Identification of fibroblast subpopulations facilitating to chemotherapy resistance in metastasis of ovarian cancer

Identification of fibroblast subpopulations facilitating to chemotherapy resistance in metastasis of 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 Research Article Identification of fibroblast subpopulations facilitating to chemotherapy resistance in metastasis of ovarian cancer Xiaolin Zhong, Weihong Lu, Qiufeng Su, Jinhua Wei, Ting Li, Fan Chao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8999242/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Ovarian cancer, currently the deadliest gynecological malignancy, remains a challenging therapeutic issue due to chemotherapy resistance leading to tumor relapse. Chemoresistance in ovarian cancer is associated with various factors, and the role and mechanisms of cancer-associated fibroblasts (CAFs) in this process remain poorly understood. In this study, we analyzed single-cell RNA sequencing data from pre- and post-chemotherapy ovarian cancer samples to examine and compare the differences in tumor stromal cell composition. We found that the proportion of tumor-associated fibroblasts in the post-chemotherapy tumor microenvironment was significantly upregulated. Further analysis of fibroblast subpopulations revealed that chemotherapy altered the subtyping and transcriptional expression patterns of ovarian cancer fibroblasts. Through the analysis of cell composition, transcriptional expression, tissue metastasis, and functional state differences of fibroblast subpopulations before and after chemotherapy, we identified three fibroblast subpopulations with the potential to promote chemoresistance and metastasis in ovarian cancer: CCL3 + fibroblasts, MGP + fibroblasts, and MMP1 + fibroblasts. The CCL3 + and MGP + fibroblast subpopulations were upregulated in metastatic omental tissues after chemotherapy, while the MMP1 + fibroblast subpopulation was upregulated in metastatic peritoneal tissues. Differential gene expression analyses showed that CCL3 + and MGP + fibroblasts upregulated several cytokines promoting cell growth and angiogenesis, while MMP1 + fibroblasts upregulated cytokines mediating immune suppression. Moreover, secretory factors such as CCL3, MGP, MMP1, PTX3, GSN, and MMP3, which promote tumor growth and metastasis, were highly expressed in these upregulated post-chemotherapy tumor-associated fibroblasts. Pathway enrichment analysis revealed that PI3K-Akt, MAPK, TNF, NFκB, and IL-17 signaling pathways were significantly activated in these macrophage subpopulations. Finally, immunofluorescence staining of omental and peritoneal metastatic tissues from ovarian cancer patients confirmed the post-chemotherapy emergence of the CCL3 + , MGP + , and MMP1 + fibroblast subpopulations. These findings contribute to understanding the role and mechanisms of fibroblasts in ovarian cancer chemoresistance and may aid in developing therapeutic strategies targeting fibroblasts for overcoming chemoresistance in ovarian cancer. cancer-associated fibroblast ovarian cancer tumor metastasis single-cell transcriptome chemotherapy resistance Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Ovarian cancer is the most lethal gynecological malignancy, predominantly represented by epithelial ovarian cancer ( 1 ). Due to the lack of effective early screening methods, approximately 80% of patients are diagnosed at advanced stages, often accompanied by tumor spread and metastasis, leading to a significant decline in the five-year survival rate of patients with advanced ovarian cancer. Advanced ovarian cancer tends to widely disseminate in the abdominal cavity, invading the omentum, peritoneum, and intestinal tissues, with metastasis being the primary cause of mortality in ovarian cancer patients. Currently, surgery and chemotherapy remain the first-line and critical treatments for ovarian cancer patients. Platinum-based chemotherapy agents (cisplatin/carboplatin) are the mainstay drugs for ovarian cancer treatment, but repeated relapse after chemotherapy ultimately results in platinum drug resistance, creating a situation where no further drug options are available. Understanding the mechanisms of chemotherapy resistance and developing drugs for the treatment of chemotherapy-resistant ovarian cancer are urgent tasks ( 2 , 3 ). Interactions between tumors and stromal cells greatly promote tumor initiation, progression, and therapeutic resistance. Cancer-associated fibroblasts (CAFs), typically located within or near the tumor, are key components of the tumor microenvironment ( 4 ). Existing studies have shown that CAFs participate in various tumor biological processes, including inflammation, tumor growth and metastasis, immune suppression, and particularly chemotherapy resistance ( 5 ). In ovarian cancer chemotherapy resistance research, specific fibroblast subtypes have been found to promote chemotherapy resistance by maintaining cancer stem cell characteristics, such as CD10 + GPR77 + fibroblasts or CD44 + fibroblasts ( 6 , 7 ). Fibroblasts have also been shown to suppress ovarian cancer tumor cell apoptosis through exosomal microRNA-21 (miR21) or direct action on tumor cell XIAP, regulating the PI3K/AKT signaling pathway, thus promoting resistance to chemotherapy ( 8 , 9 ). Moreover, fibroblasts can interfere with ovarian cancer treatment by creating an immune-suppressive tumor microenvironment. Fibroblast-derived INHBA has been reported to induce PD-L1 autocrine expression via SMAD2-dependent signaling, promoting Treg cell differentiation ( 10 ). The development of single-cell RNA sequencing has advanced the study of ovarian cancer chemoresistance mechanisms and has been used to analyze fibroblast heterogeneity. Studies have shown that fibroblasts characterized by TGF-β signaling are associated with immune suppression and promote primary chemoresistance. TGF-β secreted by fibroblasts can upregulate PD-1 and CTLA-4 protein levels in Treg cells, thereby increasing the abundance of fibroblasts ( 11 ). Recently, C3 + fibroblasts and CD34 + fibroblasts were found to be upregulated in post-chemotherapy metastatic tissues, correlating with poor clinical prognosis. Chemotherapy not only kills tumor cells but also remodels the tumor microenvironment, with C3 + fibroblasts and CD34 + fibroblasts possibly contributing to chemoresistance by promoting immune exclusion and tumor growth ( 12 ). However, there are currently no studies using single-cell analysis to explore the heterogeneity and tissue specificity of fibroblasts in ovarian cancer metastatic tissues before and after chemotherapy. Therefore, in our study, we analyzed the microenvironmental changes and cellular composition in different metastatic tissues of ovarian cancer before and after chemotherapy, with a focus on examining the subpopulation changes and gene expression differences of tumor-infiltrating fibroblasts in peritoneal and omental metastases. MATERIALS AND METHODS Collection of Single-Cell RNA Sequencing Dataset The human ovarian cancer dataset used in this study includes GSE165897 from GEO (Gene Expression Omnibus). The sequencing data for GSE165897 were generated from treatment-naïve and post-neoadjuvant chemotherapy pairs from 11 homogeneously treated high-grade serous ovarian cancer (HGSOC) patients. The sc-RNAseq data for GSE165897 were obtained using the 10x Genomics platform. These data include malignant epithelial cells (tumor cells), stromal cells, and immune cells. In this study, we specifically extracted and analyzed the stromal cells ( 13 ). Analysis of single-cell RNA sequencing data The raw single-cell data used in this study were obtained from published research, and the analysis was performed using R software with the Seurat package. The full analysis workflow includes: setting up the Seurat object, standard pre-processing, normalizing the data, identifying highly variable features, scaling the data, performing linear dimensional reduction, determining the dimensionality of the dataset, clustering cells, performing non-linear dimensional reduction (UMAP/tSNE), identifying differentially expressed features (cluster biomarkers), assigning cell type identities to clusters, and data visualization ( 14 , 15 ). Cell Type Identification Based on the reported marker genes for each cell type and the SingleR package for cell-type identification ( 16 ), we identified the following cell types and their representative marker genes: epithelial cell (TACSTD2, KRT7), mesothelial cell (LOX, TGFBI), fibroblast (GAL, SFRP2), endothelial cell (RGS5, ANGPT2). Visualization of Single-Cell RNA sequencing data The data visualization in this study was also conducted using Seurat, including UMAP scatter plots, violin plots, dot plots, volcano plots, and heatmaps ( 15 ). UMAP scatter plots represent the spatial distribution of each cell according to different gene expression patterns, with similar expression patterns clustering together. Violin plots show the expression levels of specific genes in certain cell types. Dot plots use color to indicate the average expression of genes in specific cell types, with darker colors indicating higher expression levels. The size of the dots indicates the proportion of positive expression for a gene in a particular cell type. Volcano plots display differential gene expression between cell types, highlighting upregulated and downregulated genes. Heatmaps compare the average expression levels of certain genes across different cell types. Gene Enrichment Analysis Differentially expressed genes in clusters were analyzed using Seurat. The list of upregulated or downregulated genes from the clusters was imported into the DAVID online tool ( https://david.ncifcrf.gov/summary.jsp ), a comprehensive annotation and visualization tool to understand the biological significance of large gene lists ( 17 ). Gene enrichment analysis was performed for biological processes using Gene Ontology (GO). Immunofluorescence Staining Fresh tissue samples were embedded in OCT compound (4583, Solarbio) and frozen at -80°C. Cryosections were prepared using a cryostat and incubated with a blocking/permeabilization solution for 30 minutes at room temperature, followed by overnight incubation at 4°C with primary antibodies, including anti-α-SMA antibody (1:200, 67735-1-Ig, Proteintech), anti-CCL3 antibody (1:200, 16748-1-AP, Proteintech), anti-MGP antibody (1:200, 10734-1-AP, Proteintech), and anti-MMP1 antibody (1:200, 10371-2-AP, Proteintech). After primary incubation, sections were incubated with secondary antibodies at room temperature for 2 hours, including Donkey Anti-Mouse IgG (H + L) conjugated Alexa Fluor 488 (1:1000, 715-545-150, Jackson ImmunoResearch) and Donkey Anti-Rabbit IgG (H + L) conjugated Alexa Fluor 647 (1:1000, 711-605-152, Jackson ImmunoResearch), followed by incubation with DAPI (1:50,000, 10236276001, Roche) for 15 minutes. Images were captured using a confocal microscope. RESULTS Single-Cell Transcriptomic Analysis of Stromal Cells in Ovarian Cancer Before and After Chemotherapy To investigate the role of stromal cells in the chemoresistance mechanism of ovarian cancer, we collected paired single-cell RNA sequencing data of ovarian cancer metastatic tissues before and after chemotherapy from 11 patients, available in the GEO database (NCBI). The data include metastatic tissues from both peritoneal and omental sites. In this study, we primarily focused on the role and differences of stromal cell populations in ovarian cancer chemoresistance. Thus, based on the cell annotations provided by the original study, we isolated 8045 stromal cells and re-analyzed them using standard single-cell analysis methods through the Seurat package based on R language. Using the SingleR package for cell-type identification, we identified four cell populations: epithelial cells (TACSTD2, KRT7), mesothelial cells (LOX, TGFBI), fibroblasts (GAL, SFRP2), and endothelial cells (RGS5, ANGPT2). UMAP plots were generated to show the spatial clustering of these four stromal cell populations ( Fig. 1 A ) , revealing significant expression pattern among them. Differential gene expression analysis was performed to identify unique gene expression profiles for each cell type. Violin plots were used to show the expression of two representative genes for each cell type ( Fig. 1 B ) . We then analyzed the stromal cell composition across different patient samples, and the results indicated that while there were variations in cell composition and proportion between patients, the proportions of epithelial cells and fibroblasts showed relatively minimal differences across all patients ( Fig. 1 C ) . The most significant differences in stromal cell populations between peritoneal and omental metastatic tissues were observed in epithelial cells and fibroblasts ( Fig. 1 D ) . To explore the impact of chemotherapy on the stromal cells in the tumor microenvironment, we performed clustering analysis on stromal cells from pre- and post-chemotherapy groups ( Fig. 1 E ) . The results indicated transcriptional expression changes in all four stromal cell populations after chemotherapy. Cell proportion analysis showed that fibroblasts had the most significant increase in proportion after chemotherapy, making them the most differentially altered cell type among stromal cells ( Fig. 1 F ) . These results suggest that cancer-associated fibroblasts may be significantly influenced by chemotherapy and may play an important role in chemotherapy resistance mechanisms. Single-Cell Transcriptomic Analysis of Cancer-Associated Fibroblast Subpopulations To understand the heterogeneity of fibroblasts in ovarian cancer peritoneal and omental metastases before and after chemotherapy and to clarify their role in ovarian cancer chemoresistance, we extracted and re-analyzed the fibroblasts from the single-cell data. After performing the standard single-cell analysis pipeline with Seurat, we identified 13 fibroblast subpopulations and visualized their spatial clustering distribution using UMAP ( Fig. 2 A ) . The results showed significant transcriptional expression differences between fibroblast subpopulations. We analyzed the expression levels of fibroblast marker genes in each subpopulation ( Fig. 2 B ) , and although expression levels varied, all subpopulations expressed representative fibroblast genes. We further analyzed the ratio of mitochondrial and ribosomal genes in these subpopulations to compare their cell state differences ( Fig. 2 C ) . The results showed that the mitochondrial gene proportion was low in all subpopulations, indicating that these subpopulations had low apoptotic activity. Subpopulation 10 had a higher ribosomal gene proportion, suggesting it might be in a period of active protein synthesis. To analyze the transcriptomic differences between the subpopulations, we used heatmap to display the top upregulated genes for each of the 13 subpopulations ( Fig. 2 D ) . The representative upregulated genes specific to these subpopulations included IL11, APOD, CFD, RARRES2, RSAD2, IGFBP2, MMP1, THY1, IL33, CDKN2A, KRT8, CXCL6, and CD74. Differentiation Analysis of Fibroblast Subpopulations in Ovarian Cancer Metastatic Tissues Before and After Chemotherapy To investigate post-chemotherapy changes in fibroblast subpopulations, we compared pre- and post-chemotherapy groups. UMAP plots demonstrated significant differences in the subpopulation types and proportions between the two groups ( Fig. 3 A ) . Fibroblast subpopulations increased significantly after chemotherapy. To visualize these differences in more detail, we used bar charts to show the cell proportions of each subpopulation in both groups ( Fig. 3 B ) . The results indicated that cluster 3 and 5 were primarily present in pre-chemotherapy tissues and showed a marked decrease in cell number and proportion after chemotherapy. In contrast, cluster 0, 1, 2, 4, and 7 exhibited significantly increased proportions after chemotherapy, with their rise likely linked to the chemotherapy effects. We also identified chemotherapy-specific fibroblast subpopulations, although their cell numbers were small. Cluster 11 was present only before chemotherapy, while cluster 6, 8, 9, and 10 appeared only after chemotherapy. Cluster 12 had too few cells to show specific group differences. Additionally, we analyzed the specificity of these subpopulations in peritoneal and omental metastatic tissues before and after chemotherapy ( Fig. 3 C and D) . UMAP plots and bar charts showed distinct subpopulation distribution patterns between pre- and post-chemotherapy in both tissues. Red and blue boxes in the bar charts highlighted the clusters that were specifically upregulated in post-chemotherapy omental and peritoneal tissues. Our analysis indicated that cluster 1, 2, and 7 were specifically increased in post-chemotherapy omental metastatic tissues, while cluster 0, 4, 6, 8, and 9 were specifically increased in post-chemotherapy peritoneal metastatic tissues. Interestingly, the omental and peritoneal chemotherapy-specific fibroblast clusters did not overlap, with cluster 3 and 5, which were present in pre-chemotherapy tissues, only appearing in the omental and peritoneal tissues before chemotherapy, respectively. This suggests that there were substantial differences in fibroblast subpopulations between ovarian cancer metastases in the omentum and peritoneum even before chemotherapy. These differences in fibroblast differentiation after chemotherapy likely reflect the distinct tumor microenvironments in the two metastasis sites. Therefore, analyzing chemotherapy-specific fibroblast subpopulations in particular metastatic tissues is of significant clinical relevance and may provide new directions for drug development and therapeutic strategies targeting specific subpopulations to overcome chemoresistance in ovarian cancer. Transcriptomic Functional Analysis of Chemotherapy-Related Fibroblast Subpopulations To explore the transcriptomic features of these fibroblast subpopulations that were upregulated or downregulated after chemotherapy and to investigate their impact on tumor progression, chemoresistance, and immune suppression, we analyzed the differences in functional gene sets related to cell states, functions, and differentiation. First, we selected a series of gene functional sets from the GSEA database, including fibroblast functional states, tumor chemoresistance, tumor metastasis and growth promotion, ovarian cancer poor prognosis factors, tumor angiogenesis, and immune suppression-related gene functional sets. Using the Gene Set Variation Analysis (GSVA) package based on R language, we visualized the enrichment of these gene sets in each subpopulation ( 18 ). The heatmap displayed the relative levels of gene function set enrichment for each cancer-associated fibroblast subpopulation ( Fig. 4 A ) . Cluster 3 and 5 are shown in black font, representing the subpopulations with decreased proportions after chemotherapy. Cluster 1, 2, and 7 are in red font, indicating subpopulations that specifically increased in omental metastatic tissues after chemotherapy. Cluster 0, 4, 6, 8, and 9 are in blue font, representing subpopulations that increased in peritoneal metastatic tissues after chemotherapy. The gradient from red to blue in the heatmap indicates the enrichment scores from high to low, reflecting the activation levels of corresponding cell functions. The results revealed that cluster 3 and 5 were particularly enriched in fibroblast proliferation, migration, and metabolic activity, while cluster 1 and 2 were significantly enriched in angiogenesis and growth factor secretion. Additionally, some peritoneal chemotherapy-related clusters exhibited higher enrichment scores in negative regulation of T-cell mediated immunity. To further explore the mechanisms by which these subpopulations promote tumor progression after chemotherapy, we analyzed the differential expression of secretory cytokines related to cell proliferation, angiogenesis, and immune suppression. Heatmaps displayed the gene expression differences in these cytokines ( Figs. 4 B, C, and D) . The results showed that omental chemotherapy-related cluster 1 and 2 were markedly upregulated in almost all cytokines related to cell proliferation and angiogenesis, while peritoneal chemotherapy-related clusters, particularly cluster 6, exhibited significant upregulation in immune suppression-related cytokines. The upregulation of these tumor-promoting cytokines suggests that the fibroblast subpopulations significantly increased after chemotherapy may promote ovarian cancer chemoresistance through the expression and secretion of these factors. Gene Differential Analysis of Chemoresistance-Related Fibroblast Subpopulations Previous studies have largely identified the types of cancer-associated fibroblast subpopulations related to chemoresistance in ovarian cancer peritoneal and omental metastatic tissues at the transcriptomic level. To further clarify the cellular gene characteristics and gene expression differences of cluster 1, 2, and 6, we analyzed the specific differential genes of these clusters relative to other clusters. We used volcano plots to visualize the upregulated and downregulated genes for each subpopulation, with upregulated genes marked by red circles and downregulated genes marked by blue circles. The Y-axis represents the P-value, with higher values indicating smaller P-values, while the absolute value of the X-axis indicates the fold change ( Fig. 5 A, B, and C) . Cluster 1 mainly upregulated inflammation-related factors such as CCL3, CCL2, CXCL2, and CCL19. Among these, CCL3 not only has chemotactic functions but can also promote the infiltration of immune cells with immunosuppressive functions, such as Treg cells, tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells (MDSCs), which contribute to creating an immunosuppressive microenvironment that promotes tumor growth and metastasis ( 19 ). Additionally, CCL3 has been reported to directly promote the proliferation of certain tumor cells ( 20 ). PTX3 (Pentraxin 3), a secreted protein involved in innate immune responses and inflammation, was also significantly upregulated in cluster 1. Interestingly, PTX3 has been reported to promote ovarian cancer progression and metastasis ( 21 ). Cluster 2 significantly upregulated the expression of MGP (Matrix Gla Protein), which was also upregulated in cluster 1. Initially discovered to be related to cartilage tissue matrix, MGP is a secretory protein that inhibits bone formation. Later studies revealed that MGP promotes the progression and metastasis of cancers such as colorectal cancer, breast cancer, and ovarian cancer ( 22 – 24 ). Meanwhile, GSN (Gelsolin), a calcium-regulated protein that modulates actin dynamics, was also significantly upregulated in cluster 2, and its extracellular form has been reported to promote ovarian cancer progression and chemoresistance by suppressing immune responses ( 25 ). Cluster 6 primarily upregulated matrix metalloproteinases (MMPs) such as MMP1, MMP3, and MMP10. MMPs, enzymes capable of remodeling the extracellular matrix, have been found to be closely associated with tumor growth and metastasis ( 26 ). Specifically, MMP1 has been shown to evade immune cell-mediated cytotoxicity by upregulating PD-L1 expression in head and neck tumors ( 27 ), and excessive MMP1 expression in esophageal squamous cell carcinoma promotes tumor proliferation and metastasis via PI3K/AKT signaling ( 28 ). MMP3 has been reported to enhance the invasiveness and growth of pancreatic cancer cells by upregulating the expression of Rac1b, a tumorigenic splice isoform of Rac1 ( 29 ). We then performed KEGG pathway analysis for the three clusters to explore the activation status of signal transduction pathways ( Fig. 5 D, E, and F) . The results showed that pathways related to cell adhesion, cytoskeleton regulation, apoptosis, and splicing were significantly enriched in cluster 1 (CCL3 + ), while PI3K-Akt, TNF, and IL-17 pathways were more activated in cluster 2 (MGP + ) and 6 (MMP1 + ). MAPK and NOD-like receptor pathways were also highly enriched in cluster 6. These findings suggest that the differential activation of signaling pathways across these clusters provides valuable insights for further exploring the molecular mechanisms behind chemoresistance in these fibroblast clusters. Identification of Chemoresistance-Related Fibroblast Subpopulations in Human Ovarian Cancer Samples Before and After Chemotherapy To verify the previously identified subpopulation-specific genes, we conducted a validation analysis using the single-cell transcriptomic data. In the omental metastatic tissue of ovarian cancer, CCL3 and MGP were significantly upregulated after chemotherapy, and they were expressed in different cell populations in the UMAP plot with distinct distribution patterns, primarily in cluster 1 and cluster 2 ( Fig. 6 A ) . In the peritoneal metastatic tissue, MMP1 was significantly upregulated after chemotherapy, primarily expressed in cluster 6 ( Fig. 6 B ) . To confirm the transcriptomic findings at the protein and cellular levels, we performed immunofluorescence staining on human ovarian cancer metastatic tissue samples before and after chemotherapy. We used α-SMA, a well-established fibroblast marker, to indicate all fibroblasts in the tumor microenvironment ( 30 , 31 ). DAPI (blue) and α-SMA (green) double-positive cells indicated fibroblasts. We combined CCL3, MGP, and MMP1, which were specifically upregulated in cluster 1, 2, and 6, respectively, with α-SMA to identify these three chemoresistance-related fibroblast subpopulations. DAPI (blue), α-SMA (green), and specific proteins (red) identified triple-positive cells representing the chemoresistance-associated fibroblasts. The results showed that in human ovarian cancer omental metastatic tissue, after chemotherapy, CCL3 and MGP protein signals were significantly upregulated. Double staining with α-SMA showed a significant presence of CCL3 + and MGP + fibroblasts in the omental metastatic tissue after chemotherapy, whereas they were nearly absent in the pre-chemotherapy tissue ( Fig. 6 C and D) . In human ovarian cancer peritoneal metastatic tissue, after chemotherapy, MMP1 protein signals were also significantly upregulated, and double staining with α-SMA revealed a large number of MMP1 + fibroblasts in the peritoneal metastatic tissue after chemotherapy, which were absent in the pre-chemotherapy tissue ( Fig. 6 E ) . DISCUSSION In this study, we analyzed the single-cell transcriptomic data of ovarian cancer omental and peritoneal metastatic tissues before and after chemotherapy, comparing stromal cell composition differences in these tissues. We focused on the cancer-associated fibroblasts (CAFs) and explored the differences in fibroblast subpopulations and transcriptional patterns before and after chemotherapy in both omental and peritoneal metastatic tissues. Our findings revealed significant differences between the fibroblast subpopulations in pre- and post-chemotherapy tissues, with some subpopulations being dominant pre-chemotherapy and nearly disappearing after chemotherapy. Chemotherapy also led to a higher diversity of fibroblast subpopulations, with more subpopulations emerging after treatment. Additionally, we observed that different metastatic tissues exhibited specific fibroblast subpopulations that were chemotherapy-dependent and tissue-specific. Specifically, we found that CCL3 + fibroblasts and MGP + fibroblasts were present in the omental metastatic tissue after chemotherapy, where they secreted a range of tumor-promoting and angiogenesis-related cytokines to sustain tumor growth in the omental metastases. On the other hand, MMP1 + fibroblasts were associated with peritoneal metastasis, where they appeared in large numbers after chemotherapy and contributed to the formation of an immune-excluding tumor microenvironment by secreting immune-suppressive factors, thereby participating in chemotherapy resistance in ovarian cancer peritoneal metastasis. Fibroblasts, as common stromal cells in both normal tissues and tumor microenvironments, have been widely reported to promote tumor progression and metastasis in a variety of cancers ( 32 ). Cancer-associated fibroblasts are highly heterogeneous, and numerous studies have shown a close relationship between CAF subpopulations and tumor progression or poor clinical prognosis. Two widely reported phenotypes of CAFs are inflammatory cancer-associated fibroblasts (iCAFs) and myofibroblastic CAFs (myCAFs), while other phenotypes such as antigen-presenting CAFs (apCAFs), vascular CAFs (vCAFs), and matrix CAFs (mCAFs) have also been identified in cancer studies ( 5 ). In our study, cluster 1 was found to upregulate several inflammation- and immune-related secretory factors such as CCL3, CCL2, CXCL2, CCL19, and C7, similar to the function of iCAFs. However, iCAFs are typically characterized by the upregulation of inflammatory markers like CXCL12, C3, and IL6 ( 33 ), which were not the major upregulated genes in cluster 1. This suggests that within the same CAF functional phenotype, there may be different polarization states with diverse cellular subtypes. Furthermore, some upregulated factors in the fibroblast subpopulations identified in this study align with previous reports. For example, CFD was significantly upregulated in cluster 1 and 2. CFD + fibroblasts have been reported to be more immunologically active, suggesting that ovarian cancer patients with CFD + fibroblasts in their omental metastasis after chemotherapy might benefit from immunosuppressive therapies ( 34 ). Cluster 2 also upregulated SFRP2, which has been shown to enhance the tumor-promoting potential of fibroblasts by regulating SOX2 ( 35 ). MMP1, upregulated in cluster 6, has been reported to promote tumor resistance to immune therapies by suppressing immune responses ( 36 ). In addition, our study identified several interesting factors upregulated in these tumor-promoting cancer-associated fibroblast subpopulations that have rarely been reported in other CAF studies. MGP (Matrix Gla protein), primarily upregulated in cluster 2, and moderately expressed in cluster 1, has been widely reported to promote tumor progression in various cancers, including its role in enhancing the metastasis of malignant glioma cells ( 37 ), promoting ovarian cancer stemness through the Hedgehog effector GLI1 ( 24 ), and promoting liver metastasis of colorectal cancer by facilitating CD8 + T cell exhaustion ( 22 ). Although previous studies mainly identified MGP expression in tumor cells, macrophages, and mast cells, our research reveals a novel source of MGP expression and identifies a CAF subpopulation that specifically expresses MGP. Additionally, GAL (Galanin peptides), highly expressed in cluster 6, has been recognized as a small neuropeptide involved in regulating various physiological functions, including smooth muscle contraction, growth hormone and insulin release, and adrenal secretion ( 38 ). Galanin has been found to play controversial roles in cancer, where it has tumor-suppressive functions in gastrointestinal tumors, yet it promotes tumor proliferation and growth in adenomas ( 39 ). Notably, a correlation study found that galanin expression increases the risk of endometrial cancer ( 40 ). Recent studies have suggested that galanin secreted by tumor cells can promote the progression of head and neck cancers by inhibiting immune responses ( 41 ). Our study shows that after chemotherapy, fibroblast subpopulations upregulate galanin expression, along with other immune-suppressive factors, and exhibit an immune-suppressive functional phenotype at the transcriptomic level. This aligns with previous reports and indicates that GAL and galanin in CAFs warrant further investigation. Our findings on fibroblast subpopulations provide important insights and directions for future research. While our study offers valuable transcriptomic-level insights into CAF heterogeneity and its role in chemotherapy resistance in ovarian cancer, it has certain limitations. Despite the identification of new tumor-promoting fibroblast subpopulations potentially associated with chemoresistance, the precise molecular mechanisms and their relevance to metastatic tissues still need further investigation and validation. Future studies should include larger datasets and validation at the protein level to confirm the functional roles of these subpopulations and their molecular mechanisms. In conclusion, our study provides a comprehensive analysis of the role and heterogeneity of fibroblasts in ovarian cancer chemoresistance. We identified differential subpopulations of fibroblasts in omental and peritoneal metastases after chemotherapy and found several fibroblast subpopulations that contribute to chemoresistance. By analyzing the differential gene expression of these subpopulations, we predicted their potential functional molecular mechanisms. These findings contribute to understanding ovarian cancer chemoresistance and offer insights into developing chemotherapy combination strategies or post-chemotherapy treatment options for patients with different types of metastatic ovarian cancer. Declarations CONFLICT OF INTEREST The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. Ethical Approval This study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of Zhongshan Hospital Fudan University (Xiamen Branch) (Approval No. B2025-106 ). Informed Consent Written informed consent was obtained from all individual participants included in the study for the use of their pathological specimens and clinical data. FUNDING This study was supported by the National Natural Science Foundation of China (82203743), Fujian Provincial Natural Science Foundation (2025GGA105), and Xiamen Municipal Healthcare Directed Project (3502Z20254ZD1214). Authorship Certification All authors have read and agreed to the published version of the manuscript. Author Contribution Conceptualization: [Xiaolin Zhong], [ Hongyang Xiao]; Methodology: [Xiaolin Zhong], [ Ruiqing Tu], [ Weihong Lu]; Formal analysis and investigation: [Xiaolin Zhong], [ Weihong Lu],[ Fan Chao]; Data Curation: [Qiufeng Su], [Jinhua Wei], [Ting Li]; Writing-original draft preparation: [Xiaolin Zhong]; Writing-review and editing: [Xiaolin Zhong], [Hongyang Xiao], [Ruiqing Tu]; Funding acquisition: [Fan Chao]; Supervision: [Hongyang Xiao], [Ruiqing Tu], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript. Data Availability Publicly available datasets analyzed in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE165897, GSE165897, GEO, NCBI. References Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30. 10.3322/caac.21590 . Webb PM, Jordan SJ. Global epidemiology of epithelial ovarian cancer. Nat Rev Clin Oncol. 2024;21(5):389–400. 10.1038/s41571-024-00881-3 . Kuroki L, Guntupalli SR. Treatment of epithelial ovarian cancer. BMJ. 2020;371:m3773. 10.1136/bmj.m3773 . Desbois M, Wang Y. Cancer-associated fibroblasts: Key players in shaping the tumor immune microenvironment. Immunol Rev. 2021;302(1):241–58. 10.1111/imr.12982 . Biffi G, Tuveson DA. Diversity and Biology of Cancer-Associated Fibroblasts. Physiol Rev. 2021;101(1):147–76. 10.1152/physrev.00048.2019 . 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Int Immunopharmacol. 2024;131:111818. 10.1016/j.intimp.2024.111818 . Nieddu V, Melocchi V, Battistini C, Franciosa G, Lupia M, Stellato C, Bertalot G, Olsen JV, Colombo N, Bianchi F, Cavallaro U. Matrix Gla Protein drives stemness and tumor initiation in ovarian cancer. Cell Death Dis. 2023;14(3):220. 10.1038/s41419-023-05760-w . Onuma T, Asare-Werehene M, Yoshida Y, Tsang BK. Exosomal Plasma Gelsolin Is an Immunosuppressive Mediator in the Ovarian Tumor Microenvironment and a Determinant of Chemoresistance. Cells. 2022;11(20). 10.3390/cells11203305 . de Almeida LGN, Thode H, Eslambolchi Y, Chopra S, Young D, Gill S, Devel L, Dufour A. Matrix Metalloproteinases: From Molecular Mechanisms to Physiology, Pathophysiology, and Pharmacology. Pharmacol Rev. 2022;74(3):712–68. 10.1124/pharmrev.121.000349 . Fang Q, Chen X, Cao F, Xu P, Zhao Z, Lin R, Wu D, Deng W, Liu X. SPHK1 promotes HNSCC immune evasion by regulating the MMP1-PD-L1 axis. 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Kasashima H, Duran A, Martinez-Ordoñez A, Nakanishi Y, Kinoshita H, Linares JF, Reina-Campos M, Kudo Y, L'Hermitte A, Yashiro M, Ohira M, Bao F, Tauriello DVF, Batlle E, Diaz-Meco MT, Moscat J. Stromal SOX2 Upregulation Promotes Tumorigenesis through the Generation of a SFRP1/2-Expressing Cancer-Associated Fibroblast Population. Dev Cell. 2021;56(1):95–e11010. 10.1016/j.devcel.2020.10.014 . Koplev S, Teichmann SA. Universal and tissue-specific fibroblasts in chronic inflammation and cancer. Cancer Cell. 2024;42(10):1648–50. 10.1016/j.ccell.2024.08.022 . Mertsch S, Schurgers LJ, Weber K, Paulus W, Senner V. Matrix gla protein (MGP): an overexpressed and migration-promoting mesenchymal component in glioblastoma. BMC Cancer. 2009;9:302. 10.1186/1471-2407-9-302 . Schmidt WE, Kratzin H, Eckart K, Drevs D, Mundkowski G, Clemens A, Katsoulis S, Schäfer H, Gallwitz B, Creutzfeldt W. Isolation and primary structure of pituitary human galanin, a 30-residue nonamidated neuropeptide. Proc Natl Acad Sci U S A. 1991;88(24):11435–9. 10.1073/pnas.88.24.11435 . Rauch I, Kofler B. The galanin system in cancer. Exp Suppl. 2010;102:223–41. 10.1007/978-3-0346-0228-0_16 . Nergiz Avcıoğlu S, Yüksel H. Adipocyte related peptides - galanin and resistin in endometrioid type endometrium cancer. Ginekol Pol. 2022;93(12):941–7. 10.5603/GP.a2021.0229 . de Medeiros MC, Liu M, Banerjee R, Bellile E, D'Silva NJ, Rossa C Jr.. Galanin mediates tumor-induced immunosuppression in head and neck squamous cell carcinoma. Cell Oncol (Dordr). 2022;45(2):241–56. 10.1007/s13402-021-00631-y . Additional Declarations No competing interests reported. Supplementary Files B2025106EthicsCommitteeApproval.pdf Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 23 Apr, 2026 Reviewers agreed at journal 16 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 06 Apr, 2026 Reviewers agreed at journal 05 Apr, 2026 Reviewers invited by journal 31 Mar, 2026 Editor invited by journal 09 Mar, 2026 Editor assigned by journal 03 Mar, 2026 Submission checks completed at journal 03 Mar, 2026 First submitted to journal 28 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8999242","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":616511710,"identity":"31e4c84b-f259-42ae-baa8-c7f15b2ddf86","order_by":0,"name":"Xiaolin Zhong","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University (Xiamen Branch)","correspondingAuthor":false,"prefix":"","firstName":"Xiaolin","middleName":"","lastName":"Zhong","suffix":""},{"id":616511711,"identity":"f0242d01-2f11-457b-a97e-962c825586f1","order_by":1,"name":"Weihong Lu","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University (Xiamen Branch)","correspondingAuthor":false,"prefix":"","firstName":"Weihong","middleName":"","lastName":"Lu","suffix":""},{"id":616511712,"identity":"e1d2f1cf-b9fd-4a65-91da-ecde76102b98","order_by":2,"name":"Qiufeng Su","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University (Xiamen Branch)","correspondingAuthor":false,"prefix":"","firstName":"Qiufeng","middleName":"","lastName":"Su","suffix":""},{"id":616511713,"identity":"e7963103-efe6-46b9-bc84-819359cecb50","order_by":3,"name":"Jinhua Wei","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University (Xiamen Branch)","correspondingAuthor":false,"prefix":"","firstName":"Jinhua","middleName":"","lastName":"Wei","suffix":""},{"id":616511714,"identity":"7248a5a8-ad14-48b5-aad3-3dce253d6cbf","order_by":4,"name":"Ting Li","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University (Xiamen Branch)","correspondingAuthor":false,"prefix":"","firstName":"Ting","middleName":"","lastName":"Li","suffix":""},{"id":616511715,"identity":"3e648dda-ca94-49d3-9dae-d6e56ed5ab83","order_by":5,"name":"Fan Chao","email":"","orcid":"","institution":"Zhongshan Hospital Fudan University (Xiamen Branch)","correspondingAuthor":false,"prefix":"","firstName":"Fan","middleName":"","lastName":"Chao","suffix":""},{"id":616511716,"identity":"615bbab4-ad38-4470-8321-71cab8bec0db","order_by":6,"name":"Hongyang Xiao","email":"data:image/png;base64,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","orcid":"","institution":"Zhongshan Hospital","correspondingAuthor":true,"prefix":"","firstName":"Hongyang","middleName":"","lastName":"Xiao","suffix":""},{"id":616511717,"identity":"61110fc8-a4f2-4561-8c9b-51631121dcce","order_by":7,"name":"Ruiqin Tu","email":"","orcid":"","institution":"Zhongshan Hospital","correspondingAuthor":false,"prefix":"","firstName":"Ruiqin","middleName":"","lastName":"Tu","suffix":""}],"badges":[],"createdAt":"2026-03-01 04:54:40","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8999242/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8999242/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106405024,"identity":"6cf84033-23dd-415f-9134-7d8fb8bd6958","added_by":"auto","created_at":"2026-04-08 09:20:29","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1925610,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSc-RNAseq analysis of stroma cells in ovarian cancer with chemotherapy. (A)\u003c/strong\u003e UMAP plot displaying stroma cell clustering of ovarian cancer. \u003cstrong\u003e(B) \u003c/strong\u003eViolin plots showing representative marker genes of all cell types.\u003cstrong\u003e (C) \u003c/strong\u003eHistogram plot showing proportions of all cell types in patient samples. \u003cstrong\u003e(D)\u003c/strong\u003e Pie plots showing proportions of all cell types in omentum and peritoneum. \u003cstrong\u003e(E)\u003c/strong\u003eUMAP plots displaying stroma cell clustering of treatment-navie and chemotherapy groups. \u003cstrong\u003e(F)\u003c/strong\u003e Pie plots comparing proportions of all cell types in treatment-navie and chemotherapy groups.\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999242/v1/3b7ec365cdbf2feb6d2fbd7e.jpg"},{"id":106406630,"identity":"c008bfbd-ac86-4b4d-9463-a7d9c55b2f57","added_by":"auto","created_at":"2026-04-08 09:33:15","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":4358846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSc-RNAseq analysis of cancer-associated fibroblasts. (A)\u003c/strong\u003e UMAP plot displaying cell clustering of cancer-associated fibroblasts from ovarian cancer. \u003cstrong\u003e(B)\u003c/strong\u003e Violin plots showing expression of representative fibroblast genes in all clusters. \u003cstrong\u003e(C)\u003c/strong\u003eViolin plots showing proportions of mitochondrial and ribosomal genes of all fibroblast clusters. \u003cstrong\u003e(D)\u003c/strong\u003e Heatmap showing representative genes of all fibroblast clusters.\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999242/v1/9e6a0899b6c9db9750cbb307.jpg"},{"id":106405518,"identity":"75bcaf74-23c5-472a-8484-d8dc7101bd75","added_by":"auto","created_at":"2026-04-08 09:27:00","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1950779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAnalysis of fibroblast clusters before and after chemotherapy. (A)\u003c/strong\u003e UMAP plots displaying cell clustering of treatment-navie and chemotherapy groups of all fibroblast clusters. \u003cstrong\u003e(B)\u003c/strong\u003e Histogram plot showing proportions of all clusters in treatment-navie and chemotherapy groups. \u003cstrong\u003e(C)\u003c/strong\u003e UMAP plots displaying cell clustering of treatment-navie and chemotherapy groups of all fibroblast clusters in omentum, summarized in histogram plots. \u003cstrong\u003e(D) \u003c/strong\u003eUMAP plots displaying cell clustering of treatment-navie and chemotherapy groups of all fibroblast clusters in peritoneum, summarized in histogram plots.\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999242/v1/3ab3237c21e133592c787867.jpg"},{"id":106405586,"identity":"4fcd79c7-8982-4e97-861a-f0f1d3d06bc3","added_by":"auto","created_at":"2026-04-08 09:27:39","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2117459,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFunctional analysis of fibroblast clusters. (A)\u003c/strong\u003e Heatmap showing GSVA analysis scores of functional geneset in all fibroblast clusters.\u003cstrong\u003e (B-D)\u003c/strong\u003e Heatmap showing GSVA analysis scores of growth activity factors (B), agiogenesis factors (C) and immune suppression factors (D) in all fibroblast clusters.\u003c/p\u003e","description":"","filename":"Figure4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999242/v1/d726dfcb368749c3bc7dc9ef.jpg"},{"id":106405520,"identity":"a6c8cff5-d416-4923-ad7e-889b0d13f86b","added_by":"auto","created_at":"2026-04-08 09:27:00","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1083847,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDifferential analysis of gene expression of chemotherapy-associated clusters. (A-C)\u003c/strong\u003eVolcano plots showing differentially expressed genes of fibroblast clusters associated with chemotherapy, including cluster 1 (A), cluster 2 (B), cluster 6 (C). \u003cstrong\u003e(D-F)\u003c/strong\u003e Dot plots showing KEGG analysis of differentially expressed genes of fibroblast clusters associated with chemotherapy, including cluster 1 (D), cluster 2 (E), cluster 6 (F).\u003c/p\u003e","description":"","filename":"Figure5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999242/v1/533bf05c2b2b7a280bbd9ce7.jpg"},{"id":106405553,"identity":"940955db-bcf5-4e97-83b0-10bb2673bf6a","added_by":"auto","created_at":"2026-04-08 09:27:23","extension":"jpg","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":954431,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVerification of chemotherapy-associated clusters in human ovarian cancer tissue. (A)\u003c/strong\u003e UMAP plots respectively displaying expression of CCL3 and MGP in all fibroblast clusters of treatment-navie and chemotherapy groups of omentum.\u003cstrong\u003e (B)\u003c/strong\u003e UMAP plots displaying expression of MMP1 in all fibroblast clusters of treatment-navie and chemotherapy groups of peritoneum. \u003cstrong\u003e(C, D) \u003c/strong\u003eThe immunofluorescence shoots showing signaling of α-SMA and CCL3 (C), α-SMA and MGP (D) in human ovarian cancer omentum metastasis tissues of treatment-navie and chemotherapy. (E) The immunofluorescence shoots showing signaling of α-SMA and MMP1 in human ovarian cancer peritoneum metastasis tissues of treatment-navie and chemotherapy. Scale bars indicate 20 μm in immunofluorescence stain.\u003c/p\u003e","description":"","filename":"Figure6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-8999242/v1/bcb2540164647f5e1d5460ec.jpg"},{"id":106408761,"identity":"19b38167-f2a3-4d46-943a-ce10b20e4a2b","added_by":"auto","created_at":"2026-04-08 09:44:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":13517226,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8999242/v1/eaa3ed05-d03b-4ab9-a985-9137c45d4adf.pdf"},{"id":106405585,"identity":"f2ff0ed9-cf44-43ad-8328-58d9ddcdc6a5","added_by":"auto","created_at":"2026-04-08 09:27:38","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":8777882,"visible":true,"origin":"","legend":"","description":"","filename":"B2025106EthicsCommitteeApproval.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8999242/v1/fab9a5688f4d2f5cf70ad448.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Identification of fibroblast subpopulations facilitating to chemotherapy resistance in metastasis of ovarian cancer","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eOvarian cancer is the most lethal gynecological malignancy, predominantly represented by epithelial ovarian cancer (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Due to the lack of effective early screening methods, approximately 80% of patients are diagnosed at advanced stages, often accompanied by tumor spread and metastasis, leading to a significant decline in the five-year survival rate of patients with advanced ovarian cancer. Advanced ovarian cancer tends to widely disseminate in the abdominal cavity, invading the omentum, peritoneum, and intestinal tissues, with metastasis being the primary cause of mortality in ovarian cancer patients. Currently, surgery and chemotherapy remain the first-line and critical treatments for ovarian cancer patients. Platinum-based chemotherapy agents (cisplatin/carboplatin) are the mainstay drugs for ovarian cancer treatment, but repeated relapse after chemotherapy ultimately results in platinum drug resistance, creating a situation where no further drug options are available. Understanding the mechanisms of chemotherapy resistance and developing drugs for the treatment of chemotherapy-resistant ovarian cancer are urgent tasks (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInteractions between tumors and stromal cells greatly promote tumor initiation, progression, and therapeutic resistance. Cancer-associated fibroblasts (CAFs), typically located within or near the tumor, are key components of the tumor microenvironment (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). Existing studies have shown that CAFs participate in various tumor biological processes, including inflammation, tumor growth and metastasis, immune suppression, and particularly chemotherapy resistance (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In ovarian cancer chemotherapy resistance research, specific fibroblast subtypes have been found to promote chemotherapy resistance by maintaining cancer stem cell characteristics, such as CD10\u003csup\u003e+\u003c/sup\u003eGPR77\u003csup\u003e+\u003c/sup\u003e fibroblasts or CD44\u003csup\u003e+\u003c/sup\u003e fibroblasts (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Fibroblasts have also been shown to suppress ovarian cancer tumor cell apoptosis through exosomal microRNA-21 (miR21) or direct action on tumor cell XIAP, regulating the PI3K/AKT signaling pathway, thus promoting resistance to chemotherapy (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Moreover, fibroblasts can interfere with ovarian cancer treatment by creating an immune-suppressive tumor microenvironment. Fibroblast-derived INHBA has been reported to induce PD-L1 autocrine expression via SMAD2-dependent signaling, promoting Treg cell differentiation (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe development of single-cell RNA sequencing has advanced the study of ovarian cancer chemoresistance mechanisms and has been used to analyze fibroblast heterogeneity. Studies have shown that fibroblasts characterized by TGF-β signaling are associated with immune suppression and promote primary chemoresistance. TGF-β secreted by fibroblasts can upregulate PD-1 and CTLA-4 protein levels in Treg cells, thereby increasing the abundance of fibroblasts (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Recently, C3\u003csup\u003e+\u003c/sup\u003e fibroblasts and CD34\u003csup\u003e+\u003c/sup\u003e fibroblasts were found to be upregulated in post-chemotherapy metastatic tissues, correlating with poor clinical prognosis. Chemotherapy not only kills tumor cells but also remodels the tumor microenvironment, with C3\u003csup\u003e+\u003c/sup\u003e fibroblasts and CD34\u003csup\u003e+\u003c/sup\u003e fibroblasts possibly contributing to chemoresistance by promoting immune exclusion and tumor growth (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). However, there are currently no studies using single-cell analysis to explore the heterogeneity and tissue specificity of fibroblasts in ovarian cancer metastatic tissues before and after chemotherapy. Therefore, in our study, we analyzed the microenvironmental changes and cellular composition in different metastatic tissues of ovarian cancer before and after chemotherapy, with a focus on examining the subpopulation changes and gene expression differences of tumor-infiltrating fibroblasts in peritoneal and omental metastases.\u003c/p\u003e"},{"header":"MATERIALS AND METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of Single-Cell RNA Sequencing Dataset\u003c/h2\u003e \u003cp\u003eThe human ovarian cancer dataset used in this study includes GSE165897 from GEO (Gene Expression Omnibus). The sequencing data for GSE165897 were generated from treatment-na\u0026iuml;ve and post-neoadjuvant chemotherapy pairs from 11 homogeneously treated high-grade serous ovarian cancer (HGSOC) patients. The sc-RNAseq data for GSE165897 were obtained using the 10x Genomics platform. These data include malignant epithelial cells (tumor cells), stromal cells, and immune cells. In this study, we specifically extracted and analyzed the stromal cells (\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eAnalysis of single-cell RNA sequencing data\u003c/h3\u003e\n\u003cp\u003eThe raw single-cell data used in this study were obtained from published research, and the analysis was performed using R software with the Seurat package. The full analysis workflow includes: setting up the Seurat object, standard pre-processing, normalizing the data, identifying highly variable features, scaling the data, performing linear dimensional reduction, determining the dimensionality of the dataset, clustering cells, performing non-linear dimensional reduction (UMAP/tSNE), identifying differentially expressed features (cluster biomarkers), assigning cell type identities to clusters, and data visualization (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eCell Type Identification\u003c/h3\u003e\n\u003cp\u003eBased on the reported marker genes for each cell type and the SingleR package for cell-type identification (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), we identified the following cell types and their representative marker genes: epithelial cell (TACSTD2, KRT7), mesothelial cell (LOX, TGFBI), fibroblast (GAL, SFRP2), endothelial cell (RGS5, ANGPT2).\u003c/p\u003e\n\u003ch3\u003eVisualization of Single-Cell RNA sequencing data\u003c/h3\u003e\n\u003cp\u003eThe data visualization in this study was also conducted using Seurat, including UMAP scatter plots, violin plots, dot plots, volcano plots, and heatmaps (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). UMAP scatter plots represent the spatial distribution of each cell according to different gene expression patterns, with similar expression patterns clustering together. Violin plots show the expression levels of specific genes in certain cell types. Dot plots use color to indicate the average expression of genes in specific cell types, with darker colors indicating higher expression levels. The size of the dots indicates the proportion of positive expression for a gene in a particular cell type. Volcano plots display differential gene expression between cell types, highlighting upregulated and downregulated genes. Heatmaps compare the average expression levels of certain genes across different cell types.\u003c/p\u003e\n\u003ch3\u003eGene Enrichment Analysis\u003c/h3\u003e\n\u003cp\u003eDifferentially expressed genes in clusters were analyzed using Seurat. The list of upregulated or downregulated genes from the clusters was imported into the DAVID online tool (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://david.ncifcrf.gov/summary.jsp\u003c/span\u003e\u003cspan address=\"https://david.ncifcrf.gov/summary.jsp\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), a comprehensive annotation and visualization tool to understand the biological significance of large gene lists (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Gene enrichment analysis was performed for biological processes using Gene Ontology (GO).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eImmunofluorescence Staining\u003c/h2\u003e \u003cp\u003eFresh tissue samples were embedded in OCT compound (4583, Solarbio) and frozen at -80\u0026deg;C. Cryosections were prepared using a cryostat and incubated with a blocking/permeabilization solution for 30 minutes at room temperature, followed by overnight incubation at 4\u0026deg;C with primary antibodies, including anti-α-SMA antibody (1:200, 67735-1-Ig, Proteintech), anti-CCL3 antibody (1:200, 16748-1-AP, Proteintech), anti-MGP antibody (1:200, 10734-1-AP, Proteintech), and anti-MMP1 antibody (1:200, 10371-2-AP, Proteintech). After primary incubation, sections were incubated with secondary antibodies at room temperature for 2 hours, including Donkey Anti-Mouse IgG (H\u0026thinsp;+\u0026thinsp;L) conjugated Alexa Fluor 488 (1:1000, 715-545-150, Jackson ImmunoResearch) and Donkey Anti-Rabbit IgG (H\u0026thinsp;+\u0026thinsp;L) conjugated Alexa Fluor 647 (1:1000, 711-605-152, Jackson ImmunoResearch), followed by incubation with DAPI (1:50,000, 10236276001, Roche) for 15 minutes. Images were captured using a confocal microscope.\u003c/p\u003e \u003c/div\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSingle-Cell Transcriptomic Analysis of Stromal Cells in Ovarian Cancer Before and After Chemotherapy\u003c/h2\u003e \u003cp\u003eTo investigate the role of stromal cells in the chemoresistance mechanism of ovarian cancer, we collected paired single-cell RNA sequencing data of ovarian cancer metastatic tissues before and after chemotherapy from 11 patients, available in the GEO database (NCBI). The data include metastatic tissues from both peritoneal and omental sites. In this study, we primarily focused on the role and differences of stromal cell populations in ovarian cancer chemoresistance. Thus, based on the cell annotations provided by the original study, we isolated 8045 stromal cells and re-analyzed them using standard single-cell analysis methods through the Seurat package based on R language. Using the SingleR package for cell-type identification, we identified four cell populations: epithelial cells (TACSTD2, KRT7), mesothelial cells (LOX, TGFBI), fibroblasts (GAL, SFRP2), and endothelial cells (RGS5, ANGPT2). UMAP plots were generated to show the spatial clustering of these four stromal cell populations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e, revealing significant expression pattern among them. Differential gene expression analysis was performed to identify unique gene expression profiles for each cell type. Violin plots were used to show the expression of two representative genes for each cell type \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then analyzed the stromal cell composition across different patient samples, and the results indicated that while there were variations in cell composition and proportion between patients, the proportions of epithelial cells and fibroblasts showed relatively minimal differences across all patients \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. The most significant differences in stromal cell populations between peritoneal and omental metastatic tissues were observed in epithelial cells and fibroblasts \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. To explore the impact of chemotherapy on the stromal cells in the tumor microenvironment, we performed clustering analysis on stromal cells from pre- and post-chemotherapy groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e. The results indicated transcriptional expression changes in all four stromal cell populations after chemotherapy. Cell proportion analysis showed that fibroblasts had the most significant increase in proportion after chemotherapy, making them the most differentially altered cell type among stromal cells \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eF\u003cb\u003e)\u003c/b\u003e. These results suggest that cancer-associated fibroblasts may be significantly influenced by chemotherapy and may play an important role in chemotherapy resistance mechanisms.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eSingle-Cell Transcriptomic Analysis of Cancer-Associated Fibroblast Subpopulations\u003c/h2\u003e \u003cp\u003eTo understand the heterogeneity of fibroblasts in ovarian cancer peritoneal and omental metastases before and after chemotherapy and to clarify their role in ovarian cancer chemoresistance, we extracted and re-analyzed the fibroblasts from the single-cell data. After performing the standard single-cell analysis pipeline with Seurat, we identified 13 fibroblast subpopulations and visualized their spatial clustering distribution using UMAP \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. The results showed significant transcriptional expression differences between fibroblast subpopulations. We analyzed the expression levels of fibroblast marker genes in each subpopulation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e, and although expression levels varied, all subpopulations expressed representative fibroblast genes. We further analyzed the ratio of mitochondrial and ribosomal genes in these subpopulations to compare their cell state differences \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC\u003cb\u003e)\u003c/b\u003e. The results showed that the mitochondrial gene proportion was low in all subpopulations, indicating that these subpopulations had low apoptotic activity. Subpopulation 10 had a higher ribosomal gene proportion, suggesting it might be in a period of active protein synthesis. To analyze the transcriptomic differences between the subpopulations, we used heatmap to display the top upregulated genes for each of the 13 subpopulations \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD\u003cb\u003e)\u003c/b\u003e. The representative upregulated genes specific to these subpopulations included IL11, APOD, CFD, RARRES2, RSAD2, IGFBP2, MMP1, THY1, IL33, CDKN2A, KRT8, CXCL6, and CD74.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eDifferentiation Analysis of Fibroblast Subpopulations in Ovarian Cancer Metastatic Tissues Before and After Chemotherapy\u003c/h2\u003e \u003cp\u003eTo investigate post-chemotherapy changes in fibroblast subpopulations, we compared pre- and post-chemotherapy groups. UMAP plots demonstrated significant differences in the subpopulation types and proportions between the two groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Fibroblast subpopulations increased significantly after chemotherapy. To visualize these differences in more detail, we used bar charts to show the cell proportions of each subpopulation in both groups \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e. The results indicated that cluster 3 and 5 were primarily present in pre-chemotherapy tissues and showed a marked decrease in cell number and proportion after chemotherapy. In contrast, cluster 0, 1, 2, 4, and 7 exhibited significantly increased proportions after chemotherapy, with their rise likely linked to the chemotherapy effects. We also identified chemotherapy-specific fibroblast subpopulations, although their cell numbers were small. Cluster 11 was present only before chemotherapy, while cluster 6, 8, 9, and 10 appeared only after chemotherapy. Cluster 12 had too few cells to show specific group differences.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAdditionally, we analyzed the specificity of these subpopulations in peritoneal and omental metastatic tissues before and after chemotherapy \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC \u003cb\u003eand D)\u003c/b\u003e. UMAP plots and bar charts showed distinct subpopulation distribution patterns between pre- and post-chemotherapy in both tissues. Red and blue boxes in the bar charts highlighted the clusters that were specifically upregulated in post-chemotherapy omental and peritoneal tissues. Our analysis indicated that cluster 1, 2, and 7 were specifically increased in post-chemotherapy omental metastatic tissues, while cluster 0, 4, 6, 8, and 9 were specifically increased in post-chemotherapy peritoneal metastatic tissues. Interestingly, the omental and peritoneal chemotherapy-specific fibroblast clusters did not overlap, with cluster 3 and 5, which were present in pre-chemotherapy tissues, only appearing in the omental and peritoneal tissues before chemotherapy, respectively. This suggests that there were substantial differences in fibroblast subpopulations between ovarian cancer metastases in the omentum and peritoneum even before chemotherapy. These differences in fibroblast differentiation after chemotherapy likely reflect the distinct tumor microenvironments in the two metastasis sites. Therefore, analyzing chemotherapy-specific fibroblast subpopulations in particular metastatic tissues is of significant clinical relevance and may provide new directions for drug development and therapeutic strategies targeting specific subpopulations to overcome chemoresistance in ovarian cancer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eTranscriptomic Functional Analysis of Chemotherapy-Related Fibroblast Subpopulations\u003c/h2\u003e \u003cp\u003eTo explore the transcriptomic features of these fibroblast subpopulations that were upregulated or downregulated after chemotherapy and to investigate their impact on tumor progression, chemoresistance, and immune suppression, we analyzed the differences in functional gene sets related to cell states, functions, and differentiation. First, we selected a series of gene functional sets from the GSEA database, including fibroblast functional states, tumor chemoresistance, tumor metastasis and growth promotion, ovarian cancer poor prognosis factors, tumor angiogenesis, and immune suppression-related gene functional sets. Using the Gene Set Variation Analysis (GSVA) package based on R language, we visualized the enrichment of these gene sets in each subpopulation (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). The heatmap displayed the relative levels of gene function set enrichment for each cancer-associated fibroblast subpopulation \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. Cluster 3 and 5 are shown in black font, representing the subpopulations with decreased proportions after chemotherapy. Cluster 1, 2, and 7 are in red font, indicating subpopulations that specifically increased in omental metastatic tissues after chemotherapy. Cluster 0, 4, 6, 8, and 9 are in blue font, representing subpopulations that increased in peritoneal metastatic tissues after chemotherapy. The gradient from red to blue in the heatmap indicates the enrichment scores from high to low, reflecting the activation levels of corresponding cell functions. The results revealed that cluster 3 and 5 were particularly enriched in fibroblast proliferation, migration, and metabolic activity, while cluster 1 and 2 were significantly enriched in angiogenesis and growth factor secretion. Additionally, some peritoneal chemotherapy-related clusters exhibited higher enrichment scores in negative regulation of T-cell mediated immunity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo further explore the mechanisms by which these subpopulations promote tumor progression after chemotherapy, we analyzed the differential expression of secretory cytokines related to cell proliferation, angiogenesis, and immune suppression. Heatmaps displayed the gene expression differences in these cytokines \u003cb\u003e(\u003c/b\u003eFigs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB, C, \u003cb\u003eand D)\u003c/b\u003e. The results showed that omental chemotherapy-related cluster 1 and 2 were markedly upregulated in almost all cytokines related to cell proliferation and angiogenesis, while peritoneal chemotherapy-related clusters, particularly cluster 6, exhibited significant upregulation in immune suppression-related cytokines. The upregulation of these tumor-promoting cytokines suggests that the fibroblast subpopulations significantly increased after chemotherapy may promote ovarian cancer chemoresistance through the expression and secretion of these factors.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eGene Differential Analysis of Chemoresistance-Related Fibroblast Subpopulations\u003c/h2\u003e \u003cp\u003ePrevious studies have largely identified the types of cancer-associated fibroblast subpopulations related to chemoresistance in ovarian cancer peritoneal and omental metastatic tissues at the transcriptomic level. To further clarify the cellular gene characteristics and gene expression differences of cluster 1, 2, and 6, we analyzed the specific differential genes of these clusters relative to other clusters. We used volcano plots to visualize the upregulated and downregulated genes for each subpopulation, with upregulated genes marked by red circles and downregulated genes marked by blue circles. The Y-axis represents the P-value, with higher values indicating smaller P-values, while the absolute value of the X-axis indicates the fold change \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA, B, \u003cb\u003eand C)\u003c/b\u003e. Cluster 1 mainly upregulated inflammation-related factors such as CCL3, CCL2, CXCL2, and CCL19. Among these, CCL3 not only has chemotactic functions but can also promote the infiltration of immune cells with immunosuppressive functions, such as Treg cells, tumor-associated macrophages (TAMs), and myeloid-derived suppressor cells (MDSCs), which contribute to creating an immunosuppressive microenvironment that promotes tumor growth and metastasis (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Additionally, CCL3 has been reported to directly promote the proliferation of certain tumor cells (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). PTX3 (Pentraxin 3), a secreted protein involved in innate immune responses and inflammation, was also significantly upregulated in cluster 1. Interestingly, PTX3 has been reported to promote ovarian cancer progression and metastasis (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Cluster 2 significantly upregulated the expression of MGP (Matrix Gla Protein), which was also upregulated in cluster 1. Initially discovered to be related to cartilage tissue matrix, MGP is a secretory protein that inhibits bone formation. Later studies revealed that MGP promotes the progression and metastasis of cancers such as colorectal cancer, breast cancer, and ovarian cancer (\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). Meanwhile, GSN (Gelsolin), a calcium-regulated protein that modulates actin dynamics, was also significantly upregulated in cluster 2, and its extracellular form has been reported to promote ovarian cancer progression and chemoresistance by suppressing immune responses (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e). Cluster 6 primarily upregulated matrix metalloproteinases (MMPs) such as MMP1, MMP3, and MMP10. MMPs, enzymes capable of remodeling the extracellular matrix, have been found to be closely associated with tumor growth and metastasis (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). Specifically, MMP1 has been shown to evade immune cell-mediated cytotoxicity by upregulating PD-L1 expression in head and neck tumors (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e), and excessive MMP1 expression in esophageal squamous cell carcinoma promotes tumor proliferation and metastasis via PI3K/AKT signaling (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). MMP3 has been reported to enhance the invasiveness and growth of pancreatic cancer cells by upregulating the expression of Rac1b, a tumorigenic splice isoform of Rac1 (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eWe then performed KEGG pathway analysis for the three clusters to explore the activation status of signal transduction pathways \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eD, E, \u003cb\u003eand F)\u003c/b\u003e. The results showed that pathways related to cell adhesion, cytoskeleton regulation, apoptosis, and splicing were significantly enriched in cluster 1 (CCL3\u003csup\u003e+\u003c/sup\u003e), while PI3K-Akt, TNF, and IL-17 pathways were more activated in cluster 2 (MGP\u003csup\u003e+\u003c/sup\u003e) and 6 (MMP1\u003csup\u003e+\u003c/sup\u003e). MAPK and NOD-like receptor pathways were also highly enriched in cluster 6. These findings suggest that the differential activation of signaling pathways across these clusters provides valuable insights for further exploring the molecular mechanisms behind chemoresistance in these fibroblast clusters.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eIdentification of Chemoresistance-Related Fibroblast Subpopulations in Human Ovarian Cancer Samples Before and After Chemotherapy\u003c/h2\u003e \u003cp\u003eTo verify the previously identified subpopulation-specific genes, we conducted a validation analysis using the single-cell transcriptomic data. In the omental metastatic tissue of ovarian cancer, CCL3 and MGP were significantly upregulated after chemotherapy, and they were expressed in different cell populations in the UMAP plot with distinct distribution patterns, primarily in cluster 1 and cluster 2 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA\u003cb\u003e)\u003c/b\u003e. In the peritoneal metastatic tissue, MMP1 was significantly upregulated after chemotherapy, primarily expressed in cluster 6 \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo confirm the transcriptomic findings at the protein and cellular levels, we performed immunofluorescence staining on human ovarian cancer metastatic tissue samples before and after chemotherapy. We used α-SMA, a well-established fibroblast marker, to indicate all fibroblasts in the tumor microenvironment (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). DAPI (blue) and α-SMA (green) double-positive cells indicated fibroblasts. We combined CCL3, MGP, and MMP1, which were specifically upregulated in cluster 1, 2, and 6, respectively, with α-SMA to identify these three chemoresistance-related fibroblast subpopulations. DAPI (blue), α-SMA (green), and specific proteins (red) identified triple-positive cells representing the chemoresistance-associated fibroblasts. The results showed that in human ovarian cancer omental metastatic tissue, after chemotherapy, CCL3 and MGP protein signals were significantly upregulated. Double staining with α-SMA showed a significant presence of CCL3\u003csup\u003e+\u003c/sup\u003e and MGP\u003csup\u003e+\u003c/sup\u003e fibroblasts in the omental metastatic tissue after chemotherapy, whereas they were nearly absent in the pre-chemotherapy tissue \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC \u003cb\u003eand D)\u003c/b\u003e. In human ovarian cancer peritoneal metastatic tissue, after chemotherapy, MMP1 protein signals were also significantly upregulated, and double staining with α-SMA revealed a large number of MMP1\u003csup\u003e+\u003c/sup\u003e fibroblasts in the peritoneal metastatic tissue after chemotherapy, which were absent in the pre-chemotherapy tissue \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn this study, we analyzed the single-cell transcriptomic data of ovarian cancer omental and peritoneal metastatic tissues before and after chemotherapy, comparing stromal cell composition differences in these tissues. We focused on the cancer-associated fibroblasts (CAFs) and explored the differences in fibroblast subpopulations and transcriptional patterns before and after chemotherapy in both omental and peritoneal metastatic tissues. Our findings revealed significant differences between the fibroblast subpopulations in pre- and post-chemotherapy tissues, with some subpopulations being dominant pre-chemotherapy and nearly disappearing after chemotherapy. Chemotherapy also led to a higher diversity of fibroblast subpopulations, with more subpopulations emerging after treatment. Additionally, we observed that different metastatic tissues exhibited specific fibroblast subpopulations that were chemotherapy-dependent and tissue-specific. Specifically, we found that CCL3\u003csup\u003e+\u003c/sup\u003e fibroblasts and MGP\u003csup\u003e+\u003c/sup\u003e fibroblasts were present in the omental metastatic tissue after chemotherapy, where they secreted a range of tumor-promoting and angiogenesis-related cytokines to sustain tumor growth in the omental metastases. On the other hand, MMP1\u003csup\u003e+\u003c/sup\u003e fibroblasts were associated with peritoneal metastasis, where they appeared in large numbers after chemotherapy and contributed to the formation of an immune-excluding tumor microenvironment by secreting immune-suppressive factors, thereby participating in chemotherapy resistance in ovarian cancer peritoneal metastasis.\u003c/p\u003e \u003cp\u003eFibroblasts, as common stromal cells in both normal tissues and tumor microenvironments, have been widely reported to promote tumor progression and metastasis in a variety of cancers (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). Cancer-associated fibroblasts are highly heterogeneous, and numerous studies have shown a close relationship between CAF subpopulations and tumor progression or poor clinical prognosis. Two widely reported phenotypes of CAFs are inflammatory cancer-associated fibroblasts (iCAFs) and myofibroblastic CAFs (myCAFs), while other phenotypes such as antigen-presenting CAFs (apCAFs), vascular CAFs (vCAFs), and matrix CAFs (mCAFs) have also been identified in cancer studies (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In our study, cluster 1 was found to upregulate several inflammation- and immune-related secretory factors such as CCL3, CCL2, CXCL2, CCL19, and C7, similar to the function of iCAFs. However, iCAFs are typically characterized by the upregulation of inflammatory markers like CXCL12, C3, and IL6 (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e), which were not the major upregulated genes in cluster 1. This suggests that within the same CAF functional phenotype, there may be different polarization states with diverse cellular subtypes. Furthermore, some upregulated factors in the fibroblast subpopulations identified in this study align with previous reports. For example, CFD was significantly upregulated in cluster 1 and 2. CFD\u003csup\u003e+\u003c/sup\u003e fibroblasts have been reported to be more immunologically active, suggesting that ovarian cancer patients with CFD\u003csup\u003e+\u003c/sup\u003e fibroblasts in their omental metastasis after chemotherapy might benefit from immunosuppressive therapies (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e). Cluster 2 also upregulated SFRP2, which has been shown to enhance the tumor-promoting potential of fibroblasts by regulating SOX2 (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). MMP1, upregulated in cluster 6, has been reported to promote tumor resistance to immune therapies by suppressing immune responses (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn addition, our study identified several interesting factors upregulated in these tumor-promoting cancer-associated fibroblast subpopulations that have rarely been reported in other CAF studies. MGP (Matrix Gla protein), primarily upregulated in cluster 2, and moderately expressed in cluster 1, has been widely reported to promote tumor progression in various cancers, including its role in enhancing the metastasis of malignant glioma cells (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), promoting ovarian cancer stemness through the Hedgehog effector GLI1 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e), and promoting liver metastasis of colorectal cancer by facilitating CD8\u003csup\u003e+\u003c/sup\u003e T cell exhaustion (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Although previous studies mainly identified MGP expression in tumor cells, macrophages, and mast cells, our research reveals a novel source of MGP expression and identifies a CAF subpopulation that specifically expresses MGP. Additionally, GAL (Galanin peptides), highly expressed in cluster 6, has been recognized as a small neuropeptide involved in regulating various physiological functions, including smooth muscle contraction, growth hormone and insulin release, and adrenal secretion (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Galanin has been found to play controversial roles in cancer, where it has tumor-suppressive functions in gastrointestinal tumors, yet it promotes tumor proliferation and growth in adenomas (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). Notably, a correlation study found that galanin expression increases the risk of endometrial cancer (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Recent studies have suggested that galanin secreted by tumor cells can promote the progression of head and neck cancers by inhibiting immune responses (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Our study shows that after chemotherapy, fibroblast subpopulations upregulate galanin expression, along with other immune-suppressive factors, and exhibit an immune-suppressive functional phenotype at the transcriptomic level. This aligns with previous reports and indicates that GAL and galanin in CAFs warrant further investigation. Our findings on fibroblast subpopulations provide important insights and directions for future research.\u003c/p\u003e \u003cp\u003eWhile our study offers valuable transcriptomic-level insights into CAF heterogeneity and its role in chemotherapy resistance in ovarian cancer, it has certain limitations. Despite the identification of new tumor-promoting fibroblast subpopulations potentially associated with chemoresistance, the precise molecular mechanisms and their relevance to metastatic tissues still need further investigation and validation. Future studies should include larger datasets and validation at the protein level to confirm the functional roles of these subpopulations and their molecular mechanisms.\u003c/p\u003e \u003cp\u003eIn conclusion, our study provides a comprehensive analysis of the role and heterogeneity of fibroblasts in ovarian cancer chemoresistance. We identified differential subpopulations of fibroblasts in omental and peritoneal metastases after chemotherapy and found several fibroblast subpopulations that contribute to chemoresistance. By analyzing the differential gene expression of these subpopulations, we predicted their potential functional molecular mechanisms. These findings contribute to understanding ovarian cancer chemoresistance and offer insights into developing chemotherapy combination strategies or post-chemotherapy treatment options for patients with different types of metastatic ovarian cancer.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eCONFLICT OF INTEREST\u003c/h2\u003e\n\u003cp\u003eThe authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.\u003c/p\u003e\n\u003ch2\u003eEthical Approval\u003c/h2\u003e\n\u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki and approved by the Ethics Committee of \u003cstrong\u003eZhongshan Hospital Fudan University\u003c/strong\u003e (Xiamen Branch) (Approval No.\u003cstrong\u003eB2025-106\u003c/strong\u003e).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWritten informed consent was obtained from all individual participants included in the study for the use of their pathological specimens and clinical data.\u003c/p\u003e\n\u003ch2\u003eFUNDING\u003c/h2\u003e\n\u003cp\u003eThis study was supported by the National Natural Science Foundation of China (82203743), Fujian Provincial Natural Science Foundation (2025GGA105), and Xiamen Municipal Healthcare Directed Project (3502Z20254ZD1214).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthorship Certification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\n\u003cp\u003eConceptualization: [Xiaolin Zhong], [ Hongyang Xiao]; Methodology: [Xiaolin Zhong], [ Ruiqing Tu], [ Weihong Lu]; Formal analysis and investigation: [Xiaolin Zhong], [ Weihong Lu],[ Fan Chao]; Data Curation: [Qiufeng Su], [Jinhua Wei], [Ting Li]; Writing-original draft preparation: [Xiaolin Zhong]; Writing-review and editing: [Xiaolin Zhong], [Hongyang Xiao], [Ruiqing Tu]; Funding acquisition: [Fan Chao]; Supervision: [Hongyang Xiao], [Ruiqing Tu], and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eData Availability\u003c/h2\u003e\n\u003cp\u003ePublicly available datasets analyzed in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below:https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc= GSE165897, GSE165897, GEO, NCBI.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSiegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7\u0026ndash;30. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3322/caac.21590\u003c/span\u003e\u003cspan address=\"10.3322/caac.21590\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWebb PM, Jordan SJ. Global epidemiology of epithelial ovarian cancer. 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Cell Oncol (Dordr). 2022;45(2):241\u0026ndash;56. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/s13402-021-00631-y\u003c/span\u003e\u003cspan address=\"10.1007/s13402-021-00631-y\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"cancer-associated fibroblast, ovarian cancer, tumor metastasis, single-cell transcriptome, chemotherapy resistance","lastPublishedDoi":"10.21203/rs.3.rs-8999242/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8999242/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eOvarian cancer, currently the deadliest gynecological malignancy, remains a challenging therapeutic issue due to chemotherapy resistance leading to tumor relapse. Chemoresistance in ovarian cancer is associated with various factors, and the role and mechanisms of cancer-associated fibroblasts (CAFs) in this process remain poorly understood. In this study, we analyzed single-cell RNA sequencing data from pre- and post-chemotherapy ovarian cancer samples to examine and compare the differences in tumor stromal cell composition. We found that the proportion of tumor-associated fibroblasts in the post-chemotherapy tumor microenvironment was significantly upregulated. Further analysis of fibroblast subpopulations revealed that chemotherapy altered the subtyping and transcriptional expression patterns of ovarian cancer fibroblasts. Through the analysis of cell composition, transcriptional expression, tissue metastasis, and functional state differences of fibroblast subpopulations before and after chemotherapy, we identified three fibroblast subpopulations with the potential to promote chemoresistance and metastasis in ovarian cancer: CCL3\u003csup\u003e+\u003c/sup\u003e fibroblasts, MGP\u003csup\u003e+\u003c/sup\u003e fibroblasts, and MMP1\u003csup\u003e+\u003c/sup\u003e fibroblasts. The CCL3\u003csup\u003e+\u003c/sup\u003e and MGP\u003csup\u003e+\u003c/sup\u003e fibroblast subpopulations were upregulated in metastatic omental tissues after chemotherapy, while the MMP1\u003csup\u003e+\u003c/sup\u003e fibroblast subpopulation was upregulated in metastatic peritoneal tissues. Differential gene expression analyses showed that CCL3\u003csup\u003e+\u003c/sup\u003e and MGP\u003csup\u003e+\u003c/sup\u003e fibroblasts upregulated several cytokines promoting cell growth and angiogenesis, while MMP1\u003csup\u003e+\u003c/sup\u003e fibroblasts upregulated cytokines mediating immune suppression. Moreover, secretory factors such as CCL3, MGP, MMP1, PTX3, GSN, and MMP3, which promote tumor growth and metastasis, were highly expressed in these upregulated post-chemotherapy tumor-associated fibroblasts. Pathway enrichment analysis revealed that PI3K-Akt, MAPK, TNF, NFκB, and IL-17 signaling pathways were significantly activated in these macrophage subpopulations. Finally, immunofluorescence staining of omental and peritoneal metastatic tissues from ovarian cancer patients confirmed the post-chemotherapy emergence of the CCL3\u003csup\u003e+\u003c/sup\u003e, MGP\u003csup\u003e+\u003c/sup\u003e, and MMP1\u003csup\u003e+\u003c/sup\u003e fibroblast subpopulations. These findings contribute to understanding the role and mechanisms of fibroblasts in ovarian cancer chemoresistance and may aid in developing therapeutic strategies targeting fibroblasts for overcoming chemoresistance in ovarian cancer.\u003c/p\u003e","manuscriptTitle":"Identification of fibroblast subpopulations facilitating to chemotherapy resistance in metastasis of ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-05 17:09:54","doi":"10.21203/rs.3.rs-8999242/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-23T13:28:57+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"300631340911768292925761497686198496276","date":"2026-04-16T18:41:20+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-14T02:47:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"24165961739619516790889741992677555824","date":"2026-04-13T07:53:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"246252824486337389256215435289450207246","date":"2026-04-06T23:28:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"42645718113534933347497206258673917430","date":"2026-04-05T14:55:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-31T08:24:54+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-09T07:23:49+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-04T02:57:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-04T02:55:19+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Oncology","date":"2026-03-01T04:45:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-oncology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"dion","sideBox":"Learn more about [Discover Oncology](https://www.springer.com/12672)","snPcode":"","submissionUrl":"","title":"Discover Oncology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"881b2557-bcfd-496f-8481-935008c3bfd5","owner":[],"postedDate":"April 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-05T17:09:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-05 17:09:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8999242","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8999242","identity":"rs-8999242","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}