarily stems from the highly complex immunosuppressive TME of OC, which allows immune evasion and unrestrained tumor development(Cai and Jin 2017). The suppressive cellular microenvironment includes TAMs, regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and tumor-associated dendritic cells (tDCs) (Balkwill, Capasso et al. 2012). Among these, TAMs represent the major infiltrating immune subgroup in ovarian tumors and ascites, which promote the formation of an immunosuppressive microenvironment in OC, facilitating tumor growth, invasion, angiogenesis, and metastasis(Colvin 2014). Growing evidence (Le Page, Marineau et al. 2012, He, Zhang et al. 2013, Lan, Huang et al. 2013, No, Moon et al. 2013, Zhang, He et al. 2014) has shown a correlation between high levels of tumor-associated macrophage infiltration and poor patient prognosis, with macrophage-related genes influencing the immunotherapy response. Previous studies on OC (Reinartz, Schumann et al. 2014, Yuan, Zhang et al. 2017) have indicated that the expression of the alternative activation marker CD163 in malignant TAMs within ascites is closely associated with the early recurrence of serous OC following first-line treatment. However, the impact of macrophage-associated genes on OC prognosis and treatment response remains poorly understood. Thus, exploring the molecular characteristics of macrophages and their association with prognosis and immunotherapy responses in OC may reveal new prognostic markers and enhance treatment options. Single-cell RNA sequencing (scRNA-seq) has become an indispensable approach for analyzing the TME (Potter 2018, Izar, Tirosh et al. 2020). Compared to bulk RNA sequencing (bulk RNA-seq), which explores the average gene expression within a cell population, scRNA-seq analyzes most transcripts within individual cell profiles using high-throughput sequencing, providing a comprehensive perspective on the molecular diversity and tumor heterogeneity within cell populations (Wang, Zhang et al. 2023)[Ref]. Ongoing research is combining bulk RNA-seq and scRNA-seq to identify potential biomarkers for precise patient stratification and clinical benefit group selection. Liang et al. (Wang, Zhang et al. 2023) used scRNA-seq and bulk RNA-seq to construct a dual-gene (CXCL13 and IL26) signature prognostic system, which suggested the heterogeneity of OC as an immunotherapy target. Hornburg M et al.(Hornburg, Desbois et al. 2021) integrated single-cell RNA sequencing data from 15 cases of ovarian tumors and bulk RNA-seq to identify immune microenvironment traits associated with T-cell infiltration patterns, suggesting chemokine receptor-ligand interactions as potential mediators of immune infiltration. Guo et al. (Guo, Han et al. 2023) used scRNA-seq and bulk RNA-seq along with experimental validation to identify cell clusters and the key gene RAB13, which is closely associated with OC metastasis. Using RNA-seq data from The Cancer Genome Atlas (TCGA) and scRNA-seq data from the Gene Expression Omnibus (GEO), we identified independent prognostic genes associated with macrophages and built a predictive model for patients with OC. Additionally, we examined the relationship between the risk model and clinical characteristics, immune infiltration landscape, immunotherapy response, and so on, to appropriately predict prognosis risk. Overall, our study identified clinically relevant macrophage-related indicators, explained macrophage immunogenomic characteristics in OC, and provided novel insights into targeted therapies in clinical practice. Data and Methods 1. Data Acquisition and Selection OC scRNA-seq data were sourced from the GEO database of the National Center for Biotechnology Information (NCBI) with accession number GSE154600 (Geistlinger, Oh et al. 2020). This dataset included five samples: GSM4675273, GSM4675274, GSM4675275, GSM4675276, and GSM4675277. Subsequently, the cells were filtered based on the following criteria: feature count 20,000, and mitochondrial proportion > 5%. After applying these filters, 20,914 cells were obtained for subsequent single-cell analysis. RNA-seq data (log2-transformed FPKM), clinical details (including age, tumor stage, and neoplasm histological grade), and survival information (overall survival [OS] and OS times) of OC were obtained from the UCSC Xena platform(Goldman, Craft et al. 2019) ( https://toil.xenahubs.net ). In this analysis, we designated samples with the identifier "-01A" as cancer tissue samples. Normal ovarian tissue samples were obtained from the Genotype-Tissue Expression (GTEx) database (2013) ( https://www.gtexportal.org/ ). Information on 442 samples, including 88 normal and 354 cancerous tissue samples, was downloaded. Among these, 342 cancer tissue samples with available prognostic information were included in the subsequent model construction analysis. Transcriptional data for OC from the GSE19829 and GSE26193 datasets were obtained from the NCBI GEO database (Barrett, Wilhite et al. 2013). Samples with available prognostic information were extracted, batch effects were removed, and the two datasets were merged. Ultimately, 135 OC samples were included in the study as validation datasets for subsequent modeling. We directly downloaded the preprocessed and standardized probe expression matrices and the corresponding platform annotation files for gene symbol conversion. For probes corresponding to the same gene symbol, the maximum value was used as the gene expression value in subsequent analyses. 2. Cell Annotation For the Seurat objects of OC samples in the single-cell data, uniform manifold approximation and projection (UMAP) visualization revealed 20 clusters. We manually annotated nine distinct cell types based on marker genes. These included macrophages marked by C1QA and C1QB, B cells marked by CD79A and IGHG1, CD4 + T cells marked by CD4, CD8 + T cells marked by CD8A and CD8B; natural killer cells marked by NKG7; endothelial cells marked by PECAM1, Treg cells marked by FOXP3; epithelial cells marked by PECAM; and fibroblasts marked by COL1A and BGN. 3. Gene Expression Differential Analysis We used the "FindAllMarkers" function to compute differential genes among all cell clusters. Specifically, we focused on macrophages, and genes meeting the criteria of |log2FoldChange| >0 and P-Value < 0.05 were selected as single-cell differentially expressed genes (scDEGs) for further investigation within the macrophage cell cluster. Furthermore, we employed the limma package (Ritchie, Phipson et al. 2015)(Version 3.10.3, http://www.bioconductor.org/packages/2.9/bioc/html/limma.html ), which provides linear regression and empirical Bayesian methods to perform tumor vs. normal differential expression analysis on TCGA transcriptome data, resulting in gene-specific P-values and logFC information. Additionally, we conducted multiple testing corrections using the Benjamini-Hochberg method, yielding adjusted p-values (adj.P.Value). We assessed both levels of multiplicity and significance of differences, with differential expression thresholds set as follows: adj.P.Value 0.263. 4. Protein–Protein Interaction Network Analysis Using the search tool for recurring instances of neighboring genes (STRING) database (Szklarczyk, Franceschini et al. 2015) (Version 11.0, http://string-db.org/ ), which contains human protein-protein interaction relationships, we obtained a protein-protein interaction network (PPI) for the intersection of differential genes in macrophages and differential genes in the regular transcriptome. The species used in this study was Homo sapiens. Network construction was performed using Cytoscape (Shannon, Markiel et al. 2003) (version 3.6.1) ( https://cytoscape.org/ ). 5. Selection of Prognostic-Related Genes Within the TCGA samples, based on the obtained intersection of differential genes and considering clinical survival prognosis information, we conducted a single-factor Cox regression analysis using the survival package (Therneau and Lumley 2015) ( http://bioconductor.org/packages/survivalr/ ) in R 4.3.1. Genes with p-values less than 0.05 were chosen as significant prognostic-related genes. 6. Construction and Validation of Prognostic Feature Models for Intersection Genes Based on the intersection of differential genes significantly associated with survival obtained in the previous step, we used survival prognosis information from the training set samples. Combined with the gene expression values in each sample, we applied the least absolute shrinkage and selection operator (LASSO) Cox regression model(Shahraki, Salehi et al. 2015) using the glmnet package (Friedman, Hastie et al. 2021)(version 2.0–18, https://cran.r-project.org/web/packages/glmnet/index.html ) in R. We employed 5-fold cross-validation to select gene combinations relevant to prognosis. Subsequently, the following risk score model was constructed based on the regression prognostic coefficients of the genes in the gene combinations and the expression levels of the genes in the TCGA samples, which were calculated as follows: Riskscore = ∑β gene × Exp gene Here, β gene represents the LASSO regression coefficient of the gene, and Exp gene represents the gene's expression level in the TCGA dataset. To validate the accuracy of the model, we calculated the Risk Score values for each sample in the GEO dataset using the same regression coefficients. Based on the optimal Risk Score cut-off value, we divided all GEO samples into high- and low-risk groups. We evaluated the association between high- and low-risk groupings and actual survival prognosis information using the Kaplan–Meier method provided by the survival package (Version 2.41-1). 7. Association between Clinical Features and Riskscore In TCGA samples, we used the Wilcoxon test in R 4.3.1 to statistically compare and assess the association between variables such as tumor stage, neoplasm histologic grade, age, OS, and risk score. 8. Independence Analysis of the Prognostic Model and Nomogram Construction First, to determine whether the risk score model could serve as an independent prognostic factor, we conducted single- and multiple-factor Cox regression analyses for tumor stage, neoplasm histologic grade, age, and riskscore separately. We selected variables with p-values less than 0.05. A nomogram was created to make the results of multiple-factor regression more interpretable. The calibration curves are plotted to demonstrate the accuracy of the model. 9. Association of High- and Low-Risk Groups with Immune Microenvironment In this analysis, we employed three algorithms to evaluate the immune microenvironment of OC samples. 1. Using cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT)(Chen, Khodadoust et al. 2018) ( https://cibersort.stanford.edu/index.php ), we calculated the proportions of 22 immune cell types based on their expression levels in TCGA OV tumor samples. CIBERSORT deconvolves the expression matrix of immune cell subtypes based on the principle of linear support vector regression. We conducted analyses using both relative and absolute modes. (Note: Absolute mode refers to the absolute proportions of each immune cell; for example, if the overall immune cell proportion is 3%, then the absolute values for the 22 major immune cells may be less than 0.1%, but the relative mode results in proportions of up to 1.) 2. The ESTIMATE algorithm(Yoshihara, Shahmoradgoli et al. 2013) was used to estimate stromal and immune scores, and ESTIMATE scores based on expression data were used to represent the presence of stromal and immune cells. We calculated the differential P-values between the high- and low-risk groups using the Wilcoxon rank-sum test and visualized them using box plots. 3. In this study, we employed immune gene sets to calculate gene set enrichment scores (ssGSEA) for each sample(Yi, Nissley et al. 2020). This method quantifies the enrichment of gene sets in each sample based on gene expression data and biological processes. Unlike traditional GSEA methods, ssGSEA does not require data from multiple samples and can be directly applied to single-sample gene expression data, making it suitable for small-sample analyses. 10. Drug Sensitivity Analysis We estimated the sensitivity of each patient to drugs using the Genomics of Drug Sensitivity in Cancer (GDSC) database (Yang, Soares et al. 2013) ( https://www.cancerrxgene.org/ ). The half-maximal inhibitory concentration (IC50) was quantified using the pRRophetic package (Geeleher, Cox et al. 2014) in R. Wilcoxon tests that were used to compare differences in drug sensitivity between the high- and low-risk groups. 11. Prediction of Immunotherapy Response Tumor Immune Dysfunction and Exclusion (TIDE; http://tide.dfci.harvard.edu/ ) is a transcriptome-based immunotherapy prediction tool that predicts patterns of interaction between tumor and immune cells(Jiang, Gu et al. 2018). TIDE aims to identify the biological mechanisms that cause tumor immune dysfunction and rejection, providing predictions for the responsiveness of tumor immunotherapy. We compared the TIDE scores between the high- and low-risk groups using the Wilcoxon test. 12. Statistical analysis Before model construction, the "FindAllMarkers" function was employed to calculate differentially expressed genes (DEGs) across all cell clusters, with a specific focus on macrophages. Genes meeting the criteria of |log2FoldChange| >0 and P-value < 0.05 were identified as single-cell differentially expressed genes (scDEGs), allowing for an in-depth exploration of differentially expressed genes within the macrophage cluster. Additionally, the Limma package was used to perform differential gene expression analysis on TCGA transcriptomic data, followed by Benjamini-Hochberg correction for multiple testing, resulting in adjusted p-values (adj.P.Value). The threshold for differential expression was set at adj.P.Value 0.263 based on assessments of fold change and significance. For the subsequent construction of the prognostic feature model, the glmnet package in R was employed for building the LASSO Cox regression model. Survival data were analyzed using Kaplan–Meier curves, and both univariate and multivariate Cox regression analyses were conducted to identify independent prognostic risk factors. Finally, the Wilcoxon test in R 4.3.1 was employed to assess the statistical differences in categorical variables between the different risk groups. The data analysis workflow is shown in Fig. 1 . Results 1. Single-Cell Heterogeneity We analyzed the expression of 12 marker genes for single-cell subtypes across different cell clusters and represented the results using violin (Figs. 2 A-L) and bubble plots (Fig. 2 M). The annotation revealed the presence of nine cell types (CD4 + T cells, CD8 + T cells, macrophages, fibroblasts, natural killer cells, Tregs, B cells, endothelial cells, and epithelial cells) (Fig. 2 N). The proportion of each cell type is visualized using bar charts (Fig. 2 O). A-L: Violin plots depicting the expression of marker genes across different cell clusters. M: Bubble plots illustrating the expression of marker genes in different cell clusters. N: Annotation results for the cell clusters. O: proportion of cell clusters 2. Single-Cell versus Conventional Transcriptome Differential Gene Analysis As described in the Methods section, the differential analysis of single-cell data identified 845 differentially expressed genes in macrophages (Supplementary Table 1). We represented a heatmap displaying the differences in the expression of the top 20 differential genes in macrophages, as shown in Fig. 3 A (p 0). Following the described methods, differential analysis of TCGA transcriptome data for tumor vs. normal samples yielded a total of 18,020 differential genes (adj.P.Value 0.263; Supplementary Table 2). The volcano plot is illustrated in Fig. 3 B. For the differential genes identified in macrophages and the transcriptome, we obtained 470 overlapping differential genes (p < 0.05) (Fig. 3 C). Based on these overlapping genes, we used the online tool STRING to predict protein-protein interaction relationships. Subsequently, we imported the interactions into the Cytoscape software and employed the CytoHubba plugin with the MCC algorithm to visualize the top 20 genes in the PPI network, as shown in Fig. 3 D. A: Heatmap depicting the expression of differential genes in macrophages from single-cell transcriptome data (top 20 genes, with red indicating upregulation and blue indicating downregulation). B: Volcano plot displaying differential gene expression in the TCGA transcriptome data (red represents upregulation and blue represents downregulation). C: Venn diagram illustrating the intersection of differentially expressed genes between macrophages and TCGA transcriptome data. D: The top 20 genes in the Protein-Protein Interaction (PPI) network (darker colors indicate stronger interactions) 3. Construction of a Prognostic Model Based on the aforementioned intersecting genes, we initially conducted a single-factor Cox regression, resulting in 30 genes with a p-value < 0.05, as shown in Fig. 4 A (Supplementary Table 3). Among these genes, AP1S2, ARPC5, TMSB4X, PFN1, STAT1, TPM3, SH3KBP1, C6orf62, AKIRIN2, GNG5, TAP1, C1orf43, HMGN3 , and SDF2L1 had hazard ratios (HR) less than 1, indicating a better prognosis, whereas the rest were considered to have a worse prognosis. Next, following the described methodology, we used the 30 genes that were significantly associated with survival prognosis to select an optimal combination of 19 feature genes ( AKIRIN2, AP1S2, ARL4C, ARPC5, C1orf43, C5AR1, C6orf62, PIM3, RAB20, RB1, SDF2L1, SH3KBP1, STAT1, TAP1, TGFBI, THEMIS2, TPM3, TREM1, VSIG4 ) and their corresponding prognostic coefficients (coef) using the LASSO Cox regression algorithm, as depicted in Figs. 4 B and 4 C (Supplementary Table 4). Subsequently, for each element within the selected set of 19 feature genes, we employed Kaplan–Meier survival curves using the R survival package to evaluate the association between high (expression levels greater than or equal to the cut-off value) and low (expression levels lower than the cut-off value) gene expression levels and survival prognosis. In the TCGA database, high-risk groups show shorter overall survival than that in low-risk patients (p < 0.001, Figure D, Supplementary Table 5), which was validated using GEO data (p = 0.048, Fig. 4 F). To assess the effectiveness of the signature, we computed the area under the curve (AUC) for the model's predictions of the 1-year, 3-year, and 5-year survival periods to demonstrate that the prognostic signature effectively predicted the patients' clinical outcomes, which were 0.71, 0.68, and 0.73, respectively (Fig. 4 E). Finally, the model was validated using GEO data, and the Kaplan–Meier curves are shown in Fig. 4 F. A: Forest plot for the single-factor Cox analysis. B-C: λ selection plot in the LASSO model (two dashed lines indicate two specific λ values, with lambda.min on the left and lambda.1se on the right; any λ value between them is considered suitable). The model constructed with lambda.1se uses fewer genes, making it simpler, whereas lambda.min offers a slightly higher accuracy by utilizing more genes. Here, we choose lambda.min as λ. D: Kaplan-Meier survival curves for the prognostic model using TCGA data. E: ROC curves for the model's predictions of one-, three-, and 5-year survival periods (the area under the curve represents AUC values). F: Kaplan–Meier survival curves for the prognostic model in the GEO validation dataset. The results in the validation queue were verified to be consistent with those in the training queue. 4. Differences in Riskscore Clinical Parameters To assess the prognostic value of the risk score and other clinical factors, we conducted Wilcoxon tests to statistically compare the risk scores between different groups for the following variables: age (Fig. 5 A), OS (Fig. 5 B), tumor stage (Fig. 5 C), and neoplasm histologic grade (Fig. 5 D). The results revealed that among these four indicators, the risk score was significantly higher in individuals aged > 55 years (p = 0.002) and those with a deceased survival status (p < 0.001). No statistically significant differences exist in the risk scores among the remaining variables. A: Differences in risk scores among patients with OC in different age groups (with age groups divided at 55 years). B: Differences in risk scores among patients with OC with different survival statuses. C: Differences in risk scores among tumors at different stages. D: Differences in risk scores among tumors with different degrees of metastasis. 5. Prognostic Model Independence Analysis and Nomogram Construction As described in the methods section, univariate and multivariate Cox regression analyses were performed separately for tumor stage, neoplasm histologic grade, age, and risk score to select variables with a p-value < 0.05. As shown in Fig. 6 , age and risk score were ultimately considered independent prognostic factors supported by the HR and CI values. Univariate analysis revealed HR and CI values: HR = 1.019 (95% CI: 1.006–1.033), p = 0.004, and HR = 3.685 (95%CI: 2.640–5.143), p < 0.001, respectively. In multivariate analysis, the corresponding values were 1.014 (95%CI: 1.001–1.028), p = 0.032, and 3.692 (95% CI: 2.625–5.192), p < 0.001, respectively. Based on these findings, a nomogram containing the above four clinical factors was constructed. By summing the scores from each variable on the point-scale axis, a total score was calculated to predict the one-year, three-year, and five-year survival rates of patients. (Fig. 7 A). Additionally, calibration curves (Figs. 7 B-D) and decision curve analysis curves (Fig. 7 E) were plotted to demonstrate the accuracy of the model. The calibration curves for the nomogram demonstrate the predicted probabilities of OS estimated by the nomogram closely aligned with the actual disease-free survival proportion. A: Forest plot of the single-factor Cox regression analysis of clinical factors and risk scores. B: Forest plot of multifactor Cox regression analysis of clinical factors and risk scores A: Nomogram depicting the clinical factors and risk scores. Each variable was assigned a score on a point-scale axis. The total score was calculated by summing the individual scores and projecting them onto a total point scale to estimate survival probability. B-D: Calibration curves for 1-, 3-, and 5-year nomogram predictions. E: Decision curve analysis curve for the nomogram model 6. Association between High-Risk/Low-Risk Groups and the Immune Microenvironment To explore the association between the risk score and immune cell infiltration in tumors, we estimated the relative abundance of immune and stromal cell infiltration in each sample using the CIBERSORT, ssGSEA, and ESTIMATE algorithms (Supplementary Table 6). We generated a heatmap to display the relative abundance of each cell type, as shown in Fig. 8 . Further analysis involved dividing the samples into high- and low-risk groups, followed by a Wilcoxon test to compare each cell type between the two groups and calculate statistical significance. The results revealed that for the CIBERSORT method, 8 immune microenvironment cells exhibited significant differences between the two groups (p < 0.05) which were CD8 T cells, CD4 naïve T cells, T follicular helper cells, T gamma delta cells, M1 macrophages, M2 macrophages, activated mast cells, neutrophils (Fig. 9 A); for the ssGSEA method, 17 immune microenvironment cells (central memory CD8 T cells, effector memory CD8 T cells, central memory CD4 T cells, effector memory CD4 T cells, T follicular helper cells, Type 1 T helper (Th1) cells, regulatory T cells (Treg), memory B cells, natural killer cells, myeloid derived suppressor cells, activated dendritic cells, plasmacytoid dendritic cells, immature dendritic cells, macrophages, eosinophils, mast cells, neutrophils) showed significant differences between the two groups (p < 0.05, Fig. 9 B); for the ESTIMATE method, the high-risk group had higher Stromal score and ESTIMATE score compared to that in the low-risk group (p < 0.001); there was no statistically significant difference in immune score between the high-risk and low-risk groups (p = 0.051). The results are depicted in Figs. 9 C-E. The heatmap depicts the relative abundance distribution of all microenvironmental cells estimated by the three algorithms (different colored horizontal bars on the left represent different algorithms, as indicated in the legend, with different colored horizontal bars at the top representing different groups, with blue indicating low risk and red indicating high risk). A: Differences in TME cells estimated using the CIBERSORT method; B: Differences in TME cells estimated using the ssGSEA method; C-E: Differences in stromal, immune, and ESTIMATE scores estimated using the ESTIMATE method 7. Drug Sensitivity analysis between High-Risk and Low-Risk Groups As described in the methods section, we estimated the sensitivity of each patient to OC drugs (Veliparib, Talazoparib, Pazopanib, Sunitinib, Sorafenib, and Erlotinib). IC50 was quantified using the pRRophetic package in R. Differences in drug sensitivity between the high- and low-risk groups were compared using the Wilcoxon test. The results revealed significant differences in four of the six chemotherapy drugs, with pazopanib having a higher IC50 in the low-risk group than that in the high-risk group (p = 0.0071), sunitinib having a higher IC50 in the low-risk group than that in the high-risk group (p < 0.001), sorafenib having a higher IC50 in the low-risk group than that in the high-risk group (p < 0.001), and erlotinib having a higher IC50 in the low-risk group than that in the high-risk group (p = 0.015). The results are shown in Fig. 10 . A: Difference in the IC50 of celiparib between the high-risk and low-risk groups. B: Difference in IC50 of talazoparib between the high-risk and low-risk groups. C: Difference in IC50 of Pazopanib between the high-risk and low-risk groups. D: Difference in IC50 of Sunitinib between the high-risk and low-risk groups. E: Difference in IC50 of Sorafenib between the high- and low-risk groups. F: Difference in IC50 of Erlotinib between the high- and low-risk groups 8. Association between TIDE Score and Risk Groups As described in the Methods section, we calculated TIDE scores for patients online and then compared the differences between the high- and low-risk groups using the Wilcoxon test. The results revealed that the high-risk group had significantly higher TIDE scores than that in the low-risk group (p = 0.04), indicating a lower sensitivity to immune therapy in the high-risk group (Fig. 11 ). Differences in TIDE scores between high- and low-risk groups (red, high-risk patients; blue, low-risk patients). 9. Subcellular Localization of Prognostic Model Genes in Single Cells To investigate disparities in gene expression patterns between tumor and normal cell lines, we visualized the expression of prognostic model genes in single-cell clusters using UMAP plots. The results demonstrated that genes such as AP1S2, C5AR1, RB1, THEMIS2, TREM1, VSIG4 , and others exhibit significantly higher expression levels in macrophages than that in other cell clusters (Fig. 12 A-S). Discussion Through analysis of single-cell RNA sequencing data, we established a prognostic model for OC consisting of 19 genes. Validation using The TCGA and GEO datasets confirmed the predictive performance of the model, demonstrating that the risk score is an independent prognostic factor for OS in patients with OC. Algorithmic comparisons revealed that the high-risk group exhibited lower sensitivity to immunotherapy but higher sensitivity to anti-angiogenic drugs, potentially linked to differences in the immune microenvironment. The molecular and cellular heterogeneity of OC makes a single traditional classification insufficient for an accurate prognosis(Veneziani, Gonzalez-Ochoa et al. 2023). With the continuous refinement of molecular subtyping, traditional histological classifications and binary models for OC are gradually being replaced(Köbel and Kang 2022). In 2011, the TCGA research group classified patients with high-grade serous OC into four subtypes based on mRNA expression features(2011). Building on this, Jonsson et al. further subdivided high-grade serous OC into differentiated, immune-like, proliferative, and mesenchymal-like subtypes by integrating molecular pathological features(Jönsson, Johansson et al. 2014). The immune-like subtype exhibited the longest survival, whereas the proliferative and mesenchymal-like subtypes showed shorter survival periods. Another study classified epithelial OC into five subtypes based on gene expression patterns: EPI-A, EPI-B, MES, STEM-A, and STEM-B, with distinct pathological features, signaling pathway alterations, and prognosis(Tan, Miow et al. 2013). These molecular subtypes predict prognosis and aid in the identification of specific molecular targets for targeted therapy. Kommoss et al. revealed the proliferative and mesenchymal subtypes benefited from bevacizumab monotherapy, with median PFS of 10.1 months and 8.2 months, respectively(Kommoss, Winterhoff et al. 2017). Antony et al. reported AXL enrichment in Mes subtype tumor tissues, and the AXL inhibitor R428 reduced the activation of RTK and ERK, inhibiting the movement and proliferation of Mes cells(Antony, Tan et al. 2016), suggesting that patients with Mes subtype OC may benefit from AXL-targeted therapy. Previous molecular subtyping studies have primarily emphasized gene expression features in the overall TME, lacking insights into the interactions between prognosis and single-cell populations within the TME. Our unique scRNA-seq-based prognostic model, which uses macrophage-related genes, stands out from other models and comprehensively reflects macrophage function in the TME. Integrating diverse datasets and algorithmic results, validated against inconsistent training sets, our model boasts an AUC value of 0.68–0.73, ensuring heightened reliability and relevance. The TME plays a crucial role in clinical outcomes and treatment responses. In OC, various immune-infiltrating cells, such as mature dendritic cells, M1 macrophages, natural killer cells, αβT cells, and γδT cells, exhibit anti-tumor effects. Conversely, immunosuppressive cells such as immature/tolerant dendritic cells, M2 macrophages, regulatory T cells, and myeloid-derived suppressor cells hinder antitumor immunity(Deng, Terunuma et al. 2014, Santoiemma and Powell 2015). Studies have suggested that immune cell infiltration correlates with OC prognosis. Infiltration of naïve CD4 T cells, resting CD4 memory T cells, M2 macrophages, and eosinophils is associated with a poor prognosis, whereas activated CD4 memory T cells, M0 macrophages, and M1 macrophages correlate with an improved prognosis(Yan, Li et al. 2023). Moreover, the gene expression profiles of immune-infiltrating cells in the tumor microenvironment have been linked to the prognosis of patients with OC. CRMP2, secreted by cancer-associated fibroblasts (CAFs), promotes OC growth and metastasis, leading to adverse outcomes(Jin, Bian et al. 2022). The intricate functions of macrophages are related to OC prognosis and treatment outcomes(Yan, Li et al. 2023). Our prognostic model, incorporating 19 macrophage-specific genes, revealed that the risk score was associated with the infiltration of immune cells, based on ESTIMATE scores and immune cell infiltration analysis. The high-risk group exhibited higher overall immune cell infiltration and lower CD8 + T cell infiltration. The proportion of TAMs was higher, with a lower ratio of M1 to M2 macrophages. Therefore, despite the increased infiltration of immune-activated cells in high-risk individuals, they may be functionally inhibited, possibly contributing to an unfavorable prognosis. Chemotherapy remains the primary approach for the treatment of OC; however, its effectiveness is limited owing to tumor heterogeneity and complexity. Novel targeted therapies, such as poly (ADP-ribose) polymerase (PARP) inhibitors, significantly prolonged PFS(Farmer, McCabe et al. 2005). In immunotherapy, a few Phase III trials have been prematurely terminated owing to ineffectiveness(Maiorano, Maiorano et al. 2021). Reports suggest that combining PARP inhibitors with anti-angiogenic agents enhances the efficacy of immunotherapy by stimulating adaptive immune responses, turning "cold tumors" into "hot tumors"(Peyraud and Italiano 2020, Cao, Langer et al. 2023). The DUO-O study demonstrated the feasibility of first-line maintenance therapy with a triple combination of anti-angiogenic agents, PARP inhibitors, and immunotherapy(Westin, Moore et al. 2023). The TOPACIO study evaluates the efficacy of niraparib and pembrolizumab in late-stage recurrent OC, and biomarker analysis indicates a positive immune score, correlating with clinical benefits(Färkkilä, Gulhan et al. 2020). This suggests a correlation between immune-related genes and features of the immune microenvironment, which potentially influences the clinical benefits of combined immunotherapy in patients with OC. Consequently, in our study, drug sensitivity analysis revealed that high-risk patients showed greater sensitivity to certain anti-angiogenic drugs (Pazopanib, Sunitinib, and Sorafenib). Studies have indicated that VEGF and miR-501-3p derived from TAMs directly mediate vascular formation in tumor tissues, which may explain why increased sensitivity may be associated with a higher presence of M2 macrophages, inducing vascular formation(Tacconi, Ungaro et al. 2019, Yin, Ma et al. 2019). The TIDE score results indicated weaker sensitivity to immunotherapy in the high-risk group, which was possibly linked to the suppressive tumor microenvironment. In conclusion, our research reveals the necessity for more personalized treatment plans for high-risk patients and underscores the potential of drugs such as PARP inhibitors to enhance OC survival rates. This study had several limitations, including the use of retrospective data to construct and validate prognostic models, relatively singular data sources, and limited scRNA-seq sample sizes. The GDSC database used for drug sensitivity data is based on tumor cell lines and may differ from the intrinsic drug sensitivity of patients. The regulatory mechanisms involving macrophage characteristic genes in OC remain unclear and require further clinical research to validate the study results and better guide clinical treatment. This will be the focus of future research. Conclusion In conclusion, this study discovered and established a novel prognostic risk model for OC, using macrophage gene characteristics to effectively predict the prognosis and treatment response of patients with OC. Immunological analysis confirmed the correlation between risk score and TME, elucidating diverse prognoses among patients and providing a basis for further research on biomarkers and antitumor treatment strategies. This study is clinically valuable for screening populations that benefit from treatment and improving the prognosis of patients with OC. 【Acknowledgments】 This work was supported by Central Government Guidance Fund for Local Science and Technology Development(202407AB110013). 【Data availability】 All data in the article can be obtained from GEO datasets ( https://www.ncbi.nlm.nih.gov/geo/ ), UCSC Xena platform ( https://toil.xenahubs.net ), Genotype-Tissue Expression (GTEx) database ( https://www.gtexportal.org/ ). (2011). "Integrated genomic analyses of ovarian carcinoma." Nature 474 (7353): 609–615. (2013). "The Genotype-Tissue Expression (GTEx) project." Nat Genet 45 (6): 580–585. Antony, J., T. Z. Tan, Z. Kelly, J. Low, M. Choolani, C. Recchi, H. Gabra, J. P. Thiery and R. Y. Huang (2016). "The GAS6-AXL signaling network is a mesenchymal (Mes) molecular subtype-specific therapeutic target for ovarian cancer." Sci Signal 9 (448): ra97. Balkwill, F. R., M. Capasso and T. Hagemann (2012). "The tumor microenvironment at a glance." J Cell Sci 125 (Pt 23): 5591–5596. Barrett, T., S. E. Wilhite, P. Ledoux, C. Evangelista, I. F. Kim, M. Tomashevsky, K. A. Marshall, K. H. Phillippy, P. M. Sherman, M. Holko, A. Yefanov, H. Lee, N. Zhang, C. L. Robertson, N. Serova, S. Davis and A. Soboleva (2013). "NCBI GEO: archive for functional genomics data sets–update." Nucleic Acids Res 41 (Database issue): D991-995. Cai, D. L. and L. P. Jin (2017). "Immune Cell Population in Ovarian Tumor Microenvironment." J Cancer 8 (15): 2915–2923. Cao, Y., R. Langer and N. Ferrara (2023). "Targeting angiogenesis in oncology, ophthalmology and beyond." Nat Rev Drug Discov 22 (6): 476–495. Chen, B., M. S. Khodadoust, C. L. Liu, A. M. Newman and A. A. Alizadeh (2018). "Profiling Tumor Infiltrating Immune Cells with CIBERSORT." Methods Mol Biol 1711 : 243–259. Colvin, E. K. (2014). "Tumor-associated macrophages contribute to tumor progression in ovarian cancer." Front Oncol 4 : 137. Deng, X., H. Terunuma, A. Terunuma, T. Takane and M. Nieda (2014). "Ex vivo-expanded natural killer cells kill cancer cells more effectively than ex vivo-expanded γδ T cells or αβ T cells." Int Immunopharmacol 22 (2): 486–491. Färkkilä, A., D. C. Gulhan, J. Casado, C. A. Jacobson, H. Nguyen, B. Kochupurakkal, Z. Maliga, C. Yapp, Y. A. Chen, D. Schapiro, Y. Zhou, J. R. Graham, B. J. Dezube, P. Munster, S. Santagata, E. Garcia, S. Rodig, A. Lako, D. Chowdhury, G. I. Shapiro, U. A. Matulonis, P. J. Park, S. Hautaniemi, P. K. Sorger, E. M. Swisher, A. D. D'Andrea and P. A. Konstantinopoulos (2020). "Immunogenomic profiling determines responses to combined PARP and PD-1 inhibition in ovarian cancer." Nat Commun 11 (1): 1459. Farmer, H., N. McCabe, C. J. Lord, A. N. Tutt, D. A. Johnson, T. B. Richardson, M. Santarosa, K. J. Dillon, I. Hickson, C. Knights, N. M. Martin, S. P. Jackson, G. C. Smith and A. Ashworth (2005). "Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy." Nature 434 (7035): 917–921. Friedman, J., T. Hastie, R. Tibshirani, B. Narasimhan, K. Tay, N. Simon and J. Qian (2021). "Package ‘glmnet’." CRAN R Repositary 595 . Geeleher, P., N. Cox and R. S. Huang (2014). "pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels." PLoS One 9 (9): e107468. Geistlinger, L., S. Oh, M. Ramos, L. Schiffer, R. S. LaRue, C. M. Henzler, S. A. Munro, C. Daughters, A. C. Nelson, B. J. Winterhoff, Z. Chang, S. Talukdar, M. Shetty, S. A. Mullany, M. Morgan, G. Parmigiani, M. J. Birrer, L. X. Qin, M. Riester, T. K. Starr and L. Waldron (2020). "Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma." Cancer Res 80 (20): 4335–4345. Goldman, M., B. Craft, M. Hastie, K. Repečka, F. McDade, A. Kamath, A. Banerjee, Y. Luo, D. Rogers, A. N. Brooks, J. Zhu and D. Haussler (2019). "The UCSC Xena platform for public and private cancer genomics data visualization and interpretation." bioRxiv : 326470. Guo, J., X. Han, J. Li, Z. Li, J. Yi, Y. Gao, X. Zhao and W. Yue (2023). "Single-cell transcriptomics in ovarian cancer identify a metastasis-associated cell cluster overexpressed RAB13." J Transl Med 21 (1): 254. He, Y. F., M. Y. Zhang, X. Wu, X. J. Sun, T. Xu, Q. Z. He and W. Di (2013). "High MUC2 expression in ovarian cancer is inversely associated with the M1/M2 ratio of tumor-associated macrophages and patient survival time." PLoS One 8 (12): e79769. Hornburg, M., M. Desbois, S. Lu, Y. Guan, A. A. Lo, S. Kaufman, A. Elrod, A. Lotstein, T. M. DesRochers, J. L. Munoz-Rodriguez, X. Wang, J. Giltnane, O. Mayba, S. J. Turley, R. Bourgon, A. Daemen and Y. Wang (2021). "Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer." Cancer Cell 39 (7): 928–944.e926. Izar, B., I. Tirosh, E. H. Stover, I. Wakiro, M. S. Cuoco, I. Alter, C. Rodman, R. Leeson, M. J. Su, P. Shah, M. Iwanicki, S. R. Walker, A. Kanodia, J. C. Melms, S. Mei, J. R. Lin, C. B. M. Porter, M. Slyper, J. Waldman, L. Jerby-Arnon, O. Ashenberg, T. J. Brinker, C. Mills, M. Rogava, S. Vigneau, P. K. Sorger, L. A. Garraway, P. A. Konstantinopoulos, J. F. Liu, U. Matulonis, B. E. Johnson, O. Rozenblatt-Rosen, A. Rotem and A. Regev (2020). "A single-cell landscape of high-grade serous ovarian cancer." Nat Med 26 (8): 1271–1279. Jiang, P., S. Gu, D. Pan, J. Fu, A. Sahu, X. Hu, Z. Li, N. Traugh, X. Bu, B. Li, J. Liu, G. J. Freeman, M. A. Brown, K. W. Wucherpfennig and X. S. Liu (2018). "Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response." Nat Med 24 (10): 1550–1558. Jin, Y., S. Bian, H. Wang, J. Mo, H. Fei, L. Li, T. Chen and H. Jiang (2022). "CRMP2 derived from cancer associated fibroblasts facilitates progression of ovarian cancer via HIF-1α-glycolysis signaling pathway." Cell Death Dis 13 (8): 675. Jönsson, J. M., I. Johansson, M. Dominguez-Valentin, S. Kimbung, M. Jönsson, J. H. Bonde, P. Kannisto, A. Måsbäck, S. Malander, M. Nilbert and I. Hedenfalk (2014). "Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes." PLoS One 9 (9): e107643. Köbel, M. and E. Y. Kang (2022). "The Evolution of Ovarian Carcinoma Subclassification." Cancers (Basel) 14 (2). Kommoss, S., B. Winterhoff, A. L. Oberg, G. E. Konecny, C. Wang, S. M. Riska, J. B. Fan, M. J. Maurer, C. April, V. Shridhar, F. Kommoss, A. du Bois, F. Hilpert, S. Mahner, K. Baumann, W. Schroeder, A. Burges, U. Canzler, J. Chien, A. C. Embleton, M. Parmar, R. Kaplan, T. Perren, L. C. Hartmann, E. L. Goode, S. C. Dowdy and J. Pfisterer (2017). "Bevacizumab May Differentially Improve Ovarian Cancer Outcome in Patients with Proliferative and Mesenchymal Molecular Subtypes." Clin Cancer Res 23 (14): 3794–3801. Konstantinopoulos, P. A. and S. A. Cannistra (2021). "Immune Checkpoint Inhibitors in Ovarian Cancer: Can We Bridge the Gap Between IMagynation and Reality?" J Clin Oncol 39 (17): 1833–1838. Lan, C., X. Huang, S. Lin, H. Huang, Q. Cai, T. Wan, J. Lu and J. Liu (2013). "Expression of M2-polarized macrophages is associated with poor prognosis for advanced epithelial ovarian cancer." Technol Cancer Res Treat 12 (3): 259–267. Le Page, C., A. Marineau, P. K. Bonza, K. Rahimi, L. Cyr, I. Labouba, J. Madore, N. Delvoye, A. M. Mes-Masson, D. M. Provencher and J. F. Cailhier (2012). "BTN3A2 expression in epithelial ovarian cancer is associated with higher tumor infiltrating T cells and a better prognosis." PLoS One 7 (6): e38541. Maiorano, B. A., M. F. P. Maiorano, D. Lorusso and E. Maiello (2021). "Ovarian Cancer in the Era of Immune Checkpoint Inhibitors: State of the Art and Future Perspectives." Cancers (Basel) 13 (17). No, J. H., J. M. Moon, K. Kim and Y. B. Kim (2013). "Prognostic significance of serum soluble CD163 level in patients with epithelial ovarian cancer." Gynecol Obstet Invest 75 (4): 263–267. Peyraud, F. and A. Italiano (2020). "Combined PARP Inhibition and Immune Checkpoint Therapy in Solid Tumors." Cancers (Basel) 12 (6). Potter, S. S. (2018). "Single-cell RNA sequencing for the study of development, physiology and disease." Nat Rev Nephrol 14 (8): 479–492. Reinartz, S., T. Schumann, F. Finkernagel, A. Wortmann, J. M. Jansen, W. Meissner, M. Krause, A. M. Schwörer, U. Wagner, S. Müller-Brüsselbach and R. Müller (2014). "Mixed-polarization phenotype of ascites-associated macrophages in human ovarian carcinoma: correlation of CD163 expression, cytokine levels and early relapse." Int J Cancer 134 (1): 32–42. Ritchie, M. E., B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi and G. K. Smyth (2015). "limma powers differential expression analyses for RNA-sequencing and microarray studies." Nucleic Acids Res 43 (7): e47. Santoiemma, P. P. and D. J. Powell, Jr. (2015). "Tumor infiltrating lymphocytes in ovarian cancer." Cancer Biol Ther 16 (6): 807–820. Schem, C., D. O. Bauerschlag, I. Meinhold-Heerlein, D. Fischer, M. Friedrich and N. Maass (2007). "[Benign and borderline tumors of the ovary]." Ther Umsch 64 (7): 369–374. Shahraki, H. R., A. Salehi and N. Zare (2015). "Survival Prognostic Factors of Male Breast Cancer in Southern Iran: a LASSO-Cox Regression Approach." Asian Pac J Cancer Prev 16 (15): 6773–6777. Shannon, P., A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski and T. Ideker (2003). "Cytoscape: a software environment for integrated models of biomolecular interaction networks." Genome Res 13 (11): 2498–2504. Szklarczyk, D., A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K. P. Tsafou, M. Kuhn, P. Bork, L. J. Jensen and C. von Mering (2015). "STRING v10: protein-protein interaction networks, integrated over the tree of life." Nucleic Acids Res 43 (Database issue): D447-452. Tacconi, C., F. Ungaro, C. Correale, V. Arena, L. Massimino, M. Detmar, A. Spinelli, M. Carvello, M. Mazzone, A. I. Oliveira, F. Rubbino, V. Garlatti, S. Spanò, E. Lugli, F. S. Colombo, A. Malesci, L. Peyrin-Biroulet, S. Vetrano, S. Danese and S. D'Alessio (2019). "Activation of the VEGFC/VEGFR3 Pathway Induces Tumor Immune Escape in Colorectal Cancer." Cancer Res 79 (16): 4196–4210. Tan, T. Z., Q. H. Miow, R. Y. Huang, M. K. Wong, J. Ye, J. A. Lau, M. C. Wu, L. H. Bin Abdul Hadi, R. Soong, M. Choolani, B. Davidson, J. M. Nesland, L. Z. Wang, N. Matsumura, M. Mandai, I. Konishi, B. C. Goh, J. T. Chang, J. P. Thiery and S. Mori (2013). "Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer." EMBO Mol Med 5 (7): 1051–1066. Therneau, T. M. and T. Lumley (2015). "Package ‘survival’." R Top Doc 128 (10): 28–33. Torre, L. A., B. Trabert, C. E. DeSantis, K. D. Miller, G. Samimi, C. D. Runowicz, M. M. Gaudet, A. Jemal and R. L. Siegel (2018). "Ovarian cancer statistics, 2018." CA Cancer J Clin 68 (4): 284–296. Veneziani, A. C., E. Gonzalez-Ochoa, H. Alqaisi, A. Madariaga, G. Bhat, M. Rouzbahman, S. Sneha and A. M. Oza (2023). "Heterogeneity and treatment landscape of ovarian carcinoma." Nat Rev Clin Oncol 20 (12): 820–842. Wang, Z., J. Zhang, F. Dai, B. Li and Y. Cheng (2023). "Integrated analysis of single-cell RNA-seq and bulk RNA-seq unveils heterogeneity and establishes a novel signature for prognosis and tumor immune microenvironment in ovarian cancer." J Ovarian Res 16 (1): 12. Westin, S. N., K. Moore, H. S. Chon, J. Y. Lee, J. Thomes Pepin, M. Sundborg, A. Shai, J. de la Garza, S. Nishio, M. A. Gold, K. Wang, K. McIntyre, T. D. Tillmanns, S. V. Blank, J. H. Liu, M. McCollum, F. Contreras Mejia, T. Nishikawa, K. Pennington, Z. Novak, A. C. De Melo, J. Sehouli, D. Klasa-Mazurkiewicz, C. Papadimitriou, M. Gil-Martin, B. Brasiuniene, C. Donnelly, P. M. Del Rosario, X. Liu and E. Van Nieuwenhuysen (2023). "Durvalumab Plus Carboplatin/Paclitaxel Followed by Maintenance Durvalumab With or Without Olaparib as First-Line Treatment for Advanced Endometrial Cancer: The Phase III DUO-E Trial." J Clin Oncol : Jco2302132. Yan, C., K. Li, F. Meng, L. Chen, J. Zhao, Z. Zhang, D. Xu, J. Sun and M. Zhou (2023). "Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer." J Adv Res 44 : 149–160. Yang, W., J. Soares, P. Greninger, E. J. Edelman, H. Lightfoot, S. Forbes, N. Bindal, D. Beare, J. A. Smith, I. R. Thompson, S. Ramaswamy, P. A. Futreal, D. A. Haber, M. R. Stratton, C. Benes, U. McDermott and M. J. Garnett (2013). "Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells." Nucleic Acids Res 41 (Database issue): D955-961. Yi, M., D. V. Nissley, F. McCormick and R. M. Stephens (2020). "ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications." Sci Rep 10 (1): 10258. Yin, Z., T. Ma, B. Huang, L. Lin, Y. Zhou, J. Yan, Y. Zou and S. Chen (2019). "Macrophage-derived exosomal microRNA-501-3p promotes progression of pancreatic ductal adenocarcinoma through the TGFBR3-mediated TGF-β signaling pathway." J Exp Clin Cancer Res 38 (1): 310. Yoshihara, K., M. Shahmoradgoli, E. Martínez, R. Vegesna, H. Kim, W. Torres-Garcia, V. Treviño, H. Shen, P. W. Laird, D. A. Levine, S. L. Carter, G. Getz, K. Stemke-Hale, G. B. Mills and R. G. Verhaak (2013). "Inferring tumour purity and stromal and immune cell admixture from expression data." Nat Commun 4 : 2612. Yuan, X., J. Zhang, D. Li, Y. Mao, F. Mo, W. Du and X. Ma (2017). "Prognostic significance of tumor-associated macrophages in ovarian cancer: A meta-analysis." Gynecol Oncol 147 (1): 181–187. Zhang, M., Y. He, X. Sun, Q. Li, W. Wang, A. Zhao and W. Di (2014). "A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients." J Ovarian Res 7 : 19. Declarations Acknowledgments This work was supported by Central Government Guidance Fund for Local Science and Technology Development（202407AB110013）. Author Contribution Tao Yu and Guangji Yang wrote the main manuscript text,conducted the research design, data processing, and visualization.Dongyan Ren,Yantao Li and Chong Yue conducted the research design and manuscript proofreading.Qin Yang and Jie Zhang designe and conduct experiments, data analysis, prepared and revised the manuscript. Data Availability All data in the article can be obtained from GEO datasets (https://www.ncbi.nlm.nih.gov/geo/), UCSC Xena platform (https://toil.xenahubs.net), Genotype-Tissue Expression (GTEx) database (https://www.gtexportal.org/). References "Integrated genomic analyses of ovarian carcinoma." Nature 474 (7353): 609-615. "The Genotype-Tissue Expression (GTEx) project." Nat Genet 45 (6): 580-585. Antony, J., T. Z. Tan, Z. Kelly, J. Low, M. Choolani, C. Recchi, H. Gabra, J. P. Thiery and R. Y. Huang (2016). "The GAS6-AXL signaling network is a mesenchymal (Mes) molecular subtype-specific therapeutic target for ovarian cancer." Sci Signal 9 (448): ra97. Balkwill, F. R., M. Capasso and T. Hagemann (2012). "The tumor microenvironment at a glance." J Cell Sci 125 (Pt 23): 5591-5596. Barrett, T., S. E. Wilhite, P. Ledoux, C. Evangelista, I. F. Kim, M. Tomashevsky, K. A. Marshall, K. H. Phillippy, P. M. Sherman, M. Holko, A. Yefanov, H. Lee, N. Zhang, C. L. Robertson, N. Serova, S. Davis and A. Soboleva (2013). "NCBI GEO: archive for functional genomics data sets--update." Nucleic Acids Res 41 (Database issue): D991-995. Cai, D. L. and L. P. Jin (2017). "Immune Cell Population in Ovarian Tumor Microenvironment." J Cancer 8 (15): 2915-2923. Cao, Y., R. Langer and N. Ferrara (2023). "Targeting angiogenesis in oncology, ophthalmology and beyond." Nat Rev Drug Discov 22 (6): 476-495. Chen, B., M. S. Khodadoust, C. L. Liu, A. M. Newman and A. A. Alizadeh (2018). "Profiling Tumor Infiltrating Immune Cells with CIBERSORT." Methods Mol Biol 1711 : 243-259. Colvin, E. K. (2014). "Tumor-associated macrophages contribute to tumor progression in ovarian cancer." Front Oncol 4 : 137. Deng, X., H. Terunuma, A. Terunuma, T. Takane and M. Nieda (2014). "Ex vivo-expanded natural killer cells kill cancer cells more effectively than ex vivo-expanded γδ T cells or αβ T cells." Int Immunopharmacol 22 (2): 486-491. Färkkilä, A., D. C. Gulhan, J. Casado, C. A. Jacobson, H. Nguyen, B. Kochupurakkal, Z. Maliga, C. Yapp, Y. A. Chen, D. Schapiro, Y. Zhou, J. R. Graham, B. J. Dezube, P. Munster, S. Santagata, E. Garcia, S. Rodig, A. Lako, D. Chowdhury, G. I. Shapiro, U. A. Matulonis, P. J. Park, S. Hautaniemi, P. K. Sorger, E. M. Swisher, A. D. D'Andrea and P. A. Konstantinopoulos (2020). "Immunogenomic profiling determines responses to combined PARP and PD-1 inhibition in ovarian cancer." Nat Commun 11 (1): 1459. Farmer, H., N. McCabe, C. J. Lord, A. N. Tutt, D. A. Johnson, T. B. Richardson, M. Santarosa, K. J. Dillon, I. Hickson, C. Knights, N. M. Martin, S. P. Jackson, G. C. Smith and A. Ashworth (2005). "Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy." Nature 434 (7035): 917-921. Friedman, J., T. Hastie, R. Tibshirani, B. Narasimhan, K. Tay, N. Simon and J. Qian (2021). "Package ‘glmnet’." CRAN R Repositary 595 . Geeleher, P., N. Cox and R. S. Huang (2014). "pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels." PLoS One 9 (9): e107468. Geistlinger, L., S. Oh, M. Ramos, L. Schiffer, R. S. LaRue, C. M. Henzler, S. A. Munro, C. Daughters, A. C. Nelson, B. J. Winterhoff, Z. Chang, S. Talukdar, M. Shetty, S. A. Mullany, M. Morgan, G. Parmigiani, M. J. Birrer, L. X. Qin, M. Riester, T. K. Starr and L. Waldron (2020). "Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma." Cancer Res 80 (20): 4335-4345. Goldman, M., B. Craft, M. Hastie, K. Repečka, F. McDade, A. Kamath, A. Banerjee, Y. Luo, D. Rogers, A. N. Brooks, J. Zhu and D. Haussler (2019). "The UCSC Xena platform for public and private cancer genomics data visualization and interpretation." bioRxiv: 326470. Guo, J., X. Han, J. Li, Z. Li, J. Yi, Y. Gao, X. Zhao and W. Yue (2023). "Single-cell transcriptomics in ovarian cancer identify a metastasis-associated cell cluster overexpressed RAB13." J Transl Med 21 (1): 254. He, Y. F., M. Y. Zhang, X. Wu, X. J. Sun, T. Xu, Q. Z. He and W. Di (2013). "High MUC2 expression in ovarian cancer is inversely associated with the M1/M2 ratio of tumor-associated macrophages and patient survival time." PLoS One 8 (12): e79769. Hornburg, M., M. Desbois, S. Lu, Y. Guan, A. A. Lo, S. Kaufman, A. Elrod, A. Lotstein, T. M. DesRochers, J. L. Munoz-Rodriguez, X. Wang, J. Giltnane, O. Mayba, S. J. Turley, R. Bourgon, A. Daemen and Y. Wang (2021). "Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer." Cancer Cell 39 (7): 928-944.e926. Izar, B., I. Tirosh, E. H. Stover, I. Wakiro, M. S. Cuoco, I. Alter, C. Rodman, R. Leeson, M. J. Su, P. Shah, M. Iwanicki, S. R. Walker, A. Kanodia, J. C. Melms, S. Mei, J. R. Lin, C. B. M. Porter, M. Slyper, J. Waldman, L. Jerby-Arnon, O. Ashenberg, T. J. Brinker, C. Mills, M. Rogava, S. Vigneau, P. K. Sorger, L. A. Garraway, P. A. Konstantinopoulos, J. F. Liu, U. Matulonis, B. E. Johnson, O. Rozenblatt-Rosen, A. Rotem and A. Regev (2020). "A single-cell landscape of high-grade serous ovarian cancer." Nat Med 26 (8): 1271-1279. Jiang, P., S. Gu, D. Pan, J. Fu, A. Sahu, X. Hu, Z. Li, N. Traugh, X. Bu, B. Li, J. Liu, G. J. Freeman, M. A. Brown, K. W. Wucherpfennig and X. S. Liu (2018). "Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response." Nat Med 24 (10): 1550-1558. Jin, Y., S. Bian, H. Wang, J. Mo, H. Fei, L. Li, T. Chen and H. Jiang (2022). "CRMP2 derived from cancer associated fibroblasts facilitates progression of ovarian cancer via HIF-1α-glycolysis signaling pathway." Cell Death Dis 13 (8): 675. Jönsson, J. M., I. Johansson, M. Dominguez-Valentin, S. Kimbung, M. Jönsson, J. H. Bonde, P. Kannisto, A. Måsbäck, S. Malander, M. Nilbert and I. Hedenfalk (2014). "Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes." PLoS One 9 (9): e107643. Köbel, M. and E. Y. Kang (2022). "The Evolution of Ovarian Carcinoma Subclassification." Cancers (Basel) 14 (2). Kommoss, S., B. Winterhoff, A. L. Oberg, G. E. Konecny, C. Wang, S. M. Riska, J. B. Fan, M. J. Maurer, C. April, V. Shridhar, F. Kommoss, A. du Bois, F. Hilpert, S. Mahner, K. Baumann, W. Schroeder, A. Burges, U. Canzler, J. Chien, A. C. Embleton, M. Parmar, R. Kaplan, T. Perren, L. C. Hartmann, E. L. Goode, S. C. Dowdy and J. Pfisterer (2017). "Bevacizumab May Differentially Improve Ovarian Cancer Outcome in Patients with Proliferative and Mesenchymal Molecular Subtypes." Clin Cancer Res 23 (14): 3794-3801. Konstantinopoulos, P. A. and S. A. Cannistra (2021). "Immune Checkpoint Inhibitors in Ovarian Cancer: Can We Bridge the Gap Between IMagynation and Reality?" J Clin Oncol 39 (17): 1833-1838. Lan, C., X. Huang, S. Lin, H. Huang, Q. Cai, T. Wan, J. Lu and J. Liu (2013). "Expression of M2-polarized macrophages is associated with poor prognosis for advanced epithelial ovarian cancer." Technol Cancer Res Treat 12 (3): 259-267. Le Page, C., A. Marineau, P. K. Bonza, K. Rahimi, L. Cyr, I. Labouba, J. Madore, N. Delvoye, A. M. Mes-Masson, D. M. Provencher and J. F. Cailhier (2012). "BTN3A2 expression in epithelial ovarian cancer is associated with higher tumor infiltrating T cells and a better prognosis." PLoS One 7 (6): e38541. Maiorano, B. A., M. F. P. Maiorano, D. Lorusso and E. Maiello (2021). "Ovarian Cancer in the Era of Immune Checkpoint Inhibitors: State of the Art and Future Perspectives." Cancers (Basel) 13 (17). No, J. H., J. M. Moon, K. Kim and Y. B. Kim (2013). "Prognostic significance of serum soluble CD163 level in patients with epithelial ovarian cancer." Gynecol Obstet Invest 75 (4): 263-267. Peyraud, F. and A. Italiano (2020). "Combined PARP Inhibition and Immune Checkpoint Therapy in Solid Tumors." Cancers (Basel) 12 (6). Potter, S. S. (2018). "Single-cell RNA sequencing for the study of development, physiology and disease." Nat Rev Nephrol 14 (8): 479-492. Reinartz, S., T. Schumann, F. Finkernagel, A. Wortmann, J. M. Jansen, W. Meissner, M. Krause, A. M. Schwörer, U. Wagner, S. Müller-Brüsselbach and R. Müller (2014). "Mixed-polarization phenotype of ascites-associated macrophages in human ovarian carcinoma: correlation of CD163 expression, cytokine levels and early relapse." Int J Cancer 134 (1): 32-42. Ritchie, M. E., B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi and G. K. Smyth (2015). "limma powers differential expression analyses for RNA-sequencing and microarray studies." Nucleic Acids Res 43 (7): e47. Santoiemma, P. P. and D. J. Powell, Jr. (2015). "Tumor infiltrating lymphocytes in ovarian cancer." Cancer Biol Ther 16 (6): 807-820. Schem, C., D. O. Bauerschlag, I. Meinhold-Heerlein, D. Fischer, M. Friedrich and N. Maass (2007). "[Benign and borderline tumors of the ovary]." Ther Umsch 64 (7): 369-374. Shahraki, H. R., A. Salehi and N. Zare (2015). "Survival Prognostic Factors of Male Breast Cancer in Southern Iran: a LASSO-Cox Regression Approach." Asian Pac J Cancer Prev 16 (15): 6773-6777. Shannon, P., A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski and T. Ideker (2003). "Cytoscape: a software environment for integrated models of biomolecular interaction networks." Genome Res 13 (11): 2498-2504. Szklarczyk, D., A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K. P. Tsafou, M. Kuhn, P. Bork, L. J. Jensen and C. von Mering (2015). "STRING v10: protein-protein interaction networks, integrated over the tree of life." Nucleic Acids Res 43 (Database issue): D447-452. Tacconi, C., F. Ungaro, C. Correale, V. Arena, L. Massimino, M. Detmar, A. Spinelli, M. Carvello, M. Mazzone, A. I. Oliveira, F. Rubbino, V. Garlatti, S. Spanò, E. Lugli, F. S. Colombo, A. Malesci, L. Peyrin-Biroulet, S. Vetrano, S. Danese and S. D'Alessio (2019). "Activation of the VEGFC/VEGFR3 Pathway Induces Tumor Immune Escape in Colorectal Cancer." Cancer Res 79 (16): 4196-4210. Tan, T. Z., Q. H. Miow, R. Y. Huang, M. K. Wong, J. Ye, J. A. Lau, M. C. Wu, L. H. Bin Abdul Hadi, R. Soong, M. Choolani, B. Davidson, J. M. Nesland, L. Z. Wang, N. Matsumura, M. Mandai, I. Konishi, B. C. Goh, J. T. Chang, J. P. Thiery and S. Mori (2013). "Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer." EMBO Mol Med 5 (7): 1051-1066. Therneau, T. M. and T. Lumley (2015). "Package ‘survival’." R Top Doc 128 (10): 28-33. Torre, L. A., B. Trabert, C. E. DeSantis, K. D. Miller, G. Samimi, C. D. Runowicz, M. M. Gaudet, A. Jemal and R. L. Siegel (2018). "Ovarian cancer statistics, 2018." CA Cancer J Clin 68 (4): 284-296. Veneziani, A. C., E. Gonzalez-Ochoa, H. Alqaisi, A. Madariaga, G. Bhat, M. Rouzbahman, S. Sneha and A. M. Oza (2023). "Heterogeneity and treatment landscape of ovarian carcinoma." Nat Rev Clin Oncol 20 (12): 820-842. Wang, Z., J. Zhang, F. Dai, B. Li and Y. Cheng (2023). "Integrated analysis of single-cell RNA-seq and bulk RNA-seq unveils heterogeneity and establishes a novel signature for prognosis and tumor immune microenvironment in ovarian cancer." J Ovarian Res 16 (1): 12. Westin, S. N., K. Moore, H. S. Chon, J. Y. Lee, J. Thomes Pepin, M. Sundborg, A. Shai, J. de la Garza, S. Nishio, M. A. Gold, K. Wang, K. McIntyre, T. D. Tillmanns, S. V. Blank, J. H. Liu, M. McCollum, F. Contreras Mejia, T. Nishikawa, K. Pennington, Z. Novak, A. C. De Melo, J. Sehouli, D. Klasa-Mazurkiewicz, C. Papadimitriou, M. Gil-Martin, B. Brasiuniene, C. Donnelly, P. M. Del Rosario, X. Liu and E. Van Nieuwenhuysen (2023). "Durvalumab Plus Carboplatin/Paclitaxel Followed by Maintenance Durvalumab With or Without Olaparib as First-Line Treatment for Advanced Endometrial Cancer: The Phase III DUO-E Trial." J Clin Oncol: Jco2302132. Yan, C., K. Li, F. Meng, L. Chen, J. Zhao, Z. Zhang, D. Xu, J. Sun and M. Zhou (2023). "Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer." J Adv Res 44 : 149-160. Yang, W., J. Soares, P. Greninger, E. J. Edelman, H. Lightfoot, S. Forbes, N. Bindal, D. Beare, J. A. Smith, I. R. Thompson, S. Ramaswamy, P. A. Futreal, D. A. Haber, M. R. Stratton, C. Benes, U. McDermott and M. J. Garnett (2013). "Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells." Nucleic Acids Res 41 (Database issue): D955-961. Yi, M., D. V. Nissley, F. McCormick and R. M. Stephens (2020). "ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications." Sci Rep 10 (1): 10258. Yin, Z., T. Ma, B. Huang, L. Lin, Y. Zhou, J. Yan, Y. Zou and S. Chen (2019). "Macrophage-derived exosomal microRNA-501-3p promotes progression of pancreatic ductal adenocarcinoma through the TGFBR3-mediated TGF-β signaling pathway." J Exp Clin Cancer Res 38 (1): 310. Yoshihara, K., M. Shahmoradgoli, E. Martínez, R. Vegesna, H. Kim, W. Torres-Garcia, V. Treviño, H. Shen, P. W. Laird, D. A. Levine, S. L. Carter, G. Getz, K. Stemke-Hale, G. B. Mills and R. G. Verhaak (2013). "Inferring tumour purity and stromal and immune cell admixture from expression data." Nat Commun 4 : 2612. Yuan, X., J. Zhang, D. Li, Y. Mao, F. Mo, W. Du and X. Ma (2017). "Prognostic significance of tumor-associated macrophages in ovarian cancer: A meta-analysis." Gynecol Oncol 147 (1): 181-187. Zhang, M., Y. He, X. Sun, Q. Li, W. Wang, A. Zhao and W. Di (2014). "A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients." J Ovarian Res 7 : 19. Additional Declarations No competing interests reported. Supplementary Files SupplementaryInformation.zip Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5259146","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":374633418,"identity":"950da936-e2a0-4d55-8bff-7d252ba9ca45","order_by":0,"name":"Tao Yu","email":"","orcid":"","institution":"The First People’s Hospital of Yunnan Province and Affiliated Hospital of Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Tao","middleName":"","lastName":"Yu","suffix":""},{"id":374633419,"identity":"820c0c3d-d73c-4d64-a097-e1b5c24afb0d","order_by":1,"name":"Guangji Yang","email":"","orcid":"","institution":"The First People’s Hospital of Yunnan Province and Affiliated Hospital of Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Guangji","middleName":"","lastName":"Yang","suffix":""},{"id":374633422,"identity":"4d5fb802-ee7b-420f-bf5b-e40b9537ae00","order_by":2,"name":"Dongyan Ren","email":"","orcid":"","institution":"The First People’s Hospital of Yunnan Province and Affiliated Hospital of Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dongyan","middleName":"","lastName":"Ren","suffix":""},{"id":374633424,"identity":"dbfd7fc5-ee17-4f59-99ae-c40da57c46b3","order_by":3,"name":"Yantao Li","email":"","orcid":"","institution":"The First People’s Hospital of Yunnan Province and Affiliated Hospital of Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Yantao","middleName":"","lastName":"Li","suffix":""},{"id":374633425,"identity":"013abb06-e436-478d-b135-1c81c12a63f0","order_by":4,"name":"Chong Yue","email":"","orcid":"","institution":"The First People’s Hospital of Yunnan Province and Affiliated Hospital of Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Chong","middleName":"","lastName":"Yue","suffix":""},{"id":374633426,"identity":"2ad5b13a-a56c-4fd8-b237-2720e7fb47b9","order_by":5,"name":"Qin Yang","email":"","orcid":"","institution":"The First People’s Hospital of Yunnan Province and Affiliated Hospital of Kunming University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Qin","middleName":"","lastName":"Yang","suffix":""},{"id":374633427,"identity":"bcf0334e-dcfb-4dd4-9227-b6705f3a9a5b","order_by":6,"name":"Jie Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYFACNgYGxgYQ42ADwwcQn50ULYwzQHxm4rUwMDDzgEkCGgxut6Vu/LnDLk/e8XDjY5tf2+T5mBkYP3zMwaPlzrFjNyTPJBcbHjjYbJzbd9uwjZmBWXLmNjxabqS33QAqS9zYcLBNOrfnNiNQCxszLyEtiW31IC3tvy17btsToSXt2I2DbYcT5zMcBCr+cTuRoBbJG2lpNxvbjiduYDjYLNnbcDu5jZmxGa9f+G6kmd382VadOH/G8Ycffvy5bTu/vfngh494tCgcgLsQyGJsAzFh0YQDyMOk5ftBrD94FY+CUTAKRsEIBQDK4V+zyaEcdgAAAABJRU5ErkJggg==","orcid":"","institution":"The First People’s Hospital of Yunnan Province and Affiliated Hospital of Kunming University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jie","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-10-14 07:53:32","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5259146/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5259146/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":69891473,"identity":"a78a0f57-71ca-40cd-a192-b47facf1cdc3","added_by":"auto","created_at":"2024-11-26 10:35:41","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":284722,"visible":true,"origin":"","legend":"\u003cp\u003eBioinformatics Analysis Workflow\u003c/p\u003e","description":"","filename":"figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/80241504ba7843cb06713f67.jpg"},{"id":69891174,"identity":"d1b6fdd9-b0a3-4dab-b819-43095ca5964d","added_by":"auto","created_at":"2024-11-26 10:27:41","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":584747,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-Cell Heterogeneity\u003c/p\u003e\n\u003cp\u003eA-L: Violin plots depicting the expression of marker genes across different cell clusters. M: Bubble plots illustrating the expression of marker genes in different cell clusters. N: Annotation results for the cell clusters. O: proportion of cell clusters\u003c/p\u003e","description":"","filename":"figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/d4126adb968da15cb86a2a8b.jpg"},{"id":69891172,"identity":"f988bdf8-fcd4-40e1-a2cb-5ce5b2813c94","added_by":"auto","created_at":"2024-11-26 10:27:41","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":486087,"visible":true,"origin":"","legend":"\u003cp\u003eSingle-Cell vs. TCGA Transcriptome Differential Genes\u003c/p\u003e\n\u003cp\u003eA: Heatmap depicting the expression of differential genes in macrophages from single-cell transcriptome data (top 20 genes, with red indicating upregulation and blue indicating downregulation). B: Volcano plot displaying differential gene expression in the TCGA transcriptome data (red represents upregulation and blue represents downregulation). C: Venn diagram illustrating the intersection of differentially expressed genes between macrophages and TCGA transcriptome data. D: The top 20 genes in the Protein-Protein Interaction (PPI) network (darker colors indicate stronger interactions)\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/7ceb02876b735cd5522b2f24.jpg"},{"id":69891471,"identity":"b9bdfbb2-f903-49ca-8cae-b793c3da60aa","added_by":"auto","created_at":"2024-11-26 10:35:41","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":470609,"visible":true,"origin":"","legend":"\u003cp\u003eConstruction and Validation of the Prognostic Model\u003c/p\u003e\n\u003cp\u003eA: Forest plot for the single-factor Cox analysis.B-C: λ selection plot in the LASSO model (two dashed lines indicate two specific λ values, with lambda.min on the left and lambda.1se on the right; any λ value between them is considered suitable). The model constructed with lambda.1se uses fewer genes, making it simpler, whereas lambda.min offers a slightly higher accuracy by utilizing more genes. Here, we choose lambda.min as λ. D: Kaplan-Meier survival curves for the prognostic model using TCGA data. E: ROC curves for the model's predictions of one-, three-, and 5-year survival periods (the area under the curve represents AUC values). F: Kaplan–Meier survival curves for the prognostic model in the GEO validation dataset. The results in the validation queue were verified to be consistent with those in the training queue.\u003c/p\u003e","description":"","filename":"figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/2c5ad178e6b3ad248d6ad69f.jpg"},{"id":69891175,"identity":"d6477767-adc2-4bb2-a587-d792a8eaa5ee","added_by":"auto","created_at":"2024-11-26 10:27:41","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":344901,"visible":true,"origin":"","legend":"\u003cp\u003eRiskscore Clinical Parameter Differences\u003c/p\u003e\n\u003cp\u003eA: Differences in risk scores among patients with OC in different age groups (with age groups divided at 55 years). B: Differences in risk scores among patients with OC with different survival statuses. C: Differences in risk scores among tumors at different stages. D: Differences in risk scores among tumors with different degrees of metastasis.\u003c/p\u003e","description":"","filename":"figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/7c2f10f967b0441cd072a2b7.jpg"},{"id":69891169,"identity":"d62792fe-0a22-4551-b4e3-273931b0070e","added_by":"auto","created_at":"2024-11-26 10:27:41","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":124952,"visible":true,"origin":"","legend":"\u003cp\u003eCox Regression Analysis of Clinical Factors\u003c/p\u003e\n\u003cp\u003eA: Forest plot of the single-factor Cox regression analysis of clinical factors and risk scores. B: Forest plot of multifactor Cox regression analysis of clinical factors and risk scores\u003c/p\u003e","description":"","filename":"figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/3419b19c3c1821531141870d.jpg"},{"id":69892752,"identity":"4835d89c-de32-4d10-9921-3b0c72a4c8f4","added_by":"auto","created_at":"2024-11-26 10:43:41","extension":"jpg","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":401294,"visible":true,"origin":"","legend":"\u003cp\u003eNomogram Construction Based on Riskscore for Patients with OA\u003c/p\u003e\n\u003cp\u003eA: Nomogram depicting the clinical factors and risk scores. Each variable was assigned a score on a point-scale axis. The total score was calculated by summing the individual scores and projecting them onto a total point scale to estimate survival probability. B-D: Calibration curves for 1-, 3-, and 5-year nomogram predictions. E: Decision curve analysis curve for the nomogram model\u003c/p\u003e","description":"","filename":"figure7.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/d9982c54eef3100ceef3abfd.jpg"},{"id":69892750,"identity":"bfc7c4a8-cf61-4650-aed1-e4f1b7bbc68f","added_by":"auto","created_at":"2024-11-26 10:43:41","extension":"jpg","order_by":8,"title":"Figure 8","display":"","copyAsset":false,"role":"figure","size":563653,"visible":true,"origin":"","legend":"\u003cp\u003eImmune Infiltration Analysis Results\u003c/p\u003e\n\u003cp\u003eThe heatmap depicts the relative abundance distribution of all microenvironmental cells estimated by the three algorithms (different colored horizontal bars on the left represent different algorithms, as indicated in the legend, with different colored horizontal bars at the top representing different groups, with blue indicating low risk and red indicating high risk).\u003c/p\u003e","description":"","filename":"figure8.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/10bcb6b52a49b8407f7bc333.jpg"},{"id":69892751,"identity":"f30b3989-5bb8-49d3-b9eb-9f5e84a1d971","added_by":"auto","created_at":"2024-11-26 10:43:41","extension":"jpg","order_by":9,"title":"Figure 9","display":"","copyAsset":false,"role":"figure","size":611684,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in TME between High-Risk and Low-Risk Groups\u003c/p\u003e\n\u003cp\u003eA: Differences in TME cells estimated using the CIBERSORT method; B: Differences in TME cells estimated using the ssGSEA method; C-E: Differences in stromal, immune, and ESTIMATE scores estimated using the ESTIMATE method\u003c/p\u003e","description":"","filename":"figure9.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/61fddda3eebb483442529e86.jpg"},{"id":69891477,"identity":"01a33bf6-ad35-46be-bcab-b4f07e9311a6","added_by":"auto","created_at":"2024-11-26 10:35:41","extension":"jpg","order_by":10,"title":"Figure 10","display":"","copyAsset":false,"role":"figure","size":231892,"visible":true,"origin":"","legend":"\u003cp\u003eDifferences in Drug Sensitivity between High-Risk and Low-Risk Groups\u003c/p\u003e\n\u003cp\u003eA: Difference in the IC50 of celiparib between the high-risk and low-risk groups. B: Difference in IC50 of talazoparib between the high-risk and low-risk groups. C: Difference in IC50 of Pazopanib between the high-risk and low-risk groups. D: Difference in IC50 of Sunitinib between the high-risk and low-risk groups. E: Difference in IC50 of Sorafenib between the high- and low-risk groups. F: Difference in IC50 of Erlotinib between the high- and low-risk groups\u003c/p\u003e","description":"","filename":"figure10.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/a76f03a15df21a249b93bf3f.jpg"},{"id":69891179,"identity":"4cc04293-80e8-4f6e-8952-c430a3c0b220","added_by":"auto","created_at":"2024-11-26 10:27:41","extension":"jpg","order_by":11,"title":"Figure 11","display":"","copyAsset":false,"role":"figure","size":218985,"visible":true,"origin":"","legend":"\u003cp\u003eTIDE Analysis\u003c/p\u003e\n\u003cp\u003eDifferences in TIDE scores between high- and low-risk groups (red, high-risk patients; blue, low-risk patients).\u003c/p\u003e","description":"","filename":"figure11.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/0b6a2d7246d07e005a3e470d.jpg"},{"id":69891474,"identity":"31d96d73-8849-44d8-b56d-3012b99bdd94","added_by":"auto","created_at":"2024-11-26 10:35:41","extension":"jpg","order_by":12,"title":"Figure 12","display":"","copyAsset":false,"role":"figure","size":588373,"visible":true,"origin":"","legend":"\u003cp\u003eA-S. Expression of Prognostic Model Genes in Different Cell Clusters\u003c/p\u003e","description":"","filename":"figure12.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/994d5cfe632e25854cd95092.jpg"},{"id":69894265,"identity":"a6b27e67-e039-4be7-8883-47e0c1ef0723","added_by":"auto","created_at":"2024-11-26 10:59:45","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5778759,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/b684cc73-7843-4cd1-9ad8-61d79c43b136.pdf"},{"id":69891181,"identity":"f971b89d-cef3-436d-8a54-903c0265ce8e","added_by":"auto","created_at":"2024-11-26 10:27:41","extension":"zip","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":1239265,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.zip","url":"https://assets-eu.researchsquare.com/files/rs-5259146/v1/cbcfcf159818e11eb871488b.zip"}],"financialInterests":"No competing interests reported.","formattedTitle":"Integration of scRNA-Seq and bulk RNA-Seq to Establish a Macrophage-related Prognostic Model in Ovarian Cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OC) ranks third in incidence among common malignant tumors of the female reproductive system. Due to the lack of effective early detection and diagnostic techniques, 60\u0026ndash;70% of patients with OC are diagnosed at an advanced stage.(Schem, Bauerschlag et al. 2007) While most patients recover following primary cytoreductive surgery (PCS) and standard first-line chemotherapy, approximately 70% of advanced-stage patients relapse within 2\u0026ndash;3 years, eventually developing resistance with a 5-year survival rate of less than 40%(Torre, Trabert et al. 2018).\u003c/p\u003e \u003cp\u003eIn recent years, immunotherapy has emerged as a potential treatment strategy for multiple treatment-refractory cancers. However, immunotherapies, including PD-1 (PD-L1) checkpoint inhibitors (ICIs), have shown limited efficacy in OC (Maiorano, Maiorano et al. 2021). Even in combination with first-line chemotherapy, it fails to show meaningful improvement in progression-free survival (PFS) (Konstantinopoulos and Cannistra 2021). This low response rate primarily stems from the highly complex immunosuppressive TME of OC, which allows immune evasion and unrestrained tumor development(Cai and Jin 2017).\u003c/p\u003e \u003cp\u003eThe suppressive cellular microenvironment includes TAMs, regulatory T cells (Tregs), myeloid-derived suppressor cells (MDSCs), and tumor-associated dendritic cells (tDCs) (Balkwill, Capasso et al. 2012). Among these, TAMs represent the major infiltrating immune subgroup in ovarian tumors and ascites, which promote the formation of an immunosuppressive microenvironment in OC, facilitating tumor growth, invasion, angiogenesis, and metastasis(Colvin 2014).\u003c/p\u003e \u003cp\u003eGrowing evidence (Le Page, Marineau et al. 2012, He, Zhang et al. 2013, Lan, Huang et al. 2013, No, Moon et al. 2013, Zhang, He et al. 2014) has shown a correlation between high levels of tumor-associated macrophage infiltration and poor patient prognosis, with macrophage-related genes influencing the immunotherapy response. Previous studies on OC (Reinartz, Schumann et al. 2014, Yuan, Zhang et al. 2017) have indicated that the expression of the alternative activation marker CD163 in malignant TAMs within ascites is closely associated with the early recurrence of serous OC following first-line treatment. However, the impact of macrophage-associated genes on OC prognosis and treatment response remains poorly understood. Thus, exploring the molecular characteristics of macrophages and their association with prognosis and immunotherapy responses in OC may reveal new prognostic markers and enhance treatment options.\u003c/p\u003e \u003cp\u003eSingle-cell RNA sequencing (scRNA-seq) has become an indispensable approach for analyzing the TME (Potter 2018, Izar, Tirosh et al. 2020). Compared to bulk RNA sequencing (bulk RNA-seq), which explores the average gene expression within a cell population, scRNA-seq analyzes most transcripts within individual cell profiles using high-throughput sequencing, providing a comprehensive perspective on the molecular diversity and tumor heterogeneity within cell populations (Wang, Zhang et al. 2023)[Ref]. Ongoing research is combining bulk RNA-seq and scRNA-seq to identify potential biomarkers for precise patient stratification and clinical benefit group selection. Liang et al. (Wang, Zhang et al. 2023) used scRNA-seq and bulk RNA-seq to construct a dual-gene (CXCL13 and IL26) signature prognostic system, which suggested the heterogeneity of OC as an immunotherapy target. Hornburg M et al.(Hornburg, Desbois et al. 2021) integrated single-cell RNA sequencing data from 15 cases of ovarian tumors and bulk RNA-seq to identify immune microenvironment traits associated with T-cell infiltration patterns, suggesting chemokine receptor-ligand interactions as potential mediators of immune infiltration. Guo et al. (Guo, Han et al. 2023) used scRNA-seq and bulk RNA-seq along with experimental validation to identify cell clusters and the key gene RAB13, which is closely associated with OC metastasis.\u003c/p\u003e \u003cp\u003eUsing RNA-seq data from The Cancer Genome Atlas (TCGA) and scRNA-seq data from the Gene Expression Omnibus (GEO), we identified independent prognostic genes associated with macrophages and built a predictive model for patients with OC. Additionally, we examined the relationship between the risk model and clinical characteristics, immune infiltration landscape, immunotherapy response, and so on, to appropriately predict prognosis risk. Overall, our study identified clinically relevant macrophage-related indicators, explained macrophage immunogenomic characteristics in OC, and provided novel insights into targeted therapies in clinical practice.\u003c/p\u003e \n"},{"header":"Data and Methods","content":"\u003ch3\u003e1. Data Acquisition and Selection\u003c/h3\u003e\n\u003cp\u003eOC scRNA-seq data were sourced from the GEO database of the National Center for Biotechnology Information (NCBI) with accession number GSE154600 (Geistlinger, Oh et al. 2020). This dataset included five samples: GSM4675273, GSM4675274, GSM4675275, GSM4675276, and GSM4675277. Subsequently, the cells were filtered based on the following criteria: feature count\u0026thinsp;\u0026lt;\u0026thinsp;500, unique molecular identifier (UMI) count\u0026thinsp;\u0026gt;\u0026thinsp;20,000, and mitochondrial proportion\u0026thinsp;\u0026gt;\u0026thinsp;5%. After applying these filters, 20,914 cells were obtained for subsequent single-cell analysis.\u003c/p\u003e \u003cp\u003eRNA-seq data (log2-transformed FPKM), clinical details (including age, tumor stage, and neoplasm histological grade), and survival information (overall survival [OS] and OS times) of OC were obtained from the UCSC Xena platform(Goldman, Craft et al. 2019) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://toil.xenahubs.net\u003c/span\u003e\u003cspan address=\"https://toil.xenahubs.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). In this analysis, we designated samples with the identifier \"-01A\" as cancer tissue samples. Normal ovarian tissue samples were obtained from the Genotype-Tissue Expression (GTEx) database (2013) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). Information on 442 samples, including 88 normal and 354 cancerous tissue samples, was downloaded. Among these, 342 cancer tissue samples with available prognostic information were included in the subsequent model construction analysis.\u003c/p\u003e \u003cp\u003eTranscriptional data for OC from the GSE19829 and GSE26193 datasets were obtained from the NCBI GEO database (Barrett, Wilhite et al. 2013). Samples with available prognostic information were extracted, batch effects were removed, and the two datasets were merged. Ultimately, 135 OC samples were included in the study as validation datasets for subsequent modeling. We directly downloaded the preprocessed and standardized probe expression matrices and the corresponding platform annotation files for gene symbol conversion. For probes corresponding to the same gene symbol, the maximum value was used as the gene expression value in subsequent analyses.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2. Cell Annotation\u003c/h2\u003e \u003cp\u003eFor the Seurat objects of OC samples in the single-cell data, uniform manifold approximation and projection (UMAP) visualization revealed 20 clusters. We manually annotated nine distinct cell types based on marker genes. These included macrophages marked by C1QA and C1QB, B cells marked by CD79A and IGHG1, CD4\u0026thinsp;+\u0026thinsp;T cells marked by CD4, CD8\u0026thinsp;+\u0026thinsp;T cells marked by CD8A and CD8B; natural killer cells marked by NKG7; endothelial cells marked by PECAM1, Treg cells marked by FOXP3; epithelial cells marked by PECAM; and fibroblasts marked by COL1A and BGN.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3. Gene Expression Differential Analysis\u003c/h3\u003e\n\u003cp\u003eWe used the \"FindAllMarkers\" function to compute differential genes among all cell clusters. Specifically, we focused on macrophages, and genes meeting the criteria of |log2FoldChange| \u0026gt;0 and P-Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were selected as single-cell differentially expressed genes (scDEGs) for further investigation within the macrophage cell cluster.\u003c/p\u003e \u003cp\u003eFurthermore, we employed the limma package (Ritchie, Phipson et al. 2015)(Version 3.10.3, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.bioconductor.org/packages/2.9/bioc/html/limma.html\u003c/span\u003e\u003cspan address=\"http://www.bioconductor.org/packages/2.9/bioc/html/limma.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which provides linear regression and empirical Bayesian methods to perform tumor vs. normal differential expression analysis on TCGA transcriptome data, resulting in gene-specific P-values and logFC information. Additionally, we conducted multiple testing corrections using the Benjamini-Hochberg method, yielding adjusted p-values (adj.P.Value). We assessed both levels of multiplicity and significance of differences, with differential expression thresholds set as follows: adj.P.Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| \u0026gt;0.263.\u003c/p\u003e\n\u003ch3\u003e4. Protein–Protein Interaction Network Analysis\u003c/h3\u003e\n\u003cp\u003eUsing the search tool for recurring instances of neighboring genes (STRING) database (Szklarczyk, Franceschini et al. 2015) (Version 11.0, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://string-db.org/\u003c/span\u003e\u003cspan address=\"http://string-db.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), which contains human protein-protein interaction relationships, we obtained a protein-protein interaction network (PPI) for the intersection of differential genes in macrophages and differential genes in the regular transcriptome. The species used in this study was Homo sapiens. Network construction was performed using Cytoscape (Shannon, Markiel et al. 2003) (version 3.6.1) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cytoscape.org/\u003c/span\u003e\u003cspan address=\"https://cytoscape.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003e5. Selection of Prognostic-Related Genes\u003c/h3\u003e\n\u003cp\u003eWithin the TCGA samples, based on the obtained intersection of differential genes and considering clinical survival prognosis information, we conducted a single-factor Cox regression analysis using the survival package (Therneau and Lumley 2015) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://bioconductor.org/packages/survivalr/\u003c/span\u003e\u003cspan address=\"http://bioconductor.org/packages/survivalr/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in R 4.3.1. Genes with p-values less than 0.05 were chosen as significant prognostic-related genes.\u003c/p\u003e\n\u003ch3\u003e6. Construction and Validation of Prognostic Feature Models for Intersection Genes\u003c/h3\u003e\n\u003cp\u003eBased on the intersection of differential genes significantly associated with survival obtained in the previous step, we used survival prognosis information from the training set samples. Combined with the gene expression values in each sample, we applied the least absolute shrinkage and selection operator (LASSO) Cox regression model(Shahraki, Salehi et al. 2015) using the glmnet package (Friedman, Hastie et al. 2021)(version 2.0\u0026ndash;18, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cran.r-project.org/web/packages/glmnet/index.html\u003c/span\u003e\u003cspan address=\"https://cran.r-project.org/web/packages/glmnet/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) in R. We employed 5-fold cross-validation to select gene combinations relevant to prognosis.\u003c/p\u003e \u003cp\u003eSubsequently, the following risk score model was constructed based on the regression prognostic coefficients of the genes in the gene combinations and the expression levels of the genes in the TCGA samples, which were calculated as follows:\u003c/p\u003e \u003cp\u003eRiskscore = \u0026sum;β\u003csub\u003egene\u003c/sub\u003e\u0026thinsp;\u0026times;\u0026thinsp;Exp\u003csub\u003egene\u003c/sub\u003e\u003c/p\u003e \u003cp\u003eHere, β\u003csub\u003egene\u003c/sub\u003e represents the LASSO regression coefficient of the gene, and Exp\u003csub\u003egene\u003c/sub\u003e represents the gene's expression level in the TCGA dataset.\u003c/p\u003e \u003cp\u003eTo validate the accuracy of the model, we calculated the Risk Score values for each sample in the GEO dataset using the same regression coefficients. Based on the optimal Risk Score cut-off value, we divided all GEO samples into high- and low-risk groups. We evaluated the association between high- and low-risk groupings and actual survival prognosis information using the Kaplan\u0026ndash;Meier method provided by the survival package (Version 2.41-1).\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e7. Association between Clinical Features and Riskscore\u003c/h2\u003e \u003cp\u003eIn TCGA samples, we used the Wilcoxon test in R 4.3.1 to statistically compare and assess the association between variables such as tumor stage, neoplasm histologic grade, age, OS, and risk score.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e8. Independence Analysis of the Prognostic Model and Nomogram Construction\u003c/h3\u003e\n\u003cp\u003eFirst, to determine whether the risk score model could serve as an independent prognostic factor, we conducted single- and multiple-factor Cox regression analyses for tumor stage, neoplasm histologic grade, age, and riskscore separately. We selected variables with p-values less than 0.05. A nomogram was created to make the results of multiple-factor regression more interpretable. The calibration curves are plotted to demonstrate the accuracy of the model.\u003c/p\u003e\n\u003ch3\u003e9. Association of High- and Low-Risk Groups with Immune Microenvironment\u003c/h3\u003e\n\u003cp\u003eIn this analysis, we employed three algorithms to evaluate the immune microenvironment of OC samples.\u003c/p\u003e \u003cp\u003e1. Using cell type identification by estimating relative subsets of RNA transcripts (CIBERSORT)(Chen, Khodadoust et al. 2018) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://cibersort.stanford.edu/index.php\u003c/span\u003e\u003cspan address=\"https://cibersort.stanford.edu/index.php\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), we calculated the proportions of 22 immune cell types based on their expression levels in TCGA OV tumor samples. CIBERSORT deconvolves the expression matrix of immune cell subtypes based on the principle of linear support vector regression. We conducted analyses using both relative and absolute modes. (Note: Absolute mode refers to the absolute proportions of each immune cell; for example, if the overall immune cell proportion is 3%, then the absolute values for the 22 major immune cells may be less than 0.1%, but the relative mode results in proportions of up to 1.)\u003c/p\u003e \u003cp\u003e2. The ESTIMATE algorithm(Yoshihara, Shahmoradgoli et al. 2013) was used to estimate stromal and immune scores, and ESTIMATE scores based on expression data were used to represent the presence of stromal and immune cells. We calculated the differential P-values between the high- and low-risk groups using the Wilcoxon rank-sum test and visualized them using box plots.\u003c/p\u003e\u003cp\u003e3. In this study, we employed immune gene sets to calculate gene set enrichment scores (ssGSEA) for each sample(Yi, Nissley et al. 2020). This method quantifies the enrichment of gene sets in each sample based on gene expression data and biological processes. Unlike traditional GSEA methods, ssGSEA does not require data from multiple samples and can be directly applied to single-sample gene expression data, making it suitable for small-sample analyses.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e10. Drug Sensitivity Analysis\u003c/h2\u003e \u003cp\u003eWe estimated the sensitivity of each patient to drugs using the Genomics of Drug Sensitivity in Cancer (GDSC) database (Yang, Soares et al. 2013) (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerrxgene.org/\u003c/span\u003e\u003cspan address=\"https://www.cancerrxgene.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e). The half-maximal inhibitory concentration (IC50) was quantified using the pRRophetic package (Geeleher, Cox et al. 2014) in R. Wilcoxon tests that were used to compare differences in drug sensitivity between the high- and low-risk groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e11. Prediction of Immunotherapy Response\u003c/h2\u003e \u003cp\u003eTumor Immune Dysfunction and Exclusion (TIDE; \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) is a transcriptome-based immunotherapy prediction tool that predicts patterns of interaction between tumor and immune cells(Jiang, Gu et al. 2018). TIDE aims to identify the biological mechanisms that cause tumor immune dysfunction and rejection, providing predictions for the responsiveness of tumor immunotherapy. We compared the TIDE scores between the high- and low-risk groups using the Wilcoxon test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e12. Statistical analysis\u003c/h2\u003e \u003cp\u003eBefore model construction, the \"FindAllMarkers\" function was employed to calculate differentially expressed genes (DEGs) across all cell clusters, with a specific focus on macrophages. Genes meeting the criteria of |log2FoldChange| \u0026gt;0 and P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were identified as single-cell differentially expressed genes (scDEGs), allowing for an in-depth exploration of differentially expressed genes within the macrophage cluster. Additionally, the Limma package was used to perform differential gene expression analysis on TCGA transcriptomic data, followed by Benjamini-Hochberg correction for multiple testing, resulting in adjusted p-values (adj.P.Value). The threshold for differential expression was set at adj.P.Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and |logFC| \u0026gt;0.263 based on assessments of fold change and significance. For the subsequent construction of the prognostic feature model, the glmnet package in R was employed for building the LASSO Cox regression model. Survival data were analyzed using Kaplan\u0026ndash;Meier curves, and both univariate and multivariate Cox regression analyses were conducted to identify independent prognostic risk factors. Finally, the Wilcoxon test in R 4.3.1 was employed to assess the statistical differences in categorical variables between the different risk groups. The data analysis workflow is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e1. Single-Cell Heterogeneity\u003c/h2\u003e \u003cp\u003eWe analyzed the expression of 12 marker genes for single-cell subtypes across different cell clusters and represented the results using violin (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-L) and bubble plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eM). The annotation revealed the presence of nine cell types (CD4\u0026thinsp;+\u0026thinsp;T cells, CD8\u0026thinsp;+\u0026thinsp;T cells, macrophages, fibroblasts, natural killer cells, Tregs, B cells, endothelial cells, and epithelial cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eN). The proportion of each cell type is visualized using bar charts (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eO).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA-L: Violin plots depicting the expression of marker genes across different cell clusters. M: Bubble plots illustrating the expression of marker genes in different cell clusters. N: Annotation results for the cell clusters. O: proportion of cell clusters\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e2. Single-Cell versus Conventional Transcriptome Differential Gene Analysis\u003c/h2\u003e \u003cp\u003eAs described in the Methods section, the differential analysis of single-cell data identified 845 differentially expressed genes in macrophages (Supplementary Table\u0026nbsp;1). We represented a heatmap displaying the differences in the expression of the top 20 differential genes in macrophages, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, |log2FoldChange|\u0026gt;0).\u003c/p\u003e \u003cp\u003eFollowing the described methods, differential analysis of TCGA transcriptome data for tumor vs. normal samples yielded a total of 18,020 differential genes (adj.P.Value\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u0026amp;|logFC|\u0026gt;0.263; Supplementary Table\u0026nbsp;2). The volcano plot is illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB.\u003c/p\u003e \u003cp\u003eFor the differential genes identified in macrophages and the transcriptome, we obtained 470 overlapping differential genes (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC). Based on these overlapping genes, we used the online tool STRING to predict protein-protein interaction relationships. Subsequently, we imported the interactions into the Cytoscape software and employed the CytoHubba plugin with the MCC algorithm to visualize the top 20 genes in the PPI network, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA: Heatmap depicting the expression of differential genes in macrophages from single-cell transcriptome data (top 20 genes, with red indicating upregulation and blue indicating downregulation). B: Volcano plot displaying differential gene expression in the TCGA transcriptome data (red represents upregulation and blue represents downregulation). C: Venn diagram illustrating the intersection of differentially expressed genes between macrophages and TCGA transcriptome data. D: The top 20 genes in the Protein-Protein Interaction (PPI) network (darker colors indicate stronger interactions)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3. Construction of a Prognostic Model\u003c/h2\u003e \u003cp\u003eBased on the aforementioned intersecting genes, we initially conducted a single-factor Cox regression, resulting in 30 genes with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA (Supplementary Table\u0026nbsp;3). Among these genes, \u003cem\u003eAP1S2, ARPC5, TMSB4X, PFN1, STAT1, TPM3, SH3KBP1, C6orf62, AKIRIN2, GNG5, TAP1, C1orf43, HMGN3\u003c/em\u003e, and \u003cem\u003eSDF2L1\u003c/em\u003e had hazard ratios (HR) less than 1, indicating a better prognosis, whereas the rest were considered to have a worse prognosis.\u003c/p\u003e \u003cp\u003eNext, following the described methodology, we used the 30 genes that were significantly associated with survival prognosis to select an optimal combination of 19 feature genes (\u003cem\u003eAKIRIN2, AP1S2, ARL4C, ARPC5, C1orf43, C5AR1, C6orf62, PIM3, RAB20, RB1, SDF2L1, SH3KBP1, STAT1, TAP1, TGFBI, THEMIS2, TPM3, TREM1, VSIG4\u003c/em\u003e) and their corresponding prognostic coefficients (coef) using the LASSO Cox regression algorithm, as depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC (Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003cp\u003eSubsequently, for each element within the selected set of 19 feature genes, we employed Kaplan\u0026ndash;Meier survival curves using the R survival package to evaluate the association between high (expression levels greater than or equal to the cut-off value) and low (expression levels lower than the cut-off value) gene expression levels and survival prognosis. In the TCGA database, high-risk groups show shorter overall survival than that in low-risk patients (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, Figure D, Supplementary Table\u0026nbsp;5), which was validated using GEO data (p\u0026thinsp;=\u0026thinsp;0.048, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003eTo assess the effectiveness of the signature, we computed the area under the curve (AUC) for the model's predictions of the 1-year, 3-year, and 5-year survival periods to demonstrate that the prognostic signature effectively predicted the patients' clinical outcomes, which were 0.71, 0.68, and 0.73, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). Finally, the model was validated using GEO data, and the Kaplan\u0026ndash;Meier curves are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA: Forest plot for the single-factor Cox analysis. B-C: λ selection plot in the LASSO model (two dashed lines indicate two specific λ values, with lambda.min on the left and lambda.1se on the right; any λ value between them is considered suitable). The model constructed with lambda.1se uses fewer genes, making it simpler, whereas lambda.min offers a slightly higher accuracy by utilizing more genes. Here, we choose lambda.min as λ. D: Kaplan-Meier survival curves for the prognostic model using TCGA data. E: ROC curves for the model's predictions of one-, three-, and 5-year survival periods (the area under the curve represents AUC values). F: Kaplan\u0026ndash;Meier survival curves for the prognostic model in the GEO validation dataset. The results in the validation queue were verified to be consistent with those in the training queue.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4. Differences in Riskscore Clinical Parameters\u003c/h2\u003e \u003cp\u003eTo assess the prognostic value of the risk score and other clinical factors, we conducted Wilcoxon tests to statistically compare the risk scores between different groups for the following variables: age (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA), OS (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eB), tumor stage (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC), and neoplasm histologic grade (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD). The results revealed that among these four indicators, the risk score was significantly higher in individuals aged\u0026thinsp;\u0026gt;\u0026thinsp;55 years (p\u0026thinsp;=\u0026thinsp;0.002) and those with a deceased survival status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). No statistically significant differences exist in the risk scores among the remaining variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA: Differences in risk scores among patients with OC in different age groups (with age groups divided at 55 years). B: Differences in risk scores among patients with OC with different survival statuses. C: Differences in risk scores among tumors at different stages. D: Differences in risk scores among tumors with different degrees of metastasis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e5. Prognostic Model Independence Analysis and Nomogram Construction\u003c/h2\u003e \u003cp\u003eAs described in the methods section, univariate and multivariate Cox regression analyses were performed separately for tumor stage, neoplasm histologic grade, age, and risk score to select variables with a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e, age and risk score were ultimately considered independent prognostic factors supported by the HR and CI values. Univariate analysis revealed HR and CI values: HR\u0026thinsp;=\u0026thinsp;1.019 (95% CI: 1.006\u0026ndash;1.033), p\u0026thinsp;=\u0026thinsp;0.004, and HR\u0026thinsp;=\u0026thinsp;3.685 (95%CI: 2.640\u0026ndash;5.143), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively. In multivariate analysis, the corresponding values were 1.014 (95%CI: 1.001\u0026ndash;1.028), p\u0026thinsp;=\u0026thinsp;0.032, and 3.692 (95% CI: 2.625\u0026ndash;5.192), p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, respectively. Based on these findings, a nomogram containing the above four clinical factors was constructed. By summing the scores from each variable on the point-scale axis, a total score was calculated to predict the one-year, three-year, and five-year survival rates of patients. (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Additionally, calibration curves (Figs.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB-D) and decision curve analysis curves (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eE) were plotted to demonstrate the accuracy of the model. The calibration curves for the nomogram demonstrate the predicted probabilities of OS estimated by the nomogram closely aligned with the actual disease-free survival proportion.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA: Forest plot of the single-factor Cox regression analysis of clinical factors and risk scores. B: Forest plot of multifactor Cox regression analysis of clinical factors and risk scores\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA: Nomogram depicting the clinical factors and risk scores. Each variable was assigned a score on a point-scale axis. The total score was calculated by summing the individual scores and projecting them onto a total point scale to estimate survival probability. B-D: Calibration curves for 1-, 3-, and 5-year nomogram predictions. E: Decision curve analysis curve for the nomogram model\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e6. Association between High-Risk/Low-Risk Groups and the Immune Microenvironment\u003c/h2\u003e \u003cp\u003eTo explore the association between the risk score and immune cell infiltration in tumors, we estimated the relative abundance of immune and stromal cell infiltration in each sample using the CIBERSORT, ssGSEA, and ESTIMATE algorithms (Supplementary Table\u0026nbsp;6). We generated a heatmap to display the relative abundance of each cell type, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig8\" class=\"InternalRef\"\u003e8\u003c/span\u003e. Further analysis involved dividing the samples into high- and low-risk groups, followed by a Wilcoxon test to compare each cell type between the two groups and calculate statistical significance. The results revealed that for the CIBERSORT method, 8 immune microenvironment cells exhibited significant differences between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) which were CD8 T cells, CD4 na\u0026iuml;ve T cells, T follicular helper cells, T gamma delta cells, M1 macrophages, M2 macrophages, activated mast cells, neutrophils (Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eA); for the ssGSEA method, 17 immune microenvironment cells (central memory CD8 T cells, effector memory CD8 T cells, central memory CD4 T cells, effector memory CD4 T cells, T follicular helper cells, Type 1 T helper (Th1) cells, regulatory T cells (Treg), memory B cells, natural killer cells, myeloid derived suppressor cells, activated dendritic cells, plasmacytoid dendritic cells, immature dendritic cells, macrophages, eosinophils, mast cells, neutrophils) showed significant differences between the two groups (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eB); for the ESTIMATE method, the high-risk group had higher Stromal score and ESTIMATE score compared to that in the low-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001); there was no statistically significant difference in immune score between the high-risk and low-risk groups (p\u0026thinsp;=\u0026thinsp;0.051). The results are depicted in Figs.\u0026nbsp;\u003cspan refid=\"Fig9\" class=\"InternalRef\"\u003e9\u003c/span\u003eC-E.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe heatmap depicts the relative abundance distribution of all microenvironmental cells estimated by the three algorithms (different colored horizontal bars on the left represent different algorithms, as indicated in the legend, with different colored horizontal bars at the top representing different groups, with blue indicating low risk and red indicating high risk).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA: Differences in TME cells estimated using the CIBERSORT method; B: Differences in TME cells estimated using the ssGSEA method; C-E: Differences in stromal, immune, and ESTIMATE scores estimated using the ESTIMATE method\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e7. Drug Sensitivity analysis between High-Risk and Low-Risk Groups\u003c/h2\u003e \u003cp\u003eAs described in the methods section, we estimated the sensitivity of each patient to OC drugs (Veliparib, Talazoparib, Pazopanib, Sunitinib, Sorafenib, and Erlotinib). IC50 was quantified using the pRRophetic package in R. Differences in drug sensitivity between the high- and low-risk groups were compared using the Wilcoxon test. The results revealed significant differences in four of the six chemotherapy drugs, with pazopanib having a higher IC50 in the low-risk group than that in the high-risk group (p\u0026thinsp;=\u0026thinsp;0.0071), sunitinib having a higher IC50 in the low-risk group than that in the high-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), sorafenib having a higher IC50 in the low-risk group than that in the high-risk group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and erlotinib having a higher IC50 in the low-risk group than that in the high-risk group (p\u0026thinsp;=\u0026thinsp;0.015). The results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig10\" class=\"InternalRef\"\u003e10\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA: Difference in the IC50 of celiparib between the high-risk and low-risk groups. B: Difference in IC50 of talazoparib between the high-risk and low-risk groups. C: Difference in IC50 of Pazopanib between the high-risk and low-risk groups. D: Difference in IC50 of Sunitinib between the high-risk and low-risk groups. E: Difference in IC50 of Sorafenib between the high- and low-risk groups. F: Difference in IC50 of Erlotinib between the high- and low-risk groups\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e8. Association between TIDE Score and Risk Groups\u003c/h2\u003e \u003cp\u003eAs described in the Methods section, we calculated TIDE scores for patients online and then compared the differences between the high- and low-risk groups using the Wilcoxon test. The results revealed that the high-risk group had significantly higher TIDE scores than that in the low-risk group (p\u0026thinsp;=\u0026thinsp;0.04), indicating a lower sensitivity to immune therapy in the high-risk group (Fig.\u0026nbsp;\u003cspan refid=\"Fig11\" class=\"InternalRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eDifferences in TIDE scores between high- and low-risk groups (red, high-risk patients; blue, low-risk patients).\u003c/p\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e9. Subcellular Localization of Prognostic Model Genes in Single Cells\u003c/h2\u003e \u003cp\u003eTo investigate disparities in gene expression patterns between tumor and normal cell lines, we visualized the expression of prognostic model genes in single-cell clusters using UMAP plots. The results demonstrated that genes such as \u003cem\u003eAP1S2, C5AR1, RB1, THEMIS2, TREM1, VSIG4\u003c/em\u003e, and others exhibit significantly higher expression levels in macrophages than that in other cell clusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig12\" class=\"InternalRef\"\u003e12\u003c/span\u003eA-S).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThrough analysis of single-cell RNA sequencing data, we established a prognostic model for OC consisting of 19 genes. Validation using The TCGA and GEO datasets confirmed the predictive performance of the model, demonstrating that the risk score is an independent prognostic factor for OS in patients with OC. Algorithmic comparisons revealed that the high-risk group exhibited lower sensitivity to immunotherapy but higher sensitivity to anti-angiogenic drugs, potentially linked to differences in the immune microenvironment.\u003c/p\u003e \u003cp\u003eThe molecular and cellular heterogeneity of OC makes a single traditional classification insufficient for an accurate prognosis(Veneziani, Gonzalez-Ochoa et al. 2023). With the continuous refinement of molecular subtyping, traditional histological classifications and binary models for OC are gradually being replaced(K\u0026ouml;bel and Kang 2022). In 2011, the TCGA research group classified patients with high-grade serous OC into four subtypes based on mRNA expression features(2011). Building on this, Jonsson et al. further subdivided high-grade serous OC into differentiated, immune-like, proliferative, and mesenchymal-like subtypes by integrating molecular pathological features(J\u0026ouml;nsson, Johansson et al. 2014). The immune-like subtype exhibited the longest survival, whereas the proliferative and mesenchymal-like subtypes showed shorter survival periods. Another study classified epithelial OC into five subtypes based on gene expression patterns: EPI-A, EPI-B, MES, STEM-A, and STEM-B, with distinct pathological features, signaling pathway alterations, and prognosis(Tan, Miow et al. 2013). These molecular subtypes predict prognosis and aid in the identification of specific molecular targets for targeted therapy. Kommoss et al. revealed the proliferative and mesenchymal subtypes benefited from bevacizumab monotherapy, with median PFS of 10.1 months and 8.2 months, respectively(Kommoss, Winterhoff et al. 2017). Antony et al. reported AXL enrichment in Mes subtype tumor tissues, and the AXL inhibitor R428 reduced the activation of RTK and ERK, inhibiting the movement and proliferation of Mes cells(Antony, Tan et al. 2016), suggesting that patients with Mes subtype OC may benefit from AXL-targeted therapy. Previous molecular subtyping studies have primarily emphasized gene expression features in the overall TME, lacking insights into the interactions between prognosis and single-cell populations within the TME. Our unique scRNA-seq-based prognostic model, which uses macrophage-related genes, stands out from other models and comprehensively reflects macrophage function in the TME. Integrating diverse datasets and algorithmic results, validated against inconsistent training sets, our model boasts an AUC value of 0.68\u0026ndash;0.73, ensuring heightened reliability and relevance.\u003c/p\u003e \u003cp\u003eThe TME plays a crucial role in clinical outcomes and treatment responses. In OC, various immune-infiltrating cells, such as mature dendritic cells, M1 macrophages, natural killer cells, αβT cells, and γδT cells, exhibit anti-tumor effects. Conversely, immunosuppressive cells such as immature/tolerant dendritic cells, M2 macrophages, regulatory T cells, and myeloid-derived suppressor cells hinder antitumor immunity(Deng, Terunuma et al. 2014, Santoiemma and Powell 2015). Studies have suggested that immune cell infiltration correlates with OC prognosis. Infiltration of na\u0026iuml;ve CD4 T cells, resting CD4 memory T cells, M2 macrophages, and eosinophils is associated with a poor prognosis, whereas activated CD4 memory T cells, M0 macrophages, and M1 macrophages correlate with an improved prognosis(Yan, Li et al. 2023). Moreover, the gene expression profiles of immune-infiltrating cells in the tumor microenvironment have been linked to the prognosis of patients with OC. CRMP2, secreted by cancer-associated fibroblasts (CAFs), promotes OC growth and metastasis, leading to adverse outcomes(Jin, Bian et al. 2022). The intricate functions of macrophages are related to OC prognosis and treatment outcomes(Yan, Li et al. 2023). Our prognostic model, incorporating 19 macrophage-specific genes, revealed that the risk score was associated with the infiltration of immune cells, based on ESTIMATE scores and immune cell infiltration analysis. The high-risk group exhibited higher overall immune cell infiltration and lower CD8\u0026thinsp;+\u0026thinsp;T cell infiltration. The proportion of TAMs was higher, with a lower ratio of M1 to M2 macrophages. Therefore, despite the increased infiltration of immune-activated cells in high-risk individuals, they may be functionally inhibited, possibly contributing to an unfavorable prognosis.\u003c/p\u003e \u003cp\u003eChemotherapy remains the primary approach for the treatment of OC; however, its effectiveness is limited owing to tumor heterogeneity and complexity. Novel targeted therapies, such as poly (ADP-ribose) polymerase (PARP) inhibitors, significantly prolonged PFS(Farmer, McCabe et al. 2005). In immunotherapy, a few Phase III trials have been prematurely terminated owing to ineffectiveness(Maiorano, Maiorano et al. 2021). Reports suggest that combining PARP inhibitors with anti-angiogenic agents enhances the efficacy of immunotherapy by stimulating adaptive immune responses, turning \"cold tumors\" into \"hot tumors\"(Peyraud and Italiano 2020, Cao, Langer et al. 2023). The DUO-O study demonstrated the feasibility of first-line maintenance therapy with a triple combination of anti-angiogenic agents, PARP inhibitors, and immunotherapy(Westin, Moore et al. 2023). The TOPACIO study evaluates the efficacy of niraparib and pembrolizumab in late-stage recurrent OC, and biomarker analysis indicates a positive immune score, correlating with clinical benefits(F\u0026auml;rkkil\u0026auml;, Gulhan et al. 2020). This suggests a correlation between immune-related genes and features of the immune microenvironment, which potentially influences the clinical benefits of combined immunotherapy in patients with OC. Consequently, in our study, drug sensitivity analysis revealed that high-risk patients showed greater sensitivity to certain anti-angiogenic drugs (Pazopanib, Sunitinib, and Sorafenib). Studies have indicated that VEGF and miR-501-3p derived from TAMs directly mediate vascular formation in tumor tissues, which may explain why increased sensitivity may be associated with a higher presence of M2 macrophages, inducing vascular formation(Tacconi, Ungaro et al. 2019, Yin, Ma et al. 2019). The TIDE score results indicated weaker sensitivity to immunotherapy in the high-risk group, which was possibly linked to the suppressive tumor microenvironment.\u003c/p\u003e \u003cp\u003eIn conclusion, our research reveals the necessity for more personalized treatment plans for high-risk patients and underscores the potential of drugs such as PARP inhibitors to enhance OC survival rates.\u003c/p\u003e \u003cp\u003eThis study had several limitations, including the use of retrospective data to construct and validate prognostic models, relatively singular data sources, and limited scRNA-seq sample sizes. The GDSC database used for drug sensitivity data is based on tumor cell lines and may differ from the intrinsic drug sensitivity of patients. The regulatory mechanisms involving macrophage characteristic genes in OC remain unclear and require further clinical research to validate the study results and better guide clinical treatment. This will be the focus of future research.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study discovered and established a novel prognostic risk model for OC, using macrophage gene characteristics to effectively predict the prognosis and treatment response of patients with OC. Immunological analysis confirmed the correlation between risk score and TME, elucidating diverse prognoses among patients and providing a basis for further research on biomarkers and antitumor treatment strategies. This study is clinically valuable for screening populations that benefit from treatment and improving the prognosis of patients with OC.\u003c/p\u003e \u003cp\u003e【Acknowledgments】\u003c/p\u003e \u003cp\u003eThis work was supported by Central Government Guidance Fund for Local Science and Technology Development(202407AB110013).\u003c/p\u003e \u003cp\u003e【Data availability】\u003c/p\u003e \u003cp\u003eAll data in the article can be obtained from GEO datasets (\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), UCSC Xena platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://toil.xenahubs.net\u003c/span\u003e\u003cspan address=\"https://toil.xenahubs.net\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), Genotype-Tissue Expression (GTEx) database (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.gtexportal.org/\u003c/span\u003e\u003cspan address=\"https://www.gtexportal.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e(2011). \"Integrated genomic analyses of ovarian carcinoma.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNature\u003c/span\u003e \u003cb\u003e474\u003c/b\u003e(7353): 609\u0026ndash;615.\u003c/p\u003e \u003cp\u003e(2013). \"The Genotype-Tissue Expression (GTEx) project.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Genet\u003c/span\u003e \u003cb\u003e45\u003c/b\u003e(6): 580\u0026ndash;585.\u003c/p\u003e \u003cp\u003eAntony, J., T. Z. Tan, Z. Kelly, J. Low, M. Choolani, C. Recchi, H. Gabra, J. P. Thiery and R. Y. Huang (2016). \"The GAS6-AXL signaling network is a mesenchymal (Mes) molecular subtype-specific therapeutic target for ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSci Signal\u003c/span\u003e \u003cb\u003e9\u003c/b\u003e(448): ra97.\u003c/p\u003e \u003cp\u003eBalkwill, F. R., M. Capasso and T. Hagemann (2012). \"The tumor microenvironment at a glance.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Cell Sci\u003c/span\u003e \u003cb\u003e125\u003c/b\u003e(Pt 23): 5591\u0026ndash;5596.\u003c/p\u003e \u003cp\u003eBarrett, T., S. E. Wilhite, P. Ledoux, C. Evangelista, I. F. Kim, M. Tomashevsky, K. A. Marshall, K. H. Phillippy, P. M. Sherman, M. Holko, A. Yefanov, H. Lee, N. Zhang, C. L. Robertson, N. Serova, S. Davis and A. Soboleva (2013). \"NCBI GEO: archive for functional genomics data sets\u0026ndash;update.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNucleic Acids Res\u003c/span\u003e \u003cb\u003e41\u003c/b\u003e(Database issue): D991-995.\u003c/p\u003e \u003cp\u003eCai, D. L. and L. P. Jin (2017). \"Immune Cell Population in Ovarian Tumor Microenvironment.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Cancer\u003c/span\u003e \u003cb\u003e8\u003c/b\u003e(15): 2915\u0026ndash;2923.\u003c/p\u003e \u003cp\u003eCao, Y., R. Langer and N. Ferrara (2023). \"Targeting angiogenesis in oncology, ophthalmology and beyond.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Rev Drug Discov\u003c/span\u003e \u003cb\u003e22\u003c/b\u003e(6): 476\u0026ndash;495.\u003c/p\u003e \u003cp\u003eChen, B., M. S. Khodadoust, C. L. Liu, A. M. Newman and A. A. Alizadeh (2018). \"Profiling Tumor Infiltrating Immune Cells with CIBERSORT.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eMethods Mol Biol\u003c/span\u003e \u003cb\u003e1711\u003c/b\u003e: 243\u0026ndash;259.\u003c/p\u003e \u003cp\u003eColvin, E. K. (2014). \"Tumor-associated macrophages contribute to tumor progression in ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eFront Oncol\u003c/span\u003e \u003cb\u003e4\u003c/b\u003e: 137.\u003c/p\u003e \u003cp\u003eDeng, X., H. Terunuma, A. Terunuma, T. Takane and M. Nieda (2014). \"Ex vivo-expanded natural killer cells kill cancer cells more effectively than ex vivo-expanded γδ T cells or αβ T cells.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInt Immunopharmacol\u003c/span\u003e \u003cb\u003e22\u003c/b\u003e(2): 486\u0026ndash;491.\u003c/p\u003e \u003cp\u003eF\u0026auml;rkkil\u0026auml;, A., D. C. Gulhan, J. Casado, C. A. Jacobson, H. Nguyen, B. Kochupurakkal, Z. Maliga, C. Yapp, Y. A. Chen, D. Schapiro, Y. Zhou, J. R. Graham, B. J. Dezube, P. Munster, S. Santagata, E. Garcia, S. Rodig, A. Lako, D. Chowdhury, G. I. Shapiro, U. A. Matulonis, P. J. Park, S. Hautaniemi, P. K. Sorger, E. M. Swisher, A. D. D'Andrea and P. A. Konstantinopoulos (2020). \"Immunogenomic profiling determines responses to combined PARP and PD-1 inhibition in ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Commun\u003c/span\u003e \u003cb\u003e11\u003c/b\u003e(1): 1459.\u003c/p\u003e \u003cp\u003eFarmer, H., N. McCabe, C. J. Lord, A. N. Tutt, D. A. Johnson, T. B. Richardson, M. Santarosa, K. J. Dillon, I. Hickson, C. Knights, N. M. Martin, S. P. Jackson, G. C. Smith and A. Ashworth (2005). \"Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNature\u003c/span\u003e \u003cb\u003e434\u003c/b\u003e(7035): 917\u0026ndash;921.\u003c/p\u003e \u003cp\u003eFriedman, J., T. Hastie, R. Tibshirani, B. Narasimhan, K. Tay, N. Simon and J. Qian (2021). \"Package \u0026lsquo;glmnet\u0026rsquo;.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCRAN R Repositary\u003c/span\u003e \u003cb\u003e595\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eGeeleher, P., N. Cox and R. S. Huang (2014). \"pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePLoS One\u003c/span\u003e \u003cb\u003e9\u003c/b\u003e(9): e107468.\u003c/p\u003e \u003cp\u003eGeistlinger, L., S. Oh, M. Ramos, L. Schiffer, R. S. LaRue, C. M. Henzler, S. A. Munro, C. Daughters, A. C. Nelson, B. J. Winterhoff, Z. Chang, S. Talukdar, M. Shetty, S. A. Mullany, M. Morgan, G. Parmigiani, M. J. Birrer, L. X. Qin, M. Riester, T. K. Starr and L. Waldron (2020). \"Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCancer Res\u003c/span\u003e \u003cb\u003e80\u003c/b\u003e(20): 4335\u0026ndash;4345.\u003c/p\u003e \u003cp\u003eGoldman, M., B. Craft, M. Hastie, K. Repečka, F. McDade, A. Kamath, A. Banerjee, Y. Luo, D. Rogers, A. N. Brooks, J. Zhu and D. Haussler (2019). \"The UCSC Xena platform for public and private cancer genomics data visualization and interpretation.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ebioRxiv\u003c/span\u003e: 326470.\u003c/p\u003e \u003cp\u003eGuo, J., X. Han, J. Li, Z. Li, J. Yi, Y. Gao, X. Zhao and W. Yue (2023). \"Single-cell transcriptomics in ovarian cancer identify a metastasis-associated cell cluster overexpressed RAB13.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Transl Med\u003c/span\u003e \u003cb\u003e21\u003c/b\u003e(1): 254.\u003c/p\u003e \u003cp\u003eHe, Y. F., M. Y. Zhang, X. Wu, X. J. Sun, T. Xu, Q. Z. He and W. Di (2013). \"High MUC2 expression in ovarian cancer is inversely associated with the M1/M2 ratio of tumor-associated macrophages and patient survival time.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePLoS One\u003c/span\u003e \u003cb\u003e8\u003c/b\u003e(12): e79769.\u003c/p\u003e \u003cp\u003eHornburg, M., M. Desbois, S. Lu, Y. Guan, A. A. Lo, S. Kaufman, A. Elrod, A. Lotstein, T. M. DesRochers, J. L. Munoz-Rodriguez, X. Wang, J. Giltnane, O. Mayba, S. J. Turley, R. Bourgon, A. Daemen and Y. Wang (2021). \"Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCancer Cell\u003c/span\u003e \u003cb\u003e39\u003c/b\u003e(7): 928\u0026ndash;944.e926.\u003c/p\u003e \u003cp\u003eIzar, B., I. Tirosh, E. H. Stover, I. Wakiro, M. S. Cuoco, I. Alter, C. Rodman, R. Leeson, M. J. Su, P. Shah, M. Iwanicki, S. R. Walker, A. Kanodia, J. C. Melms, S. Mei, J. R. Lin, C. B. M. Porter, M. Slyper, J. Waldman, L. Jerby-Arnon, O. Ashenberg, T. J. Brinker, C. Mills, M. Rogava, S. Vigneau, P. K. Sorger, L. A. Garraway, P. A. Konstantinopoulos, J. F. Liu, U. Matulonis, B. E. Johnson, O. Rozenblatt-Rosen, A. Rotem and A. Regev (2020). \"A single-cell landscape of high-grade serous ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Med\u003c/span\u003e \u003cb\u003e26\u003c/b\u003e(8): 1271\u0026ndash;1279.\u003c/p\u003e \u003cp\u003eJiang, P., S. Gu, D. Pan, J. Fu, A. Sahu, X. Hu, Z. Li, N. Traugh, X. Bu, B. Li, J. Liu, G. J. Freeman, M. A. Brown, K. W. Wucherpfennig and X. S. Liu (2018). \"Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Med\u003c/span\u003e \u003cb\u003e24\u003c/b\u003e(10): 1550\u0026ndash;1558.\u003c/p\u003e \u003cp\u003eJin, Y., S. Bian, H. Wang, J. Mo, H. Fei, L. Li, T. Chen and H. Jiang (2022). \"CRMP2 derived from cancer associated fibroblasts facilitates progression of ovarian cancer via HIF-1α-glycolysis signaling pathway.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCell Death Dis\u003c/span\u003e \u003cb\u003e13\u003c/b\u003e(8): 675.\u003c/p\u003e \u003cp\u003eJ\u0026ouml;nsson, J. M., I. Johansson, M. Dominguez-Valentin, S. Kimbung, M. J\u0026ouml;nsson, J. H. Bonde, P. Kannisto, A. M\u0026aring;sb\u0026auml;ck, S. Malander, M. Nilbert and I. Hedenfalk (2014). \"Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePLoS One\u003c/span\u003e \u003cb\u003e9\u003c/b\u003e(9): e107643.\u003c/p\u003e \u003cp\u003eK\u0026ouml;bel, M. and E. Y. Kang (2022). \"The Evolution of Ovarian Carcinoma Subclassification.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCancers (Basel)\u003c/span\u003e \u003cb\u003e14\u003c/b\u003e(2).\u003c/p\u003e \u003cp\u003eKommoss, S., B. Winterhoff, A. L. Oberg, G. E. Konecny, C. Wang, S. M. Riska, J. B. Fan, M. J. Maurer, C. April, V. Shridhar, F. Kommoss, A. du Bois, F. Hilpert, S. Mahner, K. Baumann, W. Schroeder, A. Burges, U. Canzler, J. Chien, A. C. Embleton, M. Parmar, R. Kaplan, T. Perren, L. C. Hartmann, E. L. Goode, S. C. Dowdy and J. Pfisterer (2017). \"Bevacizumab May Differentially Improve Ovarian Cancer Outcome in Patients with Proliferative and Mesenchymal Molecular Subtypes.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eClin Cancer Res\u003c/span\u003e \u003cb\u003e23\u003c/b\u003e(14): 3794\u0026ndash;3801.\u003c/p\u003e \u003cp\u003eKonstantinopoulos, P. A. and S. A. Cannistra (2021). \"Immune Checkpoint Inhibitors in Ovarian Cancer: Can We Bridge the Gap Between IMagynation and Reality?\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Clin Oncol\u003c/span\u003e \u003cb\u003e39\u003c/b\u003e(17): 1833\u0026ndash;1838.\u003c/p\u003e \u003cp\u003eLan, C., X. Huang, S. Lin, H. Huang, Q. Cai, T. Wan, J. Lu and J. Liu (2013). \"Expression of M2-polarized macrophages is associated with poor prognosis for advanced epithelial ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTechnol Cancer Res Treat\u003c/span\u003e \u003cb\u003e12\u003c/b\u003e(3): 259\u0026ndash;267.\u003c/p\u003e \u003cp\u003eLe Page, C., A. Marineau, P. K. Bonza, K. Rahimi, L. Cyr, I. Labouba, J. Madore, N. Delvoye, A. M. Mes-Masson, D. M. Provencher and J. F. Cailhier (2012). \"BTN3A2 expression in epithelial ovarian cancer is associated with higher tumor infiltrating T cells and a better prognosis.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePLoS One\u003c/span\u003e \u003cb\u003e7\u003c/b\u003e(6): e38541.\u003c/p\u003e \u003cp\u003eMaiorano, B. A., M. F. P. Maiorano, D. Lorusso and E. Maiello (2021). \"Ovarian Cancer in the Era of Immune Checkpoint Inhibitors: State of the Art and Future Perspectives.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCancers (Basel)\u003c/span\u003e \u003cb\u003e13\u003c/b\u003e(17).\u003c/p\u003e \u003cp\u003eNo, J. H., J. M. Moon, K. Kim and Y. B. Kim (2013). \"Prognostic significance of serum soluble CD163 level in patients with epithelial ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGynecol Obstet Invest\u003c/span\u003e \u003cb\u003e75\u003c/b\u003e(4): 263\u0026ndash;267.\u003c/p\u003e \u003cp\u003ePeyraud, F. and A. Italiano (2020). \"Combined PARP Inhibition and Immune Checkpoint Therapy in Solid Tumors.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCancers (Basel)\u003c/span\u003e \u003cb\u003e12\u003c/b\u003e(6).\u003c/p\u003e \u003cp\u003ePotter, S. S. (2018). \"Single-cell RNA sequencing for the study of development, physiology and disease.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Rev Nephrol\u003c/span\u003e \u003cb\u003e14\u003c/b\u003e(8): 479\u0026ndash;492.\u003c/p\u003e \u003cp\u003eReinartz, S., T. Schumann, F. Finkernagel, A. Wortmann, J. M. Jansen, W. Meissner, M. Krause, A. M. Schw\u0026ouml;rer, U. Wagner, S. M\u0026uuml;ller-Br\u0026uuml;sselbach and R. M\u0026uuml;ller (2014). \"Mixed-polarization phenotype of ascites-associated macrophages in human ovarian carcinoma: correlation of CD163 expression, cytokine levels and early relapse.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eInt J Cancer\u003c/span\u003e \u003cb\u003e134\u003c/b\u003e(1): 32\u0026ndash;42.\u003c/p\u003e \u003cp\u003eRitchie, M. E., B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi and G. K. Smyth (2015). \"limma powers differential expression analyses for RNA-sequencing and microarray studies.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNucleic Acids Res\u003c/span\u003e \u003cb\u003e43\u003c/b\u003e(7): e47.\u003c/p\u003e \u003cp\u003eSantoiemma, P. P. and D. J. Powell, Jr. (2015). \"Tumor infiltrating lymphocytes in ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCancer Biol Ther\u003c/span\u003e \u003cb\u003e16\u003c/b\u003e(6): 807\u0026ndash;820.\u003c/p\u003e \u003cp\u003eSchem, C., D. O. Bauerschlag, I. Meinhold-Heerlein, D. Fischer, M. Friedrich and N. Maass (2007). \"[Benign and borderline tumors of the ovary].\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eTher Umsch\u003c/span\u003e \u003cb\u003e64\u003c/b\u003e(7): 369\u0026ndash;374.\u003c/p\u003e \u003cp\u003eShahraki, H. R., A. Salehi and N. Zare (2015). \"Survival Prognostic Factors of Male Breast Cancer in Southern Iran: a LASSO-Cox Regression Approach.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAsian Pac J Cancer Prev\u003c/span\u003e \u003cb\u003e16\u003c/b\u003e(15): 6773\u0026ndash;6777.\u003c/p\u003e \u003cp\u003eShannon, P., A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski and T. Ideker (2003). \"Cytoscape: a software environment for integrated models of biomolecular interaction networks.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGenome Res\u003c/span\u003e \u003cb\u003e13\u003c/b\u003e(11): 2498\u0026ndash;2504.\u003c/p\u003e \u003cp\u003eSzklarczyk, D., A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K. P. Tsafou, M. Kuhn, P. Bork, L. J. Jensen and C. von Mering (2015). \"STRING v10: protein-protein interaction networks, integrated over the tree of life.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNucleic Acids Res\u003c/span\u003e \u003cb\u003e43\u003c/b\u003e(Database issue): D447-452.\u003c/p\u003e \u003cp\u003eTacconi, C., F. Ungaro, C. Correale, V. Arena, L. Massimino, M. Detmar, A. Spinelli, M. Carvello, M. Mazzone, A. I. Oliveira, F. Rubbino, V. Garlatti, S. Span\u0026ograve;, E. Lugli, F. S. Colombo, A. Malesci, L. Peyrin-Biroulet, S. Vetrano, S. Danese and S. D'Alessio (2019). \"Activation of the VEGFC/VEGFR3 Pathway Induces Tumor Immune Escape in Colorectal Cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCancer Res\u003c/span\u003e \u003cb\u003e79\u003c/b\u003e(16): 4196\u0026ndash;4210.\u003c/p\u003e \u003cp\u003eTan, T. Z., Q. H. Miow, R. Y. Huang, M. K. Wong, J. Ye, J. A. Lau, M. C. Wu, L. H. Bin Abdul Hadi, R. Soong, M. Choolani, B. Davidson, J. M. Nesland, L. Z. Wang, N. Matsumura, M. Mandai, I. Konishi, B. C. Goh, J. T. Chang, J. P. Thiery and S. Mori (2013). \"Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eEMBO Mol Med\u003c/span\u003e \u003cb\u003e5\u003c/b\u003e(7): 1051\u0026ndash;1066.\u003c/p\u003e \u003cp\u003eTherneau, T. M. and T. Lumley (2015). \"Package \u0026lsquo;survival\u0026rsquo;.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eR Top Doc\u003c/span\u003e \u003cb\u003e128\u003c/b\u003e(10): 28\u0026ndash;33.\u003c/p\u003e \u003cp\u003eTorre, L. A., B. Trabert, C. E. DeSantis, K. D. Miller, G. Samimi, C. D. Runowicz, M. M. Gaudet, A. Jemal and R. L. Siegel (2018). \"Ovarian cancer statistics, 2018.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eCA Cancer J Clin\u003c/span\u003e \u003cb\u003e68\u003c/b\u003e(4): 284\u0026ndash;296.\u003c/p\u003e \u003cp\u003eVeneziani, A. C., E. Gonzalez-Ochoa, H. Alqaisi, A. Madariaga, G. Bhat, M. Rouzbahman, S. Sneha and A. M. Oza (2023). \"Heterogeneity and treatment landscape of ovarian carcinoma.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Rev Clin Oncol\u003c/span\u003e \u003cb\u003e20\u003c/b\u003e(12): 820\u0026ndash;842.\u003c/p\u003e \u003cp\u003eWang, Z., J. Zhang, F. Dai, B. Li and Y. Cheng (2023). \"Integrated analysis of single-cell RNA-seq and bulk RNA-seq unveils heterogeneity and establishes a novel signature for prognosis and tumor immune microenvironment in ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Ovarian Res\u003c/span\u003e \u003cb\u003e16\u003c/b\u003e(1): 12.\u003c/p\u003e \u003cp\u003eWestin, S. N., K. Moore, H. S. Chon, J. Y. Lee, J. Thomes Pepin, M. Sundborg, A. Shai, J. de la Garza, S. Nishio, M. A. Gold, K. Wang, K. McIntyre, T. D. Tillmanns, S. V. Blank, J. H. Liu, M. McCollum, F. Contreras Mejia, T. Nishikawa, K. Pennington, Z. Novak, A. C. De Melo, J. Sehouli, D. Klasa-Mazurkiewicz, C. Papadimitriou, M. Gil-Martin, B. Brasiuniene, C. Donnelly, P. M. Del Rosario, X. Liu and E. Van Nieuwenhuysen (2023). \"Durvalumab Plus Carboplatin/Paclitaxel Followed by Maintenance Durvalumab With or Without Olaparib as First-Line Treatment for Advanced Endometrial Cancer: The Phase III DUO-E Trial.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Clin Oncol\u003c/span\u003e: Jco2302132.\u003c/p\u003e \u003cp\u003eYan, C., K. Li, F. Meng, L. Chen, J. Zhao, Z. Zhang, D. Xu, J. Sun and M. Zhou (2023). \"Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Adv Res\u003c/span\u003e \u003cb\u003e44\u003c/b\u003e: 149\u0026ndash;160.\u003c/p\u003e \u003cp\u003eYang, W., J. Soares, P. Greninger, E. J. Edelman, H. Lightfoot, S. Forbes, N. Bindal, D. Beare, J. A. Smith, I. R. Thompson, S. Ramaswamy, P. A. Futreal, D. A. Haber, M. R. Stratton, C. Benes, U. McDermott and M. J. Garnett (2013). \"Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNucleic Acids Res\u003c/span\u003e \u003cb\u003e41\u003c/b\u003e(Database issue): D955-961.\u003c/p\u003e \u003cp\u003eYi, M., D. V. Nissley, F. McCormick and R. M. Stephens (2020). \"ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSci Rep\u003c/span\u003e \u003cb\u003e10\u003c/b\u003e(1): 10258.\u003c/p\u003e \u003cp\u003eYin, Z., T. Ma, B. Huang, L. Lin, Y. Zhou, J. Yan, Y. Zou and S. Chen (2019). \"Macrophage-derived exosomal microRNA-501-3p promotes progression of pancreatic ductal adenocarcinoma through the TGFBR3-mediated TGF-β signaling pathway.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Exp Clin Cancer Res\u003c/span\u003e \u003cb\u003e38\u003c/b\u003e(1): 310.\u003c/p\u003e \u003cp\u003eYoshihara, K., M. Shahmoradgoli, E. Mart\u0026iacute;nez, R. Vegesna, H. Kim, W. Torres-Garcia, V. Trevi\u0026ntilde;o, H. Shen, P. W. Laird, D. A. Levine, S. L. Carter, G. Getz, K. Stemke-Hale, G. B. Mills and R. G. Verhaak (2013). \"Inferring tumour purity and stromal and immune cell admixture from expression data.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eNat Commun\u003c/span\u003e \u003cb\u003e4\u003c/b\u003e: 2612.\u003c/p\u003e \u003cp\u003eYuan, X., J. Zhang, D. Li, Y. Mao, F. Mo, W. Du and X. Ma (2017). \"Prognostic significance of tumor-associated macrophages in ovarian cancer: A meta-analysis.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eGynecol Oncol\u003c/span\u003e \u003cb\u003e147\u003c/b\u003e(1): 181\u0026ndash;187.\u003c/p\u003e \u003cp\u003eZhang, M., Y. He, X. Sun, Q. Li, W. Wang, A. Zhao and W. Di (2014). \"A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients.\" \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eJ Ovarian Res\u003c/span\u003e \u003cb\u003e7\u003c/b\u003e: 19.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgments\u003c/h2\u003e\n\u003cp\u003eThis work was supported by Central Government Guidance Fund for Local Science and Technology Development（202407AB110013）.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eTao Yu and Guangji Yang wrote the main manuscript text,conducted the research design, data processing, and visualization.Dongyan Ren,Yantao Li and Chong Yue conducted the research design and manuscript proofreading.Qin Yang and Jie Zhang designe and conduct experiments, data analysis, prepared and revised the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll data in the article can be obtained from GEO datasets (https://www.ncbi.nlm.nih.gov/geo/), UCSC Xena platform (https://toil.xenahubs.net), Genotype-Tissue Expression (GTEx) database (https://www.gtexportal.org/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e\u0026quot;Integrated genomic analyses of ovarian carcinoma.\u0026quot; Nature \u003cstrong\u003e474\u003c/strong\u003e(7353): 609-615.\u003c/li\u003e\n \u003cli\u003e\u0026quot;The Genotype-Tissue Expression (GTEx) project.\u0026quot; Nat Genet \u003cstrong\u003e45\u003c/strong\u003e(6): 580-585.\u003c/li\u003e\n \u003cli\u003eAntony, J., T. Z. Tan, Z. Kelly, J. Low, M. Choolani, C. Recchi, H. Gabra, J. P. Thiery and R. Y. Huang (2016). \u0026quot;The GAS6-AXL signaling network is a mesenchymal (Mes) molecular subtype-specific therapeutic target for ovarian cancer.\u0026quot; Sci Signal \u003cstrong\u003e9\u003c/strong\u003e(448): ra97.\u003c/li\u003e\n \u003cli\u003eBalkwill, F. R., M. Capasso and T. Hagemann (2012). \u0026quot;The tumor microenvironment at a glance.\u0026quot; J Cell Sci \u003cstrong\u003e125\u003c/strong\u003e(Pt 23): 5591-5596.\u003c/li\u003e\n \u003cli\u003eBarrett, T., S. E. Wilhite, P. Ledoux, C. Evangelista, I. F. Kim, M. Tomashevsky, K. A. Marshall, K. H. Phillippy, P. M. Sherman, M. Holko, A. Yefanov, H. Lee, N. Zhang, C. L. Robertson, N. Serova, S. Davis and A. Soboleva (2013). \u0026quot;NCBI GEO: archive for functional genomics data sets--update.\u0026quot; Nucleic Acids Res \u003cstrong\u003e41\u003c/strong\u003e(Database issue): D991-995.\u003c/li\u003e\n \u003cli\u003eCai, D. L. and L. P. Jin (2017). \u0026quot;Immune Cell Population in Ovarian Tumor Microenvironment.\u0026quot; J Cancer \u003cstrong\u003e8\u003c/strong\u003e(15): 2915-2923.\u003c/li\u003e\n \u003cli\u003eCao, Y., R. Langer and N. Ferrara (2023). \u0026quot;Targeting angiogenesis in oncology, ophthalmology and beyond.\u0026quot; Nat Rev Drug Discov \u003cstrong\u003e22\u003c/strong\u003e(6): 476-495.\u003c/li\u003e\n \u003cli\u003eChen, B., M. S. Khodadoust, C. L. Liu, A. M. Newman and A. A. Alizadeh (2018). \u0026quot;Profiling Tumor Infiltrating Immune Cells with CIBERSORT.\u0026quot; Methods Mol Biol \u003cstrong\u003e1711\u003c/strong\u003e: 243-259.\u003c/li\u003e\n \u003cli\u003eColvin, E. K. (2014). \u0026quot;Tumor-associated macrophages contribute to tumor progression in ovarian cancer.\u0026quot; Front Oncol \u003cstrong\u003e4\u003c/strong\u003e: 137.\u003c/li\u003e\n \u003cli\u003eDeng, X., H. Terunuma, A. Terunuma, T. Takane and M. Nieda (2014). \u0026quot;Ex vivo-expanded natural killer cells kill cancer cells more effectively than ex vivo-expanded \u0026gamma;\u0026delta; T cells or \u0026alpha;\u0026beta; T cells.\u0026quot; Int Immunopharmacol \u003cstrong\u003e22\u003c/strong\u003e(2): 486-491.\u003c/li\u003e\n \u003cli\u003eF\u0026auml;rkkil\u0026auml;, A., D. C. Gulhan, J. Casado, C. A. Jacobson, H. Nguyen, B. Kochupurakkal, Z. Maliga, C. Yapp, Y. A. Chen, D. Schapiro, Y. Zhou, J. R. Graham, B. J. Dezube, P. Munster, S. Santagata, E. Garcia, S. Rodig, A. Lako, D. Chowdhury, G. I. Shapiro, U. A. Matulonis, P. J. Park, S. Hautaniemi, P. K. Sorger, E. M. Swisher, A. D. D\u0026apos;Andrea and P. A. Konstantinopoulos (2020). \u0026quot;Immunogenomic profiling determines responses to combined PARP and PD-1 inhibition in ovarian cancer.\u0026quot; Nat Commun \u003cstrong\u003e11\u003c/strong\u003e(1): 1459.\u003c/li\u003e\n \u003cli\u003eFarmer, H., N. McCabe, C. J. Lord, A. N. Tutt, D. A. Johnson, T. B. Richardson, M. Santarosa, K. J. Dillon, I. Hickson, C. Knights, N. M. Martin, S. P. Jackson, G. C. Smith and A. Ashworth (2005). \u0026quot;Targeting the DNA repair defect in BRCA mutant cells as a therapeutic strategy.\u0026quot; Nature \u003cstrong\u003e434\u003c/strong\u003e(7035): 917-921.\u003c/li\u003e\n \u003cli\u003eFriedman, J., T. Hastie, R. Tibshirani, B. Narasimhan, K. Tay, N. Simon and J. Qian (2021). \u0026quot;Package \u0026lsquo;glmnet\u0026rsquo;.\u0026quot; CRAN R Repositary \u003cstrong\u003e595\u003c/strong\u003e.\u003c/li\u003e\n \u003cli\u003eGeeleher, P., N. Cox and R. S. Huang (2014). \u0026quot;pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels.\u0026quot; PLoS One \u003cstrong\u003e9\u003c/strong\u003e(9): e107468.\u003c/li\u003e\n \u003cli\u003eGeistlinger, L., S. Oh, M. Ramos, L. Schiffer, R. S. LaRue, C. M. Henzler, S. A. Munro, C. Daughters, A. C. Nelson, B. J. Winterhoff, Z. Chang, S. Talukdar, M. Shetty, S. A. Mullany, M. Morgan, G. Parmigiani, M. J. Birrer, L. X. Qin, M. Riester, T. K. Starr and L. Waldron (2020). \u0026quot;Multiomic Analysis of Subtype Evolution and Heterogeneity in High-Grade Serous Ovarian Carcinoma.\u0026quot; Cancer Res \u003cstrong\u003e80\u003c/strong\u003e(20): 4335-4345.\u003c/li\u003e\n \u003cli\u003eGoldman, M., B. Craft, M. Hastie, K. Repečka, F. McDade, A. Kamath, A. Banerjee, Y. Luo, D. Rogers, A. N. Brooks, J. Zhu and D. Haussler (2019). \u0026quot;The UCSC Xena platform for public and private cancer genomics data visualization and interpretation.\u0026quot; bioRxiv: 326470.\u003c/li\u003e\n \u003cli\u003eGuo, J., X. Han, J. Li, Z. Li, J. Yi, Y. Gao, X. Zhao and W. Yue (2023). \u0026quot;Single-cell transcriptomics in ovarian cancer identify a metastasis-associated cell cluster overexpressed RAB13.\u0026quot; J Transl Med \u003cstrong\u003e21\u003c/strong\u003e(1): 254.\u003c/li\u003e\n \u003cli\u003eHe, Y. F., M. Y. Zhang, X. Wu, X. J. Sun, T. Xu, Q. Z. He and W. Di (2013). \u0026quot;High MUC2 expression in ovarian cancer is inversely associated with the M1/M2 ratio of tumor-associated macrophages and patient survival time.\u0026quot; PLoS One \u003cstrong\u003e8\u003c/strong\u003e(12): e79769.\u003c/li\u003e\n \u003cli\u003eHornburg, M., M. Desbois, S. Lu, Y. Guan, A. A. Lo, S. Kaufman, A. Elrod, A. Lotstein, T. M. DesRochers, J. L. Munoz-Rodriguez, X. Wang, J. Giltnane, O. Mayba, S. J. Turley, R. Bourgon, A. Daemen and Y. Wang (2021). \u0026quot;Single-cell dissection of cellular components and interactions shaping the tumor immune phenotypes in ovarian cancer.\u0026quot; Cancer Cell \u003cstrong\u003e39\u003c/strong\u003e(7): 928-944.e926.\u003c/li\u003e\n \u003cli\u003eIzar, B., I. Tirosh, E. H. Stover, I. Wakiro, M. S. Cuoco, I. Alter, C. Rodman, R. Leeson, M. J. Su, P. Shah, M. Iwanicki, S. R. Walker, A. Kanodia, J. C. Melms, S. Mei, J. R. Lin, C. B. M. Porter, M. Slyper, J. Waldman, L. Jerby-Arnon, O. Ashenberg, T. J. Brinker, C. Mills, M. Rogava, S. Vigneau, P. K. Sorger, L. A. Garraway, P. A. Konstantinopoulos, J. F. Liu, U. Matulonis, B. E. Johnson, O. Rozenblatt-Rosen, A. Rotem and A. Regev (2020). \u0026quot;A single-cell landscape of high-grade serous ovarian cancer.\u0026quot; Nat Med \u003cstrong\u003e26\u003c/strong\u003e(8): 1271-1279.\u003c/li\u003e\n \u003cli\u003eJiang, P., S. Gu, D. Pan, J. Fu, A. Sahu, X. Hu, Z. Li, N. Traugh, X. Bu, B. Li, J. Liu, G. J. Freeman, M. A. Brown, K. W. Wucherpfennig and X. S. Liu (2018). \u0026quot;Signatures of T cell dysfunction and exclusion predict cancer immunotherapy response.\u0026quot; Nat Med \u003cstrong\u003e24\u003c/strong\u003e(10): 1550-1558.\u003c/li\u003e\n \u003cli\u003eJin, Y., S. Bian, H. Wang, J. Mo, H. Fei, L. Li, T. Chen and H. Jiang (2022). \u0026quot;CRMP2 derived from cancer associated fibroblasts facilitates progression of ovarian cancer via HIF-1\u0026alpha;-glycolysis signaling pathway.\u0026quot; Cell Death Dis \u003cstrong\u003e13\u003c/strong\u003e(8): 675.\u003c/li\u003e\n \u003cli\u003eJ\u0026ouml;nsson, J. M., I. Johansson, M. Dominguez-Valentin, S. Kimbung, M. J\u0026ouml;nsson, J. H. Bonde, P. Kannisto, A. M\u0026aring;sb\u0026auml;ck, S. Malander, M. Nilbert and I. Hedenfalk (2014). \u0026quot;Molecular subtyping of serous ovarian tumors reveals multiple connections to intrinsic breast cancer subtypes.\u0026quot; PLoS One \u003cstrong\u003e9\u003c/strong\u003e(9): e107643.\u003c/li\u003e\n \u003cli\u003eK\u0026ouml;bel, M. and E. Y. Kang (2022). \u0026quot;The Evolution of Ovarian Carcinoma Subclassification.\u0026quot; Cancers (Basel) \u003cstrong\u003e14\u003c/strong\u003e(2).\u003c/li\u003e\n \u003cli\u003eKommoss, S., B. Winterhoff, A. L. Oberg, G. E. Konecny, C. Wang, S. M. Riska, J. B. Fan, M. J. Maurer, C. April, V. Shridhar, F. Kommoss, A. du Bois, F. Hilpert, S. Mahner, K. Baumann, W. Schroeder, A. Burges, U. Canzler, J. Chien, A. C. Embleton, M. Parmar, R. Kaplan, T. Perren, L. C. Hartmann, E. L. Goode, S. C. Dowdy and J. Pfisterer (2017). \u0026quot;Bevacizumab May Differentially Improve Ovarian Cancer Outcome in Patients with Proliferative and Mesenchymal Molecular Subtypes.\u0026quot; Clin Cancer Res \u003cstrong\u003e23\u003c/strong\u003e(14): 3794-3801.\u003c/li\u003e\n \u003cli\u003eKonstantinopoulos, P. A. and S. A. Cannistra (2021). \u0026quot;Immune Checkpoint Inhibitors in Ovarian Cancer: Can We Bridge the Gap Between IMagynation and Reality?\u0026quot; J Clin Oncol \u003cstrong\u003e39\u003c/strong\u003e(17): 1833-1838.\u003c/li\u003e\n \u003cli\u003eLan, C., X. Huang, S. Lin, H. Huang, Q. Cai, T. Wan, J. Lu and J. Liu (2013). \u0026quot;Expression of M2-polarized macrophages is associated with poor prognosis for advanced epithelial ovarian cancer.\u0026quot; Technol Cancer Res Treat \u003cstrong\u003e12\u003c/strong\u003e(3): 259-267.\u003c/li\u003e\n \u003cli\u003eLe Page, C., A. Marineau, P. K. Bonza, K. Rahimi, L. Cyr, I. Labouba, J. Madore, N. Delvoye, A. M. Mes-Masson, D. M. Provencher and J. F. Cailhier (2012). \u0026quot;BTN3A2 expression in epithelial ovarian cancer is associated with higher tumor infiltrating T cells and a better prognosis.\u0026quot; PLoS One \u003cstrong\u003e7\u003c/strong\u003e(6): e38541.\u003c/li\u003e\n \u003cli\u003eMaiorano, B. A., M. F. P. Maiorano, D. Lorusso and E. Maiello (2021). \u0026quot;Ovarian Cancer in the Era of Immune Checkpoint Inhibitors: State of the Art and Future Perspectives.\u0026quot; Cancers (Basel) \u003cstrong\u003e13\u003c/strong\u003e(17).\u003c/li\u003e\n \u003cli\u003eNo, J. H., J. M. Moon, K. Kim and Y. B. Kim (2013). \u0026quot;Prognostic significance of serum soluble CD163 level in patients with epithelial ovarian cancer.\u0026quot; Gynecol Obstet Invest \u003cstrong\u003e75\u003c/strong\u003e(4): 263-267.\u003c/li\u003e\n \u003cli\u003ePeyraud, F. and A. Italiano (2020). \u0026quot;Combined PARP Inhibition and Immune Checkpoint Therapy in Solid Tumors.\u0026quot; Cancers (Basel) \u003cstrong\u003e12\u003c/strong\u003e(6).\u003c/li\u003e\n \u003cli\u003ePotter, S. S. (2018). \u0026quot;Single-cell RNA sequencing for the study of development, physiology and disease.\u0026quot; Nat Rev Nephrol \u003cstrong\u003e14\u003c/strong\u003e(8): 479-492.\u003c/li\u003e\n \u003cli\u003eReinartz, S., T. Schumann, F. Finkernagel, A. Wortmann, J. M. Jansen, W. Meissner, M. Krause, A. M. Schw\u0026ouml;rer, U. Wagner, S. M\u0026uuml;ller-Br\u0026uuml;sselbach and R. M\u0026uuml;ller (2014). \u0026quot;Mixed-polarization phenotype of ascites-associated macrophages in human ovarian carcinoma: correlation of CD163 expression, cytokine levels and early relapse.\u0026quot; Int J Cancer \u003cstrong\u003e134\u003c/strong\u003e(1): 32-42.\u003c/li\u003e\n \u003cli\u003eRitchie, M. E., B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi and G. K. Smyth (2015). \u0026quot;limma powers differential expression analyses for RNA-sequencing and microarray studies.\u0026quot; Nucleic Acids Res \u003cstrong\u003e43\u003c/strong\u003e(7): e47.\u003c/li\u003e\n \u003cli\u003eSantoiemma, P. P. and D. J. Powell, Jr. (2015). \u0026quot;Tumor infiltrating lymphocytes in ovarian cancer.\u0026quot; Cancer Biol Ther \u003cstrong\u003e16\u003c/strong\u003e(6): 807-820.\u003c/li\u003e\n \u003cli\u003eSchem, C., D. O. Bauerschlag, I. Meinhold-Heerlein, D. Fischer, M. Friedrich and N. Maass (2007). \u0026quot;[Benign and borderline tumors of the ovary].\u0026quot; Ther Umsch \u003cstrong\u003e64\u003c/strong\u003e(7): 369-374.\u003c/li\u003e\n \u003cli\u003eShahraki, H. R., A. Salehi and N. Zare (2015). \u0026quot;Survival Prognostic Factors of Male Breast Cancer in Southern Iran: a LASSO-Cox Regression Approach.\u0026quot; Asian Pac J Cancer Prev \u003cstrong\u003e16\u003c/strong\u003e(15): 6773-6777.\u003c/li\u003e\n \u003cli\u003eShannon, P., A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, N. Amin, B. Schwikowski and T. Ideker (2003). \u0026quot;Cytoscape: a software environment for integrated models of biomolecular interaction networks.\u0026quot; Genome Res \u003cstrong\u003e13\u003c/strong\u003e(11): 2498-2504.\u003c/li\u003e\n \u003cli\u003eSzklarczyk, D., A. Franceschini, S. Wyder, K. Forslund, D. Heller, J. Huerta-Cepas, M. Simonovic, A. Roth, A. Santos, K. P. Tsafou, M. Kuhn, P. Bork, L. J. Jensen and C. von Mering (2015). \u0026quot;STRING v10: protein-protein interaction networks, integrated over the tree of life.\u0026quot; Nucleic Acids Res \u003cstrong\u003e43\u003c/strong\u003e(Database issue): D447-452.\u003c/li\u003e\n \u003cli\u003eTacconi, C., F. Ungaro, C. Correale, V. Arena, L. Massimino, M. Detmar, A. Spinelli, M. Carvello, M. Mazzone, A. I. Oliveira, F. Rubbino, V. Garlatti, S. Span\u0026ograve;, E. Lugli, F. S. Colombo, A. Malesci, L. Peyrin-Biroulet, S. Vetrano, S. Danese and S. D\u0026apos;Alessio (2019). \u0026quot;Activation of the VEGFC/VEGFR3 Pathway Induces Tumor Immune Escape in Colorectal Cancer.\u0026quot; Cancer Res \u003cstrong\u003e79\u003c/strong\u003e(16): 4196-4210.\u003c/li\u003e\n \u003cli\u003eTan, T. Z., Q. H. Miow, R. Y. Huang, M. K. Wong, J. Ye, J. A. Lau, M. C. Wu, L. H. Bin Abdul Hadi, R. Soong, M. Choolani, B. Davidson, J. M. Nesland, L. Z. Wang, N. Matsumura, M. Mandai, I. Konishi, B. C. Goh, J. T. Chang, J. P. Thiery and S. Mori (2013). \u0026quot;Functional genomics identifies five distinct molecular subtypes with clinical relevance and pathways for growth control in epithelial ovarian cancer.\u0026quot; EMBO Mol Med \u003cstrong\u003e5\u003c/strong\u003e(7): 1051-1066.\u003c/li\u003e\n \u003cli\u003eTherneau, T. M. and T. Lumley (2015). \u0026quot;Package \u0026lsquo;survival\u0026rsquo;.\u0026quot; R Top Doc \u003cstrong\u003e128\u003c/strong\u003e(10): 28-33.\u003c/li\u003e\n \u003cli\u003eTorre, L. A., B. Trabert, C. E. DeSantis, K. D. Miller, G. Samimi, C. D. Runowicz, M. M. Gaudet, A. Jemal and R. L. Siegel (2018). \u0026quot;Ovarian cancer statistics, 2018.\u0026quot; CA Cancer J Clin \u003cstrong\u003e68\u003c/strong\u003e(4): 284-296.\u003c/li\u003e\n \u003cli\u003eVeneziani, A. C., E. Gonzalez-Ochoa, H. Alqaisi, A. Madariaga, G. Bhat, M. Rouzbahman, S. Sneha and A. M. Oza (2023). \u0026quot;Heterogeneity and treatment landscape of ovarian carcinoma.\u0026quot; Nat Rev Clin Oncol \u003cstrong\u003e20\u003c/strong\u003e(12): 820-842.\u003c/li\u003e\n \u003cli\u003eWang, Z., J. Zhang, F. Dai, B. Li and Y. Cheng (2023). \u0026quot;Integrated analysis of single-cell RNA-seq and bulk RNA-seq unveils heterogeneity and establishes a novel signature for prognosis and tumor immune microenvironment in ovarian cancer.\u0026quot; J Ovarian Res \u003cstrong\u003e16\u003c/strong\u003e(1): 12.\u003c/li\u003e\n \u003cli\u003eWestin, S. N., K. Moore, H. S. Chon, J. Y. Lee, J. Thomes Pepin, M. Sundborg, A. Shai, J. de la Garza, S. Nishio, M. A. Gold, K. Wang, K. McIntyre, T. D. Tillmanns, S. V. Blank, J. H. Liu, M. McCollum, F. Contreras Mejia, T. Nishikawa, K. Pennington, Z. Novak, A. C. De Melo, J. Sehouli, D. Klasa-Mazurkiewicz, C. Papadimitriou, M. Gil-Martin, B. Brasiuniene, C. Donnelly, P. M. Del Rosario, X. Liu and E. Van Nieuwenhuysen (2023). \u0026quot;Durvalumab Plus Carboplatin/Paclitaxel Followed by Maintenance Durvalumab With or Without Olaparib as First-Line Treatment for Advanced Endometrial Cancer: The Phase III DUO-E Trial.\u0026quot; J Clin Oncol: Jco2302132.\u003c/li\u003e\n \u003cli\u003eYan, C., K. Li, F. Meng, L. Chen, J. Zhao, Z. Zhang, D. Xu, J. Sun and M. Zhou (2023). \u0026quot;Integrated immunogenomic analysis of single-cell and bulk tissue transcriptome profiling unravels a macrophage activation paradigm associated with immunologically and clinically distinct behaviors in ovarian cancer.\u0026quot; J Adv Res \u003cstrong\u003e44\u003c/strong\u003e: 149-160.\u003c/li\u003e\n \u003cli\u003eYang, W., J. Soares, P. Greninger, E. J. Edelman, H. Lightfoot, S. Forbes, N. Bindal, D. Beare, J. A. Smith, I. R. Thompson, S. Ramaswamy, P. A. Futreal, D. A. Haber, M. R. Stratton, C. Benes, U. McDermott and M. J. Garnett (2013). \u0026quot;Genomics of Drug Sensitivity in Cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells.\u0026quot; Nucleic Acids Res \u003cstrong\u003e41\u003c/strong\u003e(Database issue): D955-961.\u003c/li\u003e\n \u003cli\u003eYi, M., D. V. Nissley, F. McCormick and R. M. Stephens (2020). \u0026quot;ssGSEA score-based Ras dependency indexes derived from gene expression data reveal potential Ras addiction mechanisms with possible clinical implications.\u0026quot; Sci Rep \u003cstrong\u003e10\u003c/strong\u003e(1): 10258.\u003c/li\u003e\n \u003cli\u003eYin, Z., T. Ma, B. Huang, L. Lin, Y. Zhou, J. Yan, Y. Zou and S. Chen (2019). \u0026quot;Macrophage-derived exosomal microRNA-501-3p promotes progression of pancreatic ductal adenocarcinoma through the TGFBR3-mediated TGF-\u0026beta; signaling pathway.\u0026quot; J Exp Clin Cancer Res \u003cstrong\u003e38\u003c/strong\u003e(1): 310.\u003c/li\u003e\n \u003cli\u003eYoshihara, K., M. Shahmoradgoli, E. Mart\u0026iacute;nez, R. Vegesna, H. Kim, W. Torres-Garcia, V. Trevi\u0026ntilde;o, H. Shen, P. W. Laird, D. A. Levine, S. L. Carter, G. Getz, K. Stemke-Hale, G. B. Mills and R. G. Verhaak (2013). \u0026quot;Inferring tumour purity and stromal and immune cell admixture from expression data.\u0026quot; Nat Commun \u003cstrong\u003e4\u003c/strong\u003e: 2612.\u003c/li\u003e\n \u003cli\u003eYuan, X., J. Zhang, D. Li, Y. Mao, F. Mo, W. Du and X. Ma (2017). \u0026quot;Prognostic significance of tumor-associated macrophages in ovarian cancer: A meta-analysis.\u0026quot; Gynecol Oncol \u003cstrong\u003e147\u003c/strong\u003e(1): 181-187.\u003c/li\u003e\n \u003cli\u003eZhang, M., Y. He, X. Sun, Q. Li, W. Wang, A. Zhao and W. Di (2014). \u0026quot;A high M1/M2 ratio of tumor-associated macrophages is associated with extended survival in ovarian cancer patients.\u0026quot; J Ovarian Res \u003cstrong\u003e7\u003c/strong\u003e: 19.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ovarian cancer, macrophage-related genes, prognostic model, immune landscape, scRNA-seq","lastPublishedDoi":"10.21203/rs.3.rs-5259146/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5259146/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe immunosuppressive tumor microenvironment (TME) poses challenges to effective immunotherapy in ovarian cancer (OC). Tumor-associated macrophages play a crucial role in the TME and are closely linked to OC prognosis. While many studies have used bulk RNA-seq for prognostic biomarker exploration, its limitation is in discerning gene expression differences between individual cells.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eThis study integrates single-cell RNA sequencing (scRNA-seq) with bulk RNA-seq data to accurately investigate the relationship between macrophage molecular characteristics and prognosis. RNA-seq data and prognostic information were obtained from the GEO and TCGA datasets. Using the R package \"Seurat,\" this study annotates cell types and visualizes single-cell data. Differentially expressed genes in macrophages are identified using the FindAllMarkers function and further analyzed with the limma package. The resulting genes, combined with survival data, undergo single-factor COX regression and LASSO regression to construct a prognosis model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIntegrating scRNA-seq and bulk RNA-seq data, this study established a prognosis model comprising 19 macrophage-related genes. Validation confirms the risk score as an independent prognostic factor for overall survival (OS) in patients with OC. The area under the ROC curve (AUC) for 1-year, 3-year, and 5-year survival periods were 0.71, 0.68, and 0.73, respectively. Subsequent immune analysis revealed distinct TMEs between high- and low-risk groups. The high-risk group shows distinct TMEs. The high-risk group exhibited higher immune infiltration, increased M1 macrophage infiltration, elevated M2 macrophage infiltration, and reduced sensitivity to immunotherapy but enhanced sensitivity to anti-angiogenic drugs compared to that in the low-risk group.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe study analyzed differentially expressed genes related to macrophages in OC and constructed a prognosis model. Moreover, it revealed the risk score as a prognostic factor in OC, with implications for patient sensitivity to immunotherapy.\u003c/p\u003e","manuscriptTitle":"Integration of scRNA-Seq and bulk RNA-Seq to Establish a Macrophage-related Prognostic Model in Ovarian Cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-26 10:27:36","doi":"10.21203/rs.3.rs-5259146/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"721c9d3c-adec-4c9f-8af4-9f7c4248d651","owner":[],"postedDate":"November 26th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":39875231,"name":"Biological sciences/Cancer"},{"id":39875232,"name":"Biological sciences/Immunology"}],"tags":[],"updatedAt":"2024-11-26T10:27:39+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-26 10:27:36","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5259146","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5259146","identity":"rs-5259146","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}