Single-cell transcriptome analysis reveals the targeting of epithelial and fibroblast interactions in ovarian cancer

Single-cell transcriptome analysis reveals the targeting of epithelial and fibroblast interactions in ovarian cancer | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Single-cell transcriptome analysis reveals the targeting of epithelial and fibroblast interactions in ovarian cancer Yanfang Gu, Shaokai Ning, Shaojie Zhao, Hongxin Lin, Bing Zhang, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8586120/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted You are reading this latest preprint version Abstract Background Ovarian clear cell carcinoma (OCCC), a therapy-refractory epithelial ovarian cancer subtype with distinct tumor microenvironment (TME) features, has unclear links between cancer-associated fibroblasts (CAFs), extracellular matrix (ECM) remodeling (especially collagen deposition) and disease progression. Methods We characterized TME via single-cell RNA sequencing of 10 fresh tumor samples (4 OCCC, 6 non-OCCC), validated functions using CAF-ovarian cancer cell co-culture systems, verified the collagen-EMT axis in syngeneic mouse models with targeted inhibitors, and assessed clinical relevance via tissue microarray immunohistochemistry and survival correlation analysis. Results Single-cell data revealed enriched activated CAFs and abundant COL1A1 in OCCC. Collagen-rich ECM induced epithelial-mesenchymal transition (EMT), boosting cancer cell proliferation, invasion and metastasis; EMT pathway inhibition attenuated collagen-driven tumor growth in vivo. High COL1A1 and EMT marker (FAK, N-cadherin) expression correlated with poor prognosis. Conclusion CAF-driven collagen deposition promotes OCCC aggressiveness via EMT activation, and targeting the collagen-EMT axis may serve as a novel therapeutic strategy for this chemoresistant subtype. Biological sciences/Cancer/Cancer microenvironment Health sciences/Oncology/Cancer/Cancer therapy/Cancer therapeutic resistance ovarian cancer single-cell transcriptome cancer-associated fibroblast prognosis Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Ovarian cancer (OC) remains the most lethal gynecologic malignancy, accounting for the highest mortality rate among female reproductive system cancers 1 . Epithelial ovarian cancer (EOC), representing > 95% of OC cases, demonstrates remarkable histopathological and molecular heterogeneity 2 . Based on distinct morphological and immunohistochemical features, EOC is classified into four major subtypes: (i) high/low-grade serous carcinoma, (ii) endometrioid carcinoma, (iii) clear cell carcinoma, and (iv) mucinous carcinoma 3 , 4 . Each subtype exhibits unique genetic alterations and clinical behavior patterns, contributing to differential therapeutic responses and patient outcomes 1 . Compared to other subtypes, ovarian clear cell carcinoma (OCCC) is extremly more complex and challenging. Adavanced OCCC exhibibits high recurrence rate of nearly 70% 5 and is not sensitive to most treatment strategies including the chemotherapy, targeted therapy and immunotherapy 6 . Thus, it is urgent to explore the nature and extent of tumor heterogeneity within OCCC, which will help the development of preclinical, translational, and clinical research. The aggressive nature of EOC is compounded by nonspecific early symptoms, resulting in approximately 75% of patients presenting with advanced-stage disease (FIGO III-IV) characterized by widespread peritoneal dissemination 7 . Despite standard treatment involving cytoreductive surgery and platinum-based chemotherapy, the 5-year survival rate for advanced EOC remains below 40% 8 . While poly (ADP-ribose) polymerase inhibitors (PARPi) have improved outcomes for patients with homologous recombination deficiency (HRD), acquired resistance frequently develops, leading to disease recurrence 9 . Furthermore, current immunotherapeutic strategies have shown limited efficacy against EOC, highlighting the urgent need for novel treatment approaches 10 – 12 . Recent breakthroughs in biological cancer therapeutics have highlighted the pivotal role of tumor microenvironment (TME) in oncogenesis and progression 13 , 14 , establishing TME-targeting strategies as a cornerstone in solid tumor treatment paradigms 15 . In ovarian cancer, the TME serves as a critical mediator of disease pathogenesis by fostering an immunosuppressive niche that facilitates immune evasion and promotes metastatic dissemination 16 . Notably, the extracellular matrix (ECM) acts as a central orchestrator of TME dynamics, gaining prominence as a potential diagnostic marker and therapeutic target in current oncology research 17 – 19 . The ECM constitutes both the structural backbone of the TME and a bioactive signaling platform, where matrix-bound factors dynamically regulate tumor cell adhesion, migration, and tissue invasion through integrin-mediated mechanotransduction 20 . The ECM dynamically regulates its collagen-to-hyaluronan ratio, creating biomechanical changes that promote cancer progression 21 . It has been reported pathological ECM remodeling, particularly collagen deposition by CAFs, significantly contributes to EOC aggressiveness 22 . Beyond providing structural support, collagen accumulation within ECM could: (i) activate pro-invasive signaling pathways, (ii) enhance tumor cell proliferation, and (iii) promote treatment resistance 23 . Quantitative histopathology reveals that increased collagen depositionin high-grade serous ovarian cancer correlates strongly with enhanced metastatic potential, suggesting its prognostic value in disease management 24 . Moreover, experimental evidence demonstrates that excessive collagen deposition in ovarian cancer ECM compromises drug delivery efficiency by 40–60% 25 . Comparative proteomic analyses have identified significant compositional differences between normal ovarian ECM and tumor-associated ECM, particularly in collagen fiber alignment patterns 26 . Liang et al found ECM proteins correlated with the recurrence free survival in OCCC patients 27 . However, it is still unclear whether collagen content in the ECM regulates the malignancy of OCCC. We collected newly diagnosed fresh tumors for scRNA sequencing and found the proportion of fibroblasts increased in the OCCC compared to the non-OCCC. CAF-derived COL1A1 also exhibited significantly higher intensity in the OCCC and might promote tumor progression by inducing EMT. Therapeutic modulation of ECM biomechanics, particularly collagen modulation, may provide novel clinical opportunities for OCCC management, offering a breakthrough in overcoming chemoresistance and metastasis. We propose that collagen quantification in the TME could serve as a biomarker for tumor stratification and treatment guidance. Additionally, developing therapeutics that disrupt ECM-tumor interactions may enhance the efficacy of existing anti-cancer regimens. Further research is warranted to unravel the mechanistic basis of ECM remodeling and its translational potential in EOC management. Method Collection of clinical samples EOC tumor samples were collected from patients who had undergone bilateral salpingo-oophorectomy (BSO)/hysterectomy + comprehensive staging or debulking. Ethical approval for the single-cell sequencing data was granted by Clinical Research Ethics Committees at Wuxi Maternal and Child Health Care Hospital (Ethics No. 2023-01-0511-07). The experiments were undertaken with the understanding and written consent of each subject. The participants allowed the researchers to use their tissue during the tumor resection and conduct the study accordingly. EOC tumor samples were divided into 2 groups based on pathological diagnosis. 4 samples were pathologically diagnosed as ovarian clear cell carcinoma (OCCC), and 6 samples were diagnosed as non ovarian clear cell carcinoma (non-OCCC). Fresh tissues were immediately dissected into fractions for enzymatic digestion into single cells and fixed in 4% paraformaldehyde solution followed by paraffin embedding. The Clinical Research Ethics Committees at Wuxi Maternal and Child Health Care Hospital approved the recruitment. Detailed information of in-house and public cohorts can be found in Table S1 . Paraffin-embedded human EOC tissue microarrays (TMAs) were obtained from the AiFang Biological Co., LTD (Changsha, China). Comprehensive clinicopathological characteristics and follow-up information were acquired from AiFang Biological Co., LTD. The Biotechnology Ethics Review Committee at AiFang Biological Co., LTD approved the ethical use of the TMA (No. HN20250401). scRNA-seq analysis For scRNA-seq, a small piece of 10 tumor tissues from 2 groups was taken and put into the tissue storage solution (catalog cytoG100, BioMedicine Technology). scRNA-seq was performed by BioMedicine Technology. CellRanger was used to perform barcode processing and generate gene count profiles. The Seurat (4.3.01, http://satijalab.org/seurat/%3E ) R toolkit was used to perform all analyses. Cells were removed if the expression of mitochondrial genes was greater than 10% or with detected genes less than 200 or greater than 5,000. The “RunHarmony” function in the R package harmony was used to minimize the technical batch effects among individuals and experiments. To minimize dimensionality, principal components analysis (PCA) was performed based on the top 4,000 variable genes. The dimensionality of the scaled integrated data matrix was then further reduced to 2-dimensional space and visualized using t-distributed stochastic neighbor embedding (t-SNE), based on the top 30 PCs. A shared nearest neighbor modularity optimization-based clustering approach with a resolution was used to identify the cell clusters. Functional enrichment analysis The R package “limma” was used to identify the DEGs for OCCC and non-OCCC groups, respectively. Genes with fold change (FC) ≥ 1.5 and adjusted P value < 0.05 were defined as DEGs for the OCCC group compared to the non-OCCC group. To further describe the biological progresses of DEGs dys-regulated in the OCCC group, the enrichment analysis was performed by the “GSEA” function in the “clusterProfiler” package in terms of gene signatures from a previous study. In addition, signatures for collagen-related genes and interferon-γ response were obtained from our previous research and the Hallmark dataset, respectively. Human cancer tissue staining and quantification of staining results Multiple immunohistochemistry (mIHC) staining, immunohistochemistry (IHC) staining, Masson staining, and hematoxylin and eosin (H&E) staining were conducted on the above TMAs and tissue slides. Standard operating procedures were followed for mIHC, IHC and H&E staining. The primary antibodies applied in the research were as follows: anti-COL1A1 (1:300 dilution, catalog AF20100, AiFang biological), anti-CD163 (1:2,000 dilution, catalog ab182422, Abcam), anti-CD8 (1:1,000 dilution, catalog AF20211, AiFang biological), and anti-SMA (1:500 dilution, catalog AFMM0002, AiFang biological). Antibody staining was visualized with DAB and hematoxylin counterstain. Masson staining was performed on TMAs and tissue slides to determine the col- lagen deposition using the trichrome stain (Masson) kit (catalog FH115100, FreeThinking, Nanjing, China) according to the manufacturer’s instructions. Cell lines and cell culture Human cancer cell lines SKOV3 and OVCAR3 cells were purchased from the American Type Culture Collection (ATCC). Mouse cancer cell line ID8 (catalog SC0494) was purchased from Yuchi Biology (Shanghai, China). All the cells were cultured in the Dulbecco’s modified Eagle’s medium (DMEM). All media were added with 10% fetal bovine serum (FBS) at 37°C with or without 5% CO2. All human cell lines were authenticated using short tandem repeat profiling, and all assays were conducted with mycoplasma-free condition. Primary CAFs were isolated from breast tumor tissues 28 and cultured using primary cell culture medium (catalog CX0013, Yuchi, Shanghai, China). All human cell lines were authenticated using short tandem repeat profiling and all assays were conducted with mycoplasma-free. For cell culture, type I collagen (catalog A1048301, Gibco, Thermo Fisher Scientific, MA, USA) was applied to culture plates following the manufacturer’s instructions and previous studies 29 , 30 . In vitro assays for cellular functions To evaluate cell proliferation, suspended cancer cells were seeded into a 96-well plate at a density of 5 × 103 cells/ml (100 µl/well) and incubated at 37°C. Subsequently, 10 µl of CCK-8 reagent (catalog KGA317s-1000, KeyGEN, Nanjing, China) was added to each well, followed by a 2-h incubation period. The optical density at 450 nm was then measured using a microplate reader. For assessing cell migration and invasion, Transwell chambers were utilized, with or without Matrigel (Corning) coating as needed. Cancer cells (5 × 104) in 200 µl of serum-free medium were placed in the upper chamber, while 600 µl of medium containing 10% FBS was added to the lower chamber. Western blotting analysis Cellular total proteins were extracted using a lysis buffer, followed by performing SDS-PAGE (polyacrylamide gel electrophoresis) and Western blotting analysis according to established protocols 31 . The primary antibodies used were as follows: E-cadherin (1:1,000 dilution, catalog 60335-1-Ig, ProteinTech), N-cadherin (1:1,000 dilution, catalog A19083, abclonal), COL1A1 (1:1,000 dilution, catalog A24112, Abclonal, Wuhan, China), MMP2 (1:1,000 dilution, catalog 10373-2-AP, ProteinTech), MMP9 (1:1,000 dilution, catalog 10375-2-AP, ProteinTech), FAK (1:1,000 dilution, catalog #3285, Cell Signaling Technology), p-FAK (1:1,000 dilution, catalog #3283, Cell Signaling Technology), and GAPDH (1:10,000 dilution, catalog 10494-1-AP, ProteinTech). Animal models and analysis of mouse tumor tissues All experimental mice were housed in specific pathogen-free conditions and all animal procedures were approved by the Institutional Animal Care and Use Committee of Jiangnan University (JN.No20240930c0400215[513]). Six to eight weeks old female C57BL/6 mice were inoculated subcutaneously with ID8 cells. Tumor growth was measured with calipers regularly and the volume was calculated as 0.5 length × width 2 . On day 1 (average tumors reached 100 mm 3 ), mice were randomized into 4 treatment group and each group was treated with PBS, FAK inhibitor Y15 (catalog HY-12444, MCE), FAP inhibitor Talabostat (catalog HY-13233, MCE), and Y15 + Talabostat, repectively. The Talabostat group received daily oral administration of talabostat at 20µg/mouse 19 . The Y15 group received daily intraperitoneal injection of Y15 at 30mg/kg. On day 13, mice were sacrificed for downstream analyses. Removed tumors were submitted for immunofluorescence. Statistical analysis Statistical analysis and data visualization were conducted using R language version 4.0.2 and GraphPad Prism version 6.0. Continuous variables between 2 groups were compared using Student’s t test or Mann–Whitney test as appropriate. Differences among multiple groups were assessed using one-way analysis of variance (ANOVA) or Kruskal–Wallis test with multiple comparisons as applicable. Categorical variables were analyzed using the chi-square test or Fisher exact probability test based on the conditions. Correlation between 2 variables was evalu- ated using Pearson or Spearman correlation test depending on the conditions. The prognostic significance of categorical vari- ables was determined using the log-rank test. A P value of < 0.05 was considered statistically significant and denoted as *P < 0.05; **P < 0.01; ***P < 0.001 for clarity. Result Single-cell profiling of non-OCCC and OCCC tumor ecosystems To systematically interrogate the intratumoral heterogeneity of EOC, we performed deep scRNA-seq on ten tumor tissues diagnosed as EOC obtained during first surgery, of which 6 tumors were pathologically splited into the non-OCCC group and 4 tumors were splited into the OCCC group (Fig. 1 A). Detailed clinical information is shown in Table S1 . Following quality control, the remaining 59,173 cells were harmonized to remove batch effects and visualized using the Uniform Manifold Approximation and Projection (UMAP) method (Fig. 1 B). These cells were categorized into 12 major cell types (Materials and Methods) and then annotated according to the established gene marker list (Fig. 1 C). The cell subtypes are similar in the 10 tumor samples, however, the cell distribution in the tumor ecosystem varies significantly (Fig. 1 D). Contrastingly, all non-immune stroma lineages including fibroblasts, endothelial cells, and pericytes were significantly more abundant in OCCC group than in non-OCCC group, while immune cells varied slightly, in which macrophages remianed similar predominance in both groups accompanied by fewer T cells, monocytes and neutrophils in OCCC group (Fig. 1 E). Ecosystematic T cell features in non-OCCC and OCCC groups To analyze the T cell features in further, we rearranged the T cell cluster (marked by CD3D, CD3E, CD3G, CD4 and CD8A) into 4 T cell subtypes, including T naïve cells (marked by CCR7, SELL, IL7R and TCF7), CD8 T cells (marked by GZMB, NKG7 and PRF1), Treg cells (marked by FOXP3, CTLA4 and IL2RA) and Th2 cells (marked by PDCD1, IL21, GATA3, CCR7, CD44 and TCF7) (Fig. 2 A). Visualization of four T cell subtypes was performed using UMAP approaches, as shown in Fig. 2 B. The majoraty in OCCC group was T naïve cells (> 75%) while the T cell differentiation in non-OCCC group was reshaped towards anti-tumor immunity with more inhibitory T cells (Th2 and Treg cells) (Fig. 2 C). To better understand the roles of T cells in OCCCs, differential gene expression analysis was performed and identified 471 upregulated genes and 622 downregulated genes in OCCC group compared to non-OCCC group (Fig. 2 D). It revealed OCCCs contained significantly less of the effector T cell signatures in comparison and the activiation markers (CCL5, IFNG, GZMA and GZMB)were among the topranked genes (Fig. 2 E). Enrichment pathway analysis was then conducted on the different expressed genes, and the results indicated they were probably involved in antigen processing and presentation, oxidative phosphorylation,chemokine signaling pathway, T cell differentiation and so on (Fig. 2 F). Ecosystematic macrophages features in non-OCCC and OCCC groups As shown in Fig. 3 , there was no significant difference of macrophages abundance between non-OCCC and OCCC group. Distribution of M1 and M2 macrophages was visualized by UMAP method according to classical macrophage signatures (Fig. 3 A). The proportation of M2 macrophages was higher in OCCC group than that in non-OCCC group, which indicated an immunosuppressive state and was consistent with immune tolerance of OCCC patients (Fig. 3 B). The gene expression pattern indentified 440 upregulated genes and 315 downregulated genes in OCCC group (Fig. 3 C), of which heat map also showed that macrophages in OCCC group were thought to exhibit an M2-like phenotype which associated with poor prognosis in several malignancies (Fig. 3 D). Enrichment pathway analysis showed that DEGs were associated with immune-related signaling pathway, including TLR signaling pathway, TNF signaling pathway, chemokine signaling pathway and so on (Fig. 3 E). Then IF staining was conducted to verify the TME heterogeneity between OCCC and non-OCCC patient samples. It was consistent with aforementioned single-cell RNA sequencing analysis that FOXP3 + cells (Treg cells) were more in the non-OCCC tumor while CD163 + cells (M2 macrophage) were accumulated in the OCCC tumor (Fig. 3 F). Ecosystematic CAF features in non-OCCC and OCCC groups OCCC tumor ecosystems were quite different from that in non-OCCC tumor with obviouly higher of CAFs (Fig. 1 ). To further invesgate the heterogeneity of CAFs population, hierarchical clustering was used to seprarte CAFs into 11 subclusters (Fig. 4 A). Monocle algorithm was applied in pseudotime analysis and all the 11 clusters were aggregated on the basis of gene expression similarities to project their developmental trajectories (Fig. 4 B). Cluster 1, 5, 6 were located at the middle of CAF cell differentiation and were enriched in OCCC patients, which meant most CAFs in OCCC were in an active state. CAFs in non-OCCC mainly consisted of cluster 0, 4, 7, which were located at the beginning of CAF cell differentiation (Fig. 4 C-D). We continued to study the features of CAFs in the OCCC group. There were 1119 differentially expressed genes in CAF cells from OCCC group compared with non-OCCC group (Fig. 4 E). The types of collagen in the two groups were different as COL15A1 were highly expressed in the OCCC group while COL8A1 were more abundant in the non-OCCC group (Fig. 4 F). Enrichment analysis showed the DEGs participated in several functional pathways, including focal adhesion, ECM-receptor interaction, the TGF-beta signaling pathway, PI3K-Akt signaling pathway, and cGMP-PKG signaling pathway (Fig. 4 G). In addition, CAFs from OCCC exhibited a strong signature of epithelial mesenchymal transition, such as DLK1, APOE, SULF2 and so on. The density of ECM in OCCC and non-OCCC tumor tissues was evaluated by IF staining. The results indicated that CAFs in OCCC tumors were positive for the canonical CAF markers a-SMA and COL1A1 (Fig. 4 H). Furthermore, these CAFs in OCCC possessed active EMT properties, evidenced by remarkble expression of mesenchymal biomarkers such as FAK and N-cadherin at protein levels (Fig. 4 I). Based on the above results, we validated there was more abundant CAF-derived collagen and more active EMT progression in OCCC tumor ecosystems compared with non-OCCC tumor ecosystems. ECM was reported to influence the ascending of tumor grade. We could propose that collagen deposition in OCCC could induce the EMT and lead to poor clinical prognosis of OCCC patients as a result. Collagen deposition promoted tumor progeression by stimulating EMT To better understand the effect of collagen deposition on the tumor progeression, we constructed stable cell lines with high expression of collagen (3T3-Coll/3T3-Ctr) and then estabilished a a CAF (3T3-Coll/3T3-Ctr)/ovarian cancer cell (SKOV3 and OVCAR3) transwell coculture system. CCK8 assays showed that the increase of collagen secretion promoted the proliferation of ovarian cancer cells (Fig. 5 A-B). Transwell assays showed the increase of collagen promoted tumor cell invasion and metastasis compared to the control (Fig. 5 C-D). The expressions of migration markers (MMP2 and MMP9) and EMT markers (N-cadherin, E-cadherin, p-FAK and FAK) were also significantly upregulated in ovarian cancer cells cocultured with collagen-high 3T3 cells in comparison to the control (Fig. 5 E), which comfirmed collagen accumulation remodeled ECM composition and significantly enhanced epithelial-mesenchymal transition (EMT) of ovarian cancer cells consequently. These findings corroborate our hypothesis demonstrating that extensive collagen deposition within the TME drives activation of EMT in the ovarian cancer cells, thereby promoting OCCC proliferation, invasion, and metastatic dissemination while concurrently diminishing platinum sensitivity. EMT inhibitors offset pro-tumorigenic effects induced by collagen deposition We then tested whether EMT inhibitors could suppress ovarian cancer growth and reverse the pro-tumorigenic effects brought by collagen deposition in TME. Firstly, a mixture of 3T3 cells and MFC cells at the ratio of 1:1 were subcutaneously injected into 8 female C57BL/6 mice. The tumor-bearing mice were then divided into four treatment groups, which were treated respectively with FAK inhibition (Y15) alone, EZH inhibition (Talabostat) alone, combination of FAK inhibition and EZH inhibition (Y15 plus Talabostat), or control reagents (Fig. 6 A). FAK inhibition or EZH inhibition did not affect the weights of mice (Fig. 6 B). As expected, monotherapy of Y15 or Talabostat treatment slightly reduced the tumor burden (Fig. 6 C), while the combination therapy (Y15 + Talabostat) significantly enhanced this tumor-suppressing effect, as shown by retarded tumor growth, lower tumor volume (Fig. 6 D) and tumor weight at the endpoint (Fig. 6 E). Y15 or Talabiostats had no significant side effects on various tissues, such as heart, liver, spleen, lung and kidney (Fig. 6 F). IF staining comfirmed Y15 or Talabostat inhibted EMT effectively and IHC exhibited collagen deposition was sighnificantly reduced in treated groups as a result. The expression of Ki67 also decreased remarkbly in the combination group (Y15 + Talabostat) (Fig. 6 H). Taken together, EMT inhibitors offset the EMT activation induced by collagen deposition and alleviated the tumor burden in tumor-bearing mice model. Collagen deposition and EMT activation unfavored the survival of patients with ovarian cancer Tissue microarray was ulitized for multi-labeled immunofluorescence staining to check the expression of a-SMA, COL1A1, FAK and N-cadherin in OCCC tumors. The results showed that the density of CAFs derived from different subtypes of ovarian cancers were notably various, and CAFs accounts for the highest proportion in the clear cell carcinoma, where COL1A1 was significantly overexpressed (Fig. 7 A). Next, we correlated the positive cell rates with clinical prognosis and found high expression of COL1A1 predicted poor prognosis in the ovarian cancer (Fig. 7 B). We also observed a trend that CAFs were unfavor for overall survival of ovarian cancer patients (Fig. 7 C). The colocation of COL1A1 and a-SMA in the ovarian cancer cohorts further supported the above point that high expression of CAFs-derived COL1A1 was associated the grade malignancy of the clear cell carcinoma (Fig. 7 F). To investigate the clinical significance of FAK and N-cadherin, we performed mIHC staining on tumor microarray of ovarian cancers. EMT was obviouly promoted in the clear cell carcinoma as shown by the extremely high expression of FAK and N-cadherin (Fig. 7 A). Ovarian cancer patients with the high expressions of FAK and N-cadherin tended to have shorter survival time (Fig. 7 D-E), meanwhile the positive cell rates of FAK and N-cadherin showed a positive correlation with the COL1A1 + cell rate in the ovarian cancer, respectively (Fig. 7 G-H). It corroborated our experimental results that CAFs-derived collagen deposition in TME facilitated EMT progression. Overall, the integrated analysis revealed that COL1A1 was overexpressed in the clear cell carcinoma and favored the progression of EMT, highlighting as key target in predicting poor prognosis. Discussion The profound molecular and histological heterogeneity of EOC significantly contributes to its distinct clinical characteristics, including epidemiological distribution, diagnostic presentation, biological aggressiveness, and therapeutic responsiveness 32 . Among histological subtypes, OCCC is one of the most common EOC subtypes, and its incidence rate is just second to HGSC 33 . Patients with OCCC typically present at a median age of 50–55 years, with approximately 70% diagnosed at early stages (FIGO I-II) 34 . This early-stage detection correlates with favorable prognosis, demonstrating 10-year survival rates of 80–90% 35 . In contrast, non-OCCC patients, most diagnosed as HGSC, present with nonspecific symptoms at advanced stages, exhibiting highly aggressive biological behavior and poor clinical outcomes 36 . Remarkably, the clinical trajectory of the OCCC subtypes diverges dramatically in advanced disease. While HGSC maintains consistent aggressiveness throughout its course, OCCC undergoes a malignant transformation during diffierent stages. When it progress to advanced stage (FIGO III-IV), OCCC demonstrates exceptional chemoresistance, with response rates to first-line platinum-based chemotherapy as low as 30%, compared to 70–80% in HGSC 35 . This therapeutic recalcitrance, compounded by the lack of effective targeted therapies, results in rapid disease progression and dismal survival outcomes for advanced OCCC patients. The mechanism behind still remains unclear. Most of non-OCCC are thought to arise from the fallopian tube or ovarian epithelium, typically exhibiting characteristic pathological features including solid growth patterns and papillary structures. In contrast, OCCC is believed to originate from endometriotic lesions 37 , pathologically presenting with tubular cystic architectures and distinctive clear cells. Our clinical observations indicate that most OCCC manifest as solid-cystic tumors, with more than 90% occurring unilaterally. These tumors typically achieve substantial dimensions, reaching diameters of 20–30 cm 38 . Imaging studies reveal that early-stage OCCC commonly displays high CT attenuation values and T1-weighted hyperintensity on MRI, whereas advanced cases exhibit low CT attenuation in cystic components and T2-hyperintense solid components. A small amount of OCCC cases presents with predominantly solid or fibrotic morphology. The distinct cellular origins of these tumor types necessarily result in neoplasms with divergent physicochemical characteristics, each associated with unique tumor microenvironment (TME) compositions. This observation leads us to hypothesize that specific TME components may critically influence OCCC tumor cell behavior, modulating key biological processes such as proliferation, invasion, and sensitivity to chemotherapy. Through comparative scRNA-seq analysis of OCCC and non-OCCC tumor samples, we identified a noteworthy phenomenon that the TME of OCCC tumors was significantly different from that of non-OCCC tumors. All non-immune stroma lineages including fibroblasts, endothelial cells, and pericytes were significantly more abundant in OCCC group than in the non-OCCC group, while immune cells varied slightly. Notbly, CAF population dominated in extracellular components of OCCC tumors while the proportions of extracellular component cell populations was more equally in the non-OCCC tumors. It was also worth noting that CAF in the OCCC tumors was more active and showed stronger ability to secret collagen. TME is a complex ecosystem composed of tumor cells, surrounding non tumor cells (such as immune cells, fibroblasts, and endothelial cells), and ECM. CAFs represent crucial stromal components that are predominantly localized perivascularly or within the peritumoral fibrous stroma 39 . These activated fibroblasts secrete various cytokines, ECM components, and matrix-modifying enzymes 40 . ECM is the core non cellular component of TME, secreted by cells and distributed in the extracellular space, forming a complex network structure. ECM remodeling is one of the hallmark features of solid tumors, and drives malignant biological behaviors such as tumor cell proliferation, stemness maintenance, and immune escape through mechanical transduction 41 . In addition, ECM hardness not only increases interstitial fluid pressure, hinders immune cell infiltration and drug delivery, but also affects the function of immune cells through mechanical signal transduction 25 . Collagen is the main structural protein of ECM, accounting for over 90% 42 . At present, 28 different types of collagen have been discovered, among which type I collagen (marked as COL1A1) is the most abundant in connective tissue and provides mechanical support 43 . In normal tissues, collagen maintains tissue homeostasis by providing structural support and regulating cellular behavior. However, during tumor progression, there is a significant imbalance in the synthesis, degradation, and remodeling of collagen 25 . A recent study shows that the growth and metastasis of pancreatic cancer cells were dependent on collagen cleavage which activates DDR1 signaling 44 . Collagen remodeling in the TME could promote a more efficient energy procuring process by soluble fragments and then accumulate tumor cell growth 45 . Linearly aligned collagen fiber provides a highway for tumor cell migration and dissemination in breast cancer 46 . In lung cancers, collagen around tumor cells dictates the migratory trajectory of immune cells and restrict them from infiltration 47 . Through binding to the receptors, collagens can even directly affect the characteristics of tumor-infiltrating immune cells, including proportion, function and phenotype 48 . Furthermore, biomechanical studies demonstrate that collagen deposition increased ECM hardness and such ECM could activate mechanotransduction signaling pathways, which induced epithelial-mesenchymal transition (EMT) and consequently enhanced tumor cell invasiveness and metastatic potential 49 – 51 . The collagen-based densified matrix architecture creates a physical diffusion barrier that reduces intratumoral drug penetration by 40–60%, significantly compromising chemotherapeutic efficacy. Furthermore, our results indicated that CAFs in OCCC possessed active EMT properties, evidenced by remarkble expression of mesenchymal biomarkers such as FAK and N-cadherin at protein levels. We then proposed that collagen deposition in OCCC could induce the EMT and lead to poor clinical prognosis of OCCC patients as a result. In the current study, collagen deposition promoted the proliferation and metastasis of ovarian cancer. The expressions of migration markers (MMP2 and MMP9) and EMT markers (N-cadherin, E-cadherin, p-FAK and FAK) were significantly upregulated in ovarian cancer cells cocultured with collagen-high cells in comparison to the control. EMT inhibitors could offset the EMT activation induced by collagen deposition and alleviated the tumor burden in tumor-bearing mice model. The IF staining results of TMA further confirmed the aforementationed experimental conclusion. Yue et al identified 15 ECM proteins with prognostic significance in OCCC, including LAMB3 and LAMC2, which were shown to confer platinum resistance and promote tumor cell invasion and metastasis 27 . Their study further revealed that increased IFITM1 expression significantly correlated with disease recurrence in OCCC patients. Histopathological evaluation of recurrent versus primary OCCC specimens demonstrated prominent fibrotic remodeling, featuring substantial stromal infiltration of fibroblasts, endothelial cells, and pericytes. Notably, immunohistochemical analysis confirmed marked activation of CAFs in recurrent tumors, with a significant expansion of the POSTN + CAF subset 52 . Clinical data from Taiwanese cohorts indicate that while OCCC patients demonstrate substantially lower response rates to first-line platinum-based chemotherapy compared to HGSC 53 , the incorporation of bevacizumab in combination with paclitaxel-carboplatin (PTX + CBDCA) regimens significantly enhances therapeutic efficacy 54 . These findings corroborate our experimental results demonstrating that CAF activation drives extensive collagen deposition within the TME, thereby promoting OCCC proliferation, invasion, and metastatic dissemination while concurrently diminishing platinum sensitivity. Conclusion Overall, our current study demostrated collagen deposition stimulated EMT progression of OCCC and then contributed to its poor clinical progrosis and innovatively proposed EMT inhibitors for OCCC patients as a new treatment strategy. Future research will focus more on effect of ECM on the growth and progression of OCCC. Declarations Ethics approval and consent to participate Ethical approval for the single-cell sequencing data was granted by Clinical Research Ethics Committees at Wuxi Maternal and Child Health Care Hospital (Ethics No. 2023-01-0511-07) and was conducted in accordance with the Declaration of Helsinki. The Biotechnology Ethics Review Committee at AiFang Biological Co., LTD approved the ethical use of the TMA (No. HN20250401) and was conducted in accordance with the Declaration of Helsinki. The experiments were undertaken with the understanding and written consent of each subject. The participants allowed the researchers to use their tissue during the tumor resection and conduct the study accordingly. Consent for publication Not applicable. Availability of data and material All data are available from the authors upon reasonable request. Competing interests The authors declare no competing interests. Authors' contributions YG and JC conceived the study and participated in the study design, performance, coordination, and project supervision. SN, BZ, JZ, and WL collected the public data and conducted the bioinformatics analysis. SZ collected the tumor samples and analyzed the clinical data. HL and JM performed in vitro and in vivo experiments and the tissues staining. SN and HL wrote the draft. YG and JC revised the manuscript. Acknowledgements We thank Shuqi Li for data visualization and manuscript writing; GENE for single-cell sequencing. 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(B)\u003cstrong\u003e \u003c/strong\u003eUniform manifold approximation and projection (UMAP) analysis of major cell types. (C) Stacked histogram showing percentage of each cell subtypes across the samples. (D) Classcial cell markers of each cell subtypes. (E) Difference in cell subtypes in non-OCCC and OCCC groups.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/988015a4239ed6a4bb64a83c.png"},{"id":102778916,"identity":"b1ed2690-03dc-44ee-b369-701b9fa7fb3f","added_by":"auto","created_at":"2026-02-16 14:39:23","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":537231,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEcosystematic T cell features in non-OCCC and OCCC groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Classcial cell markers of each T cell subtypes. (B) Uniform manifold approximation and projection (UMAP) analysis of T cell subtypes. (C) Stacked histogram showing percentage of each T cell subtypes across the samples. (D) Volcano plot revealing DEGs between the OCCC group and the non-OCCC group with the criterion of FC ≥ 1.5 and adjusted P value ≤ 0.05. (E) Differential expression of genes between the OCCC group and the non-OCCC group. Significance was calculated with Student’s t test. (F) KEGG pathways of the differentially expressed genes between the OCCC group and the non-OCCC group.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/ac776f9dd0b62f2f200b78b2.png"},{"id":102779390,"identity":"0c25a01b-565b-4d44-bf67-2752a6663307","added_by":"auto","created_at":"2026-02-16 14:41:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":563935,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEcosystematic macrophages features in non-OCCC and OCCC groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Uniform manifold approximation and projection (UMAP) analysis of M1 and M2 macrophages. (B) Stacked histogram showing percentage of M1 and M2 macrophages across the samples. (C) Volcano plot revealing DEGs between the OCCC group and the non-OCCC group with the criterion of FC ≥ 1.5 and adjusted P value ≤ 0.05. (D) Differential expression of genes between the OCCC group and the non-OCCC group. Significance was calculated with Student’s t test. (E) KEGG pathways of the differentially expressed genes between the OCCC group and the non-OCCC group. (F) Representative images uncovering FOXP3+ T cell and CD163+ macrophage infiltration in the OCCC group and the non-OCCC group.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/2f686164023412bc103be888.png"},{"id":102779196,"identity":"779c40f9-c864-47e1-903b-12bf1a94af8b","added_by":"auto","created_at":"2026-02-16 14:40:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":945765,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEcosystematic CAF features in non-OCCC and OCCC groups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Uniform manifold approximation and projection (UMAP) analysis of CAF cell subtypes. (B-C) Pseudotime trajectories analysis from UMAP embedding of different subtypes of CAF cells. (D) Stacked histogram showing percentage of CAF subtypes across the samples. (E) Volcano plot revealing DEGs between the OCCC group and the non-OCCC group with the criterion of FC ≥ 1.5 and adjusted P value ≤ 0.05. (F) Differential expression of genes between the OCCC group and the non-OCCC group. Significance was calculated with Student’s t test. (G) KEGG pathways of the differentially expressed genes between the OCCC group and the non-OCCC group. (H) Representative images uncovering stromal marker and collagen marker in OCCC and non-OCCC patients. (I) Representative images uncovering the co-location between COL1A1 and α-SMA in OCCC and non-OCCC patients.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/1feff0c390e2c8238a8fca40.png"},{"id":102779037,"identity":"e41e75a6-904f-40bc-8cf3-98110b87489b","added_by":"auto","created_at":"2026-02-16 14:40:01","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1095063,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCollagen deposition promoted tumor progeression by stimulating EMT\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A-B) The proliferative capacity of SKOV3/OVCAR3 after colcultured with collagen-high CAF or control was examined by CCK-8 assay. Data are presented as mean ± SD. Significance was calculated with Student’s t test. **P \u0026lt; 0.01. (C-D) The migratory and invasive capacities of SKOV3/OVCAR3 after colcultured with collagen-high CAF or control were examined at 24 h by Boyden chamber assay. Total original magnification, 200×. Significance was calculated with Student’s t test. **P \u0026lt; 0.01. (E) Protein levels of E-cad, N-cad, MMP2, MMP9, p-FAK, FAK in the SKOV3 cells after colcultured with collagen-high CAF or control. GAPDH was used as the loading control. Significance was calculated with Student’s t test. *P \u0026lt; 0.05, **P \u0026lt; 0.01. (F) Protein levels of E-cad, N-cad, MMP2, MMP9, p-FAK, FAK in the OVCAR3 cells after colcultured with collagen-high CAF or control. GAPDH was used as the loading control. Significance was calculated with Student’s t test. *P \u0026lt; 0.05, **P \u0026lt; 0.01. (G) The migratory capacities of SKOV3/OVCAR3 after colcultured with control and losartan-treated CAFs were examined at 24 h by Boyden chamber assay. Total original magnification, 200×. Significance was calculated with Student’s t test. **P \u0026lt; 0.01.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/dacd936d65bfc93a5a7263a2.png"},{"id":102779178,"identity":"4d288b7a-0054-4568-b541-128dd020fa47","added_by":"auto","created_at":"2026-02-16 14:40:36","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":1912457,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEMT inhibitors offset pro-tumorigenic effects induced by collagen deposition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) A schematic illustration of ID8 tumor model and subsequent treatment schedule. (B) Weight of mice treated with PBS, Y15, Talabostat, and the combination. (C) Representative images showing the tumors harvested from mice bearing a mixture of ID8 cells and 3T3 cells treated with PBS, Y15, Talabostat, and the combination. (D) Tumor growth curve of mice treated with PBS, Y15, Talabostat, and the combination. (E) Tumor weight of the harvested tumors. Data was presented as mean±SD. Significance was calculated with one-way ANOVA with Tukey’s multiple-comparison test. ns, non-significance, *p\u0026lt;0.05, ***p\u0026lt;0.001. (F) Representative images showing structure of heart, liver, spleen, lung and kidney from mice in different groups. (H) Representative images showing the levels of collagen, E-cadherin, N-cadherin, FAK, and Ki67 in tumor tissues from mice in different groups.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/dcdb33cd3a07e350f16369e2.png"},{"id":102779292,"identity":"879ab36e-8952-484f-8419-ff12974c9ce0","added_by":"auto","created_at":"2026-02-16 14:41:11","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":437460,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCollagen deposition and EMT activation unfavored the survival of patients with ovarian cancer\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e(A) Representative images showing the levels of COL1A1, FAK, N-cadherin, FAK, and a-SMA in OCCC patients. (B) Comparison of overall survival (OS) with log-rank test in patients with low and high COL1A1 expression in the in-house EOC cohort. *p\u0026lt;0.05. (C) Comparison of OS with log-rank test in patients with low and high a-SMAexpression in the in-house EOC cohort. P\u0026gt;0.05. (D) Comparison of OS with log-rank test in patients with low and high FAK expression in the in-house EOC cohort. *p\u0026lt;0.05. (E) Comparison of OS with log-rank test in patients with low and high N-cadherin expression in the in-house EOC cohort. *p\u0026lt;0.05. \u0026nbsp;(F) Correlation between a-SMA+ cells and COL1A1+ cells in the in-house EOC cohort. Significance was calculated with the Spearman test. (G) Correlation between FAK+ cells and COL1A1+ cells in the in-house EOC cohort. Significance was calculated with the Spearman test. (H) Correlation between N-cadherin+ cells and COL1A1+ cells in the in-house EOC cohort. Significance was calculated with the Spearman test.\u003c/p\u003e","description":"","filename":"floatimage7.png","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/6f60ca7d2ea4921470e46a9d.png"},{"id":102779460,"identity":"b1a14fd7-3d99-440c-a295-aa7aac21907c","added_by":"auto","created_at":"2026-02-16 14:41:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":6171355,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/d46c63ad-da3b-4d08-bab5-18fac3690e71.pdf"},{"id":102778963,"identity":"f401351b-a31b-4e39-af9d-28b07eb9b010","added_by":"auto","created_at":"2026-02-16 14:39:38","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":10053,"visible":true,"origin":"","legend":"Sample information","description":"","filename":"Supplementalinformation.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/65ed89d38d19d8bb0c120bb1.xlsx"},{"id":102779079,"identity":"6339e3ce-11f0-43fe-a082-3df785c3ce28","added_by":"auto","created_at":"2026-02-16 14:40:06","extension":"pptx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":8327891,"visible":true,"origin":"","legend":"Uncropped Gels and Blots","description":"","filename":"supplementaryfile.pptx","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/b7d19937e2930f1710ff7d7b.pptx"},{"id":102779243,"identity":"f2f17789-a554-4bda-bc36-23ee2797e9a9","added_by":"auto","created_at":"2026-02-16 14:41:02","extension":"pdf","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":76103,"visible":true,"origin":"","legend":"Reporting Summary","description":"","filename":"RS1101.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8586120/v1/26871458547913046ef8e245.pdf"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Single-cell transcriptome analysis reveals the targeting of epithelial and fibroblast interactions in ovarian cancer","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OC) remains the most lethal gynecologic malignancy, accounting for the highest mortality rate among female reproductive system cancers\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Epithelial ovarian cancer (EOC), representing\u0026thinsp;\u0026gt;\u0026thinsp;95% of OC cases, demonstrates remarkable histopathological and molecular heterogeneity\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Based on distinct morphological and immunohistochemical features, EOC is classified into four major subtypes: (i) high/low-grade serous carcinoma, (ii) endometrioid carcinoma, (iii) clear cell carcinoma, and (iv) mucinous carcinoma\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Each subtype exhibits unique genetic alterations and clinical behavior patterns, contributing to differential therapeutic responses and patient outcomes\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. Compared to other subtypes, ovarian clear cell carcinoma (OCCC) is extremly more complex and challenging. Adavanced OCCC exhibibits high recurrence rate of nearly 70%\u003csup\u003e5\u003c/sup\u003e and is not sensitive to most treatment strategies including the chemotherapy, targeted therapy and immunotherapy\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. Thus, it is urgent to explore the nature and extent of tumor heterogeneity within OCCC, which will help the development of preclinical, translational, and clinical research.\u003c/p\u003e \u003cp\u003eThe aggressive nature of EOC is compounded by nonspecific early symptoms, resulting in approximately 75% of patients presenting with advanced-stage disease (FIGO III-IV) characterized by widespread peritoneal dissemination\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e. Despite standard treatment involving cytoreductive surgery and platinum-based chemotherapy, the 5-year survival rate for advanced EOC remains below 40%\u003csup\u003e8\u003c/sup\u003e. While poly (ADP-ribose) polymerase inhibitors (PARPi) have improved outcomes for patients with homologous recombination deficiency (HRD), acquired resistance frequently develops, leading to disease recurrence\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e. Furthermore, current immunotherapeutic strategies have shown limited efficacy against EOC, highlighting the urgent need for novel treatment approaches\u003csup\u003e\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRecent breakthroughs in biological cancer therapeutics have highlighted the pivotal role of tumor microenvironment (TME) in oncogenesis and progression\u003csup\u003e\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e, establishing TME-targeting strategies as a cornerstone in solid tumor treatment paradigms\u003csup\u003e\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. In ovarian cancer, the TME serves as a critical mediator of disease pathogenesis by fostering an immunosuppressive niche that facilitates immune evasion and promotes metastatic dissemination\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Notably, the extracellular matrix (ECM) acts as a central orchestrator of TME dynamics, gaining prominence as a potential diagnostic marker and therapeutic target in current oncology research\u003csup\u003e\u003cspan additionalcitationids=\"CR18\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. The ECM constitutes both the structural backbone of the TME and a bioactive signaling platform, where matrix-bound factors dynamically regulate tumor cell adhesion, migration, and tissue invasion through integrin-mediated mechanotransduction\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e. The ECM dynamically regulates its collagen-to-hyaluronan ratio, creating biomechanical changes that promote cancer progression\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIt has been reported pathological ECM remodeling, particularly collagen deposition by CAFs, significantly contributes to EOC aggressiveness\u003csup\u003e\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u003c/sup\u003e. Beyond providing structural support, collagen accumulation within ECM could: (i) activate pro-invasive signaling pathways, (ii) enhance tumor cell proliferation, and (iii) promote treatment resistance\u003csup\u003e\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u003c/sup\u003e. Quantitative histopathology reveals that increased collagen depositionin high-grade serous ovarian cancer correlates strongly with enhanced metastatic potential, suggesting its prognostic value in disease management\u003csup\u003e\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Moreover, experimental evidence demonstrates that excessive collagen deposition in ovarian cancer ECM compromises drug delivery efficiency by 40\u0026ndash;60%\u003csup\u003e25\u003c/sup\u003e. Comparative proteomic analyses have identified significant compositional differences between normal ovarian ECM and tumor-associated ECM, particularly in collagen fiber alignment patterns\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e. Liang et al found ECM proteins correlated with the recurrence free survival in OCCC patients\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. However, it is still unclear whether collagen content in the ECM regulates the malignancy of OCCC.\u003c/p\u003e \u003cp\u003eWe collected newly diagnosed fresh tumors for scRNA sequencing and found the proportion of fibroblasts increased in the OCCC compared to the non-OCCC. CAF-derived COL1A1 also exhibited significantly higher intensity in the OCCC and might promote tumor progression by inducing EMT. Therapeutic modulation of ECM biomechanics, particularly collagen modulation, may provide novel clinical opportunities for OCCC management, offering a breakthrough in overcoming chemoresistance and metastasis. We propose that collagen quantification in the TME could serve as a biomarker for tumor stratification and treatment guidance. Additionally, developing therapeutics that disrupt ECM-tumor interactions may enhance the efficacy of existing anti-cancer regimens. Further research is warranted to unravel the mechanistic basis of ECM remodeling and its translational potential in EOC management.\u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eCollection of clinical samples\u003c/h2\u003e \u003cp\u003eEOC tumor samples were collected from patients who had undergone bilateral salpingo-oophorectomy (BSO)/hysterectomy\u0026thinsp;+\u0026thinsp;comprehensive staging or debulking. Ethical approval for the single-cell sequencing data was granted by Clinical Research Ethics Committees at Wuxi Maternal and Child Health Care Hospital (Ethics No. 2023-01-0511-07). The experiments were undertaken with the understanding and written consent of each subject. The participants allowed the researchers to use their tissue during the tumor resection and conduct the study accordingly. EOC tumor samples were divided into 2 groups based on pathological diagnosis. 4 samples were pathologically diagnosed as ovarian clear cell carcinoma (OCCC), and 6 samples were diagnosed as non ovarian clear cell carcinoma (non-OCCC). Fresh tissues were immediately dissected into fractions for enzymatic digestion into single cells and fixed in 4% paraformaldehyde solution followed by paraffin embedding. The Clinical Research Ethics Committees at Wuxi Maternal and Child Health Care Hospital approved the recruitment. Detailed information of in-house and public cohorts can be found in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eParaffin-embedded human EOC tissue microarrays (TMAs) were obtained from the AiFang Biological Co., LTD (Changsha, China). Comprehensive clinicopathological characteristics and follow-up information were acquired from AiFang Biological Co., LTD. The Biotechnology Ethics Review Committee at AiFang Biological Co., LTD approved the ethical use of the TMA (No. HN20250401).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003escRNA-seq analysis\u003c/h3\u003e\n\u003cp\u003eFor scRNA-seq, a small piece of 10 tumor tissues from 2 groups was taken and put into the tissue storage solution (catalog cytoG100, BioMedicine Technology). scRNA-seq was performed by BioMedicine Technology. CellRanger was used to perform barcode processing and generate gene count profiles. The Seurat (4.3.01, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://satijalab.org/seurat/%3E\u003c/span\u003e\u003cspan address=\"http://satijalab.org/seurat/%3E\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) R toolkit was used to perform all analyses. Cells were removed if the expression of mitochondrial genes was greater than 10% or with detected genes less than 200 or greater than 5,000. The \u0026ldquo;RunHarmony\u0026rdquo; function in the R package harmony was used to minimize the technical batch effects among individuals and experiments. To minimize dimensionality, principal components analysis (PCA) was performed based on the top 4,000 variable genes. The dimensionality of the scaled integrated data matrix was then further reduced to 2-dimensional space and visualized using t-distributed stochastic neighbor embedding (t-SNE), based on the top 30 PCs. A shared nearest neighbor modularity optimization-based clustering approach with a resolution was used to identify the cell clusters.\u003c/p\u003e\n\u003ch3\u003eFunctional enrichment analysis\u003c/h3\u003e\n\u003cp\u003eThe R package \u0026ldquo;limma\u0026rdquo; was used to identify the DEGs for OCCC and non-OCCC groups, respectively. Genes with fold change (FC)\u0026thinsp;\u0026ge;\u0026thinsp;1.5 and adjusted P value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 were defined as DEGs for the OCCC group compared to the non-OCCC group. To further describe the biological progresses of DEGs dys-regulated in the OCCC group, the enrichment analysis was performed by the \u0026ldquo;GSEA\u0026rdquo; function in the \u0026ldquo;clusterProfiler\u0026rdquo; package in terms of gene signatures from a previous study. In addition, signatures for collagen-related genes and interferon-γ response were obtained from our previous research and the Hallmark dataset, respectively.\u003c/p\u003e\n\u003ch3\u003eHuman cancer tissue staining and quantification of staining results\u003c/h3\u003e\n\u003cp\u003eMultiple immunohistochemistry (mIHC) staining, immunohistochemistry (IHC) staining, Masson staining, and hematoxylin and eosin (H\u0026amp;E) staining were conducted on the above TMAs and tissue slides. Standard operating procedures were followed for mIHC, IHC and H\u0026amp;E staining. The primary antibodies applied in the research were as follows: anti-COL1A1 (1:300 dilution, catalog AF20100, AiFang biological), anti-CD163 (1:2,000 dilution, catalog ab182422, Abcam), anti-CD8 (1:1,000 dilution, catalog AF20211, AiFang biological), and anti-SMA (1:500 dilution, catalog AFMM0002, AiFang biological). Antibody staining was visualized with DAB and hematoxylin counterstain. Masson staining was performed on TMAs and tissue slides to determine the col- lagen deposition using the trichrome stain (Masson) kit (catalog FH115100, FreeThinking, Nanjing, China) according to the manufacturer\u0026rsquo;s instructions.\u003c/p\u003e\n\u003ch3\u003eCell lines and cell culture\u003c/h3\u003e\n\u003cp\u003eHuman cancer cell lines SKOV3 and OVCAR3 cells were purchased from the American Type Culture Collection (ATCC). Mouse cancer cell line ID8 (catalog SC0494) was purchased from Yuchi Biology (Shanghai, China). All the cells were cultured in the Dulbecco\u0026rsquo;s modified Eagle\u0026rsquo;s medium (DMEM). All media were added with 10% fetal bovine serum (FBS) at 37\u0026deg;C with or without 5% CO2. All human cell lines were authenticated using short tandem repeat profiling, and all assays were conducted with mycoplasma-free condition. Primary CAFs were isolated from breast tumor tissues \u003csup\u003e\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u003c/sup\u003e and cultured using primary cell culture medium (catalog CX0013, Yuchi, Shanghai, China). All human cell lines were authenticated using short tandem repeat profiling and all assays were conducted with mycoplasma-free. For cell culture, type I collagen (catalog A1048301, Gibco, Thermo Fisher Scientific, MA, USA) was applied to culture plates following the manufacturer\u0026rsquo;s instructions and previous studies \u003csup\u003e\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e,\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eIn vitro assays for cellular functions\u003c/h2\u003e \u003cp\u003eTo evaluate cell proliferation, suspended cancer cells were seeded into a 96-well plate at a density of 5 \u0026times; 103 cells/ml (100 \u0026micro;l/well) and incubated at 37\u0026deg;C. Subsequently, 10 \u0026micro;l of CCK-8 reagent (catalog KGA317s-1000, KeyGEN, Nanjing, China) was added to each well, followed by a 2-h incubation period. The optical density at 450 nm was then measured using a microplate reader. For assessing cell migration and invasion, Transwell chambers were utilized, with or without Matrigel (Corning) coating as needed. Cancer cells (5 \u0026times; 104) in 200 \u0026micro;l of serum-free medium were placed in the upper chamber, while 600 \u0026micro;l of medium containing 10% FBS was added to the lower chamber.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eWestern blotting analysis\u003c/h3\u003e\n\u003cp\u003eCellular total proteins were extracted using a lysis buffer, followed by performing SDS-PAGE (polyacrylamide gel electrophoresis) and Western blotting analysis according to established protocols \u003csup\u003e\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e\u003c/sup\u003e. The primary antibodies used were as follows: E-cadherin (1:1,000 dilution, catalog 60335-1-Ig, ProteinTech), N-cadherin (1:1,000 dilution, catalog A19083, abclonal), COL1A1 (1:1,000 dilution, catalog A24112, Abclonal, Wuhan, China), MMP2 (1:1,000 dilution, catalog 10373-2-AP, ProteinTech), MMP9 (1:1,000 dilution, catalog 10375-2-AP, ProteinTech), FAK (1:1,000 dilution, catalog #3285, Cell Signaling Technology), p-FAK (1:1,000 dilution, catalog #3283, Cell Signaling Technology), and GAPDH (1:10,000 dilution, catalog 10494-1-AP, ProteinTech).\u003c/p\u003e\n\u003ch3\u003eAnimal models and analysis of mouse tumor tissues\u003c/h3\u003e\n\u003cp\u003eAll experimental mice were housed in specific pathogen-free conditions and all animal procedures were approved by the Institutional Animal Care and Use Committee of Jiangnan University (JN.No20240930c0400215[513]). Six to eight weeks old female C57BL/6 mice were inoculated subcutaneously with ID8 cells. Tumor growth was measured with calipers regularly and the volume was calculated as 0.5 length \u0026times; width\u003csup\u003e2\u003c/sup\u003e. On day 1 (average tumors reached 100 mm\u003csup\u003e3\u003c/sup\u003e), mice were randomized into 4 treatment group and each group was treated with PBS, FAK inhibitor Y15 (catalog HY-12444, MCE), FAP inhibitor Talabostat (catalog HY-13233, MCE), and Y15\u0026thinsp;+\u0026thinsp;Talabostat, repectively. The Talabostat group received daily oral administration of talabostat at 20\u0026micro;g/mouse\u003csup\u003e19\u003c/sup\u003e. The Y15 group received daily intraperitoneal injection of Y15 at 30mg/kg. On day 13, mice were sacrificed for downstream analyses. Removed tumors were submitted for immunofluorescence.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eStatistical analysis and data visualization were conducted using R language version 4.0.2 and GraphPad Prism version 6.0. Continuous variables between 2 groups were compared using Student\u0026rsquo;s t test or Mann\u0026ndash;Whitney test as appropriate. Differences among multiple groups were assessed using one-way analysis of variance (ANOVA) or Kruskal\u0026ndash;Wallis test with multiple comparisons as applicable. Categorical variables were analyzed using the chi-square test or Fisher exact probability test based on the conditions. Correlation between 2 variables was evalu- ated using Pearson or Spearman correlation test depending on the conditions. The prognostic significance of categorical vari- ables was determined using the log-rank test. A P value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant and denoted as *P\u0026thinsp;\u0026lt;\u0026thinsp;0.05; **P\u0026thinsp;\u0026lt;\u0026thinsp;0.01; ***P\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for clarity.\u003c/p\u003e \u003c/div\u003e "},{"header":"Result","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003eSingle-cell profiling of non-OCCC and OCCC tumor ecosystems\u003c/h2\u003e \u003cp\u003eTo systematically interrogate the intratumoral heterogeneity of EOC, we performed deep scRNA-seq on ten tumor tissues diagnosed as EOC obtained during first surgery, of which 6 tumors were pathologically splited into the non-OCCC group and 4 tumors were splited into the OCCC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA). Detailed clinical information is shown in Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e. Following quality control, the remaining 59,173 cells were harmonized to remove batch effects and visualized using the Uniform Manifold Approximation and Projection (UMAP) method (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB). These cells were categorized into 12 major cell types (Materials and Methods) and then annotated according to the established gene marker list (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). The cell subtypes are similar in the 10 tumor samples, however, the cell distribution in the tumor ecosystem varies significantly (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Contrastingly, all non-immune stroma lineages including fibroblasts, endothelial cells, and pericytes were significantly more abundant in OCCC group than in non-OCCC group, while immune cells varied slightly, in which macrophages remianed similar predominance in both groups accompanied by fewer T cells, monocytes and neutrophils in OCCC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eE).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eEcosystematic T cell features in non-OCCC and OCCC groups\u003c/h2\u003e \u003cp\u003eTo analyze the T cell features in further, we rearranged the T cell cluster (marked by CD3D, CD3E, CD3G, CD4 and CD8A) into 4 T cell subtypes, including T na\u0026iuml;ve cells (marked by CCR7, SELL, IL7R and TCF7), CD8 T cells (marked by GZMB, NKG7 and PRF1), Treg cells (marked by FOXP3, CTLA4 and IL2RA) and Th2 cells (marked by PDCD1, IL21, GATA3, CCR7, CD44 and TCF7) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). Visualization of four T cell subtypes was performed using UMAP approaches, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB. The majoraty in OCCC group was T na\u0026iuml;ve cells (\u0026gt;\u0026thinsp;75%) while the T cell differentiation in non-OCCC group was reshaped towards anti-tumor immunity with more inhibitory T cells (Th2 and Treg cells) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC). To better understand the roles of T cells in OCCCs, differential gene expression analysis was performed and identified 471 upregulated genes and 622 downregulated genes in OCCC group compared to non-OCCC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). It revealed OCCCs contained significantly less of the effector T cell signatures in comparison and the activiation markers (CCL5, IFNG, GZMA and GZMB)were among the topranked genes (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE). Enrichment pathway analysis was then conducted on the different expressed genes, and the results indicated they were probably involved in antigen processing and presentation, oxidative phosphorylation,chemokine signaling pathway, T cell differentiation and so on (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eEcosystematic macrophages features in non-OCCC and OCCC groups\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, there was no significant difference of macrophages abundance between non-OCCC and OCCC group. Distribution of M1 and M2 macrophages was visualized by UMAP method according to classical macrophage signatures (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). The proportation of M2 macrophages was higher in OCCC group than that in non-OCCC group, which indicated an immunosuppressive state and was consistent with immune tolerance of OCCC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). The gene expression pattern indentified 440 upregulated genes and 315 downregulated genes in OCCC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), of which heat map also showed that macrophages in OCCC group were thought to exhibit an M2-like phenotype which associated with poor prognosis in several malignancies (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD). Enrichment pathway analysis showed that DEGs were associated with immune-related signaling pathway, including TLR signaling pathway, TNF signaling pathway, chemokine signaling pathway and so on (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eE). Then IF staining was conducted to verify the TME heterogeneity between OCCC and non-OCCC patient samples. It was consistent with aforementioned single-cell RNA sequencing analysis that FOXP3\u0026thinsp;+\u0026thinsp;cells (Treg cells) were more in the non-OCCC tumor while CD163\u0026thinsp;+\u0026thinsp;cells (M2 macrophage) were accumulated in the OCCC tumor (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eF).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eEcosystematic CAF features in non-OCCC and OCCC groups\u003c/h2\u003e \u003cp\u003eOCCC tumor ecosystems were quite different from that in non-OCCC tumor with obviouly higher of CAFs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). To further invesgate the heterogeneity of CAFs population, hierarchical clustering was used to seprarte CAFs into 11 subclusters (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Monocle algorithm was applied in pseudotime analysis and all the 11 clusters were aggregated on the basis of gene expression similarities to project their developmental trajectories (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Cluster 1, 5, 6 were located at the middle of CAF cell differentiation and were enriched in OCCC patients, which meant most CAFs in OCCC were in an active state. CAFs in non-OCCC mainly consisted of cluster 0, 4, 7, which were located at the beginning of CAF cell differentiation (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC-D). We continued to study the features of CAFs in the OCCC group. There were 1119 differentially expressed genes in CAF cells from OCCC group compared with non-OCCC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eE). The types of collagen in the two groups were different as COL15A1 were highly expressed in the OCCC group while COL8A1 were more abundant in the non-OCCC group (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eF). Enrichment analysis showed the DEGs participated in several functional pathways, including focal adhesion, ECM-receptor interaction, the TGF-beta signaling pathway, PI3K-Akt signaling pathway, and cGMP-PKG signaling pathway (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eG). In addition, CAFs from OCCC exhibited a strong signature of epithelial mesenchymal transition, such as DLK1, APOE, SULF2 and so on. The density of ECM in OCCC and non-OCCC tumor tissues was evaluated by IF staining. The results indicated that CAFs in OCCC tumors were positive for the canonical CAF markers a-SMA and COL1A1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eH). Furthermore, these CAFs in OCCC possessed active EMT properties, evidenced by remarkble expression of mesenchymal biomarkers such as FAK and N-cadherin at protein levels (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eI). Based on the above results, we validated there was more abundant CAF-derived collagen and more active EMT progression in OCCC tumor ecosystems compared with non-OCCC tumor ecosystems. ECM was reported to influence the ascending of tumor grade. We could propose that collagen deposition in OCCC could induce the EMT and lead to poor clinical prognosis of OCCC patients as a result.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCollagen deposition promoted tumor progeression by stimulating EMT\u003c/h2\u003e \u003cp\u003eTo better understand the effect of collagen deposition on the tumor progeression, we constructed stable cell lines with high expression of collagen (3T3-Coll/3T3-Ctr) and then estabilished a a CAF (3T3-Coll/3T3-Ctr)/ovarian cancer cell (SKOV3 and OVCAR3) transwell coculture system. CCK8 assays showed that the increase of collagen secretion promoted the proliferation of ovarian cancer cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B). Transwell assays showed the increase of collagen promoted tumor cell invasion and metastasis compared to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-D). The expressions of migration markers (MMP2 and MMP9) and EMT markers (N-cadherin, E-cadherin, p-FAK and FAK) were also significantly upregulated in ovarian cancer cells cocultured with collagen-high 3T3 cells in comparison to the control (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eE), which comfirmed collagen accumulation remodeled ECM composition and significantly enhanced epithelial-mesenchymal transition (EMT) of ovarian cancer cells consequently. These findings corroborate our hypothesis demonstrating that extensive collagen deposition within the TME drives activation of EMT in the ovarian cancer cells, thereby promoting OCCC proliferation, invasion, and metastatic dissemination while concurrently diminishing platinum sensitivity.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eEMT inhibitors offset pro-tumorigenic effects induced by collagen deposition\u003c/h2\u003e \u003cp\u003eWe then tested whether EMT inhibitors could suppress ovarian cancer growth and reverse the pro-tumorigenic effects brought by collagen deposition in TME. Firstly, a mixture of 3T3 cells and MFC cells at the ratio of 1:1 were subcutaneously injected into 8 female C57BL/6 mice. The tumor-bearing mice were then divided into four treatment groups, which were treated respectively with FAK inhibition (Y15) alone, EZH inhibition (Talabostat) alone, combination of FAK inhibition and EZH inhibition (Y15 plus Talabostat), or control reagents (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA). FAK inhibition or EZH inhibition did not affect the weights of mice (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). As expected, monotherapy of Y15 or Talabostat treatment slightly reduced the tumor burden (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC), while the combination therapy (Y15\u0026thinsp;+\u0026thinsp;Talabostat) significantly enhanced this tumor-suppressing effect, as shown by retarded tumor growth, lower tumor volume (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eD) and tumor weight at the endpoint (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eE). Y15 or Talabiostats had no significant side effects on various tissues, such as heart, liver, spleen, lung and kidney (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eF). IF staining comfirmed Y15 or Talabostat inhibted EMT effectively and IHC exhibited collagen deposition was sighnificantly reduced in treated groups as a result. The expression of Ki67 also decreased remarkbly in the combination group (Y15\u0026thinsp;+\u0026thinsp;Talabostat) (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eH). Taken together, EMT inhibitors offset the EMT activation induced by collagen deposition and alleviated the tumor burden in tumor-bearing mice model.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCollagen deposition and EMT activation unfavored the survival of patients with ovarian cancer\u003c/h2\u003e \u003cp\u003eTissue microarray was ulitized for multi-labeled immunofluorescence staining to check the expression of a-SMA, COL1A1, FAK and N-cadherin in OCCC tumors. The results showed that the density of CAFs derived from different subtypes of ovarian cancers were notably various, and CAFs accounts for the highest proportion in the clear cell carcinoma, where COL1A1 was significantly overexpressed (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Next, we correlated the positive cell rates with clinical prognosis and found high expression of COL1A1 predicted poor prognosis in the ovarian cancer (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB). We also observed a trend that CAFs were unfavor for overall survival of ovarian cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eC). The colocation of COL1A1 and a-SMA in the ovarian cancer cohorts further supported the above point that high expression of CAFs-derived COL1A1 was associated the grade malignancy of the clear cell carcinoma (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eF). To investigate the clinical significance of FAK and N-cadherin, we performed mIHC staining on tumor microarray of ovarian cancers. EMT was obviouly promoted in the clear cell carcinoma as shown by the extremely high expression of FAK and N-cadherin (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA). Ovarian cancer patients with the high expressions of FAK and N-cadherin tended to have shorter survival time (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eD-E), meanwhile the positive cell rates of FAK and N-cadherin showed a positive correlation with the COL1A1\u0026thinsp;+\u0026thinsp;cell rate in the ovarian cancer, respectively (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eG-H). It corroborated our experimental results that CAFs-derived collagen deposition in TME facilitated EMT progression. Overall, the integrated analysis revealed that COL1A1 was overexpressed in the clear cell carcinoma and favored the progression of EMT, highlighting as key target in predicting poor prognosis.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe profound molecular and histological heterogeneity of EOC significantly contributes to its distinct clinical characteristics, including epidemiological distribution, diagnostic presentation, biological aggressiveness, and therapeutic responsiveness\u003csup\u003e\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e\u003c/sup\u003e. Among histological subtypes, OCCC is one of the most common EOC subtypes, and its incidence rate is just second to HGSC\u003csup\u003e\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u003c/sup\u003e. Patients with OCCC typically present at a median age of 50\u0026ndash;55 years, with approximately 70% diagnosed at early stages (FIGO I-II)\u003csup\u003e\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e\u003c/sup\u003e. This early-stage detection correlates with favorable prognosis, demonstrating 10-year survival rates of 80\u0026ndash;90%\u003csup\u003e35\u003c/sup\u003e. In contrast, non-OCCC patients, most diagnosed as HGSC, present with nonspecific symptoms at advanced stages, exhibiting highly aggressive biological behavior and poor clinical outcomes\u003csup\u003e\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eRemarkably, the clinical trajectory of the OCCC subtypes diverges dramatically in advanced disease. While HGSC maintains consistent aggressiveness throughout its course, OCCC undergoes a malignant transformation during diffierent stages. When it progress to advanced stage (FIGO III-IV), OCCC demonstrates exceptional chemoresistance, with response rates to first-line platinum-based chemotherapy as low as 30%, compared to 70\u0026ndash;80% in HGSC\u003csup\u003e\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u003c/sup\u003e. This therapeutic recalcitrance, compounded by the lack of effective targeted therapies, results in rapid disease progression and dismal survival outcomes for advanced OCCC patients. The mechanism behind still remains unclear.\u003c/p\u003e \u003cp\u003eMost of non-OCCC are thought to arise from the fallopian tube or ovarian epithelium, typically exhibiting characteristic pathological features including solid growth patterns and papillary structures. In contrast, OCCC is believed to originate from endometriotic lesions\u003csup\u003e\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u003c/sup\u003e, pathologically presenting with tubular cystic architectures and distinctive clear cells. Our clinical observations indicate that most OCCC manifest as solid-cystic tumors, with more than 90% occurring unilaterally. These tumors typically achieve substantial dimensions, reaching diameters of 20\u0026ndash;30 cm\u003csup\u003e38\u003c/sup\u003e. Imaging studies reveal that early-stage OCCC commonly displays high CT attenuation values and T1-weighted hyperintensity on MRI, whereas advanced cases exhibit low CT attenuation in cystic components and T2-hyperintense solid components. A small amount of OCCC cases presents with predominantly solid or fibrotic morphology. The distinct cellular origins of these tumor types necessarily result in neoplasms with divergent physicochemical characteristics, each associated with unique tumor microenvironment (TME) compositions. This observation leads us to hypothesize that specific TME components may critically influence OCCC tumor cell behavior, modulating key biological processes such as proliferation, invasion, and sensitivity to chemotherapy.\u003c/p\u003e \u003cp\u003eThrough comparative scRNA-seq analysis of OCCC and non-OCCC tumor samples, we identified a noteworthy phenomenon that the TME of OCCC tumors was significantly different from that of non-OCCC tumors. All non-immune stroma lineages including fibroblasts, endothelial cells, and pericytes were significantly more abundant in OCCC group than in the non-OCCC group, while immune cells varied slightly. Notbly, CAF population dominated in extracellular components of OCCC tumors while the proportions of extracellular component cell populations was more equally in the non-OCCC tumors. It was also worth noting that CAF in the OCCC tumors was more active and showed stronger ability to secret collagen.\u003c/p\u003e \u003cp\u003eTME is a complex ecosystem composed of tumor cells, surrounding non tumor cells (such as immune cells, fibroblasts, and endothelial cells), and ECM. CAFs represent crucial stromal components that are predominantly localized perivascularly or within the peritumoral fibrous stroma\u003csup\u003e\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u003c/sup\u003e. These activated fibroblasts secrete various cytokines, ECM components, and matrix-modifying enzymes\u003csup\u003e\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u003c/sup\u003e. ECM is the core non cellular component of TME, secreted by cells and distributed in the extracellular space, forming a complex network structure. ECM remodeling is one of the hallmark features of solid tumors, and drives malignant biological behaviors such as tumor cell proliferation, stemness maintenance, and immune escape through mechanical transduction\u003csup\u003e\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u003c/sup\u003e. In addition, ECM hardness not only increases interstitial fluid pressure, hinders immune cell infiltration and drug delivery, but also affects the function of immune cells through mechanical signal transduction\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCollagen is the main structural protein of ECM, accounting for over 90%\u003csup\u003e42\u003c/sup\u003e. At present, 28 different types of collagen have been discovered, among which type I collagen (marked as COL1A1) is the most abundant in connective tissue and provides mechanical support\u003csup\u003e\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u003c/sup\u003e. In normal tissues, collagen maintains tissue homeostasis by providing structural support and regulating cellular behavior. However, during tumor progression, there is a significant imbalance in the synthesis, degradation, and remodeling of collagen\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e. A recent study shows that the growth and metastasis of pancreatic cancer cells were dependent on collagen cleavage which activates DDR1 signaling\u003csup\u003e\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u003c/sup\u003e. Collagen remodeling in the TME could promote a more efficient energy procuring process by soluble fragments and then accumulate tumor cell growth\u003csup\u003e\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Linearly aligned collagen fiber provides a highway for tumor cell migration and dissemination in breast cancer\u003csup\u003e\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e. In lung cancers, collagen around tumor cells dictates the migratory trajectory of immune cells and restrict them from infiltration\u003csup\u003e\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e. Through binding to the receptors, collagens can even directly affect the characteristics of tumor-infiltrating immune cells, including proportion, function and phenotype\u003csup\u003e\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e. Furthermore, biomechanical studies demonstrate that collagen deposition increased ECM hardness and such ECM could activate mechanotransduction signaling pathways, which induced epithelial-mesenchymal transition (EMT) and consequently enhanced tumor cell invasiveness and metastatic potential\u003csup\u003e\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e. The collagen-based densified matrix architecture creates a physical diffusion barrier that reduces intratumoral drug penetration by 40\u0026ndash;60%, significantly compromising chemotherapeutic efficacy. Furthermore, our results indicated that CAFs in OCCC possessed active EMT properties, evidenced by remarkble expression of mesenchymal biomarkers such as FAK and N-cadherin at protein levels. We then proposed that collagen deposition in OCCC could induce the EMT and lead to poor clinical prognosis of OCCC patients as a result. In the current study, collagen deposition promoted the proliferation and metastasis of ovarian cancer. The expressions of migration markers (MMP2 and MMP9) and EMT markers (N-cadherin, E-cadherin, p-FAK and FAK) were significantly upregulated in ovarian cancer cells cocultured with collagen-high cells in comparison to the control. EMT inhibitors could offset the EMT activation induced by collagen deposition and alleviated the tumor burden in tumor-bearing mice model. The IF staining results of TMA further confirmed the aforementationed experimental conclusion.\u003c/p\u003e \u003cp\u003eYue et al identified 15 ECM proteins with prognostic significance in OCCC, including LAMB3 and LAMC2, which were shown to confer platinum resistance and promote tumor cell invasion and metastasis\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e. Their study further revealed that increased IFITM1 expression significantly correlated with disease recurrence in OCCC patients. Histopathological evaluation of recurrent versus primary OCCC specimens demonstrated prominent fibrotic remodeling, featuring substantial stromal infiltration of fibroblasts, endothelial cells, and pericytes. Notably, immunohistochemical analysis confirmed marked activation of CAFs in recurrent tumors, with a significant expansion of the POSTN\u0026thinsp;+\u0026thinsp;CAF subset\u003csup\u003e\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. Clinical data from Taiwanese cohorts indicate that while OCCC patients demonstrate substantially lower response rates to first-line platinum-based chemotherapy compared to HGSC\u003csup\u003e\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e, the incorporation of bevacizumab in combination with paclitaxel-carboplatin (PTX\u0026thinsp;+\u0026thinsp;CBDCA) regimens significantly enhances therapeutic efficacy\u003csup\u003e\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. These findings corroborate our experimental results demonstrating that CAF activation drives extensive collagen deposition within the TME, thereby promoting OCCC proliferation, invasion, and metastatic dissemination while concurrently diminishing platinum sensitivity.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eOverall, our current study demostrated collagen deposition stimulated EMT progression of OCCC and then contributed to its poor clinical progrosis and innovatively proposed EMT inhibitors for OCCC patients as a new treatment strategy. Future research will focus more on effect of ECM on the growth and progression of OCCC.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval for the single-cell sequencing data was granted by Clinical Research Ethics Committees at Wuxi Maternal and Child Health Care Hospital (Ethics No. 2023-01-0511-07) and was conducted in accordance with the Declaration of Helsinki. The Biotechnology Ethics Review Committee at AiFang Biological Co., LTD approved the ethical use of the TMA (No. HN20250401) and was conducted in accordance with the Declaration of Helsinki. The experiments were undertaken with the understanding and written consent of each subject. The participants allowed the researchers to use their tissue during the tumor resection and conduct the study accordingly.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll data are available from the authors upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYG and JC conceived the study and participated in the study design, performance, coordination, and project supervision. SN, BZ, JZ, and WL collected the public data and conducted the bioinformatics analysis. SZ collected the tumor samples and analyzed the clinical data. HL and JM performed in vitro and \u003cem\u003ein vivo\u003c/em\u003e experiments and the tissues staining. SN and HL wrote the draft. YG and JC revised the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Shuqi Li for data visualization and manuscript writing; GENE for single-cell sequencing.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was supported by Top Medical Expert Team of Wuxi City's \"Taihu Talent Program\" in 2025; Jiangsu Funding Program for Excellent Postdoctoral Talent (No.2023ZB135).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWebb, P.M., Jordan, S.J.: Global epidemiology of epithelial ovarian cancer. Nat. Rev. Clin. 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Ann Oncol 30, 672\u0026ndash;705\u003c/em\u003e (2019)\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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"ovarian cancer, single-cell transcriptome, cancer-associated fibroblast, prognosis","lastPublishedDoi":"10.21203/rs.3.rs-8586120/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8586120/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eOvarian clear cell carcinoma (OCCC), a therapy-refractory epithelial ovarian cancer subtype with distinct tumor microenvironment (TME) features, has unclear links between cancer-associated fibroblasts (CAFs), extracellular matrix (ECM) remodeling (especially collagen deposition) and disease progression.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe characterized TME via single-cell RNA sequencing of 10 fresh tumor samples (4 OCCC, 6 non-OCCC), validated functions using CAF-ovarian cancer cell co-culture systems, verified the collagen-EMT axis in syngeneic mouse models with targeted inhibitors, and assessed clinical relevance via tissue microarray immunohistochemistry and survival correlation analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSingle-cell data revealed enriched activated CAFs and abundant COL1A1 in OCCC. Collagen-rich ECM induced epithelial-mesenchymal transition (EMT), boosting cancer cell proliferation, invasion and metastasis; EMT pathway inhibition attenuated collagen-driven tumor growth in vivo. High COL1A1 and EMT marker (FAK, N-cadherin) expression correlated with poor prognosis.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eCAF-driven collagen deposition promotes OCCC aggressiveness via EMT activation, and targeting the collagen-EMT axis may serve as a novel therapeutic strategy for this chemoresistant subtype.\u003c/p\u003e","manuscriptTitle":"Single-cell transcriptome analysis reveals the targeting of epithelial and fibroblast interactions in ovarian cancer","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-16 14:38:03","doi":"10.21203/rs.3.rs-8586120/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"communications-biology","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"commsbio","sideBox":"Learn more about [Communications Biology](http://www.nature.com/commsbio/)","snPcode":"","submissionUrl":"","title":"Communications Biology","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Communications Series","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"4cb39a05-cb2a-4a28-83b8-6f9262b5988a","owner":[],"postedDate":"February 16th, 2026","published":true,"recentEditorialEvents":[{"type":"editorInvitedReview","content":"This content is not available.","date":"2026-05-07T17:43:14+00:00","index":3,"fulltext":"This content is not available."}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":62723628,"name":"Biological sciences/Cancer/Cancer microenvironment"},{"id":62723629,"name":"Health sciences/Oncology/Cancer/Cancer therapy/Cancer therapeutic resistance"}],"tags":[],"updatedAt":"2026-02-16T14:38:03+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-16 14:38:03","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8586120","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8586120","identity":"rs-8586120","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}