Metabolic molecular subtyping of ovarian cancer reveals the role of oleic acid-CD36 in facilitating cisplatin resistance

Metabolic molecular subtyping of ovarian cancer reveals the role of oleic acid-CD36 in facilitating cisplatin resistance | 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 Metabolic molecular subtyping of ovarian cancer reveals the role of oleic acid-CD36 in facilitating cisplatin resistance Lixiang Xue, jiagui Song, Xiao Huo, Yunyun Guo, Tianhui He, Yinjia Li, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7929093/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Current histopathological classification systems for ovarian cancer cannot adequately predict platinum chemotherapy response, hindering personalized therapeutic strategies. Here, through integrative analysis of multiple datasets from TCGA, ICGC and GEO databases, we stratify patients into metabolic associated chemotherapy-refractory and chemotherapy-sensitive types (MCRT, MCST for short) with distinct prognosis by 20 genes. Lipidomic profiling of ascites from patients with ovarian cancer further identifies oleic acid as a hallmark metabolite in MCRT cases. Oleic acid treatment facilitates the resistance to cisplatin in ovarian cancer cells and patient-derived organoids. Mechanistically, oleic acid activates and promotes nuclear translocation of YAP through CD36. Inhibition of SCD1—genetically or pharmacologically—synergizes with cisplatin in 26 patient-derived organoids and suppresses tumor growth in xenograft models. Our study proposes a new metabolic classification of ovarian cancer that can predict chemotherapy sensitivity. Moreover, it discovers the role of the oleic acid-CD36-YAP signaling axis in promoting cisplatin resistance. These findings shed light on how oleic acid facilitates the resistance to cisplatin in ovarian cancer and provide a potential therapeutic strategy for treating patients with ovarian cancer. Health sciences/Biomarkers/Predictive markers Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance ovarian cancer metabolic subtyping platinum resistance oleic acid CD36 Hippo/YAP pathway SCD1 inhibition patient-derived organoids precision oncology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Ovarian cancer is the most aggressive gynecologic malignancy with the poorest prognosis (1, 2). Current standard treatment for ovarian cancer involves cytoreductive surgery and combination chemotherapy based on platinum agents. However, approximately 80% of patients experience recurrence after standard treatment and ultimately succumb to chemotherapy resistance (3). Chemoresistance in ovarian cancer is closely related to metabolic characteristics. In 2019, Gentric et al. uncover the metabolic heterogeneity of high-grade serous ovarian cancer (HGSOC), categorizing it into two distinct metabolic subtypes: low-OXPHOS and high-OXPHOS (4). Furthermore, substantial evidence indicates that "metabo-typing" of a patient's tumor represents a highly promising approach, offering critical insights for the rational design of metabolic combination therapies that can be effectively translated into clinical practice (5, 6). Emerging evidence highlights the critical role of lipid metabolism in tumorigenesis and therapeutic resistance across diverse cancer types. For instance, aberrant fatty acid synthesis and uptake are hallmarks of breast cancer, where lipid droplet accumulation drives tumor growth and metastasis (7, 8). In prostate cancer, androgen receptor signaling synergizes with lipid metabolic reprogramming to promote castration resistance (9). These findings underscore the broad significance of lipid metabolic pathways as therapeutic targets. Oleic acid (FA 18:1 n-9) belongs to monounsaturated fatty acids (MUFA), which can be obtained via both diet and endogenous synthesis. Stearoyl-CoA Desaturase 1 (SCD1) is a key enzyme in oleic acid de novo synthesis, catalyzing desaturation of saturated fatty acids (SFA) (10). Oleic acid can be imported into cells by CD36 fatty acid translocase. CD36 protects cells from SFA-induced toxicity through selective MUFA uptake during tumor progression (11). Although oleic acid has many beneficial effects on human body, such as improving cardiovascular health, stabilizing blood glucose levels, and anti-inflammatory and antioxidant effects (12), it has also been reported in recent years to promote tumor occurrence and metastasis, such as enhancing cell proliferation and reducing apoptosis in colorectal cancer and protecting melanoma cells from ferroptosis and increasing their metastatic tumor-forming capacity (13), (14). Given these conflicting effects of oleic acid, it becomes crucial to further explore the underlying mechanisms that govern its dual roles in health and disease, as well as to investigate potential strategies to harness its benefits while mitigating the risks associated with tumor promotion. In this study, we used metabolic genes to classify clinical patients with ovarian cancer into chemotherapy-refractory and chemotherapy-sensitive types (MCRT, MCST for short). Through lipid metabolomics, we identified oleic acid was significantly enriched in ascites of chemotherapy-refractory patients. We further elucidated specific mechanisms of oleic acid induced chemoresistance through multiple dimensions, including cell lines, patient-derived organoids, and animal models. Additionally, we explored targeting oleic acid synthesis as a therapeutic strategy for chemotherapy-refractory patients, providing new treatment options for the clinical management of ovarian cancer. Results Molecular subtyping of ovarian cancers using metabolic profiling. Patients with ovarian cancer react differently to combination chemotherapy based on platinum agents. To investigate whether different metabolic characteristics of ovarian cancer can distinguish chemotherapy sensitive and resistant patients, we used metabolism-related genes to classify patients and analyze their prognosis. We employed 2752 metabolism-associated genes encoding all known human metabolic enzymes and transport proteins obtained from previous studies (Table 1) and selected 1650 metabolism-related genes with high variability (MAD ≤ 0.5) and significant prognostic value (p < 0.05) (15). Samples were separated into three subtypes based on the Non-negative Matrix Factorization (NMF) clustering method (Supplementary Figure 1, Figure 1A). Interestingly, we found that C1 subtype owns the poorest prognosis and C2/C3 owns better prognosis, which indicates C1 subtype represents the chemo-refractory and C2/C3 represents the chemo-sensitive (Figure 1B). For convenience, we termed C1 subtype “metabolic signature in chemotherapy-refractory types” (MCRT for short) and C2/C3 subtypes “metabolic signature in chemotherapy-sensitivity types” (MCST for short). To simplify the 1650 metabolism-related genes mentioned above, we analyzed and showed top genes expressed most in both subtypes (Figure 1C). In addition, this gene classifier was used to replicate subtype prediction in the RNASeqdat dataset. Evaluation of the consistency between this gene classifier and the original metabolic gene-based prediction indicated a consistency of 54% for C1 (MCRT subtype), 93% for subtype C2, and 79% for subtype C3 (C2 and C3 consist MCST subtype, Figure 1D). These results suggest that a signature of 60 genes can reliably identify subtypes of ovarian cancer in a reproducible manner. In addition, we performed RNAseq detection and analysis on four platinum-resistant patients with ovarian cancer (#46-49) and four platinum-sensitive patients (#50-53, Supplementary Table 1). The experimental results showed that the top 20 genes of the MCRT subtype were highly expressed in platinum-resistant patients with ovarian cancer (Figure 1E). The MCRT subtype exhibits elevated lipid metabolism compared to the MCST subtype. To elucidate the specific metabolic characteristics in MCRT and MCST subtypes, we compared the ssGSEA scores of metabolic processes between the two subtypes, and defined subtypes based on enriched metabolic pathways (Supplementary Figure 2A). The classification of MCRT and MCST is independent of classical TNM subtype (Figure 2A, Supplementary Figure 2B-E). MCRT subtype is considered as unsaturated fatty acids and glycolysis related subtype, MCST subtype is characterized by hormone biosynthesis and oxidative phosphorylation. It is noteworthy that among the 42 specific metabolic features in the poor-prognosis MCRT subtype, 11 are related to lipid metabolism, including biosynthesis of unsaturated fatty acids, ether lipid metabolism, and fatty acid degradation. Among these, fatty acid transporter CD36 in lipid metabolism (Figure 2B), glycerolipid metabolism (Figure 2C), glycerophospholipid metabolism (Figure 2D), arachidonic acid metabolism (Figure 2E) is significantly higher in MCRT subtype compared to MCST subtype. These results suggest a positive correlation between lipid metabolism and chemotherapy resistance in ovarian cancer. Oleic acid is elevated in the ascites of patients with platinum resistance. To investigate key lipid metabolites in the tumor microenvironment (TME) that may be involved in chemoresistance, we analyzed ascites samples of patients with platinum sensitivity information. We identified 3 cases of platinum-sensitive ovarian cancer without recurrence (patient #5, #7, #17) and 3 cases of platinum-resistant ovarian cancer with recurrence (patient #8, #10, #11) based on the follow-up prognosis information (Supplementary Table 1). We performed qPCR assay and found that patients with recurrence did express higher levels of MCRT subtype gene signature (Figure 2F). Therefore, these 6 patients could be divided into MCRT and MCST subtypes, and their recurrence was consistent with the prediction of subtypes. Since high expression of CD36 was enriched in MCRT subtype (Figure 2B), we further analyzed FFA (free fatty acid) levels in ascites samples. The results showed that the levels of FFA 18:1 and FFA18:2 in ascites from MCRT subtype were significantly elevated comparing to those of the MCST subtype, and FFA 18:1 was more abundant than FFA18:2 (Figure 2G). Moreover, the mRNA levels of SCD1 were higher in MCRT subtype (Figure 2H). These findings suggest that oleic acid is enriched in TME of MCRT patients and may contribute to cisplatin resistance and recurrence in patients with ovarian cancer. Oleic acid reduces sensitivity to cisplatin in both ovarian cancer cell lines and patient-derived organoids. To further verify the correlation between MCRT/MCST subtypes and drug resistance in vitro , we first divided ovarian cancer cell lines into two groups as described above. Analysis of 47 human ovarian cancer cell lines from the Cancer Cell Line Encyclopedia (Ghandi et al., 2019), allowed us to stratify the ovarian cancer cell lines into metabolic subtypes MCRT and MCST (Figure 3A). Ovarian cancer cell lines SKOV3 and HEY which fit the MCRT metabolic subtype, and A2780 and OVCAR8 which fit the MCST metabolic subtype were chosen for further studies. It was validated that SKOV3 and HEY cells did express MCRT genes at higher levels than A2780 and OVCAR8 cells (Figure 3B). We further verify the sensitivity of these two types including four cell lines to cisplatin. The results showed that MCST subtype, A2780 and OVCAR8 were more sensitive to cisplatin, while MCRT subtype, SKOV3 and HEY exhibited resistance to cisplatin (Figure 3C-F). These findings suggest a close association between ovarian cancer metabolic subtypes and sensitivity to cisplatin. To investigate the role of oleic acid in chemoresistance, we treated ovarian cancer cells with oleic acid and detected MCRT genes expression and drug sensitivity. Notably, we found that supplementation of oleic acid increased the expression of MCRT genes in MCST cells (OVCAR8), suggesting that oleic acid induced subtype transformation of ovarian cancer from MCST to MCRT (Figure 3G). Furthermore, oleic acid increased cell viability under cisplatin treatment, blunting the sensitivity of cisplatin in both MCRT subtype cells (SKOV3 and HEY, Figure 3H-I) and MCST subtype cells (A2780 and OVCAR8 Figure 3J-K). Consistent with this, oleic acid promoted the survival of three patient derived organoids under cisplatin treatment (Figure 3L-M). These results indicate that the increase of oleic acid in TME contributes to metabolism reprogramming and cisplatin resistance of ovarian cancer cells. Oleic acid promotes cisplatin resistance through YAP activation. To explore the mechanism by which oleic acid promotes cisplatin resistance in ovarian cancer, we treated SKOV3 cells with oleic acid and conducted RNA-seq analysis. Our data showed that oleic acid altered the Hippo/YAP signaling pathway and promoted the expression of downstream target genes of YAP (Figure 4A-B). Moreover, we found that YAP signatures (including YAP1, TEAD1, and CYR61) and lipid synthesis signatures (including SCD, FASN, ACACA, SREBF1, and SREBF2) exhibit a significant positive correlation in ovarian cancer of TCGA (Figure 4C). YAP (Yes-associated protein) is a mechanosensitive transcriptional co-factor positively correlated with cancer progression and chemoresistance, which is phosphorylated and inhibited by Hippo pathway (16) (17). Therefore, we treated both MCRT (SKOV3) and MCST (OVCAR8) ovarian cancer cell lines with varying concentrations of oleic acid and measured the transcription level of Hippo signaling factors, including LATS1, YAP, CYR61, TEAD1-4 . We found that oleic acid activates YAP and its downstream targets in a dose-dependent manner (Figure 4D, E). Moreover, protein levels of YAP and its downstream target CYR61 are upregulated in the presence of oleic acid (Figure 4F, G). It is reported that CYR61 is a potential predictive molecule that may play an important role in the development of platinum resistance, and CYR61 + subpopulation was identified as the relapse-initiators in ovarian cancer (18, 19). Additionally, the nuclear translocation of YAP in both MCRT and MCST ovarian cancer cell lines following oleic acid treatment was demonstrated using immunofluorescent analysis (Figure 4H, I, Supplementary Figure 3). However, oleic acid did not decrease the phosphorylation of YAP and affect the total level and the phosphorylation of LATS1, which is an upstream YAP inhibitor in classical Hippo pathway by phosphorylating YAP (Figure 4F, G). These findings suggest that oleic acid activated YAP in other ways. Oleic acid can be synthesized through the conversion of stearoyl-CoA by the enzymatic action of SCD1 (20). In this study, we found that SCD1 was highly expressed in MCRT subtype ovarian cancer (Figure 2H), suggesting its association with oleic acid enrichment in TME. We found the addition of oleic acid after knocking down SCD1 can reverse the downregulation of YAP, which is manifested by TEAD and CYR61 protein levels (Figure 6D, E). It indicates that SCD1 regulates YAP activation and downstream target gene expression through oleic acid. Moreover, we found that loss of YAP inhibits the promoting effects of oleic acid on cell viability under cisplatin treatment (Figure 4L, M). These data indicate that oleic acid promotes cisplatin resistance by activating YAP-CYR61 function in ovarian cancer cells. Oleic acid promotes YAP activation and nuclear translocation via CD36. To explore the mechanism by which oleic acid activates YAP, we examined the correlation between oleic acid transporters (CD36, FATP1-6), free fatty acid receptors (FFAR1, FFAR4), and YAP along with its downstream target genes (YAP1, TEAD1-4, and CYR61) in both normal ovarian tissues and ovarian cancer. Our results indicate that CD36 shows a significant positive correlation with YAP and its downstream target genes (YAP1, TEAD1-4, and CYR61) in both normal ovarian tissues (Supplementary Figure 4 and Figure 5A) and ovarian cancer (Supplementary Figure 4 and Figure 5B). Furthermore, knockdown of CD36 via siRNA downregulated the protein levels of YAP, TEAD, and CYR61 in both MCRT and MCST subtype ovarian cancer cell lines (Figure 5C, D). The above results indicate that CD36 can activate YAP and its downstream target genes. To further validate whether oleic acid activates YAP and promotes its nuclear translocation through CD36, we observed an interaction between the key enzyme in MUFA synthesis, stearoyl-CoA desaturase SCD and CD36 in MCRT subtype (Figure 5E). Interestingly, analysis of TCGA data revealed that patients with ovarian cancer with low co-expression of both SCD1 and CD36 had significantly better clinical outcomes compared to those with high co-expression of both genes (Figure 5F). Moreover, our results showed that CD36 inhibitor SOS eliminated the activation of YAP and its downstream target by oleic acid in both MCRT and MCST subtype ovarian cancer cell lines (Figure 5G, H). Furthermore, inhibition of CD36 eliminated the nuclear translocation of YAP promoted by oleic acid in both MCRT subtype ovarian cancer cell lines (Figure 5I). These results suggest that oleic acid activates YAP through oleic acid transporter CD36. We also observed that CD36 expression was significantly induced by oleic acid treatment (Figure 5G, H), suggesting the regulatory role of oleic acid on CD36 and the downstream signaling. Inhibition of SCD1 enhances the chemotherapy effect of cisplatin in vitro and in vivo . To test whether inhibiting oleic acid synthesis would affect cancer cell sensitivity to cisplatin, we treated ovarian cancer cell lines and patient-derived organoids with cisplatin and SCD1 inhibitors MF438 in combination, and measured cell viability. Our data indicate that SCD1 inhibitors significantly enhance the sensitivity to cisplatin in MCRT and MCST subtype cell lines (Figure 6A-B, Supplementary Figure 5A-B) and patient-derived organoids (Figure 6C). We also treated ovarian cancer cell lines with a combination of cisplatin and the CD36 inhibitor SOS, but the effect was not as significant as that of the MF438 and cisplatin combination (Supplementary Figure 5C, D). Furthermore, both MF438 alone and in combination with cisplatin were able to downregulate the protein levels of YAP and CYR61 in both MCRT and MCST ovarian cancer cell lines (Figure 6D, E), which indicate that both MF438 alone and its combination with cisplatin exert therapeutic effects by inhibiting YAP activation. We further validated these findings in patient-derived ovarian cancer organoids. The results demonstrated that the combination of MF438 and cisplatin enhanced inhibitory effects across all tested patient-derived organoids (PDOs) (n=26) (Figure 6F, patient #20-45 in Supplementary table 1). Interestingly, we cultivated PDOs of MCRT (#28, #31, #34) and MCST (#29, #36, #37) subtypes (Figure 6G). We found that MCRT subtype PDOs (Figure 6H-K) were more resistant to cisplatin than MCST subtype (Figure 6L-O), and both subtypes were sensitive to cisplatin combined with MF438 (Figure 6H-O). These results suggest that inhibiting oleic acid synthesis by targeting SCD1 can increase the sensitivity to cisplatin of platinum-resistant ovarian cancer (MCRT subtype). To determine whether SCD1 inhibition affects the progression of ovarian cancer in vivo , we knocked down SCD1 by shRNA in mouse ovarian cancer cell line ID8 (Figure 7A, B). ID8-luci cells stably transfected with shNC or shSCD1 plasmid were injected into the peritoneal cavity, and the tumor progression was monitored weekly by an in vivo imaging system (IVIS) (Figure 7C, D). Knockdown of SCD1 resulted in attenuated tumor growth and ascites formation (Figure 7E, F). We then examined the chemotherapy effect of combination of SCD1 inhibitor and cisplatin in vivo . Nude mice were subcutaneously inoculated with ID8 cells, and treated with MF438, cisplatin or drug combination (n = 5, Figure 7G). Tumor xenografts grew slower and smaller (Figure 7H-J) under combined treatment compared to the control or monotherapy groups. Thus, we concluded that combination of SCD1 inhibitor with cisplatin enhances the chemotherapy effect of cisplatin in nude mice. Discussion At present, the clinical classification of ovarian cancer follows the WHO histological classification system, which categorizes epithelial ovarian cancer into five main histological subtypes, including high-grade serous carcinoma (70%), endometrioid carcinoma (10%), clear cell carcinoma (10%), mucinous carcinoma (3%), and low-grade serous carcinoma (<5%) (21). Nevertheless, up to now, this existing classification fails to effectively predict the prognosis of chemotherapy. Meanwhile, substantial evidence has indicated that disordered fatty acid metabolism represents a crucial hallmark of ovarian cancer. Therefore, we developed a 20-genes model which can stratify ovarian cancer into metabolic subtypes including “metabolic signature in chemotherapy-refractory types” (MCRT for short), and “metabolic signature in chemotherapy-sensitivity types” (MCST for short). Based on this novel classification, we further demonstrated that oleic acid was elevated significantly in TME of the MCRT subtype patients with ovarian cancer, and oleic acid contributed to cisplatin resistance through CD36-YAP axis. Thus, this work offers a new perspective on precisely classifying ovarian cancer and guiding subsequent chemotherapy and prognosis evaluation. In recent years, extensive research has underscored the close relationship between lipid metabolism and the initiation and progression of ovarian cancer. Besides providing energy, lipids are widely distributed in cellular organelles and serve as essential bioactive molecules involved in multiple signaling pathways regulating functions such as inflammation, immunity, and cell proliferation and differentiation (22). In previous study, we have demonstrated that genes expression related to polyunsaturated fatty acid metabolism is correlated with prognosis in ovarian cancer (23). Oleic acid is reported to be one of the most abundant fatty acids in ovarian cancer ascites (24), and our assay showed similar results. However, the role of oleic acid in TME in regulating cancer progression has not been fully studied. Oleic acid stimulates cell proliferation and glucose transporter transcription through PPARα activation in ovarian cancer cells (25). Our recent study has revealed that oleic acid activated TGFβ-Smad3 signaling and promoted ovarian cancer metastasis (26). In this study, we found that higher levels of oleic acid exist in TME of patients with MCRT subtype ovarian cancer, correlated with poor prognosis. Supplementation of oleic acid can transform MCST to MCRT subtype, indicating the metabolic remodeling role of oleic acid in TME on cancer cells. In addition, we found supplementation of oleic acid promotes cisplatin resistance of ovarian cancer. RNA-seq and the validation results revealed that oleic acid inhibits Hippo signaling pathway and promotes the nuclear translocation of YAP, which is reported to be an oncogene in ovarian cancer progression and drug resistance (27). The Hippo/YAP signaling pathway plays critical roles in development (28), fibrosis (29), and the pathogenesis of various tumors (30). Previous studies have showed that inhibiting SCD1, the key enzyme of oleic acid synthesis, blocks nuclear translocation of YAP in gastric cancer and lung cancer stem cells (10), which is consistent with our results. In this study, SCD1 is found upregulated in MCRT subtype ovarian cancer, and play an important role in inducing cisplatin resistance by oleic acid synthesis and YAP activation. Fatty acid (FA) uptake is a cellular process facilitated by various FA transporters, including CD36, FATPs, and FABPs (31). Previous studies have shown that cisplatin-resistant ovarian cancer cells exhibit increased FA uptake (32). In this study, the results show that high levels of CD36 expression are enriched in MCRT subtype ovarian cancer. Oleic acid treatment significantly induced the expression of CD36, suggesting that CD36 is upregulated by oleic acid in TME. CD36 plays an essential role in the bioenergetic adaptation of ovarian cancer cells in the adipocyte-rich microenvironment and governs their metabolic plasticity (33). Our results indicated that CD36 knockdown significantly reduced YAP and its target genes. We also prove that oleic acid regulates Hippo/YAP signaling and promotes the YAP nuclear translocation dependent on CD36, suggesting the role of CD36 in mediating oleic acid induced chemoresistance in ovarian cancer. Oleic acid upregulates the mRNA and protein levels of YAP, but does not inhibit the phosphorylation of LATS and YAP. Phosphorylation of YAP proteins by LATS promotes their cytoplasmic localization and their degradation (34). Therefore, our findings indicate oleic acid activates YAP signaling independent of Hippo pathway. However, the mechanism by which oleic acid-CD36 activates YAP still requires further study. We attempted to overcome cisplatin resistance by targeting oleic acid intake or production using CD36 or SCD1 inhibitors, and the results show that SCD1 inhibitor MF438 increases the sensitivity of ovarian cancer cells to cisplatin. Organoids could maintain the genetic features, histopathological characteristics, and tumor heterogeneity of the originating ovarian cancer tissue. They accurately reflected the sensitivity of different subtypes of ovarian cancer to standard platinum-based therapy. (35). As the excellent models for drug screening experiments and personalized therapy (36-38), we cultured 26 patient-derived organoids, and proved that combination of SCD1 inhibitor MF438 can effectively enhance the sensitivity of cisplatin in patient-derived organoids, especially in MCRT subtype patients. Our results indicated that combined targeting of SCD1 could be a promising therapeutic strategy for ovarian cancer chemotherapy. In summary, this study firstly developed the new molecular typing based on metabolic genes is increasingly helpful for precise stratification and personalized treatment of patients with ovarian cancer; secondly, a new perspective has been proposed on the biological role of oleic acid in pathological conditions. Additionally, although oleic acid is associated with a reduction in cardiovascular diseases, the intake of oleic acid may need to be reconsidered for patients with cancer, especially those MCRT subtype which is fatty acid metabolic dominant and cisplatin resistance. Lastly, we propose a new metabolic intervention combination strategy that can further enhance sensitivity to platinum-based drugs. Materials and methods Human ovarian ascites and tissues Ovarian cancer ascites and tissues are clinical surgery samples with informed consent from Peking university third hospital, and was approved by Medical Ethics committee of Peking University Third Hospital with approval number LM2024117. Ascites was collected from 19 ovarian patients and another 26 ovarian cancer tissues were generated into ovarian cancer organoids. Patient-derived ovarian cancer organoids Fresh surgical samples of ovarian cancer were directly obtained from clinical sources and treated under sterile conditions. The samples were divided into three parts for subsequent protein extraction, RNA isolation, and immunohistochemistry analysis, respectively. Tissue designated for organoid culture was initially rinsed multiple times with cold PBS and then transferred to clean culture dishes. Following this, the tissue was minced with sterile scissors in aseptic conditions and transferred to sterile 15 mL centrifuge tubes. A mild cell dissociation reagent (Stem Cell) was added to the tubes, resulting in a final volume of 10 mL, and left to incubate on a room temperature shaker for 0.5-1 hour. After centrifugation at 290g for 5 minutes at room temperature, the supernatant was discarded, and the cell pellet was resuspended in 1 mL of DMEM/F12 culture medium supplemented with 1% BSA and antibiotics. The suspension was then passed through a 70 μm cell strainer into new centrifuge tubes. Following cell counting, the cells were mixed with Matrigel at a 1:1 ratio and dispensed into pre-incubated 24-well plates within a 37 ℃ cell culture incubator. After allowing the Matrigel to solidify for 10 minutes, ovarian cancer organoid culture medium was added to each well. This medium, comprising DMEM/F12 with HEPES and penicillin-streptomycin, along with WNT3A, RSPO1, B27 (50×), hEGF, Nicotinamide (1M), FGF10 (100 μg/mL), Noggin, and other components, was refreshed every 3-4 days based on the growth status of the organoids. Passaging was performed approximately every two weeks. Organoid chemosensitivity assay After preparation, the organoids were cultured for 7 days before undergoing the chemosensitivity assay. Subsequently, the organoids were passaged and transferred into a 96-well plate, with each well containing 2000 cells (10 μL) of the culture. Following a 72 hours incubation period, cisplatin, MF438 (SCD1 inhibitor) or combination was added to the medium and incubated for an additional three days. Organoid viability was then assessed using the CellTiter-Glo 3D Cell Viability Assay (Promega, USA) and measured with the GloMax Luminometer (Promega). Ovarian cancer data aource and preprocessing RNA-Seq data and corresponding clinical follow-up information data for ovarian cancer (OV) from the TCGA database (https://portal.gdc.cancer.gov/) was downloaded. Additionally, we obtained the RNA-Seq dataset and corresponding clinical follow-up information data for the OV-AU dataset from the ICGC website (https://dcc.icgc.org/). TCGA and ICGC expression profile data preprocessing method: (1) Convert the expression profiles to gene symbol format; (2) Perform log2 transformation on the expression profile data; (3) Utilize the ComBat function in the R package sva to eliminate batch effects between these two datasets, merging them into a single dataset, hereinafter referred to as RNASeqdat (Supplementary Figure 1A, B). The CEL files for the following five datasets from the GEO database were downloaded: GSE18520, GSE19829, GSE26193, GSE30161, and GSE63885. The preprocessing method for the GEO expression profile data is as follows: (1) Use the RMA function in the affy R package to preprocess the expression profile data and standardize it to obtain the expression profile of the dataset. (2) Utilize the ComBat function in the sva R package to eliminate batch effects between these five datasets, merging them into a single dataset, hereinafter referred to as GSEdat (Supplementary Figure 1 E, F). (3) Convert the probes to gene symbols based on the annotation file of GPL570 (when multiple probes correspond to the same gene symbol, take the median value as the expression profile of the gene symbol; when one probe corresponds to multiple gene symbols, remove the expression of that probe). (4) Retain only ovarian cancer tumor samples with survival time and survival status. The data were downloaded on February 1, 2021. For the clinical data, we removed samples without survival time and survival status. The clinical statistics of the filtered samples were shown below (Table 1). Table 1. clinical statistics of the filtered samples Identification of ovarian cancer subtypes We performed Non-negative Matrix Factorization (NMF) clustering on the set of 2752 metabolism-related genes encoding all known human metabolic enzymes and transport proteins obtained from previous studies (Table 2) (15). Firstly, we filtered out metabolism-related genes with low median absolute deviation (MAD) values (MAD ≤ 0.5) from the RNASeq dataset for all ovarian cancer (OV) patients. Subsequently, we conducted univariate Cox analysis using survival data and the coxph function from the R package survival (V3.1-12), selecting p < 0.05 as the threshold for filtering, to assess the correlation between all candidate genes and overall survival (OS) (S2_table.txt). Finally, based on the selection of 1650 metabolism-related genes with high variability (MAD ≤ 0.5) and significant prognostic value (p < 0.05) (S3_table.txt), we used the R package NMF (V0.23.0) (39) to perform molecular subtyping of the RNASeqdat samples, selecting the value of k at which the genetic correlation coefficient begins to decrease as the optimal number of clusters. Furthermore, we utilized the method of t-Distributed Stochastic Neighbor Embedding (t-SNE) based on the mRNA expression data of the aforementioned metabolism-related genes to validate subtype assignments. We applied the same approach to the GSEdat dataset using the same candidate genes. Additionally, we used Gene Pattern class label mapping (SubMap), a method for assessing similarity between molecular subtypes based on independent patient cohorts' expression profiles, to analyze the similarity between the subtypes identified in RNASeqdat and GSEdat datasets, to determine whether the subtypes identified in these two datasets are correlated. Single-sample gene set enrichment analysis (ssGSEA) based on the features of metabolism-related genes Gene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for gene set enrichment analysis, which can estimate scores for certain pathways or signatures based on transcriptomic data. Single-sample Gene Set Enrichment Analysis (ssGSEA) is an extension of the GSEA method that calculates enrichment scores for each sample and gene set pair. Each ssGSEA enrichment score represents the degree to which the members of a specific gene set are coordinately upregulated or downregulated in the sample (40). We obtained 113 metabolism-related gene features from previously published studies (41) and assessed them using the R packages GSVA and GSEABase (V1.50.1). We conducted differential analysis of metabolic process scores between subtypes using the Kruskal-Wallis Test. Classifier construction and performance validation Differential expression analysis of genes between ovarian cancer (OV) subtypes was performed using the R software package limma (42). Genes with |fold change| > 1.5 and false discovery rate (FDR) 0 were chosen, resulting in a total of 60 genes across the three subtypes), thus generating a 60-gene classifier. Nearest template prediction (NTP) analyses were conducted for predictive analysis, and the results were compared with previous classification results based on the NMF algorithm. Cell line classification The transcriptomes of 47 human ovarian cancer cell lines from the Cancer Cell Line Encyclopedia database was downloaded. Using the R package "pamr," we performed sample classification using the nearest shrunken centroids algorithm. Based on the prediction results obtained from "pamr" analysis, we classified the cell lines into ovarian cancer metabolic pathway subtypes. RNA sequencing sample preparation and data processing RNA extraction from samples was performed using Trizol (Thermo Fisher Scientific, 15596018) following the manufacturer's instructions. Subsequently, specific libraries were prepared using the NEBNext UltraTM RNA Library Prep Kit (NEB #E7490). The purified libraries were then processed with beads (AMPure XP system). Sequencing was conducted on the Novaseq 6000 platform (Illumina, USA). Library preparation and sequencing were carried out by Shanghai Jiayin Biotechnology Co., Ltd. For data analysis, the raw RNA-seq reads were filtered using Trimmomatic software to remove adapter sequences and low-quality reads (43). Following that, STAR was utilized to align the clean reads to the hg38 reference genome (44). Subsequently, read counts for individual transcripts of each sample were generated using HTSeq-count (45). After performing PCA on the gene expression data from all samples, the plotPCA function will sort the principal components based on the amount of variability they explain. Consequently, a plot depicting the eigenvalues of the top two principal components will be generated. We utilized the DESeq2 algorithm to filter the differentially expressed genes, considering significant analysis and false discovery rate (FDR) analysis with the following criteria: i) |log2FC| > 1; ii) P-value < 0.05. Heatmap plots were generated using R based on the analysis of differentially expressed genes, and the color scheme was determined by the filtering criteria. Additionally, GSEA analysis was conducted using the GSEA software (Broad Institute, Cambridge, MA) with eight gene sets: hallmark, C1, C2, C3, C4, C5, C6, C7, and C8. Gene ontology (GO) analysis was conducted to aid in elucidating the biological implications of unique genes within the significant or representative profiles identified in the experiment (46). Pathway analysis was employed to identify the significant pathways associated with the genes based on the KEGG database. We utilized ClusterProfiler to identify significant pathways, with significance thresholds defined by both P-value and false discovery rate (FDR) (47). Lipidomic sequencing sample preparation and data processing After preprocessing the raw data, logarithmic (LOG) transformation followed by UV scaling was performed using SIMCA software (Version 16.0.2, Sartorius Stedim Data Analytics AB, Umea, Sweden). Initially, OPLS-DA modeling analysis was conducted on the first principal component. The quality of the model was assessed using 7-fold cross-validation. Subsequently, the model's effectiveness was evaluated based on the R2Y (the explanatory ability of the model for the categorical variable Y) and Q2 (the predictability of the model) obtained from cross-validation. Finally, permutation tests were conducted to further assess the model's validity by randomly permuting the arrangement of the categorical variable Y multiple times to obtain different random Q2 values. Additionally, the variable importance in the projection (VIP) values of the first principal component in the OPLS-DA analysis were obtained. VIP summarizes the contribution of each variable to the model. Metabolites with VIP>1 and p<0.05 (student t test) were considered significantly changed metabolites. Furthermore, pathway enrichment analysis was performed using commercial databases including KEGG (http://www.genome.jp/kegg/) and MetaboAnalyst (http://www.metaboanalyst.ca/). Cell culture Human ovarian cancer cell lines (A2780, OVCAR8, HEY) were cultured in PMRI 1640 supplemented with 10% FBS (Invitrogen, Carlsbad, CA, USA) and 100 units/ml penicillin and 100 mg/ml streptomycin. Human ovarian cancer cell line SKOV3 was cultured in F12 supplemented with 10% FBS (Invitrogen, Carlsbad, CA, USA) and 100 units/ml penicillin and 100 mg/ml streptomycin. Mouse ovarian cancer cell line ID8 was cultured in DMEM supplemented with 10% FBS (Invitrogen, Carlsbad, CA, USA) and 100 units/ml penicillin and 100 mg/ml streptomycin. These cell lines were purchased from ATCC. All cells were maintained at 37°C and 5% (vol/vol) CO 2 and passaged using 0.25% trypsin / 0.02% EDTA for dissociation at 80% confluence. For transient transfection, cells at 70%–80% confluence were transfected with indicated plasmids using Lipofectamine 3000 according to the manufacturer’s instructions (Invitrogen). Immunofluorescence staining and confocal microscopy Human ovarian cancer cells were cultured on confocal culture dishes. After fixation with 4% formaldehyde and permeabilization with 0.1% Tween 20, the cells were incubated overnight at 4 °C with a primary antibody against YAP (D8H1X; #14074; Cell Signaling Technology) diluted at 1:100. Subsequently, the samples were incubated for 45 minutes at room temperature with a secondary antibody conjugated to Alexa Fluor 488 (Invitrogen). Nuclei were counterstained with 4′,6-diamidino-2-phenylindole (DAPI). Imaging was performed using a confocal microscope (Zeiss LSM 880 equipped with Airyscan). Western blot analysis Cell lysates were homogenized in RIPA buffer supplemented with 1× protease inhibitor cocktail (Roche, Basel, Switzerland). The lysates were mixed with 5× loading buffer, boiled at 100 °C for 10 minutes, and separated by SDS-PAGE. Subsequently, proteins were transferred to PVDF membranes, which were then incubated with primary antibodies overnight at 4 °C. After washing three times with TBST buffer, the membranes were probed with HRP-conjugated secondary antibodies for 1 hour at 4 °C. Protein signals were visualized using enhanced chemiluminescence (Amersham Biosciences, Sunnyvale, CA, USA). The primary antibodies used for Western blot were as follows: rabbit anti-LATS1 (3477T; Cell Signaling Technology), rabbit anti-pLATS1-Thr1079 (8654T; Cell Signaling Technology), rabbit anti-YAP (D8H1X; 14074S; Cell Signaling Technology), rabbit anti-pYAP-S127 (13008S; Cell Signaling Technology), rabbit anti-pan TEAD (D3F7L; 13295; Cell Signaling Technology), rabbit anti-SCD1 (C12H5; 2794S; Cell Signaling Technology), mouse anti-Vinculin (PTM BIO, PTM-5168), and mouse anti-GAPDH (TA-08; Zhong Shan Jin Qiao, Beijing, China). The secondary antibodies used were goat anti-mouse HRP and goat anti-rabbit HRP (both from Santa Cruz Biotechnology). Quantitative PCR Total RNA was extracted with TRIzol reagent (Invitrogen) following the manufacturer's instructions. Then, 2 μg of total RNA was reverse-transcribed into cDNA using random primers (Genestar). The synthesized cDNA was amplified by quantitative PCR with SYBR Green PCR master mix (Roche) and gene-specific primers, each at a final concentration of 10 μM. The gene-specific primers used in this study are listed below: GAPDH: Forward: 5'-CTCCTGCACCACCAACTGCT-3' Reverse: 5'-GGGCCATCCACAGTCTTCTG-3' LATS1: Forward: 5'-GCTGTCGATGTGGAGACAGA-3' Reverse: 5'-GGTTGTCCCACCAACATTTC-3' YAP: Forward: 5'-GCAGTTGGGAGCTGTTTCTC-3' Reverse: 5'-GCCATGTTGTTGTCTGATCG-3' CYR61: Forward: 5'-CTCCCTGTTTTTGGAATGGA-3' Reverse: 5'-TGGTCTTGCTGCATTTCTTG-3' TEAD1: Forward: 5'-GGAGGCCCTGGCTATCTATC-3' Reverse: 5'-AGGGCCTTATCCTTTGCAGT-3' TEAD2: Forward: 5'-TTTTGGTCTGGAGGATCTGG-3' Reverse: 5'-TTCCACGAAGGCTGAGAACT-3' TEAD3: Forward: 5'-GGACCCTCTCAGGACATCAA-3' Reverse: 5'-CACCTCCATGAAGGCTGAAT-3' TEAD4: Forward: 5'-GCTCCTTCTATGGGGTCTCC-3' Reverse: 5'-GTGCTTGAGCTTGTGGATGA-3' Each qPCR reaction was performed in triplicate. The amplification protocol included an initial denaturation at 95 °C for 10 min, followed by 40 cycles of denaturation at 95 °C for 15 s and annealing/extension at 60 °C for 1 min. Quantitative PCR was carried out on a LightCycler 96 system (Roche), and data analysis was performed using the LightCycler 96 software (Roche) along with GraphPad Prism 8.0. Compounds Cisplatin was obtained from Selleck (USA), MF438 was supplied by MedChemExpress (USA), oleic acid was sourced from Selleck (USA), and SOS was purchased from Topscience (China). Tumor inoculations and treatments To establish ID8-Luciferase cells, ID8 cells were transfected with a lentivirus carrying an overexpressed luciferase reporter gene. For the generation of stable cell lines, HEK293T cells were transfected with a lentiviral expression vector (sh-Scd1) together with packaging vectors (pMD2.G and psPAX) to produce the virus. The viral supernatants were collected at 48 and 72 hours post-transfection. Subsequently, ID8-Luciferase cells were infected with the virus and selected with puromycin to obtain stable cell lines. The plasmids sh-NC and sh-SCD1#1-2 (mouse) were obtained from Shanghai Genechem (Shanghai, China). Animals and tumor models All animal experiments were conducted with the approval of the Laboratory Animal Welfare and Ethics Committee of Peking University Third Hospital (Approval Numbers: M2021013 and S2022234). Six-week-old female C57BL/6 mice were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. The mice were housed under standard pathogen-free conditions in the animal facility of Peking University Third Hospital. To establish intraperitoneal allografts, 1×10⁷ ID8-luciferase cells were suspended in 100 μL of PBS and injected intraperitoneally into the mice. Tumor progression was monitored weekly by quantifying luciferase expression using the Vivo Imaging System (Aniview, China). Drug resistance test in vivo Male Balb/c nude mice were randomly assigned into four groups (n = 5 per group). Each mouse received a subcutaneous injection of 2 × 10⁶ ID8 cells into the flanks. On the 28th day of tumor growth, drug treatments were initiated. Mice in the cisplatin group received intraperitoneal injections of cisplatin (5 mg/kg), those in the MF438 group received MF438 (15 mg/kg), and those in the combination group received both cisplatin and MF438, administered three times per week. The remaining group received PBS as a control. Tumor sizes were measured every 3 days for the duration of the experiment. Tumor volumes were calculated using the formula: tumor volume = a² × b/2, where a is the short diameter (cm) and b is the long diameter (cm). After the experimental endpoint was reached, the mice were sacrificed, and the tumors were sectioned for further analysis. Quantification and statistical analysis Statistical analysis of WB band quantification and quantitative PCR data was conducted using unpaired one- or two-tailed Student’s t-tests. Details of these analyses, including sample sizes per group (n), are provided in the figure legends. Statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software, La Jolla, CA, USA). Data are presented as mean ± standard error of the mean (SEM). Statistical significance was defined as p < 0.05. Declarations Funding This work was supported by the General Program of National Natural Science Foundation of China (No. 82373173), the Youth Program of National Natural Science Foundation of China (No. 82203102, No. 82303801), the General Program of National Natural Science Foundation of China (No. 82073057, No. 82272745, No. 81972966), and Peking University Third Hospital Clinical Key Project (BYSY2022069, BYSYZD2023010). Authors' contributions Conceptualization, J.S., H.X. and L.X.; investigation, J.S., H.X. Y.G., Y.L., Z.H. X.M. and Y.T.; writing–original draft, J.S., H.X. and Z.G.; writing–review and editing, L.X and Z.G.; methodology, J.S., Y.Q. and X.L.; resources, H.G., T.H., X.H. and T.L.; funding acquisition, J.S., X.H., Z.G., Y.Q., L.X., T.L., and X.L; supervision, L.X. All authors have read and agreed to the published version of the manuscript. Acknowledgments We are grateful to the Department of Obstetrics and Gynecology of Peking University Third Hospital for providing OC samples and the we would like to express our gratitude to Professor Huiyong Yin for his guidance from the CAS Key Laboratory of Nutrition, Metabolism and Food Safety Research, Shanghai Institute of Nutrition and Health (SINH), Innovation Center for Intervention of Chronic Disease and Promotion of Health, Chinese Academy of Sciences (CAS). 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Anders S, Pyl PT, and Huber W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics. 2015;31(2):166-9. Ashburner M, Ball CA, Blake JA, Botstein D, Butler H, Cherry JM, et al. Gene Ontology: tool for the unification of biology. Nature Genetics. 2000;25(1):25-9. Draghici S, Khatri P, Tarca AL, Amin K, Done A, Voichita C, et al. A systems biology approach for pathway level analysis. Genome Research. 2007;17(10):1537-45. Additional Declarations There is NO conflict of interest to disclose. Supplementary Files Supplementaryfiles.docx Supplementary Files Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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07:09:05","extension":"docx","order_by":14,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":4120895,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/bfc25555a965c1d37a34f48b.docx"},{"id":95894603,"identity":"a870d989-172c-44b6-91ec-0d4bd897da01","added_by":"auto","created_at":"2025-11-14 07:09:04","extension":"png","order_by":22,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":24722,"visible":true,"origin":"","legend":"","description":"","filename":"Onlinefloatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/16f60d23649ee2bd49563336.png"},{"id":95894616,"identity":"a28352e1-58cd-41fa-aa03-3ec4bb034068","added_by":"auto","created_at":"2025-11-14 07:09:06","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2305902,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eBased on information from public databases, a molecular classification of ovarian cancer was constructed based on metabolism-related genes and discovered a positive correlation between lipid metabolism and adverse prognosis in ovarian cancer.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. t-SNE analysis supports its classification into three ovarian cancer subclasses. The samples were divided into three subtypes, named C1 (red dot), C2 (blue dot), and C3 (green dot). In this paper, we named C1 chemotherapy-refractory and C2/C3 chemotherapy-sensitive types (MCRT, MCST for short).\u003c/p\u003e\n\u003cp\u003eB.\u003cstrong\u003e \u003c/strong\u003eOverall survival curves showing the prognosis result of the two subtypes (MCRT and MCST) obtained from NMF clustering in the RNASeqdat cohort. Statistical significance was calculated using the log-rank test.\u003c/p\u003e\n\u003cp\u003eC.\u003cstrong\u003e \u003c/strong\u003eDifferential expression genes enriched in metabolic molecular subtypes MCRT and MCST of ovarian cancer (top genes).\u003c/p\u003e\n\u003cp\u003eD. Consistency after reclassification of 60 genes with 1650 metabolism-related genes.\u003c/p\u003e\n\u003cp\u003eE. RNA-seq analysis of 4 cisplatin-resistant (patient #46-49) and 4 cisplatin-sensitive (patient #50-53) patients with ovarian cancer, with validation of MCRT top 20 gene expression.\u003c/p\u003e","description":"","filename":"OnlineFigure1101.png","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/c4f7c9050e7754466f239765.png"},{"id":95894602,"identity":"8e8af324-82d8-433a-a170-f9737f00fe75","added_by":"auto","created_at":"2025-11-14 07:09:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":888558,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCompared to the MCST subtype, the MCRT subtype exhibited enhanced lipid metabolism. Targeted lipidomics analysis identified oleic acid as the most significantly altered lipid metabolite.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Comparison of the distribution of clinical features of three molecular subtypes.\u003c/p\u003e\n\u003cp\u003eB-E. Differences in lipid metabolism processes and substances among the metabolic subtypes MCRT (C1) and MCST (C2, C3) of ovarian cancer, such as CD36 (B), glycerolipid metabolism (C), glycerophospholipid metabolism (D), and arachidonic acid metabolism (E) were compared. Data are presented as mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 by Student’s t-test.\u003c/p\u003e\n\u003cp\u003eF. The high-expression genes in MCRT (among top 20 genes) were detected by qPCR. Clinical patients with ovarian cancer were classified into ovarian cancer metabolic subtypes using differential genes \u003cem\u003eAOC3, CD36, ALDH1A3, GJA1, NBL1, GLIPR1, TGM2, CYP1B1, MMP19, PLPPR4, PLN, PTGER3, PODNL1, HOPX\u003c/em\u003e. Patients numbered 8, 10, 11 were classified as subtype MCRT, while patients numbered 5, 7, 17 were classified as subtype MCST.\u003c/p\u003e\n\u003cp\u003eG. The relative abundance of free fatty acids between the MCRT and MCST subtypes.\u003c/p\u003e\n\u003cp\u003eH. The expression of SCD1 was tested by qPCR in MCRT subtype (patients 8, 10, 11) and MCST subtype (patients 5, 7, 17).\u003c/p\u003e","description":"","filename":"OnlineFigure2101.png","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/59c970afe8548d1faff86ac6.png"},{"id":95894607,"identity":"d6090a3f-269a-4e3f-979f-0eb1b1a9abf7","added_by":"auto","created_at":"2025-11-14 07:09:05","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":4517131,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOleic acid blunts the sensitivity of cisplatin in both MCRT and MCST subtypes of ovarian cancer cell lines and organoids.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA. Principal component analysis of ovarian cancer cell lines (n = 47) from the Cancer Cell Line Encyclopedia for the expression of metabolic genes according to metabolic-pathway-based subtypes.\u003c/p\u003e\n\u003cp\u003eB. The expression level of genes\u003cem\u003e CYP1B1, DIO2, GLIPR1, HOPXH, NBL1, PODNL1, PTGER3, ADAMTS1, ALDH1A3, AOC3, GUCY1A1, MICAL2\u003c/em\u003e, and\u003cem\u003e TGM2 \u003c/em\u003ewere detected in ovarian cancer cell lines SKOV3, HEY, A2780, and OVCAR8 by qPCR (genes enriched in the MCRT subtype, 3 independent experiments). Data are presented as mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 by Student’s t-test.\u003c/p\u003e\n\u003cp\u003eC．SKOV3 and OVCAR8 cells were treated with 25 μM oleic acid separately. After 48 h, RNA was extracted, cDNA was prepared, and qPCR was performed to detect \u003cem\u003eCYP1B1, DIO2, GLIPR1, HOPXH, NBL1, PODNL1, PTGER3, ADAMTS1, ALDH1A3, AOC3, GUCY1A1, MICAL2\u003c/em\u003e, and\u003cem\u003e TGM2\u003c/em\u003e using specific primers. GAPDH was used as an internal reference. Three independent experiments were\u003c/p\u003e\n\u003cp\u003einvolved. Data are mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 by Student’s t test.\u003c/p\u003e\n\u003cp\u003eD-G. Sensitivity of ovarian cancer cell lines SKOV3 (B), HEY (C), A2780 (D), and OVCAR8 (E) to cisplatin was assessed, and the IC50 values were calculated using Graph Prism 8.\u003c/p\u003e\n\u003cp\u003eH-K. Viability of ovarian cancer cell lines SKOV3 (H), HEY (I), A2780 (J), and OVCAR8 (K) was assessed after treatment with 1.56-100 μM cisplatin alone or 1.56-100 μM cisplatin plus 25 μM oleic acid for 48 hours using the CCK8 assay (3 independent experiments). Data are presented as mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 by Student’s t-test.\u003c/p\u003e\n\u003cp\u003eL. Three patient-derived ovarian cancer organoids were treated with 10 μM cisplatin or 10 μM cisplatin plus 50 μM oleic acid for 48 hours, and viability was assessed using 3D cell titer glo. Data are presented as mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 by Student’s t-test.\u003c/p\u003e\n\u003cp\u003eM. Reperesented Calcein AM staining of patient-derived ovarian cancer organoids in (L), followed by visualization with high-content screening confocal microscope (Perkin Elmer CLS Operetta). Scale bar = 20 μm.\u003c/p\u003e","description":"","filename":"OnlineFigure301.png","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/53f35bb8a7fae6a078fc0d39.png"},{"id":95894604,"identity":"bf223058-1458-4a41-a65d-1776eaea0c9f","added_by":"auto","created_at":"2025-11-14 07:09:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4571846,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eOleic acid blunts the sensitivity of cisplatin due to the activation and nuclear translocation of YAP in both MCRT and MCST subtypes of ovarian cancer cell lines.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA．KEGG pathway enrichment of differentially expressed genes after oleic acid treatment 48 hours in SKOV3 cells after RNA-seq analysis.\u003c/p\u003e\n\u003cp\u003eB. Hippo signaling pathway was enriched by Gene Set Enrichment Analysis (GSEA) of A.\u003c/p\u003e\n\u003cp\u003eC. The Spearman correlation between YAP signatures (YAP1, TEAD1, and CYR61) and lipid synthesis signatures (SCD, FASN, ACACA, SREBF1, and SREBF2) was analyzed in TCGA patients with ovarian cancer.\u003c/p\u003e\n\u003cp\u003eD-E. SKOV3 cells (D) or OVCAR8 cells (E) were treated with increasing amounts of oleic acid. After 48 h, RNA was extracted, cDNA was prepared, and qPCR was performed to detect Hippo signaling related genes, including \u003cem\u003eLATS1, YAP, CYR61, TEAD1-4\u003c/em\u003e using specific primers. GAPDH was used as an internal reference. Three independent experiments were involved. Data are mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 by Student’s t test.\u003c/p\u003e\n\u003cp\u003eF-G. SKOV3 cells (E) or OVCAR8 cells (F) were treated with increasing amounts of oleic acid, and endogenous protein levels of LATS1, pLATS1, YAP, pYAP, CYR61, GAPDH were determined (3 independent experiments).\u003c/p\u003e\n\u003cp\u003eH-I. SKOV3 (H) or A2780 (I) cells were treated with 0.1% DMSO or oleic acid for 48 hours separately, and subjected to immunostaining with anti-YAP rabbit (green), followed by visualization with confocal microscopy (Zeiss LSM 880 with Airyscan). Magnification of selected areas is shown (insets). Scale bar, 20 μm (3 independent experiments; at least 50 cells were observed).\u003c/p\u003e\n\u003cp\u003eJ-K. Representative western blot analyses of Hippo signaling pathway related genes, including YAP, TEAD and CYR61 using specific antibodies through loss of function of SCD1 and treated with 0.1% DMSO oleic acid for 48 hours separately in SKOV3 (J) or OVCAR8 (K) cell lines. GAPDH acts as an internal reference (3 independent experiments).\u003c/p\u003e\n\u003cp\u003eL-M. Control siRNAs, and YAP siRNAs (YAP si) were transiently transfected into SKOV3 (K) and OVCAR8 (L) cells separately; Viability was assessed after treatment with increasing amounts of cisplatin alone or cisplatin plus 25 μM oleic acid for 48 hours using the CCK8 assay (3 independent experiments). Data are presented as mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 by Student’s t-test.\u003c/p\u003e","description":"","filename":"OnlineFigure401.png","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/1b89ed1c6d1b23e7cf48eeae.png"},{"id":96242617,"identity":"595c495c-e53f-4148-86ec-500f7109e60f","added_by":"auto","created_at":"2025-11-19 07:13:41","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":5016835,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eCD36 positively regulates YAP and oleic acid promotes the nuclear translocation of YAP through CD36.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. The Spearman correlation between YAP signatures (YAP1, TEAD1-4, and CYR61) and CD36 was analyzed in GTEx ovary tissues (A) and TCGA patients with ovarian cancer (B).\u003c/p\u003e\n\u003cp\u003eC-D. Control siRNA (NCsi), and CD36 siRNAs (CD36 si-1 and CD36 si-2) were transiently transfected into SKOV3 (C) and OVCAR8 (D) cells separately; Representative western blots of YAP, TEAD, CD36, and CYR61 are shown. Vinculin was used as a loading control. Data are representative of three independent experiments.\u003c/p\u003e\n\u003cp\u003eE. Analysis of protein-protein interactions between highly expressed proteins in the subtype MCRT of ovarian cancer and the lipid carrier protein CD36, with the red box highlighting the indication of interaction between the desaturase SCD and CD36.\u003c/p\u003e\n\u003cp\u003eF. The overall survival of patients with low versus high expression of SCD and CD36 was compared in the TCGA ovarian cancer cohort.\u003c/p\u003e\n\u003cp\u003eG-H. SKOV3 (G) or OVCAR8 (H) cells were treated with 0.1% DMSO, 20μM SOS (CD36 inhibitor), 25 μM oleic acid or SOS plus oleic acid for 72 hours separately, representative western blots of YAP, TEAD, CD36, and CYR61 are shown. Vinculin was used as a loading control. Data are representative of three independent experiments.\u003c/p\u003e\n\u003cp\u003eI. SKOV3 cells was treated with 0.1% DMSO, 20μM SOS (CD36 inhibitor), 25 μM oleic acid or SOS plus oleic acid for 72 hours, and subjected to immunostaining with anti-YAP rabbit (green), followed by visualization with high-content screening confocal microscope (Perkin Elmer CLS Operetta). Scale bar = 20 μm. (3 independent experiments; at least 50 cells were observed).\u003c/p\u003e","description":"","filename":"OnlineFigure501.png","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/883c30d48ce518870292ce94.png"},{"id":95894609,"identity":"ce787c3e-fd2c-4dba-980f-9ed4942d51e6","added_by":"auto","created_at":"2025-11-14 07:09:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":3477876,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eInhibition of SCD1 enhances the sensitivity of cisplatin in ovarian cancer cell lines and patient derived organoids.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA-B. Viability of ovarian cancer cell lines SKOV3 (A), HEY (B) was assessed after treatment with cisplatin or cisplatin plus MF438 for 72 hours using the CCK8 assay (3 independent experiments). Data are presented as mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 by Student’s t-test.\u003c/p\u003e\n\u003cp\u003eC. Viability of ovarian cancer patient derived organoids was assessed after treatment with cisplatin or cisplatin plus MF438 for 72 hours using the 3D cell titer glo assay (3 independent experiments). Data are presented as mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001 by Student’s t-test.\u003c/p\u003e\n\u003cp\u003eD-E. SKOV3 (D) or OVCAR8 (E) cells were treated with 0.1% DMSO, 10 μM cisplatin, 10 μM MF438 or cisplatin plus MF438 for 72 hours separately, representative western blots of YAP, and CYR61 are shown. GAPDH was used as a loading control. Data are representative of three independent experiments.\u003c/p\u003e\n\u003cp\u003eF. Viability of ovarian cancer patient derived organoids (26 cases) were assessed after treatment with 0.1% DMSO, 20 μM cisplatin, 20μM MF438 or cisplatin plus MF438 for 72 hours using the 3D cell titer glo assay. Data are presented as mean ± SEM. ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 by ordinary one-way ANOVA.\u003c/p\u003e\n\u003cp\u003eG. The high-expression genes in MSRT (among top 20 genes) were detected by qPCR. Clinical patients with ovarian cancer were classified into ovarian cancer metabolic subtypes using differential genes \u003cem\u003ePTGER3, CYP1B1, DIO2, ALDH1A3, AOC3, NBL1, PLN, ITGA5, MMP19, CD36, GPR68, GJA1, GLIPR1, HOPX\u003c/em\u003e. Patients numbered 28, 31,34 were classified as subtype MCRT, while patients numbered 29, 36, 37 were classified as subtype MCST.\u003c/p\u003e\n\u003cp\u003eH-K. The viability of patient numbered 28 (H), 31 (I), 34 (J) derived ovarian cancer organoids (MCRT subtype) treated with 0.1% DMSO, 20 μM cisplatin, 20μM MF438 or cisplatin plus MF438 respectively for 72 hours, and detected by CellTiter-Glo 3D Cell Viability Assay. Data are mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, and ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001 by Student’s t test (n=6). The representative images of organoids are shown on the right (K), scale bar = 20 μm.\u003c/p\u003e\n\u003cp\u003eG-J. The viability of patient numbered 29 (G), 36 (H), 37 (I) derived ovarian cancer organoids (MCST subtype) treated with 0.1% DMSO, 20 μM cisplatin, 20μM MF438 or cisplatin plus MF438 respectively for 72 hours, and detected by CellTiter-Glo 3D Cell Viability Assay. Data are mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, and ****\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.0001 by Student’s t test (n=6). The representative images of organoids are shown on the right (J), scale bar = 20 μm.\u003c/p\u003e","description":"","filename":"OnlineFigure601.png","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/7493fbef61f6c73ce2e91444.png"},{"id":95894617,"identity":"20e79e58-4e20-40a9-b03e-b63dbaa3293a","added_by":"auto","created_at":"2025-11-14 07:09:06","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":4602141,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eLoss of SCD1 inhibited tumor growth \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003ein vivo.\u003c/strong\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eA-B. The mRNA and protein level of SCD1 in ID8 cells with stable SCD1 knockdown detected by real-time qPCR (A) and Western blot (B).\u003c/p\u003e\n\u003cp\u003eC. Schematic diagram of tumor bearing experiment with SCD1 knockdown ID8 cells in mice.\u003c/p\u003e\n\u003cp\u003eD-F. Bioluminescence images (D) and distribution of photon count of xenografted mice after 8 days (E) or 15 days (F) in shNC (n=6), shSCD1-1 (n=7) and shSCD1-2 (n=7) group. Data are mean ± SEM. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, and ***\u003cem\u003ep\u003c/em\u003e\u0026lt; 0.001 by Student’s t test (3 independent experiments).\u003c/p\u003e\n\u003cp\u003eG. Schematic diagram of tumor bearing experiment with cisplatin and MF438 treatment in nude mice.\u003c/p\u003e\n\u003cp\u003eH-J. Tumor volume (H), representative images (I) and tumor weight (J) in control (n = 5), MF438 treatment (n = 5), cisplatin treatment (n = 5) and combination (n = 5) group. *\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, **\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01, ***\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001, and ****\u003cem\u003ep\u003c/em\u003e \u0026lt; 0.0001 by Student’s t test.\u003c/p\u003e","description":"","filename":"OnlineFigure701.png","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/ecdb72f311734be0c4f2c2ac.png"},{"id":105034531,"identity":"fa824986-0005-495a-8500-18605accfd50","added_by":"auto","created_at":"2026-03-20 07:23:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":5513618,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/eaa26356-f839-492e-8300-d8d0731a1810.pdf"},{"id":95894597,"identity":"197a9692-90db-4e68-b58c-0b0b0170e103","added_by":"auto","created_at":"2025-11-14 07:09:03","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":4120895,"visible":true,"origin":"","legend":"Supplementary Files","description":"","filename":"Supplementaryfiles.docx","url":"https://assets-eu.researchsquare.com/files/rs-7929093/v1/3636a48204e29dcb1011ca53.docx"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e conflict of interest to disclose.","formattedTitle":"Metabolic molecular subtyping of ovarian cancer reveals the role of oleic acid-CD36 in facilitating cisplatin resistance","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer is the most aggressive gynecologic malignancy with the poorest prognosis\u0026nbsp;(1, 2). Current standard treatment for ovarian cancer involves cytoreductive surgery and combination chemotherapy based on platinum agents. However, approximately 80% of patients experience recurrence after standard treatment and ultimately succumb to chemotherapy resistance (3). Chemoresistance in ovarian cancer is closely related to metabolic characteristics. In 2019, Gentric et al. uncover the metabolic heterogeneity of high-grade serous ovarian cancer (HGSOC), categorizing it into two distinct metabolic subtypes: low-OXPHOS and high-OXPHOS (4). Furthermore, substantial evidence indicates that \u0026quot;metabo-typing\u0026quot; of a patient\u0026apos;s tumor represents a highly promising approach, offering critical insights for the rational design of metabolic combination therapies that can be effectively translated into clinical practice (5, 6).\u003c/p\u003e\n\u003cp\u003eEmerging evidence highlights the critical role of lipid metabolism in tumorigenesis and therapeutic resistance across diverse cancer types. For instance, aberrant fatty acid synthesis and uptake are hallmarks of breast cancer, where lipid droplet accumulation drives tumor growth and metastasis (7, 8). In prostate cancer, androgen receptor signaling synergizes with lipid metabolic reprogramming to promote castration resistance (9). These findings underscore the broad significance of lipid metabolic pathways as therapeutic targets.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOleic acid (FA 18:1 n-9) belongs to monounsaturated fatty acids (MUFA), which can be obtained via both diet and endogenous synthesis. Stearoyl-CoA Desaturase 1 (SCD1) is a key enzyme in oleic acid de novo synthesis, catalyzing desaturation of saturated fatty acids (SFA) (10). Oleic acid can be imported into cells by CD36 fatty acid translocase. CD36 protects cells from SFA-induced toxicity through selective MUFA uptake during tumor progression (11). Although oleic acid has many beneficial effects on human body, such as improving cardiovascular health, stabilizing blood glucose levels, and anti-inflammatory and antioxidant effects (12), it has also been reported in recent years to promote tumor occurrence and metastasis, such as enhancing cell proliferation and reducing apoptosis in colorectal cancer and protecting melanoma cells from ferroptosis and increasing their metastatic tumor-forming capacity (13), (14). Given these conflicting effects of oleic acid, it becomes crucial to further explore the underlying mechanisms that govern its dual roles in health and disease, as well as to investigate potential strategies to harness its benefits while mitigating the risks associated with tumor promotion.\u003c/p\u003e\n\u003cp\u003eIn this study, we used metabolic genes to classify clinical patients with ovarian cancer into chemotherapy-refractory and chemotherapy-sensitive types (MCRT, MCST for short). Through lipid metabolomics, we identified oleic acid was significantly enriched in ascites of chemotherapy-refractory patients. We further elucidated specific mechanisms of oleic acid induced chemoresistance through multiple dimensions, including cell lines, patient-derived organoids, and animal models. Additionally, we explored targeting oleic acid synthesis as a therapeutic strategy for chemotherapy-refractory patients, providing new treatment options for the clinical management of ovarian cancer.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eMolecular subtyping of ovarian cancers using metabolic profiling.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePatients with ovarian cancer react differently to combination chemotherapy based on platinum agents. To investigate whether different metabolic characteristics of ovarian cancer can distinguish chemotherapy sensitive and resistant patients, we used metabolism-related genes to classify patients and analyze their prognosis. We employed 2752 metabolism-associated genes encoding all known human metabolic enzymes and transport proteins obtained from previous studies (Table 1) and selected 1650 metabolism-related genes with high variability (MAD \u0026le; 0.5) and significant prognostic value (p \u0026lt; 0.05) (15). Samples were separated into three subtypes based on the Non-negative Matrix Factorization (NMF) clustering method (Supplementary Figure 1, Figure 1A). Interestingly, we found that C1 subtype owns the poorest prognosis and C2/C3 owns better prognosis, which indicates C1 subtype represents the chemo-refractory and C2/C3 represents the chemo-sensitive (Figure 1B). For convenience, we termed C1 subtype \u0026ldquo;metabolic signature in chemotherapy-refractory types\u0026rdquo; (MCRT for short) and C2/C3 subtypes \u0026ldquo;metabolic signature in chemotherapy-sensitivity types\u0026rdquo; (MCST for short).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo simplify the 1650 metabolism-related genes mentioned above, we analyzed and showed top genes expressed most in both subtypes (Figure 1C). In addition, this gene classifier was used to replicate subtype prediction in the RNASeqdat dataset. Evaluation of the consistency between this gene classifier and the original metabolic gene-based prediction indicated a consistency of 54% for C1 (MCRT subtype), 93% for subtype C2, and 79% for subtype C3 (C2 and C3 consist MCST subtype, Figure 1D). These results suggest that a signature of 60 genes can reliably identify subtypes of ovarian cancer in a reproducible manner. In addition, we performed RNAseq detection and analysis on four platinum-resistant patients with ovarian cancer (#46-49) and four platinum-sensitive patients (#50-53, Supplementary Table 1). The experimental results showed that the top 20 genes of the MCRT subtype were highly expressed in platinum-resistant patients with ovarian cancer (Figure 1E).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThe MCRT subtype\u0026nbsp;exhibits elevated\u0026nbsp;lipid metabolism\u0026nbsp;compared to\u0026nbsp;the MCST subtype.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo elucidate the specific metabolic characteristics in MCRT and MCST subtypes, we compared the ssGSEA scores of metabolic processes between the two subtypes, and defined subtypes based on enriched metabolic pathways (Supplementary Figure 2A). The classification of MCRT and MCST is independent of classical TNM subtype (Figure 2A, Supplementary Figure 2B-E). MCRT subtype is considered as unsaturated fatty acids and glycolysis related subtype, MCST subtype is characterized by hormone biosynthesis and oxidative phosphorylation. It is noteworthy that among the 42 specific metabolic features in the poor-prognosis MCRT subtype, 11 are related to lipid metabolism, including biosynthesis of unsaturated fatty acids, ether lipid metabolism, and fatty acid degradation. Among these, fatty acid transporter CD36 in lipid metabolism (Figure 2B), glycerolipid metabolism (Figure 2C), glycerophospholipid metabolism (Figure 2D), arachidonic acid metabolism (Figure 2E) is significantly higher in MCRT subtype compared to MCST subtype. These results suggest a positive correlation between lipid metabolism and chemotherapy resistance in ovarian cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOleic acid is elevated in the ascites of patients with platinum resistance.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo investigate key lipid metabolites in the tumor microenvironment (TME) that may be involved in chemoresistance, we analyzed ascites samples of patients with platinum sensitivity information. We identified 3 cases of platinum-sensitive ovarian cancer without recurrence (patient #5, #7, #17) and 3 cases of platinum-resistant ovarian cancer with recurrence (patient #8, #10, #11) based on the follow-up prognosis information (Supplementary Table 1). We performed qPCR assay and found that patients with recurrence did express higher levels of MCRT subtype gene signature (Figure 2F).\u0026nbsp;Therefore, these 6 patients could be divided into MCRT and MCST subtypes, and their recurrence was consistent with the prediction of subtypes.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Since high expression of CD36 was enriched in MCRT subtype (Figure 2B), we further analyzed FFA (free fatty acid) levels in ascites samples. The results showed that the levels of FFA 18:1 and FFA18:2 in ascites from MCRT subtype were significantly elevated comparing to those of the MCST subtype, and FFA 18:1 was more abundant than FFA18:2 (Figure 2G). Moreover, the mRNA levels of \u003cem\u003eSCD1\u003c/em\u003e were higher in MCRT subtype (Figure 2H). These findings suggest that oleic acid is enriched in TME of MCRT patients and may contribute to cisplatin resistance and recurrence in patients with ovarian cancer.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOleic acid\u0026nbsp;reduces\u0026nbsp;sensitivity to cisplatin in both ovarian cancer cell lines and patient-derived organoids.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further verify the correlation between MCRT/MCST subtypes and drug resistance \u003cem\u003ein vitro\u003c/em\u003e, we first divided ovarian cancer cell lines into two groups as described above. Analysis of 47 human ovarian cancer cell lines from the Cancer Cell Line Encyclopedia (Ghandi et al., 2019), allowed us to stratify the ovarian cancer cell lines into metabolic subtypes MCRT and MCST (Figure 3A). Ovarian cancer cell lines SKOV3 and HEY which fit the MCRT metabolic subtype, and A2780 and OVCAR8 which fit the MCST metabolic subtype were chosen for further studies. It was validated that SKOV3 and HEY cells did express MCRT genes at higher levels than A2780 and OVCAR8 cells (Figure 3B). We further verify the sensitivity of these two types including four cell lines to cisplatin. The results showed that MCST subtype, A2780 and OVCAR8 were more sensitive to cisplatin, while MCRT subtype, SKOV3 and HEY exhibited resistance to cisplatin (Figure 3C-F). These findings suggest a close association between ovarian cancer metabolic subtypes and sensitivity to cisplatin.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTo investigate the role of oleic acid in chemoresistance, we treated ovarian cancer cells with oleic acid and detected MCRT genes expression and drug sensitivity. Notably, we found that supplementation of oleic acid increased the expression of MCRT genes in MCST cells (OVCAR8), suggesting that oleic acid induced subtype transformation of ovarian cancer from MCST to MCRT (Figure 3G). Furthermore, oleic acid increased cell viability under cisplatin treatment, blunting the sensitivity of cisplatin in both MCRT subtype cells (SKOV3 and HEY, Figure 3H-I) and MCST subtype cells (A2780 and OVCAR8 Figure 3J-K). Consistent with this, oleic acid promoted the survival of three patient derived organoids under cisplatin treatment (Figure 3L-M). These results indicate that the increase of oleic acid in TME contributes to metabolism reprogramming and cisplatin resistance of ovarian cancer cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOleic acid promotes cisplatin resistance through YAP activation.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the mechanism by which oleic acid promotes cisplatin resistance in ovarian cancer, we treated SKOV3 cells with oleic acid and conducted RNA-seq analysis. Our data showed that oleic acid altered the Hippo/YAP signaling pathway and promoted the expression of downstream target genes of YAP (Figure 4A-B). Moreover, we found that YAP signatures (including YAP1, TEAD1, and CYR61) and lipid synthesis signatures (including SCD, FASN, ACACA, SREBF1, and SREBF2) exhibit a significant positive correlation in ovarian cancer of TCGA (Figure 4C). YAP (Yes-associated protein) is a mechanosensitive transcriptional co-factor positively correlated with cancer progression and chemoresistance, which is phosphorylated and inhibited by Hippo pathway (16) (17). Therefore, we treated both MCRT (SKOV3) and MCST (OVCAR8) ovarian cancer cell lines with varying concentrations of oleic acid and measured the\u0026nbsp;transcription level of Hippo signaling factors, including\u003cem\u003e\u0026nbsp;LATS1, YAP, CYR61, TEAD1-4\u003c/em\u003e. We found that oleic acid activates YAP and its downstream targets in a dose-dependent manner (Figure 4D, E). Moreover, protein levels of YAP and its downstream target CYR61 are upregulated in the presence of oleic acid (Figure 4F, G). It is reported that CYR61 is a potential predictive molecule that may play an important role in the development of platinum resistance, and CYR61\u003csup\u003e+\u003c/sup\u003e subpopulation was identified as the relapse-initiators in ovarian cancer\u0026nbsp;(18, 19). Additionally, the nuclear translocation of YAP in both MCRT and MCST ovarian cancer cell lines following oleic acid treatment was demonstrated using immunofluorescent analysis (Figure 4H, I, Supplementary Figure 3). However, oleic acid did not decrease the phosphorylation of YAP and affect the total level and the phosphorylation of LATS1, which is an upstream YAP inhibitor in classical Hippo pathway by phosphorylating YAP (Figure 4F, G). These findings suggest that oleic acid activated YAP in other ways.\u003c/p\u003e\n\u003cp\u003eOleic acid can be synthesized through the conversion of stearoyl-CoA by the enzymatic action of SCD1 (20). In this study, we found that SCD1 was highly expressed in MCRT subtype ovarian cancer (Figure 2H), suggesting its association with oleic acid enrichment in TME. We found the addition of oleic acid after knocking down SCD1 can reverse the downregulation of YAP, which is manifested by TEAD and CYR61 protein levels (Figure 6D, E). It indicates that SCD1 regulates YAP activation and downstream target gene expression through oleic acid.\u003c/p\u003e\n\u003cp\u003eMoreover, we found that loss of YAP inhibits the promoting effects of oleic acid on cell viability under cisplatin treatment (Figure 4L, M). These data indicate that oleic acid promotes cisplatin resistance by activating YAP-CYR61 function in ovarian cancer cells.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOleic acid promotes YAP activation and nuclear translocation via CD36.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo explore the mechanism by which oleic acid activates YAP, we examined the correlation between oleic acid transporters (CD36, FATP1-6), free fatty acid receptors (FFAR1, FFAR4), and YAP along with its downstream target genes (YAP1, TEAD1-4, and CYR61) in both normal ovarian tissues and ovarian cancer. Our results indicate that CD36 shows a significant positive correlation with YAP and its downstream target genes (YAP1, TEAD1-4, and CYR61) in both normal ovarian tissues (Supplementary Figure 4 and Figure 5A) and ovarian cancer (Supplementary Figure 4 and Figure 5B). Furthermore, knockdown of CD36 via siRNA downregulated the protein levels of YAP, TEAD, and CYR61 in both MCRT and MCST subtype ovarian cancer cell lines (Figure 5C, D). The above results indicate that CD36 can activate YAP and its downstream target genes. To further validate whether oleic acid activates YAP and promotes its nuclear translocation through CD36, we observed an interaction between the key enzyme in MUFA synthesis, stearoyl-CoA desaturase SCD and CD36 in MCRT subtype (Figure 5E). Interestingly, analysis of TCGA data revealed that patients with ovarian cancer with low co-expression of both SCD1 and CD36 had significantly better clinical outcomes compared to those with high co-expression of both genes (Figure 5F). Moreover, our results showed that CD36 inhibitor SOS eliminated the activation of YAP and its downstream target by oleic acid in both MCRT and MCST subtype ovarian cancer cell lines (Figure 5G, H). Furthermore, inhibition of CD36 eliminated the nuclear translocation of YAP promoted by oleic acid in both MCRT subtype ovarian cancer cell lines (Figure 5I). These results suggest that oleic acid activates YAP through oleic acid transporter CD36. We also observed that CD36 expression was significantly induced by oleic acid treatment (Figure 5G, H), suggesting the regulatory role of oleic acid on CD36 and the downstream signaling.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInhibition of SCD1 enhances the chemotherapy effect of cisplatin \u003cem\u003ein vitro\u0026nbsp;\u003c/em\u003eand\u003cem\u003e\u0026nbsp;in vivo\u003c/em\u003e.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo test whether inhibiting oleic acid synthesis would affect cancer cell sensitivity to cisplatin, we treated ovarian cancer cell lines and patient-derived organoids with cisplatin and SCD1 inhibitors MF438 in combination, and measured cell viability. Our data indicate that SCD1 inhibitors significantly enhance the sensitivity to cisplatin in MCRT and MCST subtype cell lines (Figure 6A-B, Supplementary Figure 5A-B) and patient-derived organoids (Figure 6C). We also treated ovarian cancer cell lines with a combination of cisplatin and the CD36 inhibitor SOS, but the effect was not as significant as that of the MF438 and cisplatin combination (Supplementary Figure 5C, D). Furthermore, both MF438 alone and in combination with cisplatin were able to downregulate the protein levels of YAP and CYR61 in both MCRT and MCST ovarian cancer cell lines (Figure 6D, E), which indicate that both MF438 alone and its combination with cisplatin exert therapeutic effects by inhibiting YAP activation.\u003c/p\u003e\n\u003cp\u003eWe further validated these findings in patient-derived ovarian cancer organoids. The results demonstrated that the combination of MF438 and cisplatin enhanced inhibitory effects across all tested patient-derived organoids (PDOs) (n=26) (Figure 6F, patient #20-45 in Supplementary table 1). Interestingly, we cultivated PDOs of MCRT (#28, #31, #34) and MCST (#29, #36, #37) subtypes (Figure 6G). We found that MCRT subtype PDOs (Figure 6H-K) were more resistant to cisplatin than MCST subtype (Figure 6L-O), and both subtypes were sensitive to cisplatin combined with MF438 (Figure 6H-O). These results suggest that inhibiting oleic acid synthesis by targeting SCD1 can increase the sensitivity to cisplatin of platinum-resistant ovarian cancer (MCRT subtype).\u003c/p\u003e\n\u003cp\u003eTo determine whether SCD1 inhibition affects the progression of ovarian cancer \u003cem\u003ein vivo\u003c/em\u003e, we knocked down SCD1 by shRNA in mouse ovarian cancer cell line ID8 (Figure 7A, B). ID8-luci cells stably transfected with shNC or shSCD1 plasmid were injected into the peritoneal cavity, and the tumor progression was monitored weekly by an \u003cem\u003ein vivo\u003c/em\u003e imaging system (IVIS) (Figure 7C, D). Knockdown of SCD1 resulted in attenuated tumor growth and ascites formation (Figure 7E, F). We then examined the chemotherapy effect of combination of SCD1 inhibitor and cisplatin\u003cem\u003e\u0026nbsp;in vivo\u003c/em\u003e. Nude mice were subcutaneously inoculated with ID8 cells, and treated with MF438, cisplatin or drug combination (n = 5, Figure 7G). Tumor xenografts grew slower and smaller (Figure 7H-J) under combined treatment compared to the control or monotherapy groups. Thus, we concluded that combination of SCD1 inhibitor with cisplatin enhances the chemotherapy effect of cisplatin in nude mice.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eAt present,\u0026nbsp;the clinical classification of ovarian cancer follows the WHO histological classification system, which categorizes epithelial ovarian cancer into five main histological subtypes, including high-grade serous carcinoma (70%), endometrioid carcinoma (10%), clear cell carcinoma (10%), mucinous carcinoma (3%), and low-grade serous carcinoma (\u0026lt;5%) (21). Nevertheless, up to now, this existing classification fails to effectively predict the prognosis of chemotherapy. Meanwhile, substantial evidence has indicated that disordered fatty acid metabolism represents a crucial hallmark of ovarian cancer. Therefore, we developed a 20-genes model which can stratify ovarian cancer into metabolic subtypes including \u0026ldquo;metabolic signature in chemotherapy-refractory types\u0026rdquo; (MCRT for short), and \u0026ldquo;metabolic signature in chemotherapy-sensitivity types\u0026rdquo; (MCST for short). Based on this novel classification, we further demonstrated that\u0026nbsp;oleic acid was elevated significantly in TME of the MCRT subtype patients with ovarian cancer, and oleic acid contributed to cisplatin resistance through CD36-YAP axis. Thus, this work offers a new perspective on precisely classifying ovarian cancer and guiding subsequent chemotherapy and prognosis evaluation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn recent years, extensive research has underscored the close relationship between lipid metabolism and the initiation and progression of ovarian cancer. Besides providing energy, lipids are widely distributed in cellular organelles and serve as essential bioactive molecules involved in multiple signaling pathways regulating functions such as inflammation, immunity, and cell proliferation and differentiation (22). In previous study, we have demonstrated that genes expression related to polyunsaturated fatty acid metabolism is correlated with prognosis in ovarian cancer (23). Oleic acid is reported to be one of the most abundant fatty acids in ovarian cancer ascites\u0026nbsp;(24), and our assay showed similar results. However, the role of oleic acid in TME in regulating cancer progression has not been fully studied. Oleic acid stimulates cell proliferation and glucose transporter transcription through PPAR\u0026alpha; activation in ovarian cancer cells\u0026nbsp;(25). Our recent study has revealed that oleic acid activated TGF\u0026beta;-Smad3 signaling and promoted ovarian cancer metastasis\u0026nbsp;(26). In this study, we found that higher levels of oleic acid exist in TME of patients with MCRT subtype ovarian cancer, correlated with poor prognosis. Supplementation of oleic acid can transform MCST to MCRT subtype, indicating the metabolic remodeling role of oleic acid in TME on cancer cells.\u003c/p\u003e\n\u003cp\u003eIn addition, we found supplementation of oleic acid promotes cisplatin resistance of ovarian cancer. RNA-seq and the validation results revealed that oleic acid inhibits Hippo signaling pathway and promotes the nuclear translocation of YAP, which is reported to be an oncogene in ovarian cancer progression and drug resistance (27). The Hippo/YAP signaling pathway plays critical roles in development (28), fibrosis (29), and the pathogenesis of various tumors (30). Previous studies have showed that inhibiting SCD1, the key enzyme of oleic acid synthesis, blocks nuclear translocation of YAP in gastric cancer and lung cancer stem cells (10), which is consistent with our results. In this study, SCD1 is found upregulated in MCRT subtype ovarian cancer, and play an important role in inducing cisplatin resistance by oleic acid synthesis and YAP activation.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFatty acid (FA) uptake is a cellular process facilitated by various FA transporters, including CD36, FATPs, and FABPs (31). Previous studies have shown that cisplatin-resistant ovarian cancer cells exhibit increased FA uptake (32). In this study, the results show that high levels of CD36 expression are enriched in MCRT subtype ovarian cancer. Oleic acid treatment significantly induced the expression of CD36, suggesting that CD36 is upregulated by oleic acid in TME. CD36 plays an essential role in the bioenergetic adaptation of ovarian cancer cells in the adipocyte-rich microenvironment and governs their metabolic plasticity (33). Our results indicated that CD36 knockdown significantly reduced YAP and its target genes. We also prove that oleic acid regulates Hippo/YAP signaling and promotes the YAP nuclear translocation dependent on CD36, suggesting the role of CD36 in mediating oleic acid induced chemoresistance in ovarian cancer. Oleic acid upregulates the mRNA and protein levels of YAP, but does not inhibit the phosphorylation of LATS and YAP. Phosphorylation of YAP proteins by LATS promotes their cytoplasmic localization and their degradation (34). Therefore, our findings indicate oleic acid activates YAP signaling independent of Hippo pathway. However, the mechanism by which oleic acid-CD36 activates YAP still requires further study.\u003c/p\u003e\n\u003cp\u003eWe attempted to overcome cisplatin resistance by targeting oleic acid intake or production using CD36 or SCD1 inhibitors, and the results show that SCD1 inhibitor MF438 increases the sensitivity of ovarian cancer cells to cisplatin. Organoids could maintain the genetic features, histopathological characteristics, and tumor heterogeneity of the originating ovarian cancer tissue. They accurately reflected the sensitivity of different subtypes of ovarian cancer to standard platinum-based therapy. (35). As the excellent models for drug screening experiments and personalized therapy (36-38), we cultured 26 patient-derived organoids, and proved that combination of SCD1 inhibitor MF438 can effectively enhance the sensitivity of cisplatin in patient-derived organoids, especially in MCRT subtype patients. Our results indicated that combined targeting of SCD1 could be a promising therapeutic strategy for ovarian cancer chemotherapy.\u003c/p\u003e\n\u003cp\u003eIn summary, this study firstly developed the new molecular typing based on metabolic genes is increasingly helpful for precise stratification and personalized treatment of patients with ovarian cancer; secondly, a new perspective has been proposed on the biological role of oleic acid in pathological conditions. Additionally, although oleic acid is associated with a reduction in cardiovascular diseases, the intake of oleic acid may need to be reconsidered for patients with cancer, especially those MCRT subtype which is fatty acid metabolic dominant and cisplatin resistance. Lastly, we propose a new metabolic intervention combination strategy that can further enhance sensitivity to platinum-based drugs.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e\u003cstrong\u003eHuman ovarian ascites and tissues\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOvarian cancer ascites and tissues are clinical surgery samples with informed consent from Peking university third hospital, and was approved by Medical Ethics committee of Peking University Third Hospital with approval number LM2024117. Ascites was collected from 19 ovarian patients and another 26 ovarian cancer tissues were generated into ovarian cancer organoids.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient-derived ovarian cancer organoids\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFresh surgical samples of ovarian cancer were directly obtained from clinical sources and treated under sterile conditions. The samples were divided into three parts for subsequent protein extraction, RNA isolation, and immunohistochemistry analysis, respectively. Tissue designated for organoid culture was initially rinsed multiple times with cold PBS and then transferred to clean culture dishes. Following this, the tissue was minced with sterile scissors in aseptic conditions and transferred to sterile 15 mL centrifuge tubes. A mild cell dissociation reagent (Stem Cell) was added to the tubes, resulting in a final volume of 10 mL, and left to incubate on a room temperature shaker for 0.5-1 hour. After centrifugation at 290g for 5 minutes at room temperature, the supernatant was discarded, and the cell pellet was resuspended in 1 mL of DMEM/F12 culture medium supplemented with 1% BSA and antibiotics. The suspension was then passed through a 70 \u0026mu;m cell strainer into new centrifuge tubes. Following cell counting, the cells were mixed with Matrigel at a 1:1 ratio and dispensed into pre-incubated 24-well plates within a 37\u0026nbsp;℃\u0026nbsp;cell culture incubator. After allowing the Matrigel to solidify for 10 minutes, ovarian cancer organoid culture medium was added to each well. This medium, comprising DMEM/F12 with HEPES and penicillin-streptomycin, along with WNT3A, RSPO1, B27 (50\u0026times;), hEGF, Nicotinamide (1M), FGF10 (100 \u0026mu;g/mL), Noggin, and other components, was refreshed every 3-4 days based on the growth status of the organoids. Passaging was performed approximately every two weeks.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOrganoid chemosensitivity assay\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter preparation, the organoids were cultured for 7 days before undergoing the chemosensitivity assay. Subsequently, the organoids were passaged and transferred into a 96-well plate, with each well containing 2000 cells (10 \u0026mu;L) of the culture. Following a 72 hours incubation period, cisplatin, MF438 (SCD1 inhibitor) or combination was added to the medium and incubated for an additional three days. Organoid viability was then assessed using the CellTiter-Glo 3D Cell Viability Assay (Promega, USA) and measured with the GloMax Luminometer (Promega).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eOvarian cancer data aource and preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA-Seq data and corresponding clinical follow-up information data for ovarian cancer (OV) from the TCGA database (https://portal.gdc.cancer.gov/) was downloaded. Additionally, we obtained the RNA-Seq dataset and corresponding clinical follow-up information data for the OV-AU dataset from the ICGC website (https://dcc.icgc.org/). TCGA and ICGC expression profile data preprocessing method: (1) Convert the expression profiles to gene symbol format; (2) Perform log2 transformation on the expression profile data; (3) Utilize the ComBat function in the R package sva to eliminate batch effects between these two datasets, merging them into a single dataset, hereinafter referred to as RNASeqdat (Supplementary Figure 1A, B).\u003c/p\u003e\n\u003cp\u003eThe CEL files for the following five datasets from the GEO database were downloaded: GSE18520, GSE19829, GSE26193, GSE30161, and GSE63885. The preprocessing method for the GEO expression profile data is as follows: (1) Use the RMA function in the affy R package to preprocess the expression profile data and standardize it to obtain the expression profile of the dataset. (2) Utilize the ComBat function in the sva R package to eliminate batch effects between these five datasets, merging them into a single dataset, hereinafter referred to as GSEdat (Supplementary Figure 1 E, F). (3) Convert the probes to gene symbols based on the annotation file of GPL570 (when multiple probes correspond to the same gene symbol, take the median value as the expression profile of the gene symbol; when one probe corresponds to multiple gene symbols, remove the expression of that probe). (4) Retain only ovarian cancer tumor samples with survival time and survival status.\u003c/p\u003e\n\u003cp\u003eThe data were downloaded on February 1, 2021. For the clinical data, we removed samples without survival time and survival status. The clinical statistics of the filtered samples were shown below (Table 1).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 1. clinical statistics of the filtered samples\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cimg width=\"413\" height=\"271\" src=\"https://myfiles.space/user_files/127393_c7e80a1c9bb65875/127393_custom_files/img1763055274.gif\" alt=\"image\"\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIdentification of ovarian cancer subtypes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe performed Non-negative Matrix Factorization (NMF) clustering on the set of 2752 metabolism-related genes encoding all known human metabolic enzymes and transport proteins obtained from previous studies (Table 2) (15). Firstly, we filtered out metabolism-related genes with low median absolute deviation (MAD) values (MAD \u0026le; 0.5) from the RNASeq dataset for all ovarian cancer (OV) patients. Subsequently, we conducted univariate Cox analysis using survival data and the coxph function from the R package survival (V3.1-12), selecting p \u0026lt; 0.05 as the threshold for filtering, to assess the correlation between all candidate genes and overall survival (OS) (S2_table.txt). Finally, based on the selection of 1650 metabolism-related genes with high variability (MAD \u0026le; 0.5) and significant prognostic value (p \u0026lt; 0.05) (S3_table.txt), we used the R package NMF (V0.23.0) (39) to perform molecular subtyping of the RNASeqdat samples, selecting the value of k at which the genetic correlation coefficient begins to decrease as the optimal number of clusters. Furthermore, we utilized the method of t-Distributed Stochastic Neighbor Embedding (t-SNE) based on the mRNA expression data of the aforementioned metabolism-related genes to validate subtype assignments. We applied the same approach to the GSEdat dataset using the same candidate genes. Additionally, we used Gene Pattern class label mapping (SubMap), a method for assessing similarity between molecular subtypes based on independent patient cohorts\u0026apos; expression profiles, to analyze the similarity between the subtypes identified in RNASeqdat and GSEdat datasets, to determine whether the subtypes identified in these two datasets are correlated.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSingle-sample gene set enrichment analysis (ssGSEA) based on the features of metabolism-related genes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGene Set Variation Analysis (GSVA) is a non-parametric, unsupervised method for gene set enrichment analysis, which can estimate scores for certain pathways or signatures based on transcriptomic data. Single-sample Gene Set Enrichment Analysis (ssGSEA) is an extension of the GSEA method that calculates enrichment scores for each sample and gene set pair. Each ssGSEA enrichment score represents the degree to which the members of a specific gene set are coordinately upregulated or downregulated in the sample (40). We obtained 113 metabolism-related gene features from previously published studies (41) and assessed them using the R packages GSVA and GSEABase (V1.50.1). We conducted differential analysis of metabolic process scores between subtypes using the Kruskal-Wallis Test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClassifier construction and performance validation\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDifferential expression analysis of genes between ovarian cancer (OV) subtypes was performed using the R software package limma (42). Genes with |fold change| \u0026gt; 1.5 and false discovery rate (FDR) \u0026lt; 0.05 were defined as differentially expressed genes (DEGs). The top 20 DEGs specific to each subtype were selected (only genes with fold change \u0026gt; 0 were chosen, resulting in a total of 60 genes across the three subtypes), thus generating a 60-gene classifier. Nearest template prediction (NTP) analyses were conducted for predictive analysis, and the results were compared with previous classification results based on the NMF algorithm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell line classification\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe transcriptomes of 47 human ovarian cancer cell lines from the Cancer Cell Line Encyclopedia database was downloaded. Using the R package \u0026quot;pamr,\u0026quot; we performed sample classification using the nearest shrunken centroids algorithm. Based on the prediction results obtained from \u0026quot;pamr\u0026quot; analysis, we classified the cell lines into ovarian cancer metabolic pathway subtypes.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRNA sequencing sample preparation and data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRNA extraction from samples was performed using Trizol (Thermo Fisher Scientific, 15596018) following the manufacturer\u0026apos;s instructions. Subsequently, specific libraries were prepared using the NEBNext UltraTM RNA Library Prep Kit (NEB #E7490). The purified libraries were then processed with beads (AMPure XP system). Sequencing was conducted on the Novaseq 6000 platform (Illumina, USA). Library preparation and sequencing were carried out by Shanghai Jiayin Biotechnology Co., Ltd. For data analysis, the raw RNA-seq reads were filtered using Trimmomatic software to remove adapter sequences and low-quality reads (43). Following that, STAR was utilized to align the clean reads to the hg38 reference genome (44). Subsequently, read counts for individual transcripts of each sample were generated using HTSeq-count (45). After performing PCA on the gene expression data from all samples, the plotPCA function will sort the principal components based on the amount of variability they explain. Consequently, a plot depicting the eigenvalues of the top two principal components will be generated. We utilized the DESeq2 algorithm to filter the differentially expressed genes, considering significant analysis and false discovery rate (FDR) analysis with the following criteria: i) |log2FC| \u0026gt; 1; ii) P-value \u0026lt; 0.05. Heatmap plots were generated using R based on the analysis of differentially expressed genes, and the color scheme was determined by the filtering criteria. Additionally, GSEA analysis was conducted using the GSEA software (Broad Institute, Cambridge, MA) with eight gene sets: hallmark, C1, C2, C3, C4, C5, C6, C7, and C8. Gene ontology (GO) analysis was conducted to aid in elucidating the biological implications of unique genes within the significant or representative profiles identified in the experiment (46). Pathway analysis was employed to identify the significant pathways associated with the genes based on the KEGG database. We utilized ClusterProfiler to identify significant pathways, with significance thresholds defined by both P-value and false discovery rate (FDR) (47).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLipidomic sequencing sample preparation and data processing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAfter preprocessing the raw data, logarithmic (LOG) transformation followed by UV scaling was performed using SIMCA software (Version 16.0.2, Sartorius Stedim Data Analytics AB, Umea, Sweden). Initially, OPLS-DA modeling analysis was conducted on the first principal component. The quality of the model was assessed using 7-fold cross-validation. Subsequently, the model\u0026apos;s effectiveness was evaluated based on the R2Y (the explanatory ability of the model for the categorical variable Y) and Q2 (the predictability of the model) obtained from cross-validation. Finally, permutation tests were conducted to further assess the model\u0026apos;s validity by randomly permuting the arrangement of the categorical variable Y multiple times to obtain different random Q2 values. Additionally, the variable importance in the projection (VIP) values of the first principal component in the OPLS-DA analysis were obtained. VIP summarizes the contribution of each variable to the model. Metabolites with VIP\u0026gt;1 and p\u0026lt;0.05 (student t test) were considered significantly changed metabolites. Furthermore, pathway enrichment analysis was performed using commercial databases including KEGG (http://www.genome.jp/kegg/) and MetaboAnalyst (http://www.metaboanalyst.ca/).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCell culture\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman ovarian cancer cell lines (A2780, OVCAR8, HEY) were cultured in PMRI 1640 supplemented with 10% FBS (Invitrogen, Carlsbad, CA, USA) and 100 units/ml penicillin and 100 mg/ml streptomycin. Human ovarian cancer cell line SKOV3 was cultured in F12 supplemented with 10% FBS (Invitrogen, Carlsbad, CA, USA) and 100 units/ml penicillin and 100 mg/ml streptomycin. Mouse ovarian cancer cell line ID8 was cultured in DMEM supplemented with 10% FBS (Invitrogen, Carlsbad, CA, USA) and 100 units/ml penicillin and 100 mg/ml streptomycin. These cell lines were purchased from ATCC. All cells were maintained at 37\u0026deg;C and 5% (vol/vol) CO\u003csub\u003e2\u003c/sub\u003e and passaged using 0.25% trypsin / 0.02% EDTA for dissociation at 80% confluence. For transient transfection, cells at 70%\u0026ndash;80% confluence were transfected with indicated plasmids using Lipofectamine 3000 according to the manufacturer\u0026rsquo;s instructions (Invitrogen).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImmunofluorescence staining and confocal microscopy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHuman ovarian cancer cells were cultured on confocal culture dishes. After fixation with 4% formaldehyde and permeabilization with 0.1% Tween 20, the cells were incubated overnight at 4 \u0026deg;C with a primary antibody against YAP (D8H1X; #14074; Cell Signaling Technology) diluted at 1:100. Subsequently, the samples were incubated for 45 minutes at room temperature with a secondary antibody conjugated to Alexa Fluor 488 (Invitrogen). Nuclei were counterstained with 4\u0026prime;,6-diamidino-2-phenylindole (DAPI). Imaging was performed using a confocal microscope (Zeiss LSM 880 equipped with Airyscan).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eWestern blot analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCell lysates were homogenized in RIPA buffer supplemented with 1\u0026times; protease inhibitor cocktail (Roche, Basel, Switzerland). The lysates were mixed with 5\u0026times; loading buffer, boiled at 100 \u0026deg;C for 10 minutes, and separated by SDS-PAGE. Subsequently, proteins were transferred to PVDF membranes, which were then incubated with primary antibodies overnight at 4 \u0026deg;C. After washing three times with TBST buffer, the membranes were probed with HRP-conjugated secondary antibodies for 1 hour at 4 \u0026deg;C. Protein signals were visualized using enhanced chemiluminescence (Amersham Biosciences, Sunnyvale, CA, USA). The primary antibodies used for Western blot were as follows: rabbit anti-LATS1 (3477T; Cell Signaling Technology), rabbit anti-pLATS1-Thr1079 (8654T; Cell Signaling Technology), rabbit anti-YAP (D8H1X; 14074S; Cell Signaling Technology), rabbit anti-pYAP-S127 (13008S; Cell Signaling Technology), rabbit anti-pan TEAD (D3F7L; 13295; Cell Signaling Technology), rabbit anti-SCD1 (C12H5; 2794S; Cell Signaling Technology), mouse anti-Vinculin (PTM BIO, PTM-5168), and mouse anti-GAPDH (TA-08; Zhong Shan Jin Qiao, Beijing, China). The secondary antibodies used were goat anti-mouse HRP and goat anti-rabbit HRP (both from Santa Cruz Biotechnology).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantitative PCR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTotal RNA was extracted with TRIzol reagent (Invitrogen) following the manufacturer\u0026apos;s instructions. Then, 2 \u0026mu;g of total RNA was reverse-transcribed into cDNA using random primers (Genestar). The synthesized cDNA was amplified by quantitative PCR with SYBR Green PCR master mix (Roche) and gene-specific primers, each at a final concentration of 10 \u0026mu;M.\u003c/p\u003e\n\u003cp\u003eThe gene-specific primers used in this study are listed below:\u003c/p\u003e\n\u003cp\u003eGAPDH:\u003c/p\u003e\n\u003cp\u003eForward: 5\u0026apos;-CTCCTGCACCACCAACTGCT-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eReverse: 5\u0026apos;-GGGCCATCCACAGTCTTCTG-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eLATS1:\u003c/p\u003e\n\u003cp\u003eForward: 5\u0026apos;-GCTGTCGATGTGGAGACAGA-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eReverse: 5\u0026apos;-GGTTGTCCCACCAACATTTC-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eYAP:\u003c/p\u003e\n\u003cp\u003eForward: 5\u0026apos;-GCAGTTGGGAGCTGTTTCTC-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eReverse: 5\u0026apos;-GCCATGTTGTTGTCTGATCG-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eCYR61:\u003c/p\u003e\n\u003cp\u003eForward: 5\u0026apos;-CTCCCTGTTTTTGGAATGGA-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eReverse: 5\u0026apos;-TGGTCTTGCTGCATTTCTTG-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eTEAD1:\u003c/p\u003e\n\u003cp\u003eForward: 5\u0026apos;-GGAGGCCCTGGCTATCTATC-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eReverse: 5\u0026apos;-AGGGCCTTATCCTTTGCAGT-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eTEAD2:\u003c/p\u003e\n\u003cp\u003eForward: 5\u0026apos;-TTTTGGTCTGGAGGATCTGG-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eReverse: 5\u0026apos;-TTCCACGAAGGCTGAGAACT-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eTEAD3:\u003c/p\u003e\n\u003cp\u003eForward: 5\u0026apos;-GGACCCTCTCAGGACATCAA-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eReverse: 5\u0026apos;-CACCTCCATGAAGGCTGAAT-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eTEAD4:\u003c/p\u003e\n\u003cp\u003eForward: 5\u0026apos;-GCTCCTTCTATGGGGTCTCC-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eReverse: 5\u0026apos;-GTGCTTGAGCTTGTGGATGA-3\u0026apos;\u003c/p\u003e\n\u003cp\u003eEach qPCR reaction was performed in triplicate. The amplification protocol included an initial denaturation at 95 \u0026deg;C for 10 min, followed by 40 cycles of denaturation at 95 \u0026deg;C for 15 s and annealing/extension at 60 \u0026deg;C for 1 min. Quantitative PCR was carried out on a LightCycler 96 system (Roche), and data analysis was performed using the LightCycler 96 software (Roche) along with GraphPad Prism 8.0.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompounds\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCisplatin was obtained from Selleck (USA), MF438 was supplied by MedChemExpress (USA), oleic acid was sourced from Selleck (USA), and SOS was purchased from Topscience (China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTumor inoculations and treatments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo establish ID8-Luciferase cells, ID8 cells were transfected with a lentivirus carrying an overexpressed luciferase reporter gene. For the generation of stable cell lines, HEK293T cells were transfected with a lentiviral expression vector (sh-Scd1) together with packaging vectors (pMD2.G and psPAX) to produce the virus. The viral supernatants were collected at 48 and 72 hours post-transfection. Subsequently, ID8-Luciferase cells were infected with the virus and selected with puromycin to obtain stable cell lines. The plasmids sh-NC and sh-SCD1#1-2 (mouse) were obtained from Shanghai Genechem (Shanghai, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAnimals and tumor models\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll animal experiments were conducted with the approval of the Laboratory Animal Welfare and Ethics Committee of Peking University Third Hospital (Approval Numbers: M2021013 and S2022234). Six-week-old female C57BL/6 mice were obtained from Beijing Vital River Laboratory Animal Technology Co., Ltd. The mice were housed under standard pathogen-free conditions in the animal facility of Peking University Third Hospital.\u003c/p\u003e\n\u003cp\u003eTo establish intraperitoneal allografts, 1\u0026times;10⁷ ID8-luciferase cells were suspended in 100 \u0026mu;L of PBS and injected intraperitoneally into the mice. Tumor progression was monitored weekly by quantifying luciferase expression using the Vivo Imaging System (Aniview, China).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDrug resistance test \u003cem\u003ein vivo\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMale Balb/c nude mice were randomly assigned into four groups (n = 5 per group). Each mouse received a subcutaneous injection of 2 \u0026times; 10⁶ ID8 cells into the flanks. On the 28th day of tumor growth, drug treatments were initiated. Mice in the cisplatin group received intraperitoneal injections of cisplatin (5 mg/kg), those in the MF438 group received MF438 (15 mg/kg), and those in the combination group received both cisplatin and MF438, administered three times per week. The remaining group received PBS as a control. Tumor sizes were measured every 3 days for the duration of the experiment. Tumor volumes were calculated using the formula: tumor volume = a\u0026sup2; \u0026times; b/2, where a is the short diameter (cm) and b is the long diameter (cm). After the experimental endpoint was reached, the mice were sacrificed, and the tumors were sectioned for further analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuantification and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analysis of WB band quantification and quantitative PCR data was conducted using unpaired one- or two-tailed Student\u0026rsquo;s t-tests. Details of these analyses, including sample sizes per group (n), are provided in the figure legends. Statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software, La Jolla, CA, USA). Data are presented as mean \u0026plusmn; standard error of the mean (SEM). Statistical significance was defined as p \u0026lt; 0.05.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch3\u003eFunding\u003c/h3\u003e\n\u003cp\u003eThis work was supported by the General Program of National Natural Science Foundation of China (No. 82373173), the Youth Program of National Natural Science Foundation of China (No. 82203102, No. 82303801), the General Program of National Natural Science Foundation of China (No. 82073057, No. 82272745, No. 81972966), and Peking University Third Hospital Clinical Key Project (BYSY2022069, BYSYZD2023010).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization, J.S., H.X. and L.X.; investigation, J.S., H.X. Y.G., Y.L., Z.H. X.M. and Y.T.; writing\u0026ndash;original draft, J.S., H.X. and Z.G.; writing\u0026ndash;review and editing, L.X and Z.G.; methodology, J.S., Y.Q. and X.L.; resources, H.G., T.H., X.H. and T.L.; funding acquisition, J.S., X.H., Z.G., Y.Q., L.X., T.L., and X.L; supervision, L.X. All authors have read and agreed to the published version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe are grateful to the Department of Obstetrics and Gynecology of Peking University Third Hospital for providing OC samples and the we would like to express our gratitude to Professor Huiyong Yin for his guidance from the CAS Key Laboratory of Nutrition, Metabolism and Food Safety Research, Shanghai Institute of Nutrition and Health (SINH), Innovation Center for Intervention of Chronic Disease and Promotion of Health, Chinese Academy of Sciences (CAS).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experiments were conducted following approval from the Laboratory Animal Welfare and Ethics Committee of Peking University Third Hospital, with approval number License No. M2021013 and No. S2022234.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data generated in this study are available upon request from the corresponding author.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eWilczynski JR, Wilczynski M, and Paradowska E. 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Here, through integrative analysis of multiple datasets from TCGA, ICGC and GEO databases, we stratify patients into metabolic associated chemotherapy-refractory and chemotherapy-sensitive types (MCRT, MCST for short) with distinct prognosis by 20 genes. Lipidomic profiling of ascites from patients with ovarian cancer further identifies oleic acid as a hallmark metabolite in MCRT cases. Oleic acid treatment facilitates the resistance to cisplatin in ovarian cancer cells and patient-derived organoids. Mechanistically, oleic acid activates and promotes nuclear translocation of YAP through CD36. Inhibition of SCD1—genetically or pharmacologically—synergizes with cisplatin in 26 patient-derived organoids and suppresses tumor growth in xenograft models. Our study proposes a new metabolic classification of ovarian cancer that can predict chemotherapy sensitivity. Moreover, it discovers the role of the oleic acid-CD36-YAP signaling axis in promoting cisplatin resistance. These findings shed light on how oleic acid facilitates the resistance to cisplatin in ovarian cancer and provide a potential therapeutic strategy for treating patients with ovarian cancer.","manuscriptTitle":"Metabolic molecular subtyping of ovarian cancer reveals the role of oleic acid-CD36 in facilitating cisplatin resistance","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-14 07:08:45","doi":"10.21203/rs.3.rs-7929093/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"39764f01-4230-47cd-87b2-46e3b6a954e8","owner":[],"postedDate":"November 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":57364443,"name":"Health sciences/Biomarkers/Predictive markers"},{"id":57364444,"name":"Biological sciences/Cancer/Cancer therapy/Cancer therapeutic resistance"}],"tags":[],"updatedAt":"2026-03-18T14:46:20+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-14 07:08:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7929093","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7929093","identity":"rs-7929093","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}