Two Cycles of Chemotherapy Modulate Itaconic Acid Levels via ATF3/TET2/NF-κB Pathways in Ovarian Cancer Among Han Chinese Women

Two Cycles of Chemotherapy Modulate Itaconic Acid Levels via ATF3/TET2/NF-κB Pathways in Ovarian Cancer Among Han Chinese Women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Two Cycles of Chemotherapy Modulate Itaconic Acid Levels via ATF3/TET2/NF-κB Pathways in Ovarian Cancer Among Han Chinese Women Ying Tang, Jia Wu, Qin Wang, Li-ya Sun, Bin Su, Lin Li, Li-ming Shen, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7296175/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Journal of Ovarian Research → Version 1 posted 13 You are reading this latest preprint version Abstract Background The optimal chemotherapy cycles after debulking surgery in ovarian cancer (OC) remain unclear. This prospective cohort was aimed to define metabolic changes induced by chemotherapy, determine the ideal regimen, and assess longitudinal effects. Methods Metabolites between treatment-naïve OC patients (n = 26) and age-matched healthy controls (n = 30) were identified by gas chromatography-mass spectrometry (GC-MS). Chemotherapy-related signaling molecules were quantified via ELISA. Results Our analysis identified 38 differentially expressed metabolites, including critical intermediates in the tricarboxylic acid (TCA) cycle and revealed significant reductions in five TCA cycle intermediates, namely itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid, in OC patients compared to controls. Following the first chemotherapy cycle, global metabolomic profiling showed minimal changes; however, targeted analysis indicated an increase in itaconic acid levels prior to the second chemotherapy cycle. Remarkably, after two chemotherapy cycles, itaconic acid concentrations normalized to levels comparable to those in healthy controls, suggesting metabolic recovery. Furthermore, significant inverse correlations were observed between HE4 levels and the concentrations of the five TCA cycle intermediates, highlighting the potential of these metabolites as biomarkers. Mechanistic studies revealed that two cycles of chemotherapy restored metabolic homeostasis, normalizing itaconic acid levels and reactivating the ATF3/TET2/NF-κB signaling pathway to levels seen in healthy participants. Conclusions Two chemotherapy cycles may represent an optimal therapeutic strategy following complete cytoreduction, offering novel insights into chemotherapy-induced metabolic reprogramming and potential directions for both clinical decision-making and mechanistic research in OC management. Trial registration: ClinicalTrials.gov ID: ChiCTR2300069160. Ovarian cancer Chemotherapy Itaconic acid Metabolomics Tricarboxylic acid cycle Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 1. Background Ovarian cancer (OC) is one of the most challenging gynecological malignancies owing to its frequent late-stage diagnosis, high propensity for metastasis, and significant risk of recurrence ( 1 – 4 ). In the United States, the American Cancer Society estimated 12,740 deaths from OC in 2024 ( 5 ). Similarly, in China, OC resulted in 40,000 patient deaths in 2020, with mortality rates continuing to rise in 2022( 6 ). According to the National Comprehensive Cancer Network (NCCN) guidelines, the standard treatment for advanced-stage or high-risk OC patients typically involves surgery followed by post-surgery chemotherapy or neoadjuvant chemotherapy (NACT) ( 2 , 7 ). Nevertheless, the optimal number of chemotherapy cycles required after optimal debulking surgery (ODS) is inconsistent based on medical biological mechanisms, and the underlying mechanisms need to be further understood. Although human epididymis protein 4 (HE4) is widely applied as a biomarker for OC diagnosis and prognosis monitoring( 8 ), its low accuracy during chemotherapy limits its utility in routine clinical practice. Therefore, there is a critical need for supplementary biomarkers, such as metabolites, to monitor metabolic changes during chemotherapy, optimize post-chemotherapy regimens, and explore potential therapeutic mechanisms. Recent research on metabolic changes during chemotherapy in OC remains limited. Cui et al . reported that the plasma arachidonic acid-to-prostaglandin E2 ratio and tumoral focal adhesion kinase (FAK) expression were significantly reduced in OC patients after chemotherapy relative to the pre-treatment levels ( 9 ). Similarly, Corvigno et al . identified the associations between specific metabolic alterations, including dysregulation of pyrimidine, aspartate and asparagine metabolism, and clinical response to chemotherapy in OC patients ( 10 ). A complete metabolic response (CMR) on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) after three cycles of NACT has been put forward as a prognostic marker for the recurrence and mortality in advanced high-grade serous carcinoma( 11 ). However, most existing studies are constrained by cross-sectional designs or a narrow focus on single chemotherapy cycles, failing to systematically explore the longitudinal metabolic mechanisms underlying OC progression following ODS across multiple post-chemotherapy regimens ( 10 ). This study was aimed to ( 1 ) characterize the chemotherapy-induced metabolic alterations of OC, ( 2 ) identify the optimal post-chemotherapy regimens, and ( 3 ) elucidate the longitudinal metabolic effects following ODS. Ultimately, this research seeks to provide actionable insights for clinical decision-making and uncover the molecular mechanisms underlying chemotherapy efficacy in OC. 2. Methods 2.1 Study populations Between March 2023 and March 2025, totally 60 healthy participants were enrolled in this prospective cohort from the Affiliated Nanchong Central Hospital of North Sichuan Medical College, including 30 OC patients and 30 age- and body mass index (BMI)-matched healthy participants (ClinicalTrials.gov ID: ChiCTR2300069160). Healthy participants were recruited from the physical examination center of the hospital and matched to OC patients by age and BMI. The inclusion criteria for the OC group were as follows: radiological suspicion (magnetic resonance imaging (MRI)/ultrasound) of OC with histopathological confirmation, scheduled postoperative chemotherapy on the basis of platinum and paclitaxel, regular oncology follow-up, and commitment to study blood collection protocol. Exclusion criteria were shown below: patients not undergoing chemotherapy after surgery, those not confirmed by histopathology, those not receiving platinum-based chemotherapy, those unwilling to provide blood according to the study schedule, and those who failed to collect blood samples according to the study schedule (Flow chart of the patient selection is displayed in Fig. 1 ). This study was approved by the Institutional Review Board of the Affiliated Nanchong Central Hospital of North Sichuan Medical College (NO.2023-010), and written informed consent was acquired from all participants. 2.2 Blood collection Blood samples were collected at three chemotherapy timepoints: pre-treatment baseline (Chemo 1-pre), prior to cycle 2 (Chemo 2-pre), and prior to cycle 3 (Chemo 3-pre). Prior to blood collection, all participants required an overnight fasting period. Subsequently, 5 ml samples of venous blood were collected using a peripheral venous catheter in the following morning. Blood samples were centrifuged (3,000 × g, 10 min, 4°C) using standardized protocols. Plasma aliquots were cryopreserved in microtubules (Lelystad, Netherlands) at -80°C until analysis( 12 ). 2.3 Gas chromatography-mass spectrometry (GC-MS) 2.3.1 Metabolite extraction and derivatization from plasma Plasma metabolite extraction was performed by mixing 100 µL aliquots with 200 µL ice-cold methanol and 4 µL isotope-labeled internal standards (d4-alanine, d5-phenylalanine, d2-tyrosine). After vortexing (30 s), samples were subject to incubation at − 20°C for 30 min under intermittent shaking. The mixture was then centrifuged at 12000 rpm for 15 min. Afterwards, the extracted samples were chemically derivatized through the MCF derivatization method, following the protocol published by Smart et al ( 13 ). Briefly, 200 µl of the extracted metabolites were transferred into a glass tube and mixed with 200 µl NaOH (1M), 34 µl pyridine, and 20 µl methyl chloroformate (MCF). After vortexing for 30 s, an additional 20 µl MCF was supplemented and vortexed for 30 s. Then, 400 µl chloroform was added and vortexed for 10 s, followed by the addition of 400 µl NaHCO 3 and vortexing for another 10 s. The upper aqueous and intermediate protein layers were carefully discarded after centrifugation of the derivatized sample at 2000 rpm for 10 min. Sodium sulfate was supplemented to absorb the remaining water. Finally, 150–200 µl of the chloroform phase was transferred to a vial for mass spectrometry analysis. 2.3.2 Gas chromatography-mass spectrometry analysis The devitalized plasma metabolites were analyzed by Agilent Intuvo9000 Gas chromatograph coupled with an MSD5977B mass spectrometer. The DB-1701 GC capillary column (20 m × 180 µm ×0.18 µm, Agilent) was used for metabolite separation. The GC-MS inlet parameters were set as follows( 12 , 14 ): injection volume (1 µl), pulsed splitless mode (290°C, 180 kPa for 1 min), guard chip (300°C), and helium carrier flow rate (1 mL/min). The GC oven temperature was initiated at 45°C for 2 min, raised to 180°C at 9°C/min and held for 5 min, subsequently raised to 220°C at 40°C/min and held for 5 min, continued to raise to 240°C at 40°C/min and held for 5 min, and finally raised to 280°C at 80°C/min and held for 3 min. The GC-MS system maintained the following temperatures: ion source at 150°C, quadrupole at 230°C, and transfer line at 300°C.The mass spectrometry was operated using electron-impact ionization at 70 eV. The solvent delay was set at 4.5 min, with the mass range being 30 to 550 m/z at a scan speed of 2.9 scan/s( 12 , 14 ). 2.3.4 Data extraction and normalization The Automated Mass Spectral Deconvolution and Identification System (AMDIS) software was used for metabolite identification and deconvolution. The metabolites were identified by comparing their MS fragmentation patterns (mass-to-charge ratio and relative intensity of mass spectra to most abundant ion) and GC retention time to an in-house MS library established with chemical standards. The Mass Omics R-based script was used for extracting the relative concentration of the metabolites through the peak height of the most abundant fragmented ion mass. To improve the quantitative robustness and minimize the human and instrumental variability, the relative abundances of the identified compounds were normalized against multiple internal standards. Subsequently, blank samples were applied to subtract background contamination and any carryover from identified metabolites( 14 ). Calibration curves were acquired from the respective chemical standard, spanning a concentration range of 0 ~ 195.8 µM( 15 ). 2.4. Metabolites in signaling pathways with chemotherapy Chemotherapy-related signaling molecules were quantified by enzyme-linked immunosorbent assay (ELISA) at Lilai Biomedical Research Center using commercial kits, with analyses performed by blinded technicians (≥ 5 years' experience) ( 16 ). ACOD1 concentrations were determined using Human ACOD1 Platinum ELISA Kit (eBioscience; Zhuocai Biotech, China) following the manufacturer's protocol. Activating transcription factor 3 (ATF3)/ten-eleven translocation 2 (TET2)/NF-kappaB (NF-κB)/inhibitor of nuclear factor kappa B zeta (IκBζ)/Interleukin 6 (IL-6) levels were measured with human ATF3/TET2/NF-κB/IκBζ/ IL-6 Platinum ELlSA Kit (eBioscience, an Zhuocai Biotechnology Company, Shanghai, China). The absorbance (OD value) was measured at 450 nm and the sample concentrations were calculated. All procedures were performed and analyzed in strict accordance with the manufacturer's instructions. 2.5. Clinic data collection Serum HE4 concentrations were retrospectively obtained from the patients’ clinical records. Following the standardized collection protocols, venous blood samples were drawn into serum separator tubes, allowed to clot for 30 min at room temperature, and later centrifuged at 3000 × g for 10 min to obtain serum aliquots( 17 , 18 ). All samples were processed and analyzed within 24 h of collection to ensure sample integrity. Quantitative measurement of HE4 was performed using automated chemiluminescent immunoassays (CLIA) on the ARCHITECT i2000SR platform (Abbott Diagnostics, Chicago, IL, USA) according to the manufacturer’s specifications( 19 ). The assay demonstrated a measurable range of 15-1500 pmol/L, with intra- and inter-assay coefficients of variation being < 5% and < 8%, respectively. All the testing procedures were conducted in a CAP-accredited central laboratory by experienced technicians blinded to clinical outcomes to eliminate potential bias( 19 ). 2.6. Statistical analysis Continuous data were expressed as mean ± standard deviation, while categorical data were presented as proportions. Metabolomic analysis was conducted using MetaboAnalyst 6.0 ( https://www.metaboanalyst.ca ), with plasma metabolite levels being log-transformed and Pareto-scaled to achieve normal distribution prior to analysis. Univariate analyses included Student’s t-tests (two-group comparisons) and one-way ANOVA (multi-group comparisons). Multivariate analyses comprised partial least squares-discriminant analysis (PLS-DA), evaluation using R²Y (goodness-of-fit) and Q²Y (predictive ability via leave-one-out cross-validation), and linear support vector machine (SVM) classification to rank metabolite importance( 12 ). Pathway enrichment analysis was conducted via KEGG (2023Q1 release) with the significance thresholds of p < 0.05 and false discovery rate (FDR) < 0.3 (Benjamini-Hochberg correction). Pearson correlation assessed associations between metabolites and key elements during chemotherapy, with multiple comparisons adjusted using the q-value R package (FDR < 0.05). Data were visualized using GraphPad Prism 10 (box/line plots), ggplot2 (heatmaps, forest plots, correlation matrices), and the CNSKnowAll platform (clustering, chord diagrams). All statistical analyses were conducted in SPSS 23.0 and ggplot 2 R package, with supplementary figures generated in Microsoft Excel 2019. 3. Results 3.1. Patient characteristics Between March 2023 and May 2023, 26 OC patients and 30 healthy participants (controls) were enrolled (4 patients were excluded, Fig. 1 ). The majority of OC patients presented with advanced-stage disease (International Federation of Gynecology and Obstetrics [FIGO] stage III/IV) and epithelial histology (serous subtype: 96.2%). Table 1 summarizes demographic and clinical characteristics. No significant differences were observed in age (median: 55.3 vs. 65.1 years, P = 0.093) or BMI 23.9 vs. 23.7 kg/m², P = 0.231) between the OC group and the control group. Table 1 Baseline clinical characteristics of the study participants (n = 56) Variable Ovarian cancer (n = 26) Healthy participants (n = 30) Pvalue* Age, years 53.5 (48.0–62.3) 65.0 (52.0–67.0) 0.659 BMI, kg/m⁻² † 23.9 (23.1–25.6) 22.3 (19.1–27.0) 0.231 FIGO stage , n (%) Early (I–II) 7 (26.9) Advanced (III–IV) 19 (73.1) Histopathological type , n (%) Epithelial 25 (96.2) Other 1 (3.8) Malignant ascites , n (%) Yes 19 (73.1) No 7 (26.9) Chemotherapy regimen , n (%) Platinumbased 20 (100) Other 0 (0) * Pvalues derived from the MannWhitney U test for continuous variables. † BMI recorded for patients with ovarian cancer before the first cycle of chemotherapy. Values for continuous variables are presented as median (interquartile range); categorical data are shown as number (percentage). 3.2. Metabolic Profiling in OC vs. healthy participants Through untargeted metabolomic analysis, 128 metabolites were identified, among which, 38 demonstrated significant alterations in OC patients compared with controls (P < 0.05, FDR < 0.05; Fig. 2 A-B). The differentially expressed metabolites encompassed amino acid derivatives (e.g., glutamine, FC = 0.72), fatty acids (e.g., palmitate, FC = 1.35), and tricarboxylic acid (TCA) cycle intermediates (e.g., citrate, FC = 0.68). The supervised PLS-DA modeling revealed distinct metabolic separation between groups (R²Y = 0.748, Q² = 0.481; Fig. 2 C), revealing 33 down-regulated and 5 up-regulated metabolites in OC (P 1.1; Fig. 2 D). Additionally, linear SVM classification identified that itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid were the most discriminatory metabolites (VIP > 2.0; Fig. 2 E-F). Notably, TCA cycle intermediates were consistently depleted in OC, and the area under the curve (AUC) of the top five metabolites was 0.852 (95% confidence interval (CI): 0.696–0.988). Further validation confirmed the persistently lower levels of TCA cycle intermediates in OC compared with controls (R²Y = 0.55, Q² = 0.39; Fig. 3 A, P < 0.05). Pathway enrichment analysis further underscored the significant dysregulation in the TCA cycle (P < 0.05) and pyruvate metabolism (P < 0.05). PLS-DA analysis indicated that chemotherapy significantly altered the metabolic profile of OC patients. The metabolic patterns progressively approached those of the healthy participants across pre-treatment time points (Chemo-1 pre, Chemo-2 pre, and Chemo-3 pre). The model demonstrated satisfactory predictive and explanatory capabilities with R²=0.55 and Q²=0.39. Seventeen metabolites showed VIP scores exceeding 1. Cluster analysis identified significant alterations in amino acids, amino acid derivatives, TCA cycle-related metabolites (including itaconic acid), and their derivatives. Volcano plot analysis indicated significantly decreased itaconic acid levels in patients relative to healthy participants (FDR 1.2), with gradual normalization of levels following successive chemotherapy cycles. The metabolic disparities diminished with treatment progression. One-way ANOVA showed statistically significant differences in TCA cycle metabolites (including itaconic acid) between OC patients and healthy participants before the first chemotherapy cycle. However, these differences became statistically insignificant after the third chemotherapy cycle. Univariate receiver operating characteristic (ROC) analysis identified 20 metabolites with AUC values > 0.7. Subsequent multivariate ROC analysis using linear support vector machine classification ranked variables by mean importance. ROC curves and AUC values were generated for models incorporating top-ranked metabolites (5, 10, 15, 25, 50, and 100 variables). The optimal biomarker panel consisted of the top 10 metabolites, demonstrating superior diagnostic performance. 3.3. Longitudinal Metabolic Changes During Chemotherapy 3.3.1 Chemotherapy Cycle 1 (Chemo-2 Pre. vs. healthy participants) PLS-DA, heatmap analysis and clustering analysis revealed 9 phyla correlated with 13 differentially abundant metabolites, including itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid, which were obviously higher in Chemo 1-pre group than those in control group (Fig. 2 B). Compared with healthy participants, those TCA cycle intermediates, such as itaconic acid, and citric acid, partially recovered. Following the first cycle, TCA intermediates showed further elevation compared with Chemo-1 ( P < 0.05), though still below healthy levels ( P < 0.05). Pathway analysis indicated reactivation of pyruvate metabolism and TCA cycle after one-cycle chemotherapy ( P < 0.05 ; Fig. 3 ). 3.3.2 Chemotherapy Cycle 2 (Chemo-3 Pre vs. healthy participants) After two cycles, five TCA cycle intermediates: itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid, resembled to healthy participant levels ( P > 0.05; Fig. 2 B). Metabolic pathway disparities between Chemo-2 and controls were not significant for both TCA cycle (P = 0.21) and pyruvate metabolism ( P = 0.18 ) (Fig. 3 , shown in Table 2 ). Table 2 Trajectory of key serum metabolites and HE4 across consecutive chemotherapy cycles in patients with ovarian cancer (n = 26) Metabolite Chemo-1 pre Chemo-2 pre Chemo-3 pre Pvalue† Pyruvic acid 2.34 (0.97–3.90) 2.74 (1.42–4.74) 5.54 (3.34–9.45) 0.015* Citric acid 0.06 (0.04–0.08) 0.07 (0.06–0.08) 0.05 (0.03–0.07) 0.235 cisAconitic acid 0.64 (0.37–0.73) 0.65 (0.57–0.77) 0.51 (0.33–0.67) 0.072 Itaconic acid 0.04 (0.03–0.06) 0.05 (0.04–0.06) 0.07 (0.06–0.07) 0.018* Fumaric acid 0.04 (0.04–0.05) 0.05 (0.04–0.06) 0.06 (0.05–0.09) 0.040* Malic acid 0.10 (0.07–0.11) 0.11 (0.09–0.13) 0.11 (0.08–0.14) 0.106 HE4, pg mL 895 (316–1 090) 69.6 (46.0–129.9) 68.0 (43.2–109.4) < 0.001*** Values are median (interquartile range). Pvalues indicate statistical significance at * P < 0.05 and *** P < 0.001 † Pvalues derived from a nonparametric Friedman test for repeated measures across the three prechemotherapy time points. 3.4. Dynamic changes in key signaling molecules in ELISA profiling ELISA profiling demonstrated dynamic changes in key signaling molecules (Fig. 4 ). Aconitate decarboxylase 1 (ACOD1), restored to near-normal levels pre-chemo-2 ( P = 0.08 vs. controls). ATF3/NF-κB/IL-6 elevated in Chemo-1 ( P 0.05) . TET2/IκBζ were suppressed in Chemo-1 (P < 0.05) and recovered pre-chemo-3 (P = 0.12) . Univariate ANOVA indicated that ACOD1 increased with chemotherapy, resembling the levels of health participants after one-cycle-chemotherapy. While levels of ATF3, TET2, NF-κB, IκBζ and IL-6 resembled those of health participants after two-cycles of chemotherapy. 3.5. Chemotherapy-induced Modulation of ATF3/TET2/NF-κB Pathways Prior to chemotherapy, OC patients exhibited significantly reduced TCA cycle intermediates, including itaconic acid, citrate, fumarate, cis-aconitate, and malate, compared with healthy participants ( P < 0.05). This metabolic suppression was associated with the diminished activity of ACOD1, resulting in the impaired conversion of cis-aconitate to itaconic acid. Following chemotherapy, a progressive restoration of TCA cycle activity was observed. Concomitantly, key pro-inflammatory mediators, which included ATF3, NF-κB, and IL-6, were significantly suppressed ( P < 0.05), while epigenetic regulators TET2 and IκBζ showed marked up-regulation ( P 0.05, Fig. 4 ). 3.6. Associations Among Serum HE4, Metabolites and Dynamic changes in key signaling molecules After one cycle of chemotherapy, HE4 levels decreased to less than 149 pg/mL (895 pg/mL [IQR: 316–1090] in pre-chemo-1 vs. 69.6 pg/mL [46.0–129.9] pre-chemo-2; Table 2 ). Pearson correlation analysis was performed to study the associations among serum HE4, metabolites and dynamic changes in key signaling molecules. Our results demonstrated that among the blood biomarkers analyzed (including CA125 cancer antigen, white blood cells, neutrophils, monocytes, lymphocytes, hemoglobin, and platelets), HE4 showed significant associations with several metabolites: itaconic acid, citrate, fumarate, cis-aconitate, and malate (all P < 0.05 ; Figs. 5 and 6 ). Notably, Figs. 6 revealed inverse correlations between serum HE4 levels and these metabolites. Furthermore, itaconic acid exhibited significant associations with ATF3 in both the pre-chemo-1 and pre-chemo-3 groups (P < 0.05, Figs. 5 and 6 ). 4. Discussion The optimal number of chemotherapy cycles following ODS in OC remains unclear. This study investigated metabolomic changes in OC to identify effective post-chemotherapy regimens. We found that the concentrations of itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid were significantly lower in OC patients than those in healthy participants. These metabolites increased with chemotherapy and reached levels comparable to those in healthy participants after two cycles. Additionally, itaconic acid levels were associated with the ATF3/TET2/NF-κB signaling pathways and HE4. Based on these findings, we propose that two chemotherapy cycles may be optimal following ODS, offering insights into chemotherapy-induced metabolic changes and informing treatment strategies for OC. 4.1 Metabolome profiles disparity between Chemo-1 pre-group and healthy participant group The Warburg effect, a hallmark of cancer metabolism, is characterized by the preference for anaerobic glycolysis in cancerous tissues, even in the presence of oxygen. This metabolic shift causes the increased glucose consumption and the secretion of large amounts of lactic acid, supporting the energy demands and biosynthetic needs of rapidly proliferating cancer cells( 20 , 21 ). In OC, this phenomenon is accompanied by a corresponding reduction in TCA cycle activity during aerobic glycolysis( 22 ). Consistent with our findings, the levels of TCA cycle derivatives and intermediates, such as itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid, were obviously lower in the Chemo-1 pre-group than the healthy participant group. This suggests a metabolic reprogramming that prioritizes alternative fuel sources and energy production to support tumor growth. These observations align with previous studies demonstrating a similar reduction in TCA cycle metabolites in cancerous tissues, further underscoring the metabolic reprogramming characteristic of OC( 23 ). For instance, Hanahan et al . ( 24 ) reported a reduction in TCA intermediates in OC cells, highlighting the altered metabolic pathways that facilitated cancer progression. Similarly, DeBerardinis et al . ( 25 ) identified a strong correlation between the diminished TCA cycle activity and the enhanced tumorigenesis in OC, emphasizing the critical role of metabolic adaptations in promoting cancer cell survival and proliferation. Collectively, these findings provide compelling evidence that OC cells rely on the reprogrammed metabolic pathways to sustain their growth and survival. This metabolic rewiring not only supports the bioenergetic and biosynthetic demands of tumors but also represents a potential target for therapeutic intervention. 4.2 At least two Chemotherapy Cycles Post-ODS was recommended for OC The NCCN guidelines recommend 3–6 cycles of chemotherapy to maintain favorable outcomes in OC( 2 , 7 ). However, these guidelines do not specify whether ODS achieves satisfactory tumor cell reduction, leaving the optimal number of chemotherapy cycles for OC post-ODS unclear based on the metabolome profiles. Based on our PLS-DA and heatmap analyses of metabolite profiles in this study, a clear overlapping metabolite profile was observed between healthy and chemo-2 pre groups (Fig. 3 ). Particularly, the levels of itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid increased following two cycles of chemotherapy, indicating the alternations of TCA cycle activity. Furthermore, after two cycles of chemotherapy, the levels of these metabolites returned to levels comparable to those in healthy participants. These results suggest that at least two cycles of chemotherapy are recommended for OC following ODS. The underlying mechanism remains unclear, though one possible explanation is the up-regulation of TCA cycle activity in aerobic glycolysis following chemotherapy( 26 ). We explored the association between TCA cycle derivatives, intermediates, and HE4, a widely used biomarker for the diagnosis and prognosis of OC. We observed that increased itaconic acid levels were associated with a reduction in serum HE4, which fell below 149 pg/ mL after one cycle of chemotherapy. This finding is consistent with previous studies demonstrating HE4 as a reliable biomarker for assessing post-chemotherapy response and predicting survival outcomes( 27 – 30 ). Above all, these results suggest that at least two cycles of chemotherapy are a recommended regimen following ODS, providing insights into chemotherapy-induced metabolic changes and guiding clinical strategies. 4.3 Chemotherapy-associated signaling pathways Our study further investigated the impact of chemotherapy on key signaling pathways, including ATF3/TET2/NF-κB, in OC patients. We observed significant alterations in the levels of ACOD1, ATF3, TET2, NF-κB, IκBζ, and IL-6 in OC patients (Fig. 7 ), which were gradually restored to levels compared with those in healthy participants following chemotherapy. ACOD1, an enzyme responsible for converting cis-aconitate to itaconic acid, returned to near-healthy levels after one cycle of chemotherapy, leading to the increased production of itaconic acid. Itaconic acid, a metabolite with emerging significance in cancer research, has been shown to exert a critical role in immune regulation and inflammation suppression( 31 , 32 ). The up-regulation of ACOD1 after one cycle of chemotherapy, coupled with the normalization of ATF3, TET2, NF-κB, IκBζ, and IL-6 levels after two cycles, suggests that chemotherapy may enhance antitumor immunity and suppress inflammation through these pathways( 33 – 35 ). These findings align with previous studies that emphasize the roles of NF-κB and IL-6 in tumor progression and chemoresistance( 36 ). Additionally, studies have demonstrated that paclitaxel, a key component of chemotherapy, is metabolized into detectable plasma metabolites in treated patients( 37 ). Platinum- and paclitaxel-based chemotherapy enhances the antitumor activity by reducing apoptosis and inhibiting tumor metastasis and invasion( 38 ). Furthermore, chemotherapy may down-regulate biosynthesis and mitochondrial metabolism in highly aggressive OC cells, thereby increasing TCA cycle activity and suppressing inflammation( 39 , 40 ). Based on these observations, we speculate that chemotherapy may enhance antitumor immunity, reduce inflammation, and restore metabolic homeostasis in OC patients after two treatment cycles. Consequently, targeting metabolic pathways in combination with platinum-based therapy may represent a novel therapeutic strategy for OC. This metabolic rewiring not only supports the bioenergetic and biosynthetic demands of tumors but also represents a potential target for therapeutic intervention. 4.4 Strengths and limitations An additional strength lies in the longitudinal collection of plasma samples at adequate intervals, enabling the differentiation of metabolic profiles across multiple chemotherapy cycles. While the evaluation of metabolic changes during chemotherapy needs to be explored, routine monitoring of metabolic profiles throughout treatment can provide a more comprehensive understanding of the dynamic metabolic alterations induced by chemotherapy. Moreover, longitudinal assessment of metabolic profiles may help establish optimal chemotherapy regimens tailored to individual patients. Nevertheless, this study still had the following several limitations. First, as a pilot study, our sample size was insufficient to fully elucidate differences in metabolic profiles and potential pathway enrichment between the post-chemotherapy and NACT groups, as well as between early-stage and advanced OC patients. Therefore, we have designed a comprehensive follow-up study with a larger cohort and increased sampling time points. Second, our analysis was limited to plasma samples. Future studies should incorporate cell lines, tissue samples, and animal models to demonstrate the metabolic mechanisms of chemotherapy across different tissue types and cancer stages. Third, while numerous metabolites except itaconic acid identified in this study are not discussed here, they will be thoroughly examined in our subsequent research. These unexplored metabolites may hold significant biological relevance, and their potential roles in the observed metabolic pathways warrant further investigation. Future studies will focus on elucidating their mechanistic contributions and clinical implications. Finally, owing to the relatively short duration of the study and the limited number of patients reporting related symptoms, chemotherapy-related toxicities and drug resistance were not included as outcomes. We plan to collect and analyze this information in future research. Finally, while our manuscript focused on a selected number of metabolites based on existing literature, we aim to expand our analysis to include a broader range of metabolites to better understand the unknown metabolic changes associated with chemotherapy in OC. 5. Conclusion To conclude, our study reveals the distinct metabolic profiles in OC patients, with itaconic acid levels returning to those observed in healthy participants after two cycles of chemotherapy. These findings underscore the importance of at least two cycles of chemotherapy following ODS as a fundamental component of OC treatment. Our results not only provide a foundation for further research into metabolic-targeted therapies but also highlight the potential of combining metabolic pathway inhibition with platinum-based chemotherapy as a promising strategy for OC treatment. Abbreviations OC, ovarian cancer GC-MS, gas chromatography-mass spectrometry TCA, tricarboxylic acid NCCN, National Comprehensive Cancer Network NACT, neoadjuvant chemotherapy ODS, optimal debulking surgery HE4, human epididymis protein 4 FAK, focal adhesion kinase CMR, complete metabolic response BMI, body mass index MRI, magnetic resonance imaging AMDIS, Automated Mass Spectral Deconvolution and Identification System ELISA, enzyme-linked immunosorbent assay ACOD1, Aconitate decarboxylase 1 CLIA, chemiluminescent immunoassays ATF3, Activating transcription factor 3 TET2, ten-eleven translocation 2 NF-κB, NF-kappaB Declarations Competing interests None declared. Completed disclosure of interest forms are available to view online as supporting information. Author contributions YT, MH, and JL designed the study. JW, QW, BS, LL, SN and LS collected the data. XC, JH and HH performed the analyses. All authors drafted the first manuscript with the help of JL and MH. All authors have given approval of the final version prior to submission. Acknowledgements Not applicable Ethics approval and consent to participate The written informed consent was acquired from each participant to gather their data anonymously for research purpose. The approval of this study was obtained from Ethics Committee of the Affiliated Nanchong Central Hospital of North Sichuan Medical College (NO.2023-010), and written informed consent was acquired from all participants. All procedures performed in studies involving human participants were in accordance with the 1963 Helsinki Declaration. Availability of data and materials The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. With regard to the publication and utilization of datasets, all contributing authors to this article have executed consent declarations. Funding This work was supported by National Key R&D Plan for Intergovernmental Cooperation, the Ministry of Science and Technology of China (Grant No.2022YFE0133100), Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (Grant No. 2024KFKT016）and the Project of North Sichuan Medical College Youth Program (Grant No. CBY23-QNA19, CBY23-ZDA12, CBY23-QNA11）. Consent for publication This work is original and has not been published elsewhere. All co-authors have reviewed and approved the final version of the manuscript. We agree to the journal’s copyright/license terms (e.g., CC BY, transfer of copyright). 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Prognostic value of complete metabolic response on ¹⁸F-FDG-PET/CT after three cycles of neoadjuvant chemotherapy in advanced high-grade serous ovarian cancer. J Gynecol Oncol . 2022;33(3):e28. Liu X, Yang J, Ran R, et al. Chorionicity-associated variation in metabolic phenotype of cord blood in twin. Nutrition & metabolism . 2023;20(1):31. Smart KF, Aggio RB, Van Houtte JR, Villas-Bôas SG. Analytical platform for metabolome analysis of microbial cells using methyl chloroformate derivatization followed by gas chromatography-mass spectrometry. Nature protocols . 2010;5(10):1709-29. Zhong X, Ran R, Gao S, et al. Complex metabolic interactions between ovary, plasma, urine, and hair in ovarian cancer. Frontiers in oncology . 2022;12:916375. Jiang W, Zhang T, Zhang H, Han T, Ji P, Ou Z. Metabolic Patterns of High-Invasive and Low-Invasive Oral Squamous Cell Carcinoma Cells Using Quantitative Metabolomics and (13)C-Glucose Tracing. Biomolecules . 2023;13(12). Ubachs J, Ziemons J, Soons Z, et al. Gut microbiota and short-chain fatty acid alterations in cachectic cancer patients. Journal of cachexia, sarcopenia and muscle . 2021;12(6):2007-21. Harter P, Sehouli J, Lorusso D, et al. A Randomized Trial of Lymphadenectomy in Patients with Advanced Ovarian Neoplasms. N Engl J Med . 2019;380(9):822-32. Tang Y, Wen MB, Su B, et al. Early return to work: Single-port vs. multiport laparoscopic surgery for benign ovarian tumor. Frontiers in surgery . 2022;9:1005898. Curtis CM, Kost GJ, Louie RF, Sonu RJ, Ammirati EB, Sumner S. POINT-OF-CARE HEMATOLOGY AND COAGULATION TESTING IN PRIMARY, RURAL EMERGENCY, AND DISASTER CARE SCENARIOS. Point of care . 2012;11(2):140-5. Ran R, Zhong X, Yang Y, et al. Metabolomic profiling identifies hair as a robust biological sample for identifying women with cervical cancer. Medical oncology (Northwood, London, England) . 2023;40(2):75. Boroughs LK, DeBerardinis RJ. Metabolic pathways promoting cancer cell survival and growth. Nature cell biology . 2015;17(4):351-9. Burk D, Schade AL. On respiratory impairment in cancer cells. Science (New York, NY) . 1956;124(3215):270-2. Zong WX, Rabinowitz JD, White E. Mitochondria and Cancer. Molecular cell . 2016;61(5):667-76. Hanahan D, Weinberg RA. Hallmarks of cancer: the next generation. Cell . 2011;144(5):646-74. DeBerardinis RJ, Chandel NS. Fundamentals of cancer metabolism. Science advances . 2016;2(5):e1600200. Fasoulakis Z, Koutras A, Ntounis T, et al. Ovarian Cancer and Glutamine Metabolism. International journal of molecular sciences . 2023;24(5). Granato T, Midulla C, Longo F, Colaprisca B, Frati L, Anastasi E. Role of HE4, CA72.4, and CA125 in monitoring ovarian cancer. Tumour biology : the journal of the International Society for Oncodevelopmental Biology and Medicine . 2012;33(5):1335-9. Potenza E, Parpinel G, Laudani ME, Macchi C, Fuso L, Zola P. Prognostic and predictive value of combined HE-4 and CA-125 biomarkers during chemotherapy in patients with epithelial ovarian cancer. The International journal of biological markers . 2020;35(4):20-7. Lee J, Kim JM, Lee YH, Chong GO, Hong DG. Correlation Between Clinical Outcomes and Serum CA-125 Levels After Standard Treatment for Epithelial Ovarian Cancer. Anticancer research . 2022;42(1):349-53. Gadducci A, Menichetti A, Guiggi I, Notarnicola M, Cosio S. Correlation between CA125 levels after sixth cycle of chemotherapy and clinical outcome in advanced ovarian carcinoma. Anticancer research . 2015;35(2):1099-104. McGettrick AF, Bourner LA, Dorsey FC, O'Neill LAJ. Metabolic Messengers: itaconate. Nature metabolism . 2024;6(9):1661-7. Shi X, Zhou H, Wei J, Mo W, Li Q, Lv X. The signaling pathways and therapeutic potential of itaconate to alleviate inflammation and oxidative stress in inflammatory diseases. Redox biology . 2022;58:102553. Gross S, Cairns RA, Minden MD, et al. Cancer-associated metabolite 2-hydroxyglutarate accumulates in acute myelogenous leukemia with isocitrate dehydrogenase 1 and 2 mutations. The Journal of experimental medicine . 2010;207(2):339-44. Jacque N, Ronchetti AM, Larrue C, et al. Targeting glutaminolysis has antileukemic activity in acute myeloid leukemia and synergizes with BCL-2 inhibition. Blood . 2015;126(11):1346-56. Anderson NM, Mucka P, Kern JG, Feng H. The emerging role and targetability of the TCA cycle in cancer metabolism. Protein & cell . 2018;9(2):216-37. Wang Y, Zong X, Mitra S, Mitra AK, Matei D, Nephew KP. IL-6 mediates platinum-induced enrichment of ovarian cancer stem cells. JCI insight . 2018;3(23). Huizing MT, Keung AC, Rosing H, et al. Pharmacokinetics of paclitaxel and metabolites in a randomized comparative study in platinum-pretreated ovarian cancer patients. Journal of clinical oncology : official journal of the American Society of Clinical Oncology . 1993;11(11):2127-35. Liu N, Yan M, Tao Q, et al. Inhibition of TCA cycle improves the anti-PD-1 immunotherapy efficacy in melanoma cells via ATF3-mediated PD-L1 expression and glycolysis. Journal for immunotherapy of cancer . 2023;11(9). Muggia F. Platinum compounds 30 years after the introduction of cisplatin: implications for the treatment of ovarian cancer. Gynecologic oncology . 2009;112(1):275-81. Yang X, Li Z, Ren H, Peng X, Fu J. New progress of glutamine metabolism in the occurrence, development, and treatment of ovarian cancer from mechanism to clinic. Frontiers in oncology . 2022;12:1018642. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 18 Mar, 2026 Read the published version in Journal of Ovarian Research → Version 1 posted Editorial decision: Revision requested 12 Dec, 2025 Reviews received at journal 08 Dec, 2025 Reviewers agreed at journal 25 Nov, 2025 Reviewers agreed at journal 11 Nov, 2025 Reviewers agreed at journal 03 Oct, 2025 Reviews received at journal 23 Sep, 2025 Reviewers agreed at journal 23 Sep, 2025 Reviewers agreed at journal 14 Aug, 2025 Reviewers agreed at journal 12 Aug, 2025 Reviewers invited by journal 12 Aug, 2025 Editor assigned by journal 06 Aug, 2025 Submission checks completed at journal 06 Aug, 2025 First submitted to journal 05 Aug, 2025 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. 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16:04:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4556422,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7296175/v1/4d41183e-8353-4f5b-8542-907b500c6e55.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Two Cycles of Chemotherapy Modulate Itaconic Acid Levels via ATF3/TET2/NF-κB Pathways in Ovarian Cancer Among Han Chinese Women","fulltext":[{"header":"1. Background","content":"\u003cp\u003eOvarian cancer (OC) is one of the most challenging gynecological malignancies owing to its frequent late-stage diagnosis, high propensity for metastasis, and significant risk of recurrence (\u003cspan additionalcitationids=\"CR2 CR3\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). In the United States, the American Cancer Society estimated 12,740 deaths from OC in 2024 (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Similarly, in China, OC resulted in 40,000 patient deaths in 2020, with mortality rates continuing to rise in 2022(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e). According to the National Comprehensive Cancer Network (NCCN) guidelines, the standard treatment for advanced-stage or high-risk OC patients typically involves surgery followed by post-surgery chemotherapy or neoadjuvant chemotherapy (NACT) (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Nevertheless, the optimal number of chemotherapy cycles required after optimal debulking surgery (ODS) is inconsistent based on medical biological mechanisms, and the underlying mechanisms need to be further understood. Although human epididymis protein 4 (HE4) is widely applied as a biomarker for OC diagnosis and prognosis monitoring(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e), its low accuracy during chemotherapy limits its utility in routine clinical practice. Therefore, there is a critical need for supplementary biomarkers, such as metabolites, to monitor metabolic changes during chemotherapy, optimize post-chemotherapy regimens, and explore potential therapeutic mechanisms.\u003c/p\u003e\u003cp\u003eRecent research on metabolic changes during chemotherapy in OC remains limited. Cui \u003cem\u003eet al\u003c/em\u003e. reported that the plasma arachidonic acid-to-prostaglandin E2 ratio and tumoral focal adhesion kinase (FAK) expression were significantly reduced in OC patients after chemotherapy relative to the pre-treatment levels (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). Similarly, Corvigno \u003cem\u003eet al\u003c/em\u003e. identified the associations between specific metabolic alterations, including dysregulation of pyrimidine, aspartate and asparagine metabolism, and clinical response to chemotherapy in OC patients (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). A complete metabolic response (CMR) on 18F-fluorodeoxyglucose positron emission tomography/computed tomography (18F-FDG-PET/CT) after three cycles of NACT has been put forward as a prognostic marker for the recurrence and mortality in advanced high-grade serous carcinoma(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). However, most existing studies are constrained by cross-sectional designs or a narrow focus on single chemotherapy cycles, failing to systematically explore the longitudinal metabolic mechanisms underlying OC progression following ODS across multiple post-chemotherapy regimens (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study was aimed to (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) characterize the chemotherapy-induced metabolic alterations of OC, (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) identify the optimal post-chemotherapy regimens, and (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) elucidate the longitudinal metabolic effects following ODS. Ultimately, this research seeks to provide actionable insights for clinical decision-making and uncover the molecular mechanisms underlying chemotherapy efficacy in OC.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003e2.1 Study populations\u003c/h2\u003e\u003cp\u003e Between March 2023 and March 2025, totally 60 healthy participants were enrolled in this prospective cohort from the Affiliated Nanchong Central Hospital of North Sichuan Medical College, including 30 OC patients and 30 age- and body mass index (BMI)-matched healthy participants (ClinicalTrials.gov ID: ChiCTR2300069160). Healthy participants were recruited from the physical examination center of the hospital and matched to OC patients by age and BMI. The inclusion criteria for the OC group were as follows: radiological suspicion (magnetic resonance imaging (MRI)/ultrasound) of OC with histopathological confirmation, scheduled postoperative chemotherapy on the basis of platinum and paclitaxel, regular oncology follow-up, and commitment to study blood collection protocol. Exclusion criteria were shown below: patients not undergoing chemotherapy after surgery, those not confirmed by histopathology, those not receiving platinum-based chemotherapy, those unwilling to provide blood according to the study schedule, and those who failed to collect blood samples according to the study schedule (Flow chart of the patient selection is displayed in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). This study was approved by the Institutional Review Board of the Affiliated Nanchong Central Hospital of North Sichuan Medical College (NO.2023-010), and written informed consent was acquired from all participants.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\u003ch2\u003e2.2 Blood collection\u003c/h2\u003e\u003cp\u003eBlood samples were collected at three chemotherapy timepoints: pre-treatment baseline (Chemo 1-pre), prior to cycle 2 (Chemo 2-pre), and prior to cycle 3 (Chemo 3-pre). Prior to blood collection, all participants required an overnight fasting period. Subsequently, 5 ml samples of venous blood were collected using a peripheral venous catheter in the following morning. Blood samples were centrifuged (3,000 \u0026times; g, 10 min, 4\u0026deg;C) using standardized protocols. Plasma aliquots were cryopreserved in microtubules (Lelystad, Netherlands) at -80\u0026deg;C until analysis(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\u003ch2\u003e2.3 Gas chromatography-mass spectrometry (GC-MS)\u003c/h2\u003e\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\u003ch2\u003e2.3.1 Metabolite extraction and derivatization from plasma\u003c/h2\u003e\u003cp\u003ePlasma metabolite extraction was performed by mixing 100 \u0026micro;L aliquots with 200 \u0026micro;L ice-cold methanol and 4 \u0026micro;L isotope-labeled internal standards (d4-alanine, d5-phenylalanine, d2-tyrosine). After vortexing (30 s), samples were subject to incubation at \u0026minus;\u0026thinsp;20\u0026deg;C for 30 min under intermittent shaking. The mixture was then centrifuged at 12000 rpm for 15 min. Afterwards, the extracted samples were chemically derivatized through the MCF derivatization method, following the protocol published by Smart \u003cem\u003eet al\u003c/em\u003e(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Briefly, 200 \u0026micro;l of the extracted metabolites were transferred into a glass tube and mixed with 200 \u0026micro;l NaOH (1M), 34 \u0026micro;l pyridine, and 20 \u0026micro;l methyl chloroformate (MCF). After vortexing for 30 s, an additional 20 \u0026micro;l MCF was supplemented and vortexed for 30 s. Then, 400 \u0026micro;l chloroform was added and vortexed for 10 s, followed by the addition of 400 \u0026micro;l NaHCO\u003csub\u003e3\u003c/sub\u003e and vortexing for another 10 s. The upper aqueous and intermediate protein layers were carefully discarded after centrifugation of the derivatized sample at 2000 rpm for 10 min. Sodium sulfate was supplemented to absorb the remaining water. Finally, 150\u0026ndash;200 \u0026micro;l of the chloroform phase was transferred to a vial for mass spectrometry analysis.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\u003ch2\u003e2.3.2 Gas chromatography-mass spectrometry analysis\u003c/h2\u003e\u003cp\u003eThe devitalized plasma metabolites were analyzed by Agilent Intuvo9000 Gas chromatograph coupled with an MSD5977B mass spectrometer. The DB-1701 GC capillary column (20 m \u0026times; 180 \u0026micro;m \u0026times;0.18 \u0026micro;m, Agilent) was used for metabolite separation. The GC-MS inlet parameters were set as follows(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e): injection volume (1 \u0026micro;l), pulsed splitless mode (290\u0026deg;C, 180 kPa for 1 min), guard chip (300\u0026deg;C), and helium carrier flow rate (1 mL/min). The GC oven temperature was initiated at 45\u0026deg;C for 2 min, raised to 180\u0026deg;C at 9\u0026deg;C/min and held for 5 min, subsequently raised to 220\u0026deg;C at 40\u0026deg;C/min and held for 5 min, continued to raise to 240\u0026deg;C at 40\u0026deg;C/min and held for 5 min, and finally raised to 280\u0026deg;C at 80\u0026deg;C/min and held for 3 min. The GC-MS system maintained the following temperatures: ion source at 150\u0026deg;C, quadrupole at 230\u0026deg;C, and transfer line at 300\u0026deg;C.The mass spectrometry was operated using electron-impact ionization at 70 eV. The solvent delay was set at 4.5 min, with the mass range being 30 to 550 m/z at a scan speed of 2.9 scan/s(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec8\" class=\"Section3\"\u003e\u003ch2\u003e2.3.4 Data extraction and normalization\u003c/h2\u003e\u003cp\u003eThe Automated Mass Spectral Deconvolution and Identification System (AMDIS) software was used for metabolite identification and deconvolution. The metabolites were identified by comparing their MS fragmentation patterns (mass-to-charge ratio and relative intensity of mass spectra to most abundant ion) and GC retention time to an in-house MS library established with chemical standards. The Mass Omics R-based script was used for extracting the relative concentration of the metabolites through the peak height of the most abundant fragmented ion mass. To improve the quantitative robustness and minimize the human and instrumental variability, the relative abundances of the identified compounds were normalized against multiple internal standards. Subsequently, blank samples were applied to subtract background contamination and any carryover from identified metabolites(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Calibration curves were acquired from the respective chemical standard, spanning a concentration range of 0\u0026thinsp;~\u0026thinsp;195.8 \u0026micro;M(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\u003ch2\u003e2.4. Metabolites in signaling pathways with chemotherapy\u003c/h2\u003e\u003cp\u003eChemotherapy-related signaling molecules were quantified by enzyme-linked immunosorbent assay (ELISA) at Lilai Biomedical Research Center using commercial kits, with analyses performed by blinded technicians (\u0026ge;\u0026thinsp;5 years' experience) (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). ACOD1 concentrations were determined using Human ACOD1 Platinum ELISA Kit (eBioscience; Zhuocai Biotech, China) following the manufacturer's protocol. Activating transcription factor 3 (ATF3)/ten-eleven translocation 2 (TET2)/NF-kappaB (NF-κB)/inhibitor of nuclear factor kappa B zeta (IκBζ)/Interleukin 6 (IL-6) levels were measured with human ATF3/TET2/NF-κB/IκBζ/ IL-6 Platinum ELlSA Kit (eBioscience, an Zhuocai Biotechnology Company, Shanghai, China). The absorbance (OD value) was measured at 450 nm and the sample concentrations were calculated. All procedures were performed and analyzed in strict accordance with the manufacturer's instructions.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\u003ch2\u003e2.5. Clinic data collection\u003c/h2\u003e\u003cp\u003eSerum HE4 concentrations were retrospectively obtained from the patients\u0026rsquo; clinical records. Following the standardized collection protocols, venous blood samples were drawn into serum separator tubes, allowed to clot for 30 min at room temperature, and later centrifuged at 3000 \u0026times; g for 10 min to obtain serum aliquots(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). All samples were processed and analyzed within 24 h of collection to ensure sample integrity. Quantitative measurement of HE4 was performed using automated chemiluminescent immunoassays (CLIA) on the ARCHITECT i2000SR platform (Abbott Diagnostics, Chicago, IL, USA) according to the manufacturer\u0026rsquo;s specifications(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). The assay demonstrated a measurable range of 15-1500 pmol/L, with intra- and inter-assay coefficients of variation being \u0026lt;\u0026thinsp;5% and \u0026lt;\u0026thinsp;8%, respectively. All the testing procedures were conducted in a CAP-accredited central laboratory by experienced technicians blinded to clinical outcomes to eliminate potential bias(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\u003ch2\u003e2.6. Statistical analysis\u003c/h2\u003e\u003cp\u003eContinuous data were expressed as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, while categorical data were presented as proportions. Metabolomic analysis was conducted using MetaboAnalyst 6.0 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.metaboanalyst.ca\u003c/span\u003e\u003cspan address=\"https://www.metaboanalyst.ca\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), with plasma metabolite levels being log-transformed and Pareto-scaled to achieve normal distribution prior to analysis. Univariate analyses included Student\u0026rsquo;s t-tests (two-group comparisons) and one-way ANOVA (multi-group comparisons). Multivariate analyses comprised partial least squares-discriminant analysis (PLS-DA), evaluation using R\u0026sup2;Y (goodness-of-fit) and Q\u0026sup2;Y (predictive ability via leave-one-out cross-validation), and linear support vector machine (SVM) classification to rank metabolite importance(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Pathway enrichment analysis was conducted via KEGG (2023Q1 release) with the significance thresholds of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and false discovery rate (FDR)\u0026thinsp;\u0026lt;\u0026thinsp;0.3 (Benjamini-Hochberg correction). Pearson correlation assessed associations between metabolites and key elements during chemotherapy, with multiple comparisons adjusted using the q-value R package (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Data were visualized using GraphPad Prism 10 (box/line plots), ggplot2 (heatmaps, forest plots, correlation matrices), and the CNSKnowAll platform (clustering, chord diagrams). All statistical analyses were conducted in SPSS 23.0 and ggplot 2 R package, with supplementary figures generated in Microsoft Excel 2019.\u003c/p\u003e\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\u003ch2\u003e3.1. Patient characteristics\u003c/h2\u003e\u003cp\u003eBetween March 2023 and May 2023, 26 OC patients and 30 healthy participants (controls) were enrolled (4 patients were excluded, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The majority of OC patients presented with advanced-stage disease (International Federation of Gynecology and Obstetrics [FIGO] stage III/IV) and epithelial histology (serous subtype: 96.2%). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes demographic and clinical characteristics. No significant differences were observed in age (median: 55.3 vs. 65.1 years, P\u0026thinsp;=\u0026thinsp;0.093) or BMI 23.9 vs. 23.7 kg/m\u0026sup2;, P\u0026thinsp;=\u0026thinsp;0.231) between the OC group and the control group.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eBaseline clinical characteristics of the study participants (n\u0026thinsp;=\u0026thinsp;56)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eVariable\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOvarian cancer (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eHealthy participants (n\u0026thinsp;=\u0026thinsp;30)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003ePvalue*\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eAge, years\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e53.5 (48.0\u0026ndash;62.3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e65.0 (52.0\u0026ndash;67.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.659\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBMI, kg/m⁻\u0026sup2; \u0026dagger;\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.9 (23.1\u0026ndash;25.6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e22.3 (19.1\u0026ndash;27.0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.231\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eFIGO stage\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEarly (I\u0026ndash;II)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAdvanced (III\u0026ndash;IV)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (73.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eHistopathological type\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eEpithelial\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e25 (96.2)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1 (3.8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eMalignant ascites\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e19 (73.1)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e7 (26.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eChemotherapy regimen\u003c/b\u003e, n (%)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePlatinumbased\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e20 (100)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eOther\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0 (0)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e* Pvalues derived from the MannWhitney U test for continuous variables.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u0026dagger; BMI recorded for patients with ovarian cancer before the first cycle of chemotherapy.\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"4\"\u003eValues for continuous variables are presented as median (interquartile range); categorical data are shown as number (percentage).\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\u003ch2\u003e3.2. Metabolic Profiling in OC vs. healthy participants\u003c/h2\u003e\u003cp\u003eThrough untargeted metabolomic analysis, 128 metabolites were identified, among which, 38 demonstrated significant alterations in OC patients compared with controls (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA-B). The differentially expressed metabolites encompassed amino acid derivatives (e.g., glutamine, FC\u0026thinsp;=\u0026thinsp;0.72), fatty acids (e.g., palmitate, FC\u0026thinsp;=\u0026thinsp;1.35), and tricarboxylic acid (TCA) cycle intermediates (e.g., citrate, FC\u0026thinsp;=\u0026thinsp;0.68).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe supervised PLS-DA modeling revealed distinct metabolic separation between groups (R\u0026sup2;Y\u0026thinsp;=\u0026thinsp;0.748, Q\u0026sup2; = 0.481; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eC), revealing 33 down-regulated and 5 up-regulated metabolites in OC (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, FC\u0026thinsp;\u0026gt;\u0026thinsp;1.1; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eD). Additionally, linear SVM classification identified that itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid were the most discriminatory metabolites (VIP\u0026thinsp;\u0026gt;\u0026thinsp;2.0; Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eE-F). Notably, TCA cycle intermediates were consistently depleted in OC, and the area under the curve (AUC) of the top five metabolites was 0.852 (95% confidence interval (CI): 0.696\u0026ndash;0.988).\u003c/p\u003e\u003cp\u003eFurther validation confirmed the persistently lower levels of TCA cycle intermediates in OC compared with controls (R\u0026sup2;Y\u0026thinsp;=\u0026thinsp;0.55, Q\u0026sup2; = 0.39; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pathway enrichment analysis further underscored the significant dysregulation in the TCA cycle (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and pyruvate metabolism (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05). PLS-DA analysis indicated that chemotherapy significantly altered the metabolic profile of OC patients. The metabolic patterns progressively approached those of the healthy participants across pre-treatment time points (Chemo-1 pre, Chemo-2 pre, and Chemo-3 pre). The model demonstrated satisfactory predictive and explanatory capabilities with R\u0026sup2;=0.55 and Q\u0026sup2;=0.39. Seventeen metabolites showed VIP scores exceeding 1. Cluster analysis identified significant alterations in amino acids, amino acid derivatives, TCA cycle-related metabolites (including itaconic acid), and their derivatives. Volcano plot analysis indicated significantly decreased itaconic acid levels in patients relative to healthy participants (FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05, fold change (FC)\u0026thinsp;\u0026gt;\u0026thinsp;1.2), with gradual normalization of levels following successive chemotherapy cycles. The metabolic disparities diminished with treatment progression. One-way ANOVA showed statistically significant differences in TCA cycle metabolites (including itaconic acid) between OC patients and healthy participants before the first chemotherapy cycle. However, these differences became statistically insignificant after the third chemotherapy cycle. Univariate receiver operating characteristic (ROC) analysis identified 20 metabolites with AUC values\u0026thinsp;\u0026gt;\u0026thinsp;0.7. Subsequent multivariate ROC analysis using linear support vector machine classification ranked variables by mean importance. ROC curves and AUC values were generated for models incorporating top-ranked metabolites (5, 10, 15, 25, 50, and 100 variables). The optimal biomarker panel consisted of the top 10 metabolites, demonstrating superior diagnostic performance.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\u003ch2\u003e3.3. Longitudinal Metabolic Changes During Chemotherapy\u003c/h2\u003e\u003cdiv id=\"Sec16\" class=\"Section3\"\u003e\u003ch2\u003e3.3.1 Chemotherapy Cycle 1 (Chemo-2 Pre. vs. healthy participants)\u003c/h2\u003e\u003cp\u003ePLS-DA, heatmap analysis and clustering analysis revealed 9 phyla correlated with 13 differentially abundant metabolites, including itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid, which were obviously higher in Chemo 1-pre group than those in control group (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Compared with healthy participants, those TCA cycle intermediates, such as itaconic acid, and citric acid, partially recovered. Following the first cycle, TCA intermediates showed further elevation compared with Chemo-1 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), though still below healthy levels (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Pathway analysis indicated reactivation of pyruvate metabolism and TCA cycle after one-cycle chemotherapy (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e; Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section3\"\u003e\u003ch2\u003e3.3.2 Chemotherapy Cycle 2 (Chemo-3 Pre vs. healthy participants)\u003c/h2\u003e\u003cp\u003eAfter two cycles, five TCA cycle intermediates: itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid, resembled to healthy participant levels (\u003cem\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05;\u003c/em\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB). Metabolic pathway disparities between Chemo-2 and controls were not significant for both TCA cycle (P\u0026thinsp;=\u0026thinsp;0.21) and pyruvate metabolism (\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.18\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eTrajectory of key serum metabolites and HE4 across consecutive chemotherapy cycles in patients with ovarian cancer (n\u0026thinsp;=\u0026thinsp;26)\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"5\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMetabolite\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eChemo-1 pre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eChemo-2 pre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eChemo-3 pre\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u003cp\u003ePvalue\u0026dagger;\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePyruvic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e2.34 (0.97\u0026ndash;3.90)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.74 (1.42\u0026ndash;4.74)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e5.54 (3.34\u0026ndash;9.45)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.015*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCitric acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.06 (0.04\u0026ndash;0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.07 (0.06\u0026ndash;0.08)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.05 (0.03\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.235\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ecisAconitic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.64 (0.37\u0026ndash;0.73)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.65 (0.57\u0026ndash;0.77)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.51 (0.33\u0026ndash;0.67)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.072\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eItaconic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04 (0.03\u0026ndash;0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05 (0.04\u0026ndash;0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.07 (0.06\u0026ndash;0.07)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.018*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFumaric acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.04 (0.04\u0026ndash;0.05)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.05 (0.04\u0026ndash;0.06)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.06 (0.05\u0026ndash;0.09)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.040*\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eMalic acid\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.10 (0.07\u0026ndash;0.11)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.11 (0.09\u0026ndash;0.13)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e0.11 (0.08\u0026ndash;0.14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e0.106\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHE4, pg mL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e895 (316\u0026ndash;1 090)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e69.6 (46.0\u0026ndash;129.9)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e68.0 (43.2\u0026ndash;109.4)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;0.001***\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003ctfoot\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003eValues are median (interquartile range).\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003ePvalues indicate statistical significance at *\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and ***\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd colspan=\"5\"\u003e\u0026dagger; Pvalues derived from a nonparametric Friedman test for repeated measures across the three prechemotherapy time points.\u003c/td\u003e\u003c/tr\u003e\u003c/tfoot\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.4. Dynamic changes in key signaling molecules in ELISA profiling\u003c/h2\u003e\u003cp\u003eELISA profiling demonstrated dynamic changes in key signaling molecules (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Aconitate decarboxylase 1 (ACOD1), restored to near-normal levels pre-chemo-2 (\u003cem\u003eP\u0026thinsp;=\u0026thinsp;0.08\u003c/em\u003e vs. controls). ATF3/NF-κB/IL-6 elevated in Chemo-1 (\u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.01\u003c/em\u003e) but were normalized pre-chemo-3 (\u003cem\u003eP\u0026thinsp;\u0026gt;\u0026thinsp;0.05)\u003c/em\u003e. TET2/IκBζ were suppressed in Chemo-1 \u003cem\u003e(P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/em\u003e and recovered pre-chemo-3 \u003cem\u003e(P\u0026thinsp;=\u0026thinsp;0.12)\u003c/em\u003e. Univariate ANOVA indicated that ACOD1 increased with chemotherapy, resembling the levels of health participants after one-cycle-chemotherapy. While levels of ATF3, TET2, NF-κB, IκBζ and IL-6 resembled those of health participants after two-cycles of chemotherapy.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.5. Chemotherapy-induced Modulation of ATF3/TET2/NF-κB Pathways\u003c/h2\u003e\u003cp\u003ePrior to chemotherapy, OC patients exhibited significantly reduced TCA cycle intermediates, including itaconic acid, citrate, fumarate, cis-aconitate, and malate, compared with healthy participants (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This metabolic suppression was associated with the diminished activity of ACOD1, resulting in the impaired conversion of cis-aconitate to itaconic acid. Following chemotherapy, a progressive restoration of TCA cycle activity was observed. Concomitantly, key pro-inflammatory mediators, which included ATF3, NF-κB, and IL-6, were significantly suppressed (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while epigenetic regulators TET2 and IκBζ showed marked up-regulation (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). After two cycles of chemotherapy, the levels of TCA cycle intermediates and associated signaling molecules (ACOD1, TET2, IκBζ, NF-κB) approached those of healthy controls (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.6. Associations Among Serum HE4, Metabolites and Dynamic changes in key signaling molecules\u003c/h2\u003e\u003cp\u003eAfter one cycle of chemotherapy, HE4 levels decreased to less than 149 pg/mL (895 pg/mL [IQR: 316\u0026ndash;1090] in pre-chemo-1 vs. 69.6 pg/mL [46.0\u0026ndash;129.9] pre-chemo-2; Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Pearson correlation analysis was performed to study the associations among serum HE4, metabolites and dynamic changes in key signaling molecules. Our results demonstrated that among the blood biomarkers analyzed (including CA125 cancer antigen, white blood cells, neutrophils, monocytes, lymphocytes, hemoglobin, and platelets), HE4 showed significant associations with several metabolites: itaconic acid, citrate, fumarate, cis-aconitate, and malate (all \u003cem\u003eP\u0026thinsp;\u0026lt;\u0026thinsp;0.05\u003c/em\u003e; Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Notably, Figs.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e revealed inverse correlations between serum HE4 levels and these metabolites. Furthermore, itaconic acid exhibited significant associations with ATF3 in both the pre-chemo-1 and pre-chemo-3 groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Figs.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e and \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe optimal number of chemotherapy cycles following ODS in OC remains unclear. This study investigated metabolomic changes in OC to identify effective post-chemotherapy regimens. We found that the concentrations of itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid were significantly lower in OC patients than those in healthy participants. These metabolites increased with chemotherapy and reached levels comparable to those in healthy participants after two cycles. Additionally, itaconic acid levels were associated with the ATF3/TET2/NF-κB signaling pathways and HE4. Based on these findings, we propose that two chemotherapy cycles may be optimal following ODS, offering insights into chemotherapy-induced metabolic changes and informing treatment strategies for OC.\u003c/p\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e4.1 \u003cb\u003eMetabolome profiles disparity between Chemo-1 pre-group and healthy participant group\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eThe Warburg effect, a hallmark of cancer metabolism, is characterized by the preference for anaerobic glycolysis in cancerous tissues, even in the presence of oxygen. This metabolic shift causes the increased glucose consumption and the secretion of large amounts of lactic acid, supporting the energy demands and biosynthetic needs of rapidly proliferating cancer cells(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). In OC, this phenomenon is accompanied by a corresponding reduction in TCA cycle activity during aerobic glycolysis(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Consistent with our findings, the levels of TCA cycle derivatives and intermediates, such as itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid, were obviously lower in the Chemo-1 pre-group than the healthy participant group. This suggests a metabolic reprogramming that prioritizes alternative fuel sources and energy production to support tumor growth. These observations align with previous studies demonstrating a similar reduction in TCA cycle metabolites in cancerous tissues, further underscoring the metabolic reprogramming characteristic of OC(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). For instance, Hanahan \u003cem\u003eet al\u003c/em\u003e. (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e) reported a reduction in TCA intermediates in OC cells, highlighting the altered metabolic pathways that facilitated cancer progression. Similarly, DeBerardinis \u003cem\u003eet al\u003c/em\u003e. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e) identified a strong correlation between the diminished TCA cycle activity and the enhanced tumorigenesis in OC, emphasizing the critical role of metabolic adaptations in promoting cancer cell survival and proliferation. Collectively, these findings provide compelling evidence that OC cells rely on the reprogrammed metabolic pathways to sustain their growth and survival. This metabolic rewiring not only supports the bioenergetic and biosynthetic demands of tumors but also represents a potential target for therapeutic intervention.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e4.2 At least two Chemotherapy Cycles Post-ODS was recommended for OC\u003c/h2\u003e\u003cp\u003eThe NCCN guidelines recommend 3\u0026ndash;6 cycles of chemotherapy to maintain favorable outcomes in OC(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). However, these guidelines do not specify whether ODS achieves satisfactory tumor cell reduction, leaving the optimal number of chemotherapy cycles for OC post-ODS unclear based on the metabolome profiles. Based on our PLS-DA and heatmap analyses of metabolite profiles in this study, a clear overlapping metabolite profile was observed between healthy and chemo-2 pre groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Particularly, the levels of itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid increased following two cycles of chemotherapy, indicating the alternations of TCA cycle activity. Furthermore, after two cycles of chemotherapy, the levels of these metabolites returned to levels comparable to those in healthy participants. These results suggest that at least two cycles of chemotherapy are recommended for OC following ODS. The underlying mechanism remains unclear, though one possible explanation is the up-regulation of TCA cycle activity in aerobic glycolysis following chemotherapy(\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). We explored the association between TCA cycle derivatives, intermediates, and HE4, a widely used biomarker for the diagnosis and prognosis of OC. We observed that increased itaconic acid levels were associated with a reduction in serum HE4, which fell below 149 pg/ mL after one cycle of chemotherapy. This finding is consistent with previous studies demonstrating HE4 as a reliable biomarker for assessing post-chemotherapy response and predicting survival outcomes(\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Above all, these results suggest that at least two cycles of chemotherapy are a recommended regimen following ODS, providing insights into chemotherapy-induced metabolic changes and guiding clinical strategies.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec24\" class=\"Section2\"\u003e\u003ch2\u003e4.3 Chemotherapy-associated signaling pathways\u003c/h2\u003e\u003cp\u003eOur study further investigated the impact of chemotherapy on key signaling pathways, including ATF3/TET2/NF-κB, in OC patients. We observed significant alterations in the levels of ACOD1, ATF3, TET2, NF-κB, IκBζ, and IL-6 in OC patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e), which were gradually restored to levels compared with those in healthy participants following chemotherapy. ACOD1, an enzyme responsible for converting cis-aconitate to itaconic acid, returned to near-healthy levels after one cycle of chemotherapy, leading to the increased production of itaconic acid. Itaconic acid, a metabolite with emerging significance in cancer research, has been shown to exert a critical role in immune regulation and inflammation suppression(\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The up-regulation of ACOD1 after one cycle of chemotherapy, coupled with the normalization of ATF3, TET2, NF-κB, IκBζ, and IL-6 levels after two cycles, suggests that chemotherapy may enhance antitumor immunity and suppress inflammation through these pathways(\u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). These findings align with previous studies that emphasize the roles of NF-κB and IL-6 in tumor progression and chemoresistance(\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Additionally, studies have demonstrated that paclitaxel, a key component of chemotherapy, is metabolized into detectable plasma metabolites in treated patients(\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e). Platinum- and paclitaxel-based chemotherapy enhances the antitumor activity by reducing apoptosis and inhibiting tumor metastasis and invasion(\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e). Furthermore, chemotherapy may down-regulate biosynthesis and mitochondrial metabolism in highly aggressive OC cells, thereby increasing TCA cycle activity and suppressing inflammation(\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). Based on these observations, we speculate that chemotherapy may enhance antitumor immunity, reduce inflammation, and restore metabolic homeostasis in OC patients after two treatment cycles. Consequently, targeting metabolic pathways in combination with platinum-based therapy may represent a novel therapeutic strategy for OC. This metabolic rewiring not only supports the bioenergetic and biosynthetic demands of tumors but also represents a potential target for therapeutic intervention.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e\u003ch2\u003e4.4 \u003cb\u003eStrengths and limitations\u003c/b\u003e\u003c/h2\u003e\u003cp\u003eAn additional strength lies in the longitudinal collection of plasma samples at adequate intervals, enabling the differentiation of metabolic profiles across multiple chemotherapy cycles. While the evaluation of metabolic changes during chemotherapy needs to be explored, routine monitoring of metabolic profiles throughout treatment can provide a more comprehensive understanding of the dynamic metabolic alterations induced by chemotherapy. Moreover, longitudinal assessment of metabolic profiles may help establish optimal chemotherapy regimens tailored to individual patients.\u003c/p\u003e\u003cp\u003eNevertheless, this study still had the following several limitations. First, as a pilot study, our sample size was insufficient to fully elucidate differences in metabolic profiles and potential pathway enrichment between the post-chemotherapy and NACT groups, as well as between early-stage and advanced OC patients. Therefore, we have designed a comprehensive follow-up study with a larger cohort and increased sampling time points. Second, our analysis was limited to plasma samples. Future studies should incorporate cell lines, tissue samples, and animal models to demonstrate the metabolic mechanisms of chemotherapy across different tissue types and cancer stages. Third, while numerous metabolites except itaconic acid identified in this study are not discussed here, they will be thoroughly examined in our subsequent research. These unexplored metabolites may hold significant biological relevance, and their potential roles in the observed metabolic pathways warrant further investigation. Future studies will focus on elucidating their mechanistic contributions and clinical implications. Finally, owing to the relatively short duration of the study and the limited number of patients reporting related symptoms, chemotherapy-related toxicities and drug resistance were not included as outcomes. We plan to collect and analyze this information in future research. Finally, while our manuscript focused on a selected number of metabolites based on existing literature, we aim to expand our analysis to include a broader range of metabolites to better understand the unknown metabolic changes associated with chemotherapy in OC.\u003c/p\u003e\u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eTo conclude, our study reveals the distinct metabolic profiles in OC patients, with itaconic acid levels returning to those observed in healthy participants after two cycles of chemotherapy. These findings underscore the importance of at least two cycles of chemotherapy following ODS as a fundamental component of OC treatment. Our results not only provide a foundation for further research into metabolic-targeted therapies but also highlight the potential of combining metabolic pathway inhibition with platinum-based chemotherapy as a promising strategy for OC treatment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eOC, ovarian cancer\u003c/p\u003e\n\u003cp\u003eGC-MS, gas chromatography-mass spectrometry\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTCA, \u0026nbsp;tricarboxylic acid\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNCCN, National Comprehensive Cancer Network\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNACT, neoadjuvant chemotherapy\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eODS, \u0026nbsp;optimal debulking surgery\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eHE4, human epididymis protein 4\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFAK, focal adhesion kinase\u003c/p\u003e\n\u003cp\u003eCMR, complete metabolic response\u003c/p\u003e\n\u003cp\u003eBMI, body mass index\u003c/p\u003e\n\u003cp\u003eMRI, magnetic resonance imaging\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAMDIS, Automated Mass Spectral Deconvolution and Identification System\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eELISA, enzyme-linked immunosorbent assay\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eACOD1, Aconitate decarboxylase 1\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eCLIA, chemiluminescent immunoassays\u003c/p\u003e\n\u003cp\u003eATF3, Activating transcription factor 3\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eTET2, ten-eleven translocation 2\u003c/p\u003e\n\u003cp\u003eNF-\u0026kappa;B, NF-kappaB\u0026nbsp;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared. Completed disclosure of interest forms are available to view online as supporting information.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYT, MH, and JL designed the study. JW, QW, BS, LL, SN and LS collected the data. XC, JH and HH performed the analyses. All authors drafted the first manuscript with the help of JL and MH. All authors have given approval of the final version prior to submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe written informed consent was acquired from each participant to gather their data anonymously for research purpose. The approval of this study was obtained from Ethics Committee of the Affiliated Nanchong Central Hospital of North Sichuan Medical College\u0026nbsp;(NO.2023-010), and written informed consent was acquired from all participants. All procedures performed in studies involving human participants were in accordance with the 1963 Helsinki Declaration.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request. With regard to the publication and utilization of datasets, all contributing authors to this article have executed consent declarations.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by National Key R\u0026amp;D Plan for Intergovernmental Cooperation, the Ministry of Science and Technology of China (Grant No.2022YFE0133100), Foundation of State Key Laboratory of Ultrasound in Medicine and Engineering (Grant No. 2024KFKT016）and the Project of North Sichuan Medical College Youth Program (Grant No. CBY23-QNA19, CBY23-ZDA12, CBY23-QNA11）.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eThis work is original and has not been published elsewhere. All co-authors have reviewed and approved the final version of the manuscript. \u0026nbsp;We agree to the journal\u0026rsquo;s copyright/license terms (e.g., CC BY, transfer of copyright).\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/strong\u003eNot applicable\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eTorre LA, Trabert B, DeSantis CE, et al. Ovarian cancer statistics, 2018.\u003cem\u003e CA: a cancer journal for clinicians\u003c/em\u003e. 2018;68(4):284-96.\u003c/li\u003e\n\u003cli\u003eArmstrong DK, Alvarez RD, Backes FJ, et al. NCCN Guidelines\u0026reg; Insights: Ovarian Cancer, Version 3.2022.\u003cem\u003e Journal of the National Comprehensive Cancer Network : JNCCN\u003c/em\u003e. 2022;20(9):972-80.\u003c/li\u003e\n\u003cli\u003eZhang W, Torres-Rojas C, Yue J, Zhu BM. 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New progress of glutamine metabolism in the occurrence, development, and treatment of ovarian cancer from mechanism to clinic.\u003cem\u003e Frontiers in oncology\u003c/em\u003e. 2022;12:1018642.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Ovarian cancer, Chemotherapy, Itaconic acid, Metabolomics, Tricarboxylic acid cycle","lastPublishedDoi":"10.21203/rs.3.rs-7296175/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7296175/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eThe optimal chemotherapy cycles after debulking surgery in ovarian cancer (OC) remain unclear. This prospective cohort was aimed to define metabolic changes induced by chemotherapy, determine the ideal regimen, and assess longitudinal effects.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eMetabolites between treatment-na\u0026iuml;ve OC patients (n\u0026thinsp;=\u0026thinsp;26) and age-matched healthy controls (n\u0026thinsp;=\u0026thinsp;30) were identified by gas chromatography-mass spectrometry (GC-MS). Chemotherapy-related signaling molecules were quantified via ELISA.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eOur analysis identified 38 differentially expressed metabolites, including critical intermediates in the tricarboxylic acid (TCA) cycle and revealed significant reductions in five TCA cycle intermediates, namely itaconic acid, citric acid, fumaric acid, cis-aconitic acid, and malic acid, in OC patients compared to controls. Following the first chemotherapy cycle, global metabolomic profiling showed minimal changes; however, targeted analysis indicated an increase in itaconic acid levels prior to the second chemotherapy cycle. Remarkably, after two chemotherapy cycles, itaconic acid concentrations normalized to levels comparable to those in healthy controls, suggesting metabolic recovery. Furthermore, significant inverse correlations were observed between HE4 levels and the concentrations of the five TCA cycle intermediates, highlighting the potential of these metabolites as biomarkers. Mechanistic studies revealed that two cycles of chemotherapy restored metabolic homeostasis, normalizing itaconic acid levels and reactivating the ATF3/TET2/NF-κB signaling pathway to levels seen in healthy participants.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e\u003cp\u003eTwo chemotherapy cycles may represent an optimal therapeutic strategy following complete cytoreduction, offering novel insights into chemotherapy-induced metabolic reprogramming and potential directions for both clinical decision-making and mechanistic research in OC management.\u003c/p\u003e\u003ch2\u003eTrial registration:\u003c/h2\u003e\u003cp\u003eClinicalTrials.gov ID: ChiCTR2300069160.\u003c/p\u003e","manuscriptTitle":"Two Cycles of Chemotherapy Modulate Itaconic Acid Levels via ATF3/TET2/NF-κB Pathways in Ovarian Cancer Among Han Chinese Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 06:48:34","doi":"10.21203/rs.3.rs-7296175/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-12-12T07:45:12+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-12-08T13:29:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"161122417427423181564728091429968430611","date":"2025-11-25T06:59:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"209817570731951965647521403010508517413","date":"2025-11-11T05:29:59+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"191159403803028584788869791665372517152","date":"2025-10-03T15:36:11+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-23T13:26:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"229416078030660897865203701541059880107","date":"2025-09-23T09:26:15+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"51172843166141895681871614705588088804","date":"2025-08-14T07:36:20+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"165242840591483391400480993716773653736","date":"2025-08-12T09:19:43+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-12T07:55:55+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-07T02:00:31+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-06T09:13:32+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Ovarian Research","date":"2025-08-05T04:24:32+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-ovarian-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jovr","sideBox":"Learn more about [Journal of Ovarian Research](http://ovarianresearch.biomedcentral.com)","snPcode":"13048","submissionUrl":"https://submission.nature.com/new-submission/13048/3","title":"Journal of Ovarian Research","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"9c426110-4e5b-4f99-a01e-5b41b621aff9","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2026-03-23T16:01:30+00:00","versionOfRecord":{"articleIdentity":"rs-7296175","link":"https://doi.org/10.1186/s13048-026-02074-1","journal":{"identity":"journal-of-ovarian-research","isVorOnly":false,"title":"Journal of Ovarian Research"},"publishedOn":"2026-03-18 15:58:42","publishedOnDateReadable":"March 18th, 2026"},"versionCreatedAt":"2025-08-21 06:48:34","video":"","vorDoi":"10.1186/s13048-026-02074-1","vorDoiUrl":"https://doi.org/10.1186/s13048-026-02074-1","workflowStages":[]},"version":"v1","identity":"rs-7296175","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7296175","identity":"rs-7296175","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}