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Updated: May 20, 2025

Characterization and Functional Prediction of Bacteria in Ovarian Tissues
Published on: October 23, 2021
Translational Implications of The Gut Microbiome in Women with A Benign or Malignant Pelvic Mass
Priya Sabu1,2,3, Harsh B Pathak4,5, Emily Nissen6
1Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, University of Kansas Medical Center, USA.
Objective:
The role of the gut microbiome in non-gastrointestinal cancers has generated growing interest in the field of gynecologic oncology. Our objective was to characterize the gut microbiome in women with a pelvic mass suspicious for ovarian cancer. We hypothesized that (1) women with a pelvic mass would have reduced gut microbiota bacterial diversity compared to healthy controls and (2) gut microbial diversity would differ between benign disease compared to ovarian cancer.
Methods:
In this case-control observational study, patients who presented with a suspicious pelvic mass were recruited from university affiliated gynecologic oncology clinics for fecal biospecimen donation. Fecal samples that were obtained from patients underwent 16S rRNA gene sequencing for microbial evaluation and statistical analysis. We used the Human Microbiome Project (HMP) Data Portal to compare gut microbiota profiles for our study to that of healthy female controls.
Results:
Fifteen patients with a pelvic mass were included ages 24-75 years. When comparing the gut microbiomes of these patients to 82 healthy females from the HMP Dataset, those with a pelvic mass had a significantly lower microbiota gut bacterial diversity. On the final pathology, 8 of the 15 patients with a suspicious pelvic mass had ovarian cancer and 7 had benign disease. Although not statistically significant, the alpha diversity was marginally reduced in patients with ovarian cancer compared to those with benign disease.
Conclusion:
These findings underscore the necessity for validation in larger patient cohorts for clinical translation as a potential tool for disease diagnostics and disease prediction in diverse populations.
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