ABSTRACT The gut microbiota is closely related to the occurrence and development of cancer. However, the characteristics of gut microbiota associated with ovarian tumors remain elusive. In

this study, fecal samples were collected from healthy control (HC) group and patients with ovarian tumor (OT) or with other benign tumor (OBT) for 16s rRNA sequencing to determine

differential flora in gut microbiota. The composition of gut microbiota in the OT group, including bacterial abundance and diversity, was significantly different form HC and OBT groups. In

the OT group, _Escherichia_Shigella_ was markedly higher than in the HC group, while _Coprococcus_, _Fusicatenibacter, Butyricicoccus_ and _Oscillibacter_ were significantly lower than in

microbiota. The random forest model was designed using different bacteria. Compared with HCs, area under curve (AUC) values for BOT and OC groups were 0.77 and 0.86, respectively. These

findings suggest that some gut microbiota such as _Escherichia_Shigella_ show a certain ability to distinguish between healthy individuals and patients with OT. SIMILAR CONTENT BEING VIEWED

BY OTHERS COMPREHENSIVE PROFILES AND DIAGNOSTIC VALUE OF MENOPAUSAL-SPECIFIC GUT MICROBIOTA IN PREMENOPAUSAL BREAST CANCER Article Open access 27 October 2021 BREAST CANCER PATIENTS FROM THE

MIDWEST REGION OF THE UNITED STATES HAVE REDUCED LEVELS OF SHORT-CHAIN FATTY ACID-PRODUCING GUT BACTERIA Article Open access 11 January 2023 DIAGNOSTIC AND PROGNOSTIC POTENTIAL OF THE

MICROBIOME IN OVARIAN CANCER TREATMENT RESPONSE Article Open access 13 January 2023 INTRODUCTION Ovarian cancer (OC) is a malignancy which poses grave threats to female health, and has the

highest mortality rates affecting the female reproductive system1. Due to hidden disease locations and a lack of good screening methods, most cases are at advanced stages at initial

diagnosis, with tumors often showing primary or secondary resistance to chemotherapeutic drugs, and Critically, 5-year survival rates in patients with OC are between 30 and 45%2. Currently,

the main OC treatments include radical surgery, platinum-based combined chemotherapy, and poly ADP-ribose polymerase inhibitor maintenance therapy, but due to low response rates, toxicity,

and drug resistance, many patients fail to benefit from such treatments3. Therefore, more convenient, non-invasive, and highly sensitive OC screening methods are required. Known as a “super

organism”, billions of symbiotic bacteria called the “gut microbiota” live in the human body, with the intestinal tract numbering approximately 1014 microorganism species4,5. Due to a

two-way influence between sex hormone levels and the microflora, gut microbiota composition in females is significantly different to that of males; _Bacteroides_ abundance in females is

lower, but α-diversity indices are higher4,5. Intestinal microbiome disorders are associated with several cancers, including colorectal, gastric, and liver cancers6,7,8, and have been

observed in various female malignant tumors9,10. Significant differences in α- and β-diversity indices have been reported between patients with cervical cancer and healthy controls.

_Prevotella, Porphyromonas,_ and _Dialister_ levels were higher in patients with cervical cancer, while _Bacteroides, Alistipes,_ and _Lachnospiracea_ levels in healthy controls were

higher10. Some studies have reported that gut microbiota diversity in breast cancer patients was lower than that in healthy controls, while _Clostridium_ abundance was increased11. However,

few studies have explored relationships between the intestinal microbiota and OC. Jacobson et al.12 reported that the abundance of _Prevotella_ bacteria were significantly increased in OC

patients compared with BOTs, regardless of their response to platinum chemotherapy. We explored gut microbiota differences between patients with ovarian tumors (OTs) and HCs, patients with

benign ovarian tumors (BOTs) and patients with OC, and patients with OTs and other benign tumors (OBTs). Critically, our research may benefit early OT diagnoses and/or screening strategies.

MATERIALS AND METHODS THE STUDY POPULATION From May 2018 to January 2022, we collected fecal samples from 382 female individuals from Zhejiang Cancer Hospital in China, including 239

patients with OTs (148 patients with OC and 91 with BOTs), 90 patients with OBTs, and 53 with HCs. This study was investigated in compliance with the Declaration of Helsinki. All subjects

provided written informed consent, and the study was approved by our local ethics committee (Approval No. IRB-2023-417). The following patients were excluded: Patients who have been exposed

to antibiotics, patients who have not signed consent forms and other patients with malignant tumors in the past eight weeks. Healthy individuals excluded people with severe cardiopulmonary

diseases and other tumors, and were recruited by the health examination center of our hospital. Clinical data were collected by consulting medical records. Stool samples were freshly

collected and immediately frozen at − 80 °C for follow-up analysis. In order to avoid the influence of medication as much as possible, we collected samples from the patients when they were

just admitted to the hospital and had not received treatment. Subject clinical data were collected by consulting medical records (Table 1), including factor such as age, FIGO stage, body

mass index (BMI), medication history and personal cancer history. Tumor staging was performed according to World Health Organization histological classification criteria and the

International Federation of Gynecology and Obstetrics (FIGO) staging criteria. We confirm that all experiments are carried out in accordance with the relevant guidelines and regulations. DNA

EXTRACTION Total bacterial genomic DNA was extracted from fecal samples using DNA isolation kits (GUhe Laboratories, Hangzhou, China). DNA concentrations and purity were tested on a

NanoDrop ND-1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA). 16S RDNA AMPLICON PYROSEQUENCING The V4 region of bacterial 16s rRNA was amplified using forward (515F

5′-GTGCCAGCMGCCGCGGTAA-3′) and reverse primers (806R 5′-GGACTACHVGGGTWTCTAAT-3′). We also used specific 6-bp sequences to incorporate bar codes into TrueSeq adapters for multiple sequencing.

Amplification included a pre-denaturation step at 98 °C for 30 s and then 25 cycles including denaturation at 98 °C for 15 s, annealing at 58 °C for 15 s, extension at 72 °C for 15 s, and a

final extension at 72 °C for 1 min. Amplicons were purified and quantified using Agencourt AMPure XP Beads (Beckman Coulter, Indianapolis, IN, USA) and a PicoGreen dsDNA assay kit

(Invitrogen, Carlsbad, CA, USA). In further analyses, GUHE Info Technology Co., Ltd (Hangzhou, China) used the Illumina NovaSeq6000 platform (Illumina, San Diego, CA, USA) for pairwise 2 × 

150 bp sequencing, after amplifier quantification and pooling. After individual quantification steps, amplicons were pooled in equal amounts, and pair-end 2 × 150 bp sequencing was performed

using the Illumina HiSeq4000 platform at Guhe Info Technology Co. Ltd (Hangzhou, China). SEQUENCE ANALYSIS Operational taxonomic unit (OTU) picking using VSEARCH v2.22.1. Exact matches with

bar codes were assigned to corresponding samples and identified as valid sequences. The average sequencing reads of the samples was 129,726, and the lowest sequencing depth was 81,116. The

criteria for screening low-quality sequences were sequence length < 150 bp, average Phred scores of < 20, the sequence containing ambiguous bases, and the single nucleotide repeat

sequence containing > 8 bp. Using VSearch, we selected amplified sequence variants (ASVs) for included dereplication (–derep_fulllength), cluster (–cluster_fast, –id 0.97), and detection

of chimeras (–uchime_ref)13. ASV sequence data in the ASV table were normalized to minimize sequencing depth differences between samples. A normalized value of 1 indicated relative

abundance. A representative sequence (REP–SEQS) was selected from each ASV using default parameters. REP–SEQs and ASV table files were then imported into QIIME2 (V2022.2)14. QIIME2 removed

ASVs with < 0.001 of total sequences. Resulting classifications were collapsed using the QIIME taxa collapse command. BIOINFORMATICS AND STATISTICAL ANALYSIS Sequence data analysis was

primarily conducted using QIIME2 and R packages (V3.6.3). Alpha-diversity was indicated by the Shannon diversity index. Because the data do not conform to normal distribution, the

statistical differences between groups were determined using Kruskal–Wallis tests. The UniFrac distance metric15 was used for β-diversity analysis to examine structural changes in microbial

communities in samples, and principal coordinate analysis (PCoA) was used for visualization16. Phylum, class, order, family, genus, and species abundance levels in groups were statistically

compared. Besides β-diversity, the differences among samples were also analyzed by linear discriminant analysis (LDA) effect size (LEfSe). We used Kruskal–Wallis or Tukey tests to test taxa

abundance differences between groups. Box charts were used for visualization. _P_ < 0.05 values indicated statistical significance. Using the R package “random Forest” with 1000 trees and

default settings, random forest analyses were used to distinguish samples from different groups17. We used 10 × cross-validation to estimate generalization errors. The expected “baseline”

error was also included, which was generated using classifiers that predicted the most common category tags. We also used the CatBoost and XGBoost algorithms to construct and test models to

distinguish between the HC and OC groups. A tenfold cross-validation strategy was employed for model training and evaluation. Output files were further analyzed using the STAMP software

package (V2.1.3)18. The R package and Microbiome Analyst (https://www.microbiomeanalyst.ca/) were used for data visualization. RESULTS INTESTINAL MICROBIAL DIVERSITY DIFFERENCES BETWEEN HCS

AND PATIENTS WITH OTS To determine gut microbiota differences between patients with OTs and HCs, gut microbiota structures in groups were compared and analyzed. Microbial community phyla and

genera were examined and described (Fig. 1A and B) to show the relatively higher phyla and genera abundance, while remaining phyla were merged under “other”. Fecal microorganisms were

mainly composed of Bacteroidota, Firmicutes_,_ and Proteobacteria at the phylum level (Fig. 1A). Genus levels (Fig. 1B) were dominated by _Bacteroides, Faecalibacterium, Prevotella,

Escherichia_Shigella, Megamonas,_ and _Phascolarctobacterium_. At phylum and genus levels, no significant differences in gut microbiota composition were identified between OT and HC groups,

but differences were recorded in the proportion of gut microbiota composition. At the phylum level, average Bacteroidota and Proteobacteria abundance in the OT group was higher than in HCs,

while average _Firmicutes_ abundance in OTs was lower than that in HCs. At genus levels, when compared to HCs, average _Bacteroides, Prevotella,_ and _Escherichia_Shigella_ abundance in the

OT group increased, while average _Faecalibacterium, Megamonas,_ and _Phascolarctobacterium_ abundance decreased. To evaluate gut microbiota diversity in OT and HC groups, α- and β-diversity

indices were evaluated. The different alpha diversity indexes (Chao 1, ACE, Shannon and Simpson) were measured. Chao1 and ACE indexes were used to determine community abundance, the Shannon

and Simpson indexes were used to determine community diversity. In this study, the ACE and Chao1 indices of the OT group were both higher than those of the HC group (_P_ < 0.001), while

the Shannon and Simpson indices were both lower than those of the HC group (_P_ < 0.001). These data indicate that compared with the control group, the diversity of the gut microbiota in

OT patients was significantly decreased, while the abundance was significantly increased (Fig. 1C). Additionally, PCoA based on weighted UniFrac distances was used to show compositional

microflora differences. A significant difference in gut microbiota between HC and OT groups was observed (_P_ = 0.001) (Fig. 1D). It was worth noting that when compared to HCs, gut

microbiota in the OT group showed different composition and diversity. We next used univariate analysis (in Microbiome Analyst) to compare specific gut microbiota between OT and HC groups.

At genus levels, significant differences in 14 gut microbiota between groups were recorded (Table 2), including _Escherichia_Shigella, Coprococcus, Fusicatenibacter, Butyricicoccus,

Oscillibacter, Blautia, Bilophila, Enterbacter, Alistipes, Lachnospira, Bacteroides, Parasutterella, Lachnospiraceae_ND3007_group,_ and _Ruminococcus_. From an analysis of the first five

flora by False Discovery Rate (FDR), _Escherichia_Shigella_ in the OT group was significantly higher when compared with HCs, while _Coprococcus, Fusicatenibacter, Butyricicoccus,_ and

_Oscillibacter_ were significantly lower than in HCs (Fig. S1). To comprehensively consider the biological consistency and effect size, taxonomic analysis using the linear discriminant

analysis effect size (LEfSe) was carried out. Different classifications at the genus level were extracted and displayed as a bar chart. The results showed that 12 genera including

_Lachnospira_ and _Faecalibacterium_ were increased and enriched in the healthy control group, while 3 genera including _Bacteroides, Escherichia_Shigella_ and _Prevotella_ were highly

enriched in the OT group (Fig. S4). INTESTINAL MICROBIAL DIVERSITY DIFFERENCES BETWEEN PATIENTS WITH BOTS AND OC Next, we divided OTs into two groups: patients with BOTs and patients with

OC, and compared gut microbiota levels between groups. Fecal microorganisms were mainly composed of Bacteroidota, Firmicutes_,_ and Proteobacteria at the phylum level (Fig. 2A). Genus levels

(Fig. 2B) were dominated by _Bacteroides, Prevotella, Faecalibacterium, Escherichia_Shigella, Phascolarctobacterium,_ and _Parabacteroides_. At the phylum level, no significant differences

in gut microbiota composition and proportions were identified between BOT and OC groups. However, differences in the proportions of gut microbiota were identified at genus levels. When

compared to HCs, average _Bacteroides, Escherichia_Shigella, Phascolarctobacterium,_ and _Parabacteroides_ abundance increased in the OC group, while _Prevotella_ and _Faecalibacterium_

decreased. Gut microbiota diversity in OC and BOT groups was also evaluated. In the OC and BOT groups, the Shannon and Simpson indices (Fig. 2C) showed no significant difference in the

diversity of gut microbiota between the two groups of patients (_P_ = 0.967, _P_ = 0.177). According to the Chao1 index, there was no significant difference in the abundance of gut

microbiota between the two groups of patients (_P_ = 0.128), but according to the ACE index, there was a significant difference in the abundance of gut microbiota between the two groups of

patients (_P_ = 0.046). Additionally, weighted PCoA results showed a significant difference in gut microbiota composition between BOT and OC groups (_P_ = 0.002) (Fig. 2D). Univariate

analysis showed eight gut microbiota differences between BOT and OC groups at genus levels (Table 3), including _Flavonifractor, Ruminococcus_gnavus_group, Prevotella, Anaerotruncus,

Veillonella, Bacteroides,_ and _Parabacteroides_. According to FDR, the first five flora were analyzed, of which, _Flavonifractor, Ruminococcus_gnavus_group,_ and _Anaerotruncus_ in the OC

group were significantly higher than in BOT, while _Prevotella_ and _Veillonella_ were significantly decreased (Fig. S2). The classification analysis results of LEfSe showed that 12 genera

including _Prevotella_ and _Agathobacter_ were increased and enriched in the BOT group, while 3 genera including _Bacteroides_, _Escherichia_Shigella_ and _Ruminococcus_gnavus_group_ were

highly enriched in the OC group (Fig. S4). INTESTINAL MICROBIAL DIVERSITY DIFFERENCES BETWEEN PATIENTS WITH OBTS AND THOSE WITH OTS We also compared gut microbiota differences between

patients with OBTs and those with OTs. Microbial composition in feces was mainly comprised of Bacteroidota_,_ Firmicutes and Proteobacteria at the phylum level (Fig. 3A). Genus levels (Fig. 

3B) were dominated by _Bacteroides, Prevotella, Faccalibacterium, Escherichia_Shigella,_ and _Megamonas._ At phylum and genus levels, no significant differences in gut microbiota composition

were identified between OBT and OT groups, but differences in the proportion of gut microbiota composition were recorded. At phylum levels, average Bacteroidota and Proteobacteria abundance

in the OT group was higher than in the OBT group, while average Firmicutes abundance in the OBT group was lower than in the OBT group. At genus levels, when compared with the OBT group,

average _Bacteroides, Prevotella,_ and _Escherichia_Shigella_ abundance in the OT group increased, while average _Faccalibacterium_ and _Megamonas_ abundance decreased. The Chao1 and ACE

indices of the OT group were significantly higher than those of the OBT group (_P_ < 0.001, _P_ = 0.012), while the Shannon and Simpson indices were lower than those of the OBT group (_P_

 = 0.020, _P_ = 0.122). These data indicate that compared with the OBT group, the diversity of the gut microbiota in OT patients was significantly decreased, while the abundance was

significantly increased (Fig. 3C). Also, weighted PCoA results showed a significant difference in gut microbiota between OBT and OT groups (_P_ = 0.001) (Fig. 3D). Microbiome Analyst

univariate analysis was next used to compare specific gut microbiota in OBT and OT groups. At genus levels, significant differences in seven gut microbiota were identified between groups

(Table 4), including _Fusicatenibacter, Butyricicoccus, Lachnospiraceae_ND3007_group, Coprococcus, Parasutterella,_ and _Blautia._ According to FDR, the first five flora were analyzed, in

which _Fusicatenibacter, Butyricicoccus, Coprococcus Parasutterella,_ and _Anaerotruncus_ in the OBT group were significantly higher than the OT group, while the

_Lachnospiraceae_ND3007_group_ was significantly decreased (Fig. S3). The classification analysis results of LEfSe showed that 13 genera including _Lachnospira_ and _Roseburia_ were

increased and enriched in the OBT group, while 2 genera including _Prevotella_ and _Streptococcus_ were highly enriched in the OT group (Fig. S4). THE VALUE OF DETECTING GUT MICROBIOTA FOR

OT DIAGNOSES To evaluate gut microbiota potential to distinguish cancer populations, we established and tested a random forest classifier model. Gut microbiota diagnostic effects were

evaluated using ROC analysis; when compared with HCs, the AUC value of the BOT group was 0.77 (Fig. 4A). From top to bottom, the main bacteria responsible for distinguishing patients with

BOTs from HCs are shown (Fig. 4C), with an error rate of 31.6%. When compared with HCs, the AUC value of the OC group was 0.86 (Fig. 4B). The performance measured by the AUC for the CatBoost

and XGBoost models of the HC and OC groups was 0.859 for both (Fig. S5). The main bacteria responsible for distinguishing patients with OC from HCs are shown (Fig. 4D), with an error rate

of 34.1%. When compared with the OC group, the AUC value of the BOT group was 0.72 (Fig. 4E). The main bacteria responsible for distinguishing patients with OC from those with BOTs are shown

(Fig. 4G), with an error rate of 42.51%. When compared with the OBT group, the AUC value of the OT group was 0.70 (Fig. 4F). The main bacteria responsible for distinguishing patients with

OTs from those with OBTs are shown (Fig. 4H), with an error rate of 43.94%. DISCUSSION In this study, fecal samples were collected from HCs (n = 53), 239 patients with OTs (patients with OC

(n = 148) and with BOTs (n = 91)), and patients with OBTs (n = 90). Through the analysis of the gut microbiota of these patients, we observed that the gut microbiota composition, including

bacterial abundance and diversity of OT group significantly differs from that of HC group and OBT group. Also, a significant difference was noted in the gut microbiota between OT and BOT

patients. Moreover, among patients with OTs, OC and BOT patients showed distinctive gut microbiota compositions. Via the analysis of the microbiota in the patient sample, unique intestinal

microbe species were discovered. We speculate that we could distinguish OT patients from HCs through these microbes. Because of the close relationship between estrogen and intestinal

microorganisms, differences in intestinal microorganisms in female tumors (cervical and breast cancer) have been extensively studied, Patients with cervical cancer and breast cancer have

their own unique gut microbiota19,20,21. Kang et al.19 reported that the abundance of _Prevotella_ in fecal samples of early cervical cancer patients was higher than that in healthy control

group. Additionally, cervical cancer stage was most significantly and negatively correlated with _Ruminococcus 2_, which was posited as a potential biomarker in predicting cervical cancer

development20. High _Bacteroides_ abundance was also found in fecal samples from patients with cervical cancer, with _Bacteroides_ identified as a dominant bacteria related to estrogen

metabolism. Thus, cervical cancer occurrence and development may be related to estrogen metabolism mediated by intestinal microorganisms20. When compared with healthy individuals, breast

cancer patients usually have lower microbial diversity and microbial composition alterations; relative _Streptomyces_ and _Bacteroides_ abundance in feces from breast cancer patients was

lower, while _Verrucous_ and _Proteus_ abundance was higher21. The bacterial metabolites secreted by gut microbiota, similar to the role of hormones, are also involved in estrogen metabolism

regulation in cancer cells22,23. Since 80% of breast cancer cases are estrogen receptor positive22, the occurrence and development of breast cancer may be related to estrogen metabolism. In

our study, when compared with HCs, _Escherichia_Shigella_ abundance was significantly increased, while _Coprococcus, Fusicatenibacter, Butyricicoccus,_ and _Oscillibacter_ abundance was

significantly decreased in patients with OTs. Some _E. coli_ and _Shigella_ strains may cause intestinal infections and diarrhea23,24. Current evidence also suggest that patients with

non-HBV/ non-HCV hepatocellular carcinoma have intestinal ecological disorders characterized by excessive amounts of pro-inflammatory bacteria such as _Escherichia coli Shigella_ and

_enterococci_ and a decrease in anti-inflammatory bacteria25. Studies have shown that _E. coli_ and _Shigella_ are _Enterobacteria_ that generate lactic acid which promotes tumor growth and

development by providing energy for tumor cells and immune defense evasion26,27,28. _Escherichia_Shigella_ may potentially promote OT development, although mechanisms remain unclear.

_Coprococcus_ is an important member of the _Pleurococcus_ genus, which mainly colonizes the intestines of healthy individuals29 and _Butyricicoccus_ is a known “probiotic”, both of which

are important butyric acid producers30. Some studies have reported that butyric acid exerts protective effects in patients with colorectal cancer by inhibiting tumor cell proliferation and

inducing tumor cell apoptosis31. It was previously reported that when compared with fecal microbiota data in healthy women, relative _Butyricimonas_ and _Coprococcus_ abundance in patients

with early breast cancer had decreased32. A study revealed that _Fusicatenibacter_ can produce short-chain fatty acids SCFAs (i.e., butyrate, propionate, and acetate). SCFAs is essential for

the integrity of the intestinal barrier and can also affect the intestinal nervous system and stimulate systemic anti-inflammatory properties33. OTs are also associated with abnormal

estrogen levels, but whether unique intestinal microorganism levels in OTs are implicated in disease occurrence and development via estrogen metabolism requires investigation. In patients

with OTs, when compared with those with BOTs, _Flavonifractor, Ruminococcus_gnavus_group,_ and _Anaerotruncus_ in malignant OTs were significantly increased, while _Prevotella_ was

significantly decreased. _Ruminococcus gnavus_ has been implicated in Crohn’s disease; its relative abundance is increased in patients with the disease and is associated with severe disease

symptoms34. _R. gnavus_ abundance was also increased in patients with viral Hepatocellular carcinoma, which eventually induced tumor necrosis factor-α in dendritic cells and led to

hepatocyte carcinogenesis8. Jacobson et al.12 reported that _Prevotella_ abundance increased significantly in patients with OC when compared with benign controls. The possible reason is that

they included only five Native American female patients with BOTs. After being included in the study and treated for OC, intestinal microbes may alter after therapy. _Prevotella_ is

generally associated with healthy plant diets and has “probiotic” roles in the body, but too much _Prevotella_ can stimulate intestinal epithelial cells to produce IL-8 and IL-6, thus

promoting intestinal mucosal auxiliary Th17 immune responses, neutrophil recruitment, and chronic inflammation35. Similar to the gut microbiota of healthy controls included in this study,

_Fusicatenibacter_ was also significantly reduced in OTs when compared with females with OBTs. In recent years, the gut microbiota has been widely investigated as early diagnostic markers in

some cancers (e.g., gastric, colorectal, and liver cancers)6,7,36. Zhang et al. and other authors reported that Lactic Acid Bacteria and _Macrococci_ abundance in patients with gastric

cancer was significantly higher than in healthy individuals; Different bacteria were used to generate a random forest model, which provided an area under the curve (AUC) value of 0.91.

Verification samples achieved a true positive rate of 0.83 in gastric cancer7. It was also reported that the combined observation of gut bacteria and metabolic biomarkers (such as branched

chain amino acids, aromatic amino acids, and amino acyl tRNA organisms) may improve the diagnostic performance of colorectal cancer. The AUC value of colorectal cancer patients and healthy

individuals is 0.94, indicating the possibility of early diagnosis of colon cancer7. Another study reported that in eight intestinal bacterial genus classification models with an average

abundance of more than 0.1%, high diagnostic accuracy was achieved when classifying liver cancer types in the verification cohort36. In our study, gut microbiota diagnostic effects were

evaluated using ROC analysis; when compared with HCs, AUC values in BOT and OC groups were 0.77 and 0.86, respectively. These findings suggest that some gut microbiota such as

_Escherichia_Shigella_ show a certain ability to distinguish between healthy individuals and patients with OT. But this is only a preliminary study and large-scale clinical verification is

needed. Our research also had some limitations. Sample size was small and conclusions were based on single-center data. Therefore, more samples and FIGO stages must be considered in future

studies. Moreover, gut microbiome is dynamic, affected by multiple factors, including genetics, lifestyle, and environmental exposure. We also did not fully consider the relationship among

menstruation, estrogen metabolism, and gut microbiota, and there was no validation through an independent cohort. These factors will be fully considered in future research. To conclude, our

work has demonstrated characteristic changes in gut microbiota in OT patients and possible key genera in the identification of HCs and OT patients. In the future, we will further construct

and verify the predictive model of OT based on gut microbiota in clinic. DATA AVAILABILITY Sequence data that support the findings of this study have been uploaded on China National GeneBank

DataBase with the primary accession code CNP0005514. https://db.cngb.org/search/?q=CNP0005514. Please contact the corresponding author for further information if necessary. REFERENCES *

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study was financially supported by the Zhejiang Province Basic Public Welfare Research Program (No. LGC22H160009), (No LY21H160006) and by Healthy Zhejiang One Million People Cohort (No.

K-20230085). AUTHOR INFORMATION Author notes * Wangang Gong and Gulei Jin contributed equally to this work. AUTHORS AND AFFILIATIONS * Zhejiang Cancer Hospital, Banshan Road, Hangzhou,

310022, Zhejiang, China Wangang Gong, Yejiang Bao, Qi Liu, Maowei Ni, Junjian Wang, Shuyu Mao, Yingli Zhang & Zhiguo Zheng * Hangzhou Institute of Medicine (HIM), Chinese Academy of

Sciences, Hangzhou, 310022, Zhejiang, China Wangang Gong, Yejiang Bao, Qi Liu, Maowei Ni, Junjian Wang, Shuyu Mao, Yingli Zhang & Zhiguo Zheng * Hangzhou Guhe Information and Technology

Company, Hangzhou, Zhejiang, China Gulei Jin Authors * Wangang Gong View author publications You can also search for this author inPubMed Google Scholar * Gulei Jin View author publications

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CONTRIBUTIONS WGG wrote the manuscript. MWN and SYM participated in the data collection. GLJ provided technical and material support and data analysis. YJB, QL, and JJW provided the samples

and clinical data. YLZ and ZGZ conceived and designed the study. All authors read and approved the final manuscript. CORRESPONDING AUTHORS Correspondence to Yingli Zhang or Zhiguo Zheng.

ETHICS DECLARATIONS COMPETING INTERESTS The authors declare no competing interests. ETHICS APPROVAL AND CONSENT TO PARTICIPATE The study was approved by the Ethics Committee of Zhejiang

Cancer Hospital (Approval No. IRB-2023-417). ADDITIONAL INFORMATION PUBLISHER’S NOTE Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional

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potential diagnostic value of gut microbiota in ovarian tumor patients. _Sci Rep_ 15, 16504 (2025). https://doi.org/10.1038/s41598-025-99912-x Download citation * Received: 04 December 2024

* Accepted: 23 April 2025 * Published: 13 May 2025 * DOI: https://doi.org/10.1038/s41598-025-99912-x SHARE THIS ARTICLE Anyone you share the following link with will be able to read this

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KEYWORDS * Ovarian tumors * 16s RNA sequencing * _Escherichia_Shigella_ * Gut microbiota