Veillonella Associates with Platinum Resistance in Ovarian Cancer: Insights from Gut Microbiota Profiling and In Vitro Functional Validation

Veillonella Associates with Platinum Resistance in Ovarian Cancer: Insights from Gut Microbiota Profiling and In Vitro Functional Validation | 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 Veillonella Associates with Platinum Resistance in Ovarian Cancer: Insights from Gut Microbiota Profiling and In Vitro Functional Validation Siyu Li, Mengyu Chen, Ningjing Lei, Ruixia Guo, Shan Jiang, Ningyao Tong, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7288124/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Ovarian cancer (OC) remains the most lethal gynecologic malignancy, primarily due to high recurrence rates and frequent development of platinum resistance. While the gut microbiome is known to influence tumor progression and therapeutic response, its role in extraintestinal malignancies like OC remains poorly understood. Methods We collected fecal samples from six platinum-sensitive and three platinum-resistant OC patients. Clinical data were collected, and gut microbiota profiles were assessed using metagenomic next-generation sequencing (mNGS). Differentially abundant taxa were determined through linear discriminant analysis effect size (LEfSe). Functional profiling was conducted with STAMP, and correlations with clinical variables were assessed using the R “psych” package. The effects of Veillonella , the most resistance-associated species, on ovarian cancer cell behavior were validated in vitro. Results Compared to the sensitive group, resistant patients demonstrated a marked depletion of beneficial commensals such as Bacteroides and Faecalibacterium , alongside an overrepresentation of Firmicutes -affiliated taxa. Notably, Veillonella abundance was significantly positively correlated with platinum resistance (p < 0.05). Functional experiments demonstrated that Veillonella promoted ovarian cancer cell proliferation, motility, invasiveness, and resistance to chemotherapy. Conclusion Our findings suggest that the fecal microbiome, particularly Veillonella , may serve as a potential biomarker for assessing platinum sensitivity in OC and provide new insights into the microbiota-mediated mechanisms of chemoresistance. ovarian cancer platinum resistance Veillonella gut microbiota metagenomic next-generation sequencing Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Introduction Ovarian cancer (OC) is one of the most lethal malignancies affecting women, with a five-year survival rate of less than 50% [ 1 ]. This dismal prognosis is primarily due to the tumor's pronounced heterogeneity, high recurrence rate, and frequent development of chemoresistance [ 2 ]. Currently, cytoreductive surgery combined with platinum-based chemotherapy remains the first-line standard treatment for epithelial ovarian cancer (EOC) [ 3 ]. Although approximately 70% of newly diagnosed patients initially respond favorably to platinum-based regimens, most eventually relapse and develop secondary platinum resistance, resulting in progressively shorter progression-free survival (PFS) [ 4 , 5 ]. Ultimately, cisplatin resistance emerges as a near-universal challenge in advanced OC, severely limiting therapeutic efficacy and long-term survival. In clinical practice, gastrointestinal symptoms such as abdominal bloating, discomfort, and anorexia are common throughout the disease course in OC patients [ 6 ]. While these symptoms are frequently observed, current understanding of how the gut microbiota affects epithelial ovarian cancer development and treatment outcomes is limited. The gut microbiota is a diverse microbial community that includes bacteria, viruses, fungi, and other microorganisms [ 7 ]. It plays a vital role in maintaining physiological balance in the host. An imbalance in this microbial environment, such as changes in composition, metabolism, or intestinal permeability, has been linked to the development of various diseases, including cancer [ 8 , 9 ]. Emerging evidence suggests that the gut microbiota can modulate tumor development and impact responses to therapy [ 10 ]. While microbial dysbiosis has been linked to gastrointestinal malignancies such as colorectal cancer [ 11 ], pancreatic cancer [ 12 ], and hepatic cancer [ 13 ], its role in extraintestinal cancers, including OC, remains underexplored. Emerging evidence suggests that microbial communities and their metabolites can serve as biomarkers or modulators of response to chemotherapy and immunotherapy [ 14 – 18 ]. Changes in the gut microbiota may influence the clinical outcomes of OC treatment [ 19 , 20 ]. For instance, microbial-derived lipopolysaccharides (LPS) can promote proinflammatory cytokine production and enhance chemoresistance in ovarian cancer cells [ 21 ]. Moreover, a retrospective clinical study demonstrated that antibiotic use was associated with shorter PFS and worse overall survival in patients with advanced EOC, indicating that disruption of microbial homeostasis may impair responsiveness to platinum-based chemotherapy [ 22 ]. These findings have also been corroborated by preclinical models [ 23 ]. Some studies have modulated the gut microbiota composition using compounds such as naringin and triptolide glycosides, while other studies have directly transplanted specific bacterial strains to regulate OC progression or enhance sensitivity to cisplatin-based chemotherapy [ 24 – 29 ]. Although increasing attention has been directed toward the role of the gut microbiota in OC, comprehensive analyses exploring its association with platinum-based chemotherapy response remain limited. In particular, microbial signatures capable of differentiating platinum-sensitive from platinum-resistant OC have yet to be clearly identified. To address this gap, we conducted a study to examine the relationship between fecal microbial composition and platinum resistance in OC patients. Fecal samples from OC patients with defined platinum responses were analyzed using mNGS to profile gut microbiota, alongside clinical data integration. Compared to the sensitive group, resistant patients demonstrated a marked depletion of beneficial commensals such as Bacteroides and Faecalibacterium, alongside an overrepresentation of Firmicutes-affiliated taxa. Notably, Veillonella was positively associated with resistance. In vitro assays further confirmed that Veillonella enhanced ovarian cancer cell proliferation, migration, invasion, and chemoresistance. These findings highlight Veillonella as a potential biomarker for platinum sensitivity and suggest a role for the gut microbiome in mediating chemoresistance in OC. Methods 2.1 Study participants We recruited platinum-sensitive and platinum-resistant OC patients who attended the First Affiliated Hospital of Zhengzhou University between February 2022 and November 2023. All participants were initially diagnosed with OC based on histopathological examination. Postoperatively, disease staging was determined using the Tumor Node Metastasis (TNM) system. Patients were included in the study and classified into platinum-sensitive and platinum-resistant groups based on the following criteria: (1) a confirmed pathological diagnosis of OC for each patient, (2) participants had not received antibiotics in the six months preceding sample collection; (3) platinum sensitivity was defined as no recurrence or a recurrence-free interval longer than 6 months following platinum-based chemotherapy, whereas platinum resistance referred to recurrence occurring within 6 months after the final platinum treatment. 2.2 Sample collection A total of 200 mg fecal samples were obtained from each ovarian cancer patient, transferred to sterile centrifuge tubes, and rapidly frozen at − 80°C for downstream analyses. 2.3 Cell culture The human ovarian cancer cell lines A2780 and SKOV3 were purchased from Meixuan Biotechnology (Shanghai, China) and the American Type Culture Collection (ATCC, USA), respectively. The platinum-resistant A2780DDP cell line was obtained from Geno Biotech (Guangzhou, China), while SKOV3DDP cells were constructed in our laboratory using a concentration-increment method. A2780 and A2780DDP cell lines were maintained in RPMI-1640 medium (31800, Solarbio, China), while SKOV3 and SKOV3DDP cells were grown in McCoy’s 5A medium (PM150710, Procell, China). All cultures were supplemented with 10% fetal bovine serum (12483020, Gibco, USA) and 1% penicillin-streptomycin (15070063, Gibco, USA), and incubated at 37°C in a humidified atmosphere containing 5% CO₂. 2.4 Culture of Veillonella parvula Veillonella parvula ( V. parvula ) (ATCC 10790) was cultured on pre-prepared Columbia blood agar plates (CA-B, Beina Biology, China) under strict anaerobic conditions. Plates were incubated at 37°C under anaerobic conditions using a jar equipped with gas-generating sachets (Mitsubishi Gas Chemical Company, Japan) or within a sealed anaerobic chamber. The anaerobic atmosphere consisted of approximately 80% N₂, 10% CO₂, and 10% H₂. Bacterial growth was typically observed after 48–72 hours of incubation. Colonies were harvested and washed in sterile phosphate-buffered saline (PBS) for use in downstream assays. All handling steps prior to anaerobic incubation were performed promptly to minimize oxygen exposure. 2.5 Library construction and sequencing Genomic DNA was isolated from fecal samples using the QIAamp PowerFecal DNA Kit (Qiagen, Hilden, Germany) in accordance with the manufacturer's instructions. Approximately 200 mg of fecal sample was homogenized with the provided buffer, and DNA was isolated using bead-beating and spin-column techniques. For mNGS, 1 mg of genomic DNA was fragmented into random pieces averaging 200–400 bp using a Covaris ultrasonic disruptor (Woburn, Massachusetts, USA). Following fragmentation, the DNA was subjected to library construction, which included end-repair, A-tailing, and adapter ligation steps. The resulting libraries were indexed and amplified to enrich for microbial DNA fragments. Sequencing of the prepared libraries was performed on the BGISEQ-500 platform (BGI, Shenzhen, China) using paired-end 100 bp reads. 2.6 Species identification and functional annotation analysis To identify microbial species using specific marker genes, the sequencing data was subjected to MetaPhlAn4 analysis. Taxonomic profiles and detailed abundances of the gut microbiota were generated [ 30 ]. The metabolic capabilities of the microbial community were analyzed using HMP Unified Metabolic Analysis Network 3 (HUMAnN3), which referenced the MetaCyc database [ 31 ]. Data normalization was performed using the HUMAnN_renorm_table for counts per million (CPM). Subsequently, the results were consolidated using HUMAnN_join_tables, and various metabolic pathways were examined using STAMP software. 2.7 α and β diversity analyses The calculations of α diversity, which were represented graphically via a boxplot, were performed using Vega Packages [ 32 ]. Bray Curtis distances determined the dissimilarities among samples, with a resulting principal coordinate analysis (PCoA) diagram illustrating β diversity outcomes. 2.8 mNGS data analysis All statistical analyses were conducted in R (version 4.2.2), with Welch’s t-test applied to assess differences between the two groups. Species abundance was analyzed using MetagenomeSeq, and its differential impact across groups was further evaluated through LEfSe. Functional predictions of the microbial communities were conducted using STAMP software. Redundancy analysis (RDA) was conducted to evaluate how environmental variables shape gut microbiota composition. Permutational multivariate analysis of variance (PERMANOVA) and RDA were used to examine the association between patient phenotypes and gut microbial profiles. The 'psych' package was used to investigate the relationships between gut microbiota and clinical variables. A heatmap was generated using a dedicated package to visualize the correlations. Statistical significance was defined as p < 0.05. Asterisks denote significance levels: * for p < 0.05 and ** for p < 0.01. 2.9 CCK8 assay For the cisplatin resistance assay, A2780/A2780DDP (8,000 cells/well) and SKOV3/SKOV3DDP (5,000 cells/well) cells were seeded into 96-well plates. After 2 hours of treatment with V. parvula (MOI = 100) or heat-inactivated V. parvula , fresh medium was added and incubated for 24 hours. After exposure to varying concentrations of cisplatin (1, 2, 4, 8, 16, and 32 ng/mL) for 48 hours, cells were incubated with 10 µL of CCK-8 reagent for an additional 2 hours. Absorbance at 450 nm was then recorded using a SpectraMAX i3x microplate reader. For the cell proliferation assay, A2780/A2780DDP (3,000 cells/well) and SKOV3/SKOV3DDP (2,000 cells/well) cells were seeded and treated similarly, but without cisplatin addition. CCK-8 reagent (10 µL) was added at 24, 48, 72, and 96 hours, and absorbance at 450 nm was measured. 2.10 EdU cell proliferation assay EdU incorporation assay was performed to assess DNA synthesis and cell proliferation. Ovarian cancer cells were plated in 96-well plates and cultured until they reached 70–80% confluence. Cells were then treated with PBS, V. parvula (MOI = 100), heat-inactivated V. parvula , or the supernatant of V. parvula culture for 2 hours. Following treatment, cells were rinsed twice with PBS to eliminate residual bacteria or metabolites. Fresh medium supplemented with 10 µM EdU (C10310-1, RiboBio, China) was then added, and cells were incubated at 37°C for 2 hours. Following EdU incorporation, cells were fixed and permeabilized with 4% paraformaldehyde and 0.3% Triton X-100, respectively. Detection of incorporated EdU was performed using the Click-iT EdU Cell Proliferation Kit (RiboBio, China) in accordance with the manufacturer’s guidelines. DAPI was used for nuclear counterstaining, and fluorescence images were captured with a fluorescence microscope (Olympus, Japan). The proportion of EdU-positive cells was quantified to assess cell proliferation. 2.11 Wound Healing Assay To assess the migratory ability of ovarian cancer cells, a wound healing assay was performed. Cells were plated in 24-well plates and grown to nearly 90% confluence. A straight scratch was introduced into the cell monolayer using a sterile 200 µL pipette tip. Detached cells were removed by gently rinsing the wells twice with PBS. Cells were then treated with PBS, V. parvula (MOI = 100), heat-inactivated V. parvula , or the supernatant of V. parvula culture for 2 hours. After treatment, cells were rinsed twice with PBS and maintained in serum-free medium. Wound areas were imaged at 0 and 24 hours using an inverted microscope (Olympus, Japan), and the closure rate was calculated by measuring the residual gap with ImageJ. 2.12 Transwell experiment A2780, A2780DDP, SKOV3, and SKOV3DDP cells were first seeded in 6-well plates and cultured to 70–80% confluency. Cells were then treated with PBS, V. parvula (MOI = 100), heat-inactivated V. parvula , or the bacterial supernatant for 2 hours. Cells were washed with PBS following treatment, enzymatically detached using trypsin, and then resuspended in medium lacking serum. Migration assays were conducted by seeding 2 × 10⁴ cells into uncoated Transwell inserts (8.0 µm, Corning, USA), while invasion assays involved Matrigel-coated chambers seeded with 4 × 10⁴ cells. Complete medium with 10% FBS was added to the lower chamber in both assays to provide chemotactic stimulation. Following a 48-hour incubation at 37°C, residual cells on the upper side of the membrane were carefully wiped away using a cotton swab. After fixation with 4% paraformaldehyde for 20 minutes, cells on the underside of the membrane were stained using 0.1% crystal violet for 15 minutes at room temperature. Following PBS washes, images of stained cells were captured using an inverted microscope (Olympus, Japan). Cell counts were quantified from five randomly chosen regions with ImageJ. 2.13 Statistical analysis All statistical analyses were conducted using GraphPad Prism 10. Group differences were assessed via one-way or two-way ANOVA. Significance was defined as p < 0.05 and annotated as *p < 0.05, **p < 0.01, ***p < 0.001, and ****p < 0.0001. Results 3.1 Overview of Clinical Characteristics of Enrolled Patients Fecal samples were collected from six platinum-sensitive OC patients (Sensitive group) and three platinum-resistant patients (Resistant group). Table 1 summarizes the clinical characteristics of all participants, indicating no statistically significant differences in age, height, weight, or BMI between the two groups (p > 0.05). Table 1 Demographics of platinum-sensitive and platinum-resistant OC patients Resistant Sensitive DTX LXL LXE YF YAL HCH YCJ QHX WYS Age 51 59 48 68 70 54 65 50 63 Height (m) 1.61 1.68 1.65 1.52 1.59 1.55 1.64 1.64 1.55 Weight (kg) 60 75.5 57 53.6 51.5 62 56 75 48 BMI 23.14725512 26.75028345 20.93663912 23.19944598 20.37102963 25.80645161 20.82093992 27.88518739 19.97918835 Recurrence Yes Yes Yes Yes No Yes No No No Stage Ⅲ Ⅲ Ⅱ Ⅲ Ⅳ Ⅲ Ⅳ Ⅲ Ⅲ Diameter 2cm > 2cm 2cm > 2cm 2cm / Lymph node metastasis Yes No No Yes No No No Yes No Immunosuppressant Yes Yes No No No No No No No BRCA1/2 - - - - - / - - - PARPi Yes No Yes Yes Yes Yes No Yes No AE1/AE3 / / + / + / + + + CK7 + + / + + / + + + ER + + + / - / + + + PR + / + / - / + + - P53 + + + + + / + + - PAX-8 + + + / + / + + / WT-1 + - / / - / + / + Ki67 + + + + + / + + + P16 / / + / / / + + + NapsinA / - / / - / / - - PERMANOVA analysis revealed no significant association between gut microbiota composition and any individual clinical parameter within the patient cohort. Among all clinical factors analyzed, platinum sensitivity exerted the greatest influence on the variation in gut microbial profiles. However, the effect did not reach statistical significance (p = 0.09). This trend suggests that platinum sensitivity may play a primary role in shaping subgroup specific microbial differences (Supplementary Figure S1 , Supplementary Table 1). 3.2 Fecal microbiome composition and diversity in the platinum-sensitive and platinum-resistant OC patients From the results of next-generation sequencing, marked differences in gut microbiota composition and structure were observed between platinum-sensitive and platinum-resistant OC patients. At the phylum level (Fig. 1 A), platinum-resistant individuals exhibited a pronounced expansion of Firmicutes , which constituted the dominant phylum in this group. In contrast, platinum-sensitive patients showed higher relative abundance of Bacteroidetes , suggesting a more balanced microbial configuration. Minor phyla such as Actinobacteria , Proteobacteria , and Verrucomicrobia were also present in both groups, with slight variations in abundance. These observations point to a phylum-level compositional shift in resistant patients, characterized by elevated Firmicutes and depleted Bacteroidetes . Genus-level profiling (Fig. 1 B) further illustrated this pattern. Platinum-sensitive patients harbored higher relative abundances of several well-known commensals, including Bacteroides , Faecalibacterium , Prevotella , and Alistipes . These genera are frequently associated with anti-inflammatory properties and mucosal health [ 33 , 34 ]. In contrast, the microbial landscape of platinum-resistant patients appeared taxonomically narrowed, with a visible reduction in these beneficial genera and no overt emergence of new dominant taxa. The absence of compensatory overgrowth suggests a loss of microbial equilibrium rather than a substitutional shift. The assessment of fecal microbiota differences between platinum-sensitive and platinum-resistant OC groups involved α and β diversity evaluations. α diversity commonly gauges species richness and evenness within a community, encompassing a comprehensive indicator of diversity. In contrast, β diversity signifies species divergence among distinct environmental communities [ 35 – 37 ]. Analysis of α diversity showed no significant differences between the two groups (Fig. 1 C). Indices including Observed species richness, Shannon entropy, Simpson index, and Pielou’s evenness were comparable, indicating that overall microbial richness and evenness were preserved regardless of platinum status. Bray–Curtis-based β diversity analysis demonstrated a partial distinction between resistant and sensitive sample groups (Fig. 1 D). Nonetheless, the overall fecal microbiota composition did not differ significantly between the two groups (Fig. 1 D, Supplementary Figure S2 ). Taken together, the data raise the possibility that platinum resistance in OC is linked to a simplified gut microbial composition, but this hypothesis warrants further investigation in studies with expanded sample sizes. 3.3 Distinct Gut Microbial Taxa Characterize Platinum-Sensitive and Platinum-Resistant OC Patients LEfSe analyses identified distinct taxonomic biomarkers differentiating the gut microbiota of platinum-sensitive and platinum-resistant OC patients. The analysis was conducted using a threshold of LDA score > 2.0 and a Kruskal–Wallis p-value < 0.05. In the platinum-sensitive group, a range of taxa were enriched across multiple phylogenetic levels. These included species such as Lachnospir sp NSJ-43 , Bifidobacterium pseudocatenulatum , and unclassified members corresponding to t_SGB5087 , t_SGB15049 , and t_SGB17237 . At higher taxonomic ranks, enriched lineages included g_GGB9614 within the family Oscillospiraceae . These features collectively indicate greater microbial heterogeneity in the sensitive group, involving representatives from the phyla Actinobacteria and Firmicutes (Fig. 2 A). In contrast, the platinum-resistant group showed enrichment of a more convergent set of taxa, predominantly affiliated with the phylum Firmicutes . These included Lacrimispora saccharolytica , t_SGB4794 , t_SGB14985 , t_SGB8047 , and related classifications such as g_GGB6020, s_GGB6020-SGB14985 , f_FGB2107 , o_OFGB2107 , and c_CFGB2107 . The taxa identified in this group appeared phylogenetically clustered, suggesting the dominance of a narrower microbial clade (Fig. 2 A). The taxonomic cladogram (Fig. 2 B) further visualizes these group-specific biomarkers. Circles represent the taxonomic hierarchy from phylum to species. Each circle denotes a distinct categorization level, with sizes according to relative abundance. Yellow indicates no significant difference, whereas different species are colored by group. Green nodes indicate important microbes in platinum-sensitive OC, and red nodes represent crucial microbes in platinum-resistant OC. Sensitive-associated taxa were dispersed across the tree, indicating a wider phylogenetic distribution, while resistant-associated taxa formed a coherent cluster within Firmicutes , particularly under the lineage defined by c_CFGB2107 . Supplementary Table 2 presents the detailed differences between the groups. These findings indicate that while both groups share microbial representatives within Firmicutes , their internal taxonomic architectures diverge markedly. Platinum-sensitive patients exhibited a broader distribution of enriched taxa, including representatives of Oscillospiraceae and Bifidobacterium , whereas platinum-resistant patients harbored a more focused enrichment of Firmicutes -related lineages. This compositional distinction, revealed through LEfSe analysis, underscores the potential for gut microbiota to stratify OC patients by treatment response phenotype. 3.4 Alterations in Microbial Metabolic Functional Profiles in Platinum-Resistant OC Patients Functional profiling using STAMP identified significant differences in predicted microbial metabolic pathways between platinum-sensitive and platinum-resistant ovarian cancer patients (Fig. 3 ). The comparison identified a distinct set of microbial functions that were differentially enriched across the two groups. In the platinum-resistant group, multiple metabolic pathways displayed higher relative abundances (Fig. 3 A). Notably, these included P108-PWY (pyruvate fermentation to propanoate I), P125-PWY (superpathway of (R, R)-butanediol biosynthesis), the PWY-7356 (thiamine diphosphate salvage IV [yeast]), PWY66-367 (ketogenesis), PWY-7234 (inosine-5'-phosphate biosynthesis III), and RIBOSYN2-PWY (flavin biosynthesis I [bacteria and plants]) pathways (Fig. 3 B). Figure 3 C-H and Supplementary Table 3 provide detailed findings showing the differences between the two groups. These pathways encompass a range of diverse metabolic functions, including the production of short-chain fatty acids (SCFAs), vitamin cofactor salvage, and nucleotide synthesis. Conversely, the platinum-sensitive group did not display dominant enrichment of any specific pathway within the top-ranked differentials. Instead, the overall functional profile suggested a relatively lower representation of the aforementioned fermentation and biosynthetic activities. These observations suggest that the gut microbiota in platinum-resistant OC patients may be functionally adapted toward enhanced fermentative and biosynthetic activity. The elevated abundance of pathways involved in SCFA and ketone body production may reflect shifts in microbial energy metabolism, while enrichment of thiamine and nucleotide salvage pathways points toward an altered metabolic niche with increased cofactor and precursor turnover. Whether such alterations support host tumor biology or arise in response to the selective pressures of chemotherapeutic resistance remains to be determined. Nonetheless, these findings underscore the functional divergence of microbial communities associated with platinum treatment response, highlighting the potential role of gut-derived metabolites in shaping the tumor microenvironment or modulating therapy efficacy. 3.5 Microbial–Clinical Correlation and Functional Validation of Veillonella in Platinum Resistance To explore microbial features associated with platinum resistance in OC patients, Spearman correlation analysis was performed between gut microbial taxa and resistance phenotype. The top 20 taxa showing the strongest positive correlations were visualized in a hierarchical clustered heatmap (Fig. 4 A). The OC group's bacterial genera' abundance at the genus level exhibited strong correlations with patient factors, such as age, height, weight, BMI, platinum sensitivity, tumor stage, tumor diameter, lymph node metastasis, immunosuppressant use, BRCA1/2 gene mutation, and immunohistochemical markers of OC pathology. Among the top 20 intestinal bacteria with the highest positive correlation with platinum resistance, 17 demonstrated significant associations with clinical indicators (p < 0.05). Among them, Veillonella ranked prominently, suggesting a strong positive association with the resistant phenotype (p < 0.05). Veillonella is a genus of Gram-negative, obligate anaerobic cocci commonly residing in the oral cavity and gastrointestinal tract [ 38 ]. Though historically considered commensal, emerging evidence has highlighted its roles in host–microbiota metabolic interactions and immunologic signaling [ 39 – 41 ]. By fermenting lactate into propionate and other metabolites, Veillonella may influence local immune tone, alter microbial competition, and potentially affect host epithelial behavior [ 42 , 43 ]. These properties make it a plausible microbial contributor to the modulation of chemotherapeutic responses. Previous studies have linked elevated abundance of V. parvula to tumorigenesis and poorer clinical outcomes across various cancer types [ 44 – 47 ]. However, its role in shaping chemotherapeutic response in OC remains largely unexplored. V. parvula , a representative species within the Veillonella genus, was selected for in vitro experiments due to its availability, prior implication in oncogenic pathways. To investigate the potential functional impact of Veillonella , a CCK8-based viability assay was conducted in four ovarian cancer cell lines: A2780, A2780DDP, SKOV3, and SKOV3DDP (Fig. 4 B). Cells were exposed to either live Veillonella or heat-inactivated controls, followed by cisplatin treatment. Across all models, co-incubation with live Veillonella led to increased cisplatin IC₅₀ values relative to controls. Together, these findings suggest that Veillonella is positively associated with platinum resistance at both the microbial community and cellular functional levels. This dual-layered evidence supports its potential role as a microbial contributor to chemotherapy insensitivity in OC. 3.6 V. parvula Enhances the Proliferative Capacity of Ovarian Cancer Cells In Vitro Following the observation that V. parvula is associated with enhanced cisplatin resistance in ovarian cancer cells, we next sought to determine whether this bacterium might also influence other malignant phenotypes, particularly cellular proliferation. To this end, two cisplatin-sensitive/resistant cell line pairs (A2780/A2780DDP and SKOV3/SKOV3DDP) were subjected to treatment with live V. parvula , heat-inactivated bacteria, bacterial culture supernatant, or PBS control. Proliferative capacity was evaluated using both CCK8 metabolic assays and EdU incorporation. Across both cell line pairs, cells treated with either live V. parvula or its culture supernatant exhibited elevated proliferative activity compared to PBS-treated controls, whereas heat-inactivated bacteria failed to induce such effects. In the A2780/A2780DDP model (Fig. 5 ), this enhancement was evident in both CCK8 assays (Fig. 5 A-B), where metabolic activity increased over time, and in EdU assays (Fig. 5 C-F), which revealed a higher fraction of DNA-synthesizing cells in the live bacteria and supernatant groups. The response was particularly robust in the cisplatin-sensitive A2780 cells but remained present in A2780DDP cells. A similar pattern was observed in the SKOV3/SKOV3DDP model (Supplementary Figure S3 ). Both live bacteria and supernatant exposure led to increased cell viability and EdU incorporation relative to the PBS and heat-inactivated groups. Although the increase in EdU-positive cells was less pronounced in SKOV3DDP following live V. parvula treatment, the direction of the effect remained consistent with the other cell lines. These results suggest that V. parvula promotes ovarian cancer cell proliferation through both direct and soluble mechanisms, with viable bacteria and their secreted products contributing to the observed phenotype. The absence of effect in the heat-inactivated group underscores the importance of bacterial viability or active metabolites in mediating these pro-proliferative interactions. 3.7 V. parvula Enhances the Migratory Capacity of Ovarian Cancer Cells To further explore the phenotypic consequences of V. parvula exposure, cell migration was assessed using wound-healing and Transwell assays across two ovarian cancer cell line pairs: A2780/A2780DDP and SKOV3/SKOV3DDP. Treatments included live V. parvula , heat-inactivated bacteria, bacterial culture supernatant, and PBS control. Enhanced wound closure was observed following exposure to either live V. parvula or its culture supernatant (Fig. 6 A, Supplementary Figure S4 A). The effect was most prominent in the live bacteria group, where scratch areas contracted substantially within 24 hours. In contrast, the heat-inactivated group demonstrated minimal improvement over PBS. These trends were consistent across both cisplatin-sensitive and -resistant cell types, with the parental A2780 and SKOV3 lines exhibiting slightly more pronounced responses. Transwell migration assays yielded concordant results (Fig. 6 B). Cells treated with live bacteria or supernatant migrated in greater numbers than controls, a finding quantitatively validated through statistical analysis (Fig. 6 C). Heat-inactivated bacteria did not significantly alter migration, indicating that bacterial viability or secreted metabolites are necessary for this phenotype. Quantification of wound-healing rates (Supplementary Figure S4 B) reinforced the observed trend: both live bacteria and supernatant groups showed significantly increased migratory indices compared to PBS and heat-inactivated conditions. Although variation existed between cell lines, the directionality of the effect remained consistent. These results collectively demonstrate that V. parvula facilitates ovarian cancer cell migration through mechanisms dependent on bacterial viability or soluble bacterial products. The consistency of this response across multiple cell lines suggests a conserved modulatory effect on tumor cell motility. 3.8 V. parvula Potentiates the Invasive Capacity of Ovarian Cancer Cells The pro-invasive effects of V. parvula on ovarian cancer cells were assessed using a Matrigel-coated Transwell assay. Cells were exposed to live bacteria, heat-inactivated bacteria, bacterial culture supernatant, or PBS control, and their ability to traverse the matrix barrier was quantified across four cell lines. Microscopic examination of the invaded cells (Fig. 7 A) revealed an increased number of cells crossing the Matrigel membrane following treatment with live V. parvula or its culture supernatant, compared to PBS and heat-inactivated conditions. This pattern was observed consistently in both cisplatin-sensitive (A2780, SKOV3) and resistant (A2780DDP, SKOV3DDP) cell lines. The heat-inactivated group exhibited little to no enhancement relative to PBS controls, indicating that bacterial viability or active metabolites may be essential for driving the invasive phenotype. Quantitative analysis (Fig. 7 B) confirmed these visual trends. Both the live V. parvula and supernatant groups displayed significantly elevated invasion indices in all four cell lines. Although the magnitude of the response varied slightly among different models, the directionality of the effect remained robust across conditions. In contrast, no significant increase in invasion was detected in the heat-inactivated group, further underscoring the functional relevance of live bacterial exposure or secreted factors. These findings suggest that V. parvula enhances the invasive behavior of ovarian cancer cells through mechanisms dependent on bacterial viability and soluble effectors. The consistency of this effect across multiple cell types highlights its potential relevance in modulating the metastatic phenotype. Discussion The gut microbiota is now widely acknowledged to function as an endocrine-like organ, exerting effects on distant tissues and regulating systemic physiological processes [ 48 ]. In this study, we identified a previously unreported link between the gut microbiota and platinum-based chemoresistance in ovarian cancer. Notably, Veillonella was significantly enriched in patients with chemoresistant disease. This taxon was not only altered at the compositional level but also appeared to participate in regulating chemoresistance and promoting malignant cellular behaviors. This study utilized MetaPhlAn 4 for species identification in the gut microbiota of patients with platinum-sensitive and resistance OC. MetaPhlAn 4 combines known microbial genomes with metagenomic assembly genomes to define a genome box at the species level, thereby offering a more comprehensive metagenomic taxonomic analysis compared to its previous three versions [ 30 ]. It is more sensitive and specific, capable of providing accurate classification, identification, and quantification. Moreover, it can accurately quantify unidentified species and maintain high precision for taxonomically classified species, thereby broadening metagenome classification [ 30 , 49 ]. Previous studies have suggested that disruption of the gut microbial ecosystem may impair cisplatin responsiveness in EOC [ 23 ], while modulation of microbial composition has been associated with both anti-tumor effects and enhanced therapeutic efficacy [ 24 , 28 , 50 ]. Through metagenomic sequencing, we demonstrate that the fecal microbiota of platinum-resistant patients undergoes a profound compositional restructuring, characterized by marked enrichment of Veillonella and other genera belonging to the phyla Bacteroidetes and Firmicutes , in contrast to the more balanced and diverse microbial architecture observed in platinum-sensitive individuals. Recent studies have highlighted the complex and context-dependent roles of the genus Veillonella in cancer biology. Rather than acting uniformly as a pro- or anti-tumorigenic agent, Veillonella appears to exert divergent effects across distinct tumor types and microenvironmental contexts. In some settings, V. parvula has been reported to inhibit tumor growth and metastasis through tumor-specific colonization and by reducing intratumoral lactate levels [ 51 ]. Supporting this anti-tumor potential, V. parvula -derived propionate has been shown to suppress malignant phenotypes in oral squamous cell carcinoma cells via metabolic modulation [ 52 ]. Conversely, in other malignancies, Veillonella may contribute to tumor progression. For instance, V. parvula has been demonstrated to promote proliferation in lung adenocarcinoma through the Nod2/CCN4/NF-κB signaling axis [ 53 ]. Veillonella abundance was found to be positively correlated with the neutrophil-to-lymphocyte ratio (NLR) in a systematic study of oral microbiota from non-small cell lung cancer patients, highlighting its potential association with systemic inflammation and adverse prognosis [ 54 ]. Moreover, enrichment of Veillonella and Streptococcus has been associated with gut microbial aging and impaired response to immune checkpoint blockade, suggesting a broader immunomodulatory influence [ 55 ]. Taken together, these findings suggest that Veillonella , particularly V. parvula , may engage in tumor–microbe interactions that are highly dependent on tissue type, immune context, and microbial metabolic output. These multifaceted roles underscore the importance of investigating its functional consequences within each specific cancer model, including OC, where its contribution to chemoresistance and malignant progression remains poorly defined. Functional experiments confirmed that exposure to V. parvula markedly increased cisplatin resistance across multiple ovarian cancer cell lines. This effect was abolished following bacterial heat-inactivation and was partially recapitulated by bacterial culture supernatant, suggesting that the pro-resistance phenotype depends on bacterial viability or secreted factors. These findings suggest that V. parvula may modulate host chemoresistance through a potential paracrine mechanism. This effect is likely mediated by microbial metabolites or signaling molecules, and may act beyond traditional tumor-intrinsic pathways [ 56 , 57 ]. More importantly, the influence of V. parvula was not limited to chemoresistance. Live bacteria and their supernatant significantly enhanced malignant phenotypes including cell proliferation, migration, and invasion, as evidenced by elevated metabolic activity, increased DNA synthesis, and enhanced motility and matrix penetration. These effects were consistently observed across multiple cell models, suggesting that V. parvula may contribute broadly to tumor heterogeneity and metastatic potential. Notably, none of these pro-malignant effects were observed with heat-inactivated bacteria, further underscoring the essential role of bacterial viability or secreted bioactive components in shaping host cell behavior. While the precise molecular mediators remain to be elucidated, candidates may include short-chain fatty acids, microbe-derived signaling peptides, or structural components such as lipoteichoic acid [ 58 , 59 ]. Future studies employing transcriptomic, metabolomic, and mechanistic approaches are warranted to dissect these pathways and explore microbiota-based therapeutic targets aimed at overcoming chemoresistance and restraining tumor aggressiveness. Nevertheless, this study is not without limitations. First, the cohort size was relatively small, comprising only nine OC patients. Although stratification by platinum sensitivity provided biologically meaningful comparisons, the findings may not fully capture the diversity of microbial signatures across different molecular subtypes or clinical stages. Larger, multi-center studies are required to validate these observations. Second, the functional characterization of V. parvula was limited to in vitro models. While the observed trends were consistent and robust, whether similar effects occur in vivo remains to be tested using animal models and patient-derived tissues. Third, although we confirmed that both bacterial viability and soluble factors affect host phenotypes, the precise molecular mechanisms remain undefined. A more comprehensive dissection of key microbial metabolites and signaling pathways is needed to clarify causality and inform intervention strategies. In summary, our study identifies Veillonella as a potential dual biomarker and functional mediator of platinum resistance and malignant progression in OC. These findings expand the current understanding of tumor–microbiota interactions and offer a new ecological perspective on chemoresistance and clinical prognosis in gynecologic malignancies. Incorporating gut microbial features into individualized treatment strategies may open new avenues for prediction, intervention, and therapeutic optimization in OC. Conclusion This study identifies Veillonella as a gut microbial species significantly associated with platinum resistance in OC and functionally capable of enhancing chemoresistance, proliferation, migration, and invasion of tumor cells in vitro. These findings shed light on the microbiota’s potential role in shaping therapeutic outcomes and malignant phenotypes, offering a novel ecological dimension to the understanding of chemoresistance. Despite current limitations in sample size and in vivo validation, our work lays a conceptual foundation for future efforts to decode the tumor–microbiome–therapy axis and to harness microbial targets for precision interventions in gynecologic oncology. Declarations Ethics approval and consent to participate The study protocol received ethical approval from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (2023-KY-1323-002). Written informed consent was obtained from all participants before enrollment. Consent for publication Not applicable. Availability of data and material The raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics & Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA028564) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa [60, 61]. Competing interests No financial or commercial affiliations were present that might be interpreted as a conflict of interest. Funding This work was supported by Henan International Science and Technology Cooperation Project (252102521064), Henan Province High-end Foreign Expert Introduction Program (HNGD2020117), Key scientific research projects of colleges and universities in Henan Province (25A320012), Provincial and Ministry Co-constructed Key Projects of Henan Medical Science and Technology Research Program (SBGJ202402055), National Health Commission Medical Science and Technology Development Research Center Project (WKZX2024DN0182), Young and Middle-aged Subject Leader of Henan Provincial Health Commission (HNSWJW-2022001), the National Natural Science Foundation of China (No. 82303343), and International training program for high-level talents in Henan Province. Authors' contributions Conceptualization: YL, RG, and LC. Funding acquisition: NL and LC. Project administration: NL and LC. Supervision: YL and LC. Visualization: SL and MC. Validation: SL and MC. Writing-original draft: SL and MC. Writing-review & editing: NL, RG, SJ, NT, KW, WW, YZ,YL and LC. The study was conducted through joint efforts of all authors. Each author was involved in the preparation of the manuscript and consented to its submission. Acknowledgments We thank the participants for their contributions to this study. References Zeng H, Zheng R, Sun K, Zhou M, Wang S, Li L, Chen R, Han B, Liu M, Zhou J et al : Cancer survival statistics in China 2019-2021: a multicenter, population-based study . J Natl Cancer Cent 2024, 4 (3):203-213. Konstantinopoulos PA, Matulonis UA: Clinical and translational advances in ovarian cancer therapy . Nat Cancer 2023, 4 (9):1239-1257. 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Supplementary Files SupplementaryMateriallegends.docx SupplementaryFigureS1.tif SupplementaryFigureS2.tif SupplementaryFigureS3.tif SupplementaryFigureS4.tif SupplementaryTable1.xlsx SupplementaryTable2.xlsx SupplementaryTable3.xlsx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7288124","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":498276328,"identity":"572615f4-e58d-4634-88a8-b6005a61c0ec","order_by":0,"name":"Siyu Li","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Siyu","middleName":"","lastName":"Li","suffix":""},{"id":498276329,"identity":"cf87d6b6-1a1e-454c-a4b6-5b13add3de2b","order_by":1,"name":"Mengyu Chen","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Mengyu","middleName":"","lastName":"Chen","suffix":""},{"id":498276330,"identity":"16c64875-4f26-434a-b1b4-d95bea47baf3","order_by":2,"name":"Ningjing Lei","email":"","orcid":"","institution":"Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ningjing","middleName":"","lastName":"Lei","suffix":""},{"id":498276331,"identity":"92056f69-8af9-4e05-936a-6194f3bd212f","order_by":3,"name":"Ruixia Guo","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ruixia","middleName":"","lastName":"Guo","suffix":""},{"id":498276332,"identity":"b3f7462c-1d89-4942-bad6-2ead3622d694","order_by":4,"name":"Shan Jiang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Shan","middleName":"","lastName":"Jiang","suffix":""},{"id":498276333,"identity":"1f9a6deb-8732-46f8-9a73-5c3ea72be0fd","order_by":5,"name":"Ningyao Tong","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Ningyao","middleName":"","lastName":"Tong","suffix":""},{"id":498276334,"identity":"960fb731-315f-43d2-88d1-afab8c343477","order_by":6,"name":"Kunmei Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Kunmei","middleName":"","lastName":"Wang","suffix":""},{"id":498276335,"identity":"eea12e6f-cdf2-49e0-b093-43bf5c7b9bcb","order_by":7,"name":"Weili Wang","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Weili","middleName":"","lastName":"Wang","suffix":""},{"id":498276336,"identity":"d0e16e4e-1a5d-4303-bc66-407c40a82c4d","order_by":8,"name":"Yamin Zhao","email":"","orcid":"","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":false,"prefix":"","firstName":"Yamin","middleName":"","lastName":"Zhao","suffix":""},{"id":498276337,"identity":"503f8d57-ca07-403a-b26c-2addc22ddb26","order_by":9,"name":"Yong Li","email":"","orcid":"","institution":"UNSW: University of New South Wales","correspondingAuthor":false,"prefix":"","firstName":"Yong","middleName":"","lastName":"Li","suffix":""},{"id":498276338,"identity":"6ce55f66-edf3-4803-ac6d-bf3b82427a86","order_by":10,"name":"Lei Chang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIie3QsQrCMBCA4SuB63LqWim+g1DQRdpXiRQ6Ozo4FAodnRV8iD5CJGBfwkEp1MVBEBzVpuga4yaYf8kN9xESAJvtB3NTAKaGrpqanPQTIfEiqKYvicdNiSuP19liH+b9U3UlmAwKweqDllAS+KtdHec+5wFBEhQCx0MdiTxCRihj9LmICeS0EISe9paW3BvS36aS4GFIOrkM0WNORiAMiHpLZyk5UsKczTAO1hJHeqJ+jG4y6rlldTnPw8GyzGoteTdN20N9FTPZb4oM92w2m+0fewLQnz7/qID0qQAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-5596-9500","institution":"The First Affiliated Hospital of Zhengzhou University","correspondingAuthor":true,"prefix":"","firstName":"Lei","middleName":"","lastName":"Chang","suffix":""}],"badges":[],"createdAt":"2025-08-04 07:27:51","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7288124/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7288124/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89258324,"identity":"d68b82f0-afae-44aa-b0bc-69de3f20037a","added_by":"auto","created_at":"2025-08-18 06:21:04","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1496905,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFecal microbiome composition and diversity in the platinum-sensitive and platinum-resistant OC patients.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e. The histogram indicates the microbiome composition at the phylum level. \u003cstrong\u003eB\u003c/strong\u003e.\u003cstrong\u003e \u003c/strong\u003eThe histogram indicates the microbiome composition at the genus level.\u003cstrong\u003e C\u003c/strong\u003e. α diversity is shown using the Observe, Shannon, Simpson, and Pielou indices. \u003cstrong\u003eD\u003c/strong\u003e. PCoA analysis based on Pearson Distance and Bray-Curtis distance matrices. Each sphere represents one sample. Samples are separated into two groups.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/b6006cc55d8d07beb1c99059.png"},{"id":89258323,"identity":"e32b0dcc-85f3-4541-aa24-3b51e738a4f7","added_by":"auto","created_at":"2025-08-18 06:21:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1259376,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDistinct Gut Microbial Taxa Characterize Platinum-Sensitive and Platinum-Resistant OC Patients. A\u003c/strong\u003e. Discriminant analysis of LEfSe species difference between platinum-sensitive and platinum-resistant OC patients. \u003cstrong\u003eB\u003c/strong\u003e. Taxonomic cladogram from LEfSe, depicting the taxonomic association between the fecal microbiome communities from platinum-sensitive and platinum-resistant OC patients.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/b0c4f5eeedc3e87ee5b0d67a.png"},{"id":89258326,"identity":"8fe4c035-43df-4f5c-9e6d-234ffee52ea1","added_by":"auto","created_at":"2025-08-18 06:21:04","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":997549,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAlterations in Microbial Metabolic Functional Profiles in Platinum-Resistant OC Patients. A\u003c/strong\u003e. Principal Component Analysis of pathways in the two groups. \u003cstrong\u003eB\u003c/strong\u003e. Extended error bar of the platinum-resistant group versus the platinum-sensitive group. The p-value indicates a significant difference between the two groups. \u003cstrong\u003eC-H\u003c/strong\u003e. The top 6 metabolic pathways with differential abundance by mNGS testing.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/636b08429a68b2a45a3a627b.png"},{"id":89259649,"identity":"0927d991-caaa-4133-93fc-71a468eaa23d","added_by":"auto","created_at":"2025-08-18 06:29:04","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":2122118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMicrobial–Clinical Correlation and Functional Validation of \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eVeillonella \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ein Platinum Resistance.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e. Heatmap shows the correlation between clinical indices and fecal microbiota\u003cstrong\u003e \u003c/strong\u003eat the genus level. \u003cstrong\u003eB\u003c/strong\u003e. CCK8 assays showing cisplatin IC₅₀ curves of ovarian cancer cells treated with \u003cem\u003eV. parvula\u003c/em\u003e or heat-inactivated \u003cem\u003eV. parvula \u003c/em\u003e(heated V.P.) to assess cisplatin resistance.\u0026nbsp;\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/fea0a27929e3617c99046421.png"},{"id":89259650,"identity":"3a5d1db8-c785-44e1-bcf2-3b89896d255a","added_by":"auto","created_at":"2025-08-18 06:29:04","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":4631605,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eV. parvula\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003eEnhances the Proliferative Capacity of Ovarian Cancer Cells In Vitro.\u003c/strong\u003e \u003cstrong\u003eA-B\u003c/strong\u003e. Proliferation curves of A2780 and A2780DDP cells treated with PBS, live \u003cem\u003eV. parvula\u003c/em\u003e, heat-inactivated \u003cem\u003eV. parvula\u003c/em\u003e (heated \u003cem\u003eV.P.\u003c/em\u003e), or supernatant. \u003cstrong\u003eC-F\u003c/strong\u003e. EdU assay showing the percentage of EdU-positive cells in A2780 and A2780DDP after the four treatments.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/2a92518853104e6a24323d9e.png"},{"id":89259657,"identity":"d7dbc3c4-514f-4d2f-b3e2-ee6b189090d6","added_by":"auto","created_at":"2025-08-18 06:29:05","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":24705617,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eV. parvula\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e Enhances the Migratory Capacity of Ovarian Cancer Cells.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e. Wound healing assays showing the migratory capacity of A2780 and A2780DDP cells after treatment with PBS, live \u003cem\u003eV. parvula\u003c/em\u003e, heat-inactivated \u003cem\u003eV. parvula\u003c/em\u003e (heated \u003cem\u003eV.P.\u003c/em\u003e), or supernatant. \u003cstrong\u003eB-C\u003c/strong\u003e. Transwell assays were used to evaluate the migration ability of four ovarian cancer cell lines under the same four treatment conditions.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/3b413ea09aa7652eb72f0f9a.png"},{"id":89259900,"identity":"3b1c8ad3-571a-4cce-b777-2bf31df82b7a","added_by":"auto","created_at":"2025-08-18 06:37:04","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":11171043,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eV. parvula \u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ePotentiates the Invasive Capacity of Ovarian Cancer Cells.\u003c/strong\u003e \u003cstrong\u003eA\u003c/strong\u003e-\u003cstrong\u003eB\u003c/strong\u003e. Transwell invasion assays with Matrigel coating showing the invasive capacity of four ovarian cancer cell lines after treatment with PBS, live \u003cem\u003eV. parvula\u003c/em\u003e, heat-inactivated \u003cem\u003eV. parvula\u003c/em\u003e (heated \u003cem\u003eV.P.\u003c/em\u003e), or supernatant.\u003c/p\u003e","description":"","filename":"Figure7.png","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/9c2386f06f6cea10a89e4476.png"},{"id":91288010,"identity":"482ed73f-ada6-4450-bd2e-921211cc17dd","added_by":"auto","created_at":"2025-09-14 20:53:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":45825424,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/ff0e3208-4f20-4af4-8c4a-237a60887d0a.pdf"},{"id":89258330,"identity":"5f19266d-f773-4925-9ba1-bfbd47a6839d","added_by":"auto","created_at":"2025-08-18 06:21:04","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":14755,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMateriallegends.docx","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/afe62a994a0ead122bf75d21.docx"},{"id":89258337,"identity":"ef3ab1a9-1c1c-446f-a03d-f7136c49671b","added_by":"auto","created_at":"2025-08-18 06:21:04","extension":"tif","order_by":12,"title":"","display":"","copyAsset":false,"role":"supplement","size":3526452,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS1.tif","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/64098a5e657dfd3cb4a29224.tif"},{"id":89258335,"identity":"01731ba1-5d6a-464a-8358-69838a474503","added_by":"auto","created_at":"2025-08-18 06:21:04","extension":"tif","order_by":13,"title":"","display":"","copyAsset":false,"role":"supplement","size":1313264,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS2.tif","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/68ce1fabfea4806c049b4d5f.tif"},{"id":89259658,"identity":"70c3376e-c65b-4614-9a33-fcc1d2dcb3ba","added_by":"auto","created_at":"2025-08-18 06:29:05","extension":"tif","order_by":14,"title":"","display":"","copyAsset":false,"role":"supplement","size":12948840,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS3.tif","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/cb9c9219760a4aada26d2ad5.tif"},{"id":89259662,"identity":"a24d6c99-b6d6-40a4-814a-75bfb5c4cd75","added_by":"auto","created_at":"2025-08-18 06:29:05","extension":"tif","order_by":15,"title":"","display":"","copyAsset":false,"role":"supplement","size":18657316,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFigureS4.tif","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/2d1a6f55a0e19657d91bd84d.tif"},{"id":89260375,"identity":"edea7819-3a02-4b5b-ad33-3d140d63c3e4","added_by":"auto","created_at":"2025-08-18 06:45:05","extension":"xlsx","order_by":16,"title":"","display":"","copyAsset":false,"role":"supplement","size":10221,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/450651e681cd7c6d4e69a00e.xlsx"},{"id":89258342,"identity":"fb5dc422-0be5-40eb-ac8f-4f00037cc579","added_by":"auto","created_at":"2025-08-18 06:21:05","extension":"xlsx","order_by":17,"title":"","display":"","copyAsset":false,"role":"supplement","size":10697,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable2.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/6b9fa795ff3d59c1cfd3c1bf.xlsx"},{"id":89258341,"identity":"29bfe0ce-25fb-4287-8d9c-ba1b3e6bfb70","added_by":"auto","created_at":"2025-08-18 06:21:05","extension":"xlsx","order_by":18,"title":"","display":"","copyAsset":false,"role":"supplement","size":156457,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryTable3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7288124/v1/a3a15bd7a3211f5f646c8d08.xlsx"}],"financialInterests":"","formattedTitle":"Veillonella Associates with Platinum Resistance in Ovarian Cancer: Insights from Gut Microbiota Profiling and In Vitro Functional Validation","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOvarian cancer (OC) is one of the most lethal malignancies affecting women, with a five-year survival rate of less than 50% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. This dismal prognosis is primarily due to the tumor's pronounced heterogeneity, high recurrence rate, and frequent development of chemoresistance [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Currently, cytoreductive surgery combined with platinum-based chemotherapy remains the first-line standard treatment for epithelial ovarian cancer (EOC) [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Although approximately 70% of newly diagnosed patients initially respond favorably to platinum-based regimens, most eventually relapse and develop secondary platinum resistance, resulting in progressively shorter progression-free survival (PFS) [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Ultimately, cisplatin resistance emerges as a near-universal challenge in advanced OC, severely limiting therapeutic efficacy and long-term survival.\u003c/p\u003e\u003cp\u003eIn clinical practice, gastrointestinal symptoms such as abdominal bloating, discomfort, and anorexia are common throughout the disease course in OC patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. While these symptoms are frequently observed, current understanding of how the gut microbiota affects epithelial ovarian cancer development and treatment outcomes is limited. The gut microbiota is a diverse microbial community that includes bacteria, viruses, fungi, and other microorganisms [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It plays a vital role in maintaining physiological balance in the host. An imbalance in this microbial environment, such as changes in composition, metabolism, or intestinal permeability, has been linked to the development of various diseases, including cancer [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Emerging evidence suggests that the gut microbiota can modulate tumor development and impact responses to therapy [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. While microbial dysbiosis has been linked to gastrointestinal malignancies such as colorectal cancer [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], pancreatic cancer [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e], and hepatic cancer [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], its role in extraintestinal cancers, including OC, remains underexplored. Emerging evidence suggests that microbial communities and their metabolites can serve as biomarkers or modulators of response to chemotherapy and immunotherapy [\u003cspan additionalcitationids=\"CR15 CR16 CR17\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e–\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Changes in the gut microbiota may influence the clinical outcomes of OC treatment [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. For instance, microbial-derived lipopolysaccharides (LPS) can promote proinflammatory cytokine production and enhance chemoresistance in ovarian cancer cells [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Moreover, a retrospective clinical study demonstrated that antibiotic use was associated with shorter PFS and worse overall survival in patients with advanced EOC, indicating that disruption of microbial homeostasis may impair responsiveness to platinum-based chemotherapy [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. These findings have also been corroborated by preclinical models [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Some studies have modulated the gut microbiota composition using compounds such as naringin and triptolide glycosides, while other studies have directly transplanted specific bacterial strains to regulate OC progression or enhance sensitivity to cisplatin-based chemotherapy [\u003cspan additionalcitationids=\"CR25 CR26 CR27 CR28\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e–\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eAlthough increasing attention has been directed toward the role of the gut microbiota in OC, comprehensive analyses exploring its association with platinum-based chemotherapy response remain limited. In particular, microbial signatures capable of differentiating platinum-sensitive from platinum-resistant OC have yet to be clearly identified. To address this gap, we conducted a study to examine the relationship between fecal microbial composition and platinum resistance in OC patients. Fecal samples from OC patients with defined platinum responses were analyzed using mNGS to profile gut microbiota, alongside clinical data integration. Compared to the sensitive group, resistant patients demonstrated a marked depletion of beneficial commensals such as Bacteroides and Faecalibacterium, alongside an overrepresentation of Firmicutes-affiliated taxa. Notably, Veillonella was positively associated with resistance. In vitro assays further confirmed that Veillonella enhanced ovarian cancer cell proliferation, migration, invasion, and chemoresistance. These findings highlight Veillonella as a potential biomarker for platinum sensitivity and suggest a role for the gut microbiome in mediating chemoresistance in OC.\u003c/p\u003e"},{"header":"Methods","content":"\u003ch2\u003e2.1 Study participants\u003c/h2\u003e\n\u003cp\u003eWe recruited platinum-sensitive and platinum-resistant OC patients who attended the First Affiliated Hospital of Zhengzhou University between February 2022 and November 2023. All participants were initially diagnosed with OC based on histopathological examination. Postoperatively, disease staging was determined using the Tumor Node Metastasis (TNM) system. Patients were included in the study and classified into platinum-sensitive and platinum-resistant groups based on the following criteria: (1) a confirmed pathological diagnosis of OC for each patient, (2) participants had not received antibiotics in the six months preceding sample collection; (3) platinum sensitivity was defined as no recurrence or a recurrence-free interval longer than 6 months following platinum-based chemotherapy, whereas platinum resistance referred to recurrence occurring within 6 months after the final platinum treatment.\u003c/p\u003e\n\u003ch2\u003e2.2 Sample collection\u003c/h2\u003e\n\u003cp\u003eA total of 200 mg fecal samples were obtained from each ovarian cancer patient, transferred to sterile centrifuge tubes, and rapidly frozen at \u0026minus;\u0026thinsp;80\u0026deg;C for downstream analyses.\u003c/p\u003e\n\u003ch2\u003e2.3 Cell culture\u003c/h2\u003e\n\u003cp\u003eThe human ovarian cancer cell lines A2780 and SKOV3 were purchased from Meixuan Biotechnology (Shanghai, China) and the American Type Culture Collection (ATCC, USA), respectively. The platinum-resistant A2780DDP cell line was obtained from Geno Biotech (Guangzhou, China), while SKOV3DDP cells were constructed in our laboratory using a concentration-increment method. A2780 and A2780DDP cell lines were maintained in RPMI-1640 medium (31800, Solarbio, China), while SKOV3 and SKOV3DDP cells were grown in McCoy\u0026rsquo;s 5A medium (PM150710, Procell, China). All cultures were supplemented with 10% fetal bovine serum (12483020, Gibco, USA) and 1% penicillin-streptomycin (15070063, Gibco, USA), and incubated at 37\u0026deg;C in a humidified atmosphere containing 5% CO₂.\u003c/p\u003e\n\u003ch2\u003e2.4 Culture of Veillonella parvula\u003c/h2\u003e\n\u003cp\u003e\u003cem\u003eVeillonella parvula\u003c/em\u003e (\u003cem\u003eV. parvula\u003c/em\u003e) (ATCC 10790) was cultured on pre-prepared Columbia blood agar plates (CA-B, Beina Biology, China) under strict anaerobic conditions. Plates were incubated at 37\u0026deg;C under anaerobic conditions using a jar equipped with gas-generating sachets (Mitsubishi Gas Chemical Company, Japan) or within a sealed anaerobic chamber. The anaerobic atmosphere consisted of approximately 80% N₂, 10% CO₂, and 10% H₂. Bacterial growth was typically observed after 48\u0026ndash;72 hours of incubation. Colonies were harvested and washed in sterile phosphate-buffered saline (PBS) for use in downstream assays. All handling steps prior to anaerobic incubation were performed promptly to minimize oxygen exposure.\u003c/p\u003e\n\u003ch2\u003e2.5 Library construction and sequencing\u003c/h2\u003e\n\u003cp\u003eGenomic DNA was isolated from fecal samples using the QIAamp PowerFecal DNA Kit (Qiagen, Hilden, Germany) in accordance with the manufacturer\u0026apos;s instructions. Approximately 200 mg of fecal sample was homogenized with the provided buffer, and DNA was isolated using bead-beating and spin-column techniques. For mNGS, 1 mg of genomic DNA was fragmented into random pieces averaging 200\u0026ndash;400 bp using a Covaris ultrasonic disruptor (Woburn, Massachusetts, USA). Following fragmentation, the DNA was subjected to library construction, which included end-repair, A-tailing, and adapter ligation steps. The resulting libraries were indexed and amplified to enrich for microbial DNA fragments. Sequencing of the prepared libraries was performed on the BGISEQ-500 platform (BGI, Shenzhen, China) using paired-end 100 bp reads.\u003c/p\u003e\n\u003ch2\u003e2.6 Species identification and functional annotation analysis\u003c/h2\u003e\n\u003cp\u003eTo identify microbial species using specific marker genes, the sequencing data was subjected to MetaPhlAn4 analysis. Taxonomic profiles and detailed abundances of the gut microbiota were generated [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. The metabolic capabilities of the microbial community were analyzed using HMP Unified Metabolic Analysis Network 3 (HUMAnN3), which referenced the MetaCyc database [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Data normalization was performed using the HUMAnN_renorm_table for counts per million (CPM). Subsequently, the results were consolidated using HUMAnN_join_tables, and various metabolic pathways were examined using STAMP software.\u003c/p\u003e\n\u003ch2\u003e2.7 \u0026alpha; and \u0026beta; diversity analyses\u003c/h2\u003e\n\u003cp\u003eThe calculations of \u0026alpha; diversity, which were represented graphically via a boxplot, were performed using Vega Packages [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]. Bray Curtis distances determined the dissimilarities among samples, with a resulting principal coordinate analysis (PCoA) diagram illustrating \u0026beta; diversity outcomes.\u003c/p\u003e\n\u003ch2\u003e2.8 mNGS data analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were conducted in R (version 4.2.2), with Welch\u0026rsquo;s t-test applied to assess differences between the two groups. Species abundance was analyzed using MetagenomeSeq, and its differential impact across groups was further evaluated through LEfSe. Functional predictions of the microbial communities were conducted using STAMP software. Redundancy analysis (RDA) was conducted to evaluate how environmental variables shape gut microbiota composition. Permutational multivariate analysis of variance (PERMANOVA) and RDA were used to examine the association between patient phenotypes and gut microbial profiles. The \u0026apos;psych\u0026apos; package was used to investigate the relationships between gut microbiota and clinical variables. A heatmap was generated using a dedicated package to visualize the correlations. Statistical significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Asterisks denote significance levels: * for p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and ** for p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e\n\u003ch2\u003e2.9 CCK8 assay\u003c/h2\u003e\n\u003cp\u003eFor the cisplatin resistance assay, A2780/A2780DDP (8,000 cells/well) and SKOV3/SKOV3DDP (5,000 cells/well) cells were seeded into 96-well plates. After 2 hours of treatment with \u003cem\u003eV. parvula\u003c/em\u003e (MOI\u0026thinsp;=\u0026thinsp;100) or heat-inactivated \u003cem\u003eV. parvula\u003c/em\u003e, fresh medium was added and incubated for 24 hours. After exposure to varying concentrations of cisplatin (1, 2, 4, 8, 16, and 32 ng/mL) for 48 hours, cells were incubated with 10 \u0026micro;L of CCK-8 reagent for an additional 2 hours. Absorbance at 450 nm was then recorded using a SpectraMAX i3x microplate reader.\u003c/p\u003e\n\u003cp\u003eFor the cell proliferation assay, A2780/A2780DDP (3,000 cells/well) and SKOV3/SKOV3DDP (2,000 cells/well) cells were seeded and treated similarly, but without cisplatin addition. CCK-8 reagent (10 \u0026micro;L) was added at 24, 48, 72, and 96 hours, and absorbance at 450 nm was measured.\u003c/p\u003e\n\u003ch2\u003e2.10 EdU cell proliferation assay\u003c/h2\u003e\n\u003cp\u003eEdU incorporation assay was performed to assess DNA synthesis and cell proliferation. Ovarian cancer cells were plated in 96-well plates and cultured until they reached 70\u0026ndash;80% confluence. Cells were then treated with PBS, \u003cem\u003eV. parvula\u003c/em\u003e (MOI\u0026thinsp;=\u0026thinsp;100), heat-inactivated \u003cem\u003eV. parvula\u003c/em\u003e, or the supernatant of \u003cem\u003eV. parvula\u003c/em\u003e culture for 2 hours. Following treatment, cells were rinsed twice with PBS to eliminate residual bacteria or metabolites. Fresh medium supplemented with 10 \u0026micro;M EdU (C10310-1, RiboBio, China) was then added, and cells were incubated at 37\u0026deg;C for 2 hours. Following EdU incorporation, cells were fixed and permeabilized with 4% paraformaldehyde and 0.3% Triton X-100, respectively. Detection of incorporated EdU was performed using the Click-iT EdU Cell Proliferation Kit (RiboBio, China) in accordance with the manufacturer\u0026rsquo;s guidelines. DAPI was used for nuclear counterstaining, and fluorescence images were captured with a fluorescence microscope (Olympus, Japan). The proportion of EdU-positive cells was quantified to assess cell proliferation.\u003c/p\u003e\n\u003ch2\u003e2.11 Wound Healing Assay\u003c/h2\u003e\n\u003cp\u003eTo assess the migratory ability of ovarian cancer cells, a wound healing assay was performed. Cells were plated in 24-well plates and grown to nearly 90% confluence. A straight scratch was introduced into the cell monolayer using a sterile 200 \u0026micro;L pipette tip. Detached cells were removed by gently rinsing the wells twice with PBS. Cells were then treated with PBS, \u003cem\u003eV. parvula\u003c/em\u003e (MOI\u0026thinsp;=\u0026thinsp;100), heat-inactivated \u003cem\u003eV. parvula\u003c/em\u003e, or the supernatant of \u003cem\u003eV. parvula\u003c/em\u003e culture for 2 hours. After treatment, cells were rinsed twice with PBS and maintained in serum-free medium. Wound areas were imaged at 0 and 24 hours using an inverted microscope (Olympus, Japan), and the closure rate was calculated by measuring the residual gap with ImageJ.\u003c/p\u003e\n\u003ch2\u003e2.12 Transwell experiment\u003c/h2\u003e\n\u003cp\u003eA2780, A2780DDP, SKOV3, and SKOV3DDP cells were first seeded in 6-well plates and cultured to 70\u0026ndash;80% confluency. Cells were then treated with PBS, \u003cem\u003eV. parvula\u003c/em\u003e (MOI\u0026thinsp;=\u0026thinsp;100), heat-inactivated \u003cem\u003eV. parvula\u003c/em\u003e, or the bacterial supernatant for 2 hours. Cells were washed with PBS following treatment, enzymatically detached using trypsin, and then resuspended in medium lacking serum. Migration assays were conducted by seeding 2 \u0026times; 10⁴ cells into uncoated Transwell inserts (8.0 \u0026micro;m, Corning, USA), while invasion assays involved Matrigel-coated chambers seeded with 4 \u0026times; 10⁴ cells. Complete medium with 10% FBS was added to the lower chamber in both assays to provide chemotactic stimulation. Following a 48-hour incubation at 37\u0026deg;C, residual cells on the upper side of the membrane were carefully wiped away using a cotton swab. After fixation with 4% paraformaldehyde for 20 minutes, cells on the underside of the membrane were stained using 0.1% crystal violet for 15 minutes at room temperature. Following PBS washes, images of stained cells were captured using an inverted microscope (Olympus, Japan). Cell counts were quantified from five randomly chosen regions with ImageJ.\u003c/p\u003e\n\u003ch2\u003e2.13 Statistical analysis\u003c/h2\u003e\n\u003cp\u003eAll statistical analyses were conducted using GraphPad Prism 10. Group differences were assessed via one-way or two-way ANOVA. Significance was defined as p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and annotated as *p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, and ****p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\u003ch2\u003e3.1 Overview of Clinical Characteristics of Enrolled Patients\u003c/h2\u003e\u003cp\u003eFecal samples were collected from six platinum-sensitive OC patients (Sensitive group) and three platinum-resistant patients (Resistant group). Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e summarizes the clinical characteristics of all participants, indicating no statistically significant differences in age, height, weight, or BMI between the two groups (p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\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\u003eDemographics of platinum-sensitive and platinum-resistant OC patients\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"11\"\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e\u003cp\u003eResistant\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"6\" nameend=\"c11\" namest=\"c6\"\u003e\u003cp\u003eSensitive\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eDTX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eLXL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eLXE\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYAL\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eHCH\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eYCJ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eQHX\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eWYS\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e51\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e70\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e54\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e50\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e63\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eHeight (m)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e1.61\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e1.68\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.65\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e1.52\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e1.59\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e1.64\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e1.55\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWeight (kg)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e60\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e75.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e57\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e53.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e51.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e62\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e56\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e75\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e48\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBMI\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e23.14725512\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e26.75028345\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e20.93663912\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e23.19944598\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e20.37102963\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e25.80645161\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e20.82093992\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e27.88518739\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e19.97918835\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eRecurrence\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eStage\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eⅡ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eⅣ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eⅣ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eⅢ\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eDiameter\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e\u0026gt;\u0026thinsp;2cm\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eLymph node metastasis\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eImmunosuppressant\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBRCA1/2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePARPi\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003eYes\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003eNo\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAE1/AE3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eCK7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eER\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePR\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP53\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePAX-8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eWT-1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKi67\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eP16\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e+\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eNapsinA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e/\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u003cp\u003e-\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003ePERMANOVA analysis revealed no significant association between gut microbiota composition and any individual clinical parameter within the patient cohort. Among all clinical factors analyzed, platinum sensitivity exerted the greatest influence on the variation in gut microbial profiles. However, the effect did not reach statistical significance (p\u0026thinsp;=\u0026thinsp;0.09). This trend suggests that platinum sensitivity may play a primary role in shaping subgroup specific microbial differences (Supplementary Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e, Supplementary Table\u0026nbsp;1).\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\u003ch2\u003e3.2 Fecal microbiome composition and diversity in the platinum-sensitive and platinum-resistant OC patients\u003c/h2\u003e\u003cp\u003eFrom the results of next-generation sequencing, marked differences in gut microbiota composition and structure were observed between platinum-sensitive and platinum-resistant OC patients. At the phylum level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA), platinum-resistant individuals exhibited a pronounced expansion of \u003cem\u003eFirmicutes\u003c/em\u003e, which constituted the dominant phylum in this group. In contrast, platinum-sensitive patients showed higher relative abundance of \u003cem\u003eBacteroidetes\u003c/em\u003e, suggesting a more balanced microbial configuration. Minor phyla such as \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, and \u003cem\u003eVerrucomicrobia\u003c/em\u003e were also present in both groups, with slight variations in abundance. These observations point to a phylum-level compositional shift in resistant patients, characterized by elevated \u003cem\u003eFirmicutes\u003c/em\u003e and depleted \u003cem\u003eBacteroidetes\u003c/em\u003e. Genus-level profiling (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) further illustrated this pattern. Platinum-sensitive patients harbored higher relative abundances of several well-known commensals, including \u003cem\u003eBacteroides\u003c/em\u003e, \u003cem\u003eFaecalibacterium\u003c/em\u003e, \u003cem\u003ePrevotella\u003c/em\u003e, and \u003cem\u003eAlistipes\u003c/em\u003e. These genera are frequently associated with anti-inflammatory properties and mucosal health [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In contrast, the microbial landscape of platinum-resistant patients appeared taxonomically narrowed, with a visible reduction in these beneficial genera and no overt emergence of new dominant taxa. The absence of compensatory overgrowth suggests a loss of microbial equilibrium rather than a substitutional shift.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThe assessment of fecal microbiota differences between platinum-sensitive and platinum-resistant OC groups involved α and β diversity evaluations. α diversity commonly gauges species richness and evenness within a community, encompassing a comprehensive indicator of diversity. In contrast, β diversity signifies species divergence among distinct environmental communities [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Analysis of α diversity showed no significant differences between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eC). Indices including Observed species richness, Shannon entropy, Simpson index, and Pielou\u0026rsquo;s evenness were comparable, indicating that overall microbial richness and evenness were preserved regardless of platinum status. Bray\u0026ndash;Curtis-based β diversity analysis demonstrated a partial distinction between resistant and sensitive sample groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD). Nonetheless, the overall fecal microbiota composition did not differ significantly between the two groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eD, Supplementary Figure \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTaken together, the data raise the possibility that platinum resistance in OC is linked to a simplified gut microbial composition, but this hypothesis warrants further investigation in studies with expanded sample sizes.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003e3.3 Distinct Gut Microbial Taxa Characterize Platinum-Sensitive and Platinum-Resistant OC Patients\u003c/h2\u003e\u003cp\u003eLEfSe analyses identified distinct taxonomic biomarkers differentiating the gut microbiota of platinum-sensitive and platinum-resistant OC patients. The analysis was conducted using a threshold of LDA score\u0026thinsp;\u0026gt;\u0026thinsp;2.0 and a Kruskal\u0026ndash;Wallis p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\u003cp\u003eIn the platinum-sensitive group, a range of taxa were enriched across multiple phylogenetic levels. These included species such as \u003cem\u003eLachnospir sp NSJ-43\u003c/em\u003e, \u003cem\u003eBifidobacterium pseudocatenulatum\u003c/em\u003e, and unclassified members corresponding to \u003cem\u003et_SGB5087\u003c/em\u003e, \u003cem\u003et_SGB15049\u003c/em\u003e, and \u003cem\u003et_SGB17237\u003c/em\u003e. At higher taxonomic ranks, enriched lineages included \u003cem\u003eg_GGB9614\u003c/em\u003e within the family \u003cem\u003eOscillospiraceae\u003c/em\u003e. These features collectively indicate greater microbial heterogeneity in the sensitive group, involving representatives from the phyla \u003cem\u003eActinobacteria\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). In contrast, the platinum-resistant group showed enrichment of a more convergent set of taxa, predominantly affiliated with the phylum \u003cem\u003eFirmicutes\u003c/em\u003e. These included \u003cem\u003eLacrimispora saccharolytica\u003c/em\u003e, \u003cem\u003et_SGB4794\u003c/em\u003e, \u003cem\u003et_SGB14985\u003c/em\u003e, \u003cem\u003et_SGB8047\u003c/em\u003e, and related classifications such as \u003cem\u003eg_GGB6020, s_GGB6020-SGB14985\u003c/em\u003e, \u003cem\u003ef_FGB2107\u003c/em\u003e, \u003cem\u003eo_OFGB2107\u003c/em\u003e, and \u003cem\u003ec_CFGB2107\u003c/em\u003e. The taxa identified in this group appeared phylogenetically clustered, suggesting the dominance of a narrower microbial clade (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eA). The taxonomic cladogram (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eB) further visualizes these group-specific biomarkers. Circles represent the taxonomic hierarchy from phylum to species. Each circle denotes a distinct categorization level, with sizes according to relative abundance. Yellow indicates no significant difference, whereas different species are colored by group. Green nodes indicate important microbes in platinum-sensitive OC, and red nodes represent crucial microbes in platinum-resistant OC. Sensitive-associated taxa were dispersed across the tree, indicating a wider phylogenetic distribution, while resistant-associated taxa formed a coherent cluster within \u003cem\u003eFirmicutes\u003c/em\u003e, particularly under the lineage defined by \u003cem\u003ec_CFGB2107\u003c/em\u003e. Supplementary Table\u0026nbsp;2 presents the detailed differences between the groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings indicate that while both groups share microbial representatives within \u003cem\u003eFirmicutes\u003c/em\u003e, their internal taxonomic architectures diverge markedly. Platinum-sensitive patients exhibited a broader distribution of enriched taxa, including representatives of \u003cem\u003eOscillospiraceae\u003c/em\u003e and \u003cem\u003eBifidobacterium\u003c/em\u003e, whereas platinum-resistant patients harbored a more focused enrichment of \u003cem\u003eFirmicutes\u003c/em\u003e-related lineages. This compositional distinction, revealed through LEfSe analysis, underscores the potential for gut microbiota to stratify OC patients by treatment response phenotype.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\u003ch2\u003e3.4 Alterations in Microbial Metabolic Functional Profiles in Platinum-Resistant OC Patients\u003c/h2\u003e\u003cp\u003eFunctional profiling using STAMP identified significant differences in predicted microbial metabolic pathways between platinum-sensitive and platinum-resistant ovarian cancer patients (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The comparison identified a distinct set of microbial functions that were differentially enriched across the two groups.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eIn the platinum-resistant group, multiple metabolic pathways displayed higher relative abundances (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA). Notably, these included P108-PWY (pyruvate fermentation to propanoate I), P125-PWY (superpathway of (R, R)-butanediol biosynthesis), the PWY-7356 (thiamine diphosphate salvage IV [yeast]), PWY66-367 (ketogenesis), PWY-7234 (inosine-5'-phosphate biosynthesis III), and RIBOSYN2-PWY (flavin biosynthesis I [bacteria and plants]) pathways (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB). Figure\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC-H and Supplementary Table\u0026nbsp;3 provide detailed findings showing the differences between the two groups. These pathways encompass a range of diverse metabolic functions, including the production of short-chain fatty acids (SCFAs), vitamin cofactor salvage, and nucleotide synthesis. Conversely, the platinum-sensitive group did not display dominant enrichment of any specific pathway within the top-ranked differentials. Instead, the overall functional profile suggested a relatively lower representation of the aforementioned fermentation and biosynthetic activities.\u003c/p\u003e\u003cp\u003eThese observations suggest that the gut microbiota in platinum-resistant OC patients may be functionally adapted toward enhanced fermentative and biosynthetic activity. The elevated abundance of pathways involved in SCFA and ketone body production may reflect shifts in microbial energy metabolism, while enrichment of thiamine and nucleotide salvage pathways points toward an altered metabolic niche with increased cofactor and precursor turnover. Whether such alterations support host tumor biology or arise in response to the selective pressures of chemotherapeutic resistance remains to be determined. Nonetheless, these findings underscore the functional divergence of microbial communities associated with platinum treatment response, highlighting the potential role of gut-derived metabolites in shaping the tumor microenvironment or modulating therapy efficacy.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec20\" class=\"Section2\"\u003e\u003ch2\u003e3.5 Microbial\u0026ndash;Clinical Correlation and Functional Validation of \u003cem\u003eVeillonella\u003c/em\u003e in Platinum Resistance\u003c/h2\u003e\u003cp\u003eTo explore microbial features associated with platinum resistance in OC patients, Spearman correlation analysis was performed between gut microbial taxa and resistance phenotype. The top 20 taxa showing the strongest positive correlations were visualized in a hierarchical clustered heatmap (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). The OC group's bacterial genera' abundance at the genus level exhibited strong correlations with patient factors, such as age, height, weight, BMI, platinum sensitivity, tumor stage, tumor diameter, lymph node metastasis, immunosuppressant use, BRCA1/2 gene mutation, and immunohistochemical markers of OC pathology. Among the top 20 intestinal bacteria with the highest positive correlation with platinum resistance, 17 demonstrated significant associations with clinical indicators (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Among them, \u003cem\u003eVeillonella\u003c/em\u003e ranked prominently, suggesting a strong positive association with the resistant phenotype (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003cem\u003eVeillonella\u003c/em\u003e is a genus of Gram-negative, obligate anaerobic cocci commonly residing in the oral cavity and gastrointestinal tract [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Though historically considered commensal, emerging evidence has highlighted its roles in host\u0026ndash;microbiota metabolic interactions and immunologic signaling [\u003cspan additionalcitationids=\"CR40\" citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. By fermenting lactate into propionate and other metabolites, \u003cem\u003eVeillonella\u003c/em\u003e may influence local immune tone, alter microbial competition, and potentially affect host epithelial behavior [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These properties make it a plausible microbial contributor to the modulation of chemotherapeutic responses. Previous studies have linked elevated abundance of \u003cem\u003eV. parvula\u003c/em\u003e to tumorigenesis and poorer clinical outcomes across various cancer types [\u003cspan additionalcitationids=\"CR45 CR46\" citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. However, its role in shaping chemotherapeutic response in OC remains largely unexplored. \u003cem\u003eV. parvula\u003c/em\u003e, a representative species within the \u003cem\u003eVeillonella\u003c/em\u003e genus, was selected for in vitro experiments due to its availability, prior implication in oncogenic pathways.\u003c/p\u003e\u003cp\u003eTo investigate the potential functional impact of \u003cem\u003eVeillonella\u003c/em\u003e, a CCK8-based viability assay was conducted in four ovarian cancer cell lines: A2780, A2780DDP, SKOV3, and SKOV3DDP (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Cells were exposed to either live \u003cem\u003eVeillonella\u003c/em\u003e or heat-inactivated controls, followed by cisplatin treatment. Across all models, co-incubation with live \u003cem\u003eVeillonella\u003c/em\u003e led to increased cisplatin IC₅₀ values relative to controls.\u003c/p\u003e\u003cp\u003eTogether, these findings suggest that \u003cem\u003eVeillonella\u003c/em\u003e is positively associated with platinum resistance at both the microbial community and cellular functional levels. This dual-layered evidence supports its potential role as a microbial contributor to chemotherapy insensitivity in OC.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec21\" class=\"Section2\"\u003e\u003ch2\u003e3.6 \u003cem\u003eV. parvula\u003c/em\u003e Enhances the Proliferative Capacity of Ovarian Cancer Cells In Vitro\u003c/h2\u003e\u003cp\u003eFollowing the observation that \u003cem\u003eV. parvula\u003c/em\u003e is associated with enhanced cisplatin resistance in ovarian cancer cells, we next sought to determine whether this bacterium might also influence other malignant phenotypes, particularly cellular proliferation. To this end, two cisplatin-sensitive/resistant cell line pairs (A2780/A2780DDP and SKOV3/SKOV3DDP) were subjected to treatment with live \u003cem\u003eV. parvula\u003c/em\u003e, heat-inactivated bacteria, bacterial culture supernatant, or PBS control. Proliferative capacity was evaluated using both CCK8 metabolic assays and EdU incorporation.\u003c/p\u003e\u003cp\u003eAcross both cell line pairs, cells treated with either live \u003cem\u003eV. parvula\u003c/em\u003e or its culture supernatant exhibited elevated proliferative activity compared to PBS-treated controls, whereas heat-inactivated bacteria failed to induce such effects. In the A2780/A2780DDP model (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e), this enhancement was evident in both CCK8 assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eA-B), where metabolic activity increased over time, and in EdU assays (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eC-F), which revealed a higher fraction of DNA-synthesizing cells in the live bacteria and supernatant groups. The response was particularly robust in the cisplatin-sensitive A2780 cells but remained present in A2780DDP cells. A similar pattern was observed in the SKOV3/SKOV3DDP model (Supplementary Figure \u003cspan refid=\"MOESM3\" class=\"InternalRef\"\u003eS3\u003c/span\u003e). Both live bacteria and supernatant exposure led to increased cell viability and EdU incorporation relative to the PBS and heat-inactivated groups. Although the increase in EdU-positive cells was less pronounced in SKOV3DDP following live \u003cem\u003eV. parvula\u003c/em\u003e treatment, the direction of the effect remained consistent with the other cell lines. These results suggest that \u003cem\u003eV. parvula\u003c/em\u003e promotes ovarian cancer cell proliferation through both direct and soluble mechanisms, with viable bacteria and their secreted products contributing to the observed phenotype. The absence of effect in the heat-inactivated group underscores the importance of bacterial viability or active metabolites in mediating these pro-proliferative interactions.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e\u003ch2\u003e3.7 \u003cem\u003eV. parvula\u003c/em\u003e Enhances the Migratory Capacity of Ovarian Cancer Cells\u003c/h2\u003e\u003cp\u003eTo further explore the phenotypic consequences of \u003cem\u003eV. parvula\u003c/em\u003e exposure, cell migration was assessed using wound-healing and Transwell assays across two ovarian cancer cell line pairs: A2780/A2780DDP and SKOV3/SKOV3DDP. Treatments included live \u003cem\u003eV. parvula\u003c/em\u003e, heat-inactivated bacteria, bacterial culture supernatant, and PBS control.\u003c/p\u003e\u003cp\u003eEnhanced wound closure was observed following exposure to either live \u003cem\u003eV. parvula\u003c/em\u003e or its culture supernatant (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eA, Supplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eA). The effect was most prominent in the live bacteria group, where scratch areas contracted substantially within 24 hours. In contrast, the heat-inactivated group demonstrated minimal improvement over PBS. These trends were consistent across both cisplatin-sensitive and -resistant cell types, with the parental A2780 and SKOV3 lines exhibiting slightly more pronounced responses. Transwell migration assays yielded concordant results (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eB). Cells treated with live bacteria or supernatant migrated in greater numbers than controls, a finding quantitatively validated through statistical analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003eC). Heat-inactivated bacteria did not significantly alter migration, indicating that bacterial viability or secreted metabolites are necessary for this phenotype. Quantification of wound-healing rates (Supplementary Figure \u003cspan refid=\"MOESM4\" class=\"InternalRef\"\u003eS4\u003c/span\u003eB) reinforced the observed trend: both live bacteria and supernatant groups showed significantly increased migratory indices compared to PBS and heat-inactivated conditions. Although variation existed between cell lines, the directionality of the effect remained consistent.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese results collectively demonstrate that \u003cem\u003eV. parvula\u003c/em\u003e facilitates ovarian cancer cell migration through mechanisms dependent on bacterial viability or soluble bacterial products. The consistency of this response across multiple cell lines suggests a conserved modulatory effect on tumor cell motility.\u003c/p\u003e\u003c/div\u003e\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e\u003ch2\u003e3.8 \u003cem\u003eV. parvula\u003c/em\u003e Potentiates the Invasive Capacity of Ovarian Cancer Cells\u003c/h2\u003e\u003cp\u003eThe pro-invasive effects of \u003cem\u003eV. parvula\u003c/em\u003e on ovarian cancer cells were assessed using a Matrigel-coated Transwell assay. Cells were exposed to live bacteria, heat-inactivated bacteria, bacterial culture supernatant, or PBS control, and their ability to traverse the matrix barrier was quantified across four cell lines.\u003c/p\u003e\u003cp\u003eMicroscopic examination of the invaded cells (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eA) revealed an increased number of cells crossing the Matrigel membrane following treatment with live \u003cem\u003eV. parvula\u003c/em\u003e or its culture supernatant, compared to PBS and heat-inactivated conditions. This pattern was observed consistently in both cisplatin-sensitive (A2780, SKOV3) and resistant (A2780DDP, SKOV3DDP) cell lines. The heat-inactivated group exhibited little to no enhancement relative to PBS controls, indicating that bacterial viability or active metabolites may be essential for driving the invasive phenotype. Quantitative analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003eB) confirmed these visual trends. Both the live \u003cem\u003eV. parvula\u003c/em\u003e and supernatant groups displayed significantly elevated invasion indices in all four cell lines. Although the magnitude of the response varied slightly among different models, the directionality of the effect remained robust across conditions. In contrast, no significant increase in invasion was detected in the heat-inactivated group, further underscoring the functional relevance of live bacterial exposure or secreted factors.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThese findings suggest that \u003cem\u003eV. parvula\u003c/em\u003e enhances the invasive behavior of ovarian cancer cells through mechanisms dependent on bacterial viability and soluble effectors. The consistency of this effect across multiple cell types highlights its potential relevance in modulating the metastatic phenotype.\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe gut microbiota is now widely acknowledged to function as an endocrine-like organ, exerting effects on distant tissues and regulating systemic physiological processes [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. In this study, we identified a previously unreported link between the gut microbiota and platinum-based chemoresistance in ovarian cancer. Notably, \u003cem\u003eVeillonella\u003c/em\u003e was significantly enriched in patients with chemoresistant disease. This taxon was not only altered at the compositional level but also appeared to participate in regulating chemoresistance and promoting malignant cellular behaviors.\u003c/p\u003e\u003cp\u003eThis study utilized MetaPhlAn 4 for species identification in the gut microbiota of patients with platinum-sensitive and resistance OC. MetaPhlAn 4 combines known microbial genomes with metagenomic assembly genomes to define a genome box at the species level, thereby offering a more comprehensive metagenomic taxonomic analysis compared to its previous three versions [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. It is more sensitive and specific, capable of providing accurate classification, identification, and quantification. Moreover, it can accurately quantify unidentified species and maintain high precision for taxonomically classified species, thereby broadening metagenome classification [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePrevious studies have suggested that disruption of the gut microbial ecosystem may impair cisplatin responsiveness in EOC [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], while modulation of microbial composition has been associated with both anti-tumor effects and enhanced therapeutic efficacy [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. Through metagenomic sequencing, we demonstrate that the fecal microbiota of platinum-resistant patients undergoes a profound compositional restructuring, characterized by marked enrichment of \u003cem\u003eVeillonella\u003c/em\u003e and other genera belonging to the phyla \u003cem\u003eBacteroidetes\u003c/em\u003e and \u003cem\u003eFirmicutes\u003c/em\u003e, in contrast to the more balanced and diverse microbial architecture observed in platinum-sensitive individuals.\u003c/p\u003e\u003cp\u003eRecent studies have highlighted the complex and context-dependent roles of the genus \u003cem\u003eVeillonella\u003c/em\u003e in cancer biology. Rather than acting uniformly as a pro- or anti-tumorigenic agent, \u003cem\u003eVeillonella\u003c/em\u003e appears to exert divergent effects across distinct tumor types and microenvironmental contexts. In some settings, \u003cem\u003eV. parvula\u003c/em\u003e has been reported to inhibit tumor growth and metastasis through tumor-specific colonization and by reducing intratumoral lactate levels [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Supporting this anti-tumor potential, \u003cem\u003eV. parvula\u003c/em\u003e-derived propionate has been shown to suppress malignant phenotypes in oral squamous cell carcinoma cells via metabolic modulation [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Conversely, in other malignancies, \u003cem\u003eVeillonella\u003c/em\u003e may contribute to tumor progression. For instance, \u003cem\u003eV. parvula\u003c/em\u003e has been demonstrated to promote proliferation in lung adenocarcinoma through the Nod2/CCN4/NF-κB signaling axis [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. \u003cem\u003eVeillonella\u003c/em\u003e abundance was found to be positively correlated with the neutrophil-to-lymphocyte ratio (NLR) in a systematic study of oral microbiota from non-small cell lung cancer patients, highlighting its potential association with systemic inflammation and adverse prognosis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. Moreover, enrichment of \u003cem\u003eVeillonella\u003c/em\u003e and \u003cem\u003eStreptococcus\u003c/em\u003e has been associated with gut microbial aging and impaired response to immune checkpoint blockade, suggesting a broader immunomodulatory influence [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Taken together, these findings suggest that \u003cem\u003eVeillonella\u003c/em\u003e, particularly \u003cem\u003eV. parvula\u003c/em\u003e, may engage in tumor\u0026ndash;microbe interactions that are highly dependent on tissue type, immune context, and microbial metabolic output. These multifaceted roles underscore the importance of investigating its functional consequences within each specific cancer model, including OC, where its contribution to chemoresistance and malignant progression remains poorly defined.\u003c/p\u003e\u003cp\u003eFunctional experiments confirmed that exposure to \u003cem\u003eV. parvula\u003c/em\u003e markedly increased cisplatin resistance across multiple ovarian cancer cell lines. This effect was abolished following bacterial heat-inactivation and was partially recapitulated by bacterial culture supernatant, suggesting that the pro-resistance phenotype depends on bacterial viability or secreted factors. These findings suggest that \u003cem\u003eV. parvula\u003c/em\u003e may modulate host chemoresistance through a potential paracrine mechanism. This effect is likely mediated by microbial metabolites or signaling molecules, and may act beyond traditional tumor-intrinsic pathways [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eMore importantly, the influence of \u003cem\u003eV. parvula\u003c/em\u003e was not limited to chemoresistance. Live bacteria and their supernatant significantly enhanced malignant phenotypes including cell proliferation, migration, and invasion, as evidenced by elevated metabolic activity, increased DNA synthesis, and enhanced motility and matrix penetration. These effects were consistently observed across multiple cell models, suggesting that \u003cem\u003eV. parvula\u003c/em\u003e may contribute broadly to tumor heterogeneity and metastatic potential. Notably, none of these pro-malignant effects were observed with heat-inactivated bacteria, further underscoring the essential role of bacterial viability or secreted bioactive components in shaping host cell behavior. While the precise molecular mediators remain to be elucidated, candidates may include short-chain fatty acids, microbe-derived signaling peptides, or structural components such as lipoteichoic acid [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. Future studies employing transcriptomic, metabolomic, and mechanistic approaches are warranted to dissect these pathways and explore microbiota-based therapeutic targets aimed at overcoming chemoresistance and restraining tumor aggressiveness.\u003c/p\u003e\u003cp\u003eNevertheless, this study is not without limitations. First, the cohort size was relatively small, comprising only nine OC patients. Although stratification by platinum sensitivity provided biologically meaningful comparisons, the findings may not fully capture the diversity of microbial signatures across different molecular subtypes or clinical stages. Larger, multi-center studies are required to validate these observations. Second, the functional characterization of \u003cem\u003eV. parvula\u003c/em\u003e was limited to in vitro models. While the observed trends were consistent and robust, whether similar effects occur in vivo remains to be tested using animal models and patient-derived tissues. Third, although we confirmed that both bacterial viability and soluble factors affect host phenotypes, the precise molecular mechanisms remain undefined. A more comprehensive dissection of key microbial metabolites and signaling pathways is needed to clarify causality and inform intervention strategies.\u003c/p\u003e\u003cp\u003eIn summary, our study identifies \u003cem\u003eVeillonella\u003c/em\u003e as a potential dual biomarker and functional mediator of platinum resistance and malignant progression in OC. These findings expand the current understanding of tumor\u0026ndash;microbiota interactions and offer a new ecological perspective on chemoresistance and clinical prognosis in gynecologic malignancies. Incorporating gut microbial features into individualized treatment strategies may open new avenues for prediction, intervention, and therapeutic optimization in OC.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study identifies \u003cem\u003eVeillonella\u003c/em\u003e as a gut microbial species significantly associated with platinum resistance in OC and functionally capable of enhancing chemoresistance, proliferation, migration, and invasion of tumor cells in vitro. These findings shed light on the microbiota\u0026rsquo;s potential role in shaping therapeutic outcomes and malignant phenotypes, offering a novel ecological dimension to the understanding of chemoresistance. Despite current limitations in sample size and in vivo validation, our work lays a conceptual foundation for future efforts to decode the tumor\u0026ndash;microbiome\u0026ndash;therapy axis and to harness microbial targets for precision interventions in gynecologic oncology.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003eThe study protocol received ethical approval from the Ethics Committee of the First Affiliated Hospital of Zhengzhou University (2023-KY-1323-002). Written informed consent was obtained from all participants before enrollment. \u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003eAvailability of data and material\u003c/p\u003e\n\u003cp\u003eThe raw sequence data reported in this paper have been deposited in the Genome Sequence Archive (Genomics, Proteomics \u0026amp; Bioinformatics 2021) in National Genomics Data Center (Nucleic Acids Res 2022), China National Center for Bioinformation / Beijing Institute of Genomics, Chinese Academy of Sciences (GSA: CRA028564) that are publicly accessible at https://ngdc.cncb.ac.cn/gsa [60, 61].\u003c/p\u003e\n\u003cp\u003eCompeting interests\u003c/p\u003e\n\u003cp\u003eNo financial or commercial affiliations were present that might be interpreted as a conflict of interest.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThis work was supported by Henan International Science and Technology Cooperation Project (252102521064), Henan Province High-end Foreign Expert Introduction Program (HNGD2020117), Key scientific research projects of colleges and universities in Henan Province (25A320012), Provincial and Ministry Co-constructed Key Projects of Henan Medical Science and Technology Research Program (SBGJ202402055), National Health Commission Medical Science and Technology Development Research Center Project (WKZX2024DN0182), Young and Middle-aged Subject Leader of Henan Provincial Health Commission (HNSWJW-2022001), the National Natural Science Foundation of China (No. 82303343), and International training program for high-level talents in Henan Province. \u003c/p\u003e\n\u003cp\u003eAuthors\u0026apos; contributions\u003c/p\u003e\n\u003cp\u003eConceptualization: YL, RG, and LC. Funding acquisition: NL and LC. Project administration: NL and LC. Supervision: YL and LC. Visualization: SL and MC. Validation: SL and MC. Writing-original draft: SL and MC. Writing-review \u0026amp; editing: NL, RG, SJ, NT, KW, WW, YZ,YL and LC. The study was conducted through joint efforts of all authors. Each author was involved in the preparation of the manuscript and consented to its submission.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eWe thank the participants for their contributions to this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eZeng H, Zheng R, Sun K, Zhou M, Wang S, Li L, Chen R, Han B, Liu M, Zhou J\u003cem\u003e et al\u003c/em\u003e: \u003cstrong\u003eCancer survival statistics in China 2019-2021: a multicenter, population-based study\u003c/strong\u003e. \u003cem\u003eJ Natl Cancer Cent \u003c/em\u003e2024, \u003cstrong\u003e4\u003c/strong\u003e(3):203-213.\u003c/li\u003e\n\u003cli\u003eKonstantinopoulos PA, Matulonis UA: \u003cstrong\u003eClinical and translational advances in ovarian cancer therapy\u003c/strong\u003e. \u003cem\u003eNat Cancer \u003c/em\u003e2023, \u003cstrong\u003e4\u003c/strong\u003e(9):1239-1257.\u003c/li\u003e\n\u003cli\u003eLiu J, Berchuck A, Backes FJ, Cohen J, Grisham R, 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Data Center, China National Center for Bioinformation in 2025\u003c/strong\u003e. \u003cem\u003eNucleic Acids Res \u003c/em\u003e2025, \u003cstrong\u003e53\u003c/strong\u003e(D1):D30-d44.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"ovarian cancer, platinum resistance, Veillonella, gut microbiota, metagenomic next-generation sequencing","lastPublishedDoi":"10.21203/rs.3.rs-7288124/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7288124/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e\u003cp\u003eOvarian cancer (OC) remains the most lethal gynecologic malignancy, primarily due to high recurrence rates and frequent development of platinum resistance. While the gut microbiome is known to influence tumor progression and therapeutic response, its role in extraintestinal malignancies like OC remains poorly understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eWe collected fecal samples from six platinum-sensitive and three platinum-resistant OC patients. Clinical data were collected, and gut microbiota profiles were assessed using metagenomic next-generation sequencing (mNGS). Differentially abundant taxa were determined through linear discriminant analysis effect size (LEfSe). Functional profiling was conducted with STAMP, and correlations with clinical variables were assessed using the R \u0026ldquo;psych\u0026rdquo; package. The effects of \u003cem\u003eVeillonella\u003c/em\u003e, the most resistance-associated species, on ovarian cancer cell behavior were validated in vitro.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eCompared to the sensitive group, resistant patients demonstrated a marked depletion of beneficial commensals such as \u003cem\u003eBacteroides\u003c/em\u003e and \u003cem\u003eFaecalibacterium\u003c/em\u003e, alongside an overrepresentation of \u003cem\u003eFirmicutes\u003c/em\u003e-affiliated taxa. Notably, \u003cem\u003eVeillonella\u003c/em\u003e abundance was significantly positively correlated with platinum resistance (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Functional experiments demonstrated that \u003cem\u003eVeillonella\u003c/em\u003e promoted ovarian cancer cell proliferation, motility, invasiveness, and resistance to chemotherapy.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eOur findings suggest that the fecal microbiome, particularly \u003cem\u003eVeillonella\u003c/em\u003e, may serve as a potential biomarker for assessing platinum sensitivity in OC and provide new insights into the microbiota-mediated mechanisms of chemoresistance.\u003c/p\u003e","manuscriptTitle":"Veillonella Associates with Platinum Resistance in Ovarian Cancer: Insights from Gut Microbiota Profiling and In Vitro Functional Validation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-18 06:20:59","doi":"10.21203/rs.3.rs-7288124/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1443b045-a193-40e9-8540-6e7917077c94","owner":[],"postedDate":"August 18th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-09-14T20:45:08+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-18 06:20:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7288124","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7288124","identity":"rs-7288124","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}