A Natural Language Processing Method Identifies an Association Between Bacterial Communities in the Upper Genital Tract and Ovarian Cancer

Andrew Polio; Vincent Wagner; David P. Bender; Michael J. Goodheart; Jesus Gonzalez Bosquet

doi:10.3390/ijms26157432

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A Natural Language Processing Method Identifies an Association Between Bacterial Communities in the Upper Genital Tract and Ovarian Cancer

Journal article

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A Natural Language Processing Method Identifies an Association Between Bacterial Communities in the Upper Genital Tract and Ovarian Cancer

Andrew Polio, Vincent Wagner, David P. Bender, Michael J. Goodheart and Jesus Gonzalez Bosquet

International journal of molecular sciences, Vol.26(15), 7432

08/01/2025

DOI: 10.3390/ijms26157432

PMCID: PMC12347966

PMID: 40806562

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Abstract

Bacterial communities within the female upper genital tract may influence the risk of ovarian cancer. In this retrospective cohort pilot study, we aim to detect different communities of bacteria between ovarian cancer and normal controls using topic modeling, a natural language processing tool. RNA was extracted and analyzed using the VITCOMIC2 pipeline. Topic modeling assessed differences in bacterial communities. Idatuning identified an optimal latent topic number and Latent Dirichlet Allocation (LDA) assessed topic differences between high-grade serous ovarian cancer (HGSOC) and controls. Results were validated using The Cancer Genome Atlas (TCGA) HGSOC dataset. A total of 801 unique taxa were identified, with 13 bacteria significantly differing between HGSOC and normal controls. LDA modeling revealed a latent topic associated with HGSOC samples, containing bacteria Escherichia/Shigella and Corynebacterineae. Pathway analysis using KEGG databases suggest differences in several biologic pathways including oocyte meiosis, aldosterone-regulated sodium reabsorption, gastric acid secretion, and long-term potentiation. These findings support the hypothesis that bacterial communities in the upper female genital tract may influence the development of HGSOC by altering the local environment, with potential functional implications between HGSOC and normal controls. However, further validation is required to confirms these associations and determine mechanistic relevance.

ovarian cancer

prediction model

natural language processing

microbiome

RNA sequencing

RNAseq

Details

Title: Subtitle: A Natural Language Processing Method Identifies an Association Between Bacterial Communities in the Upper Genital Tract and Ovarian Cancer
Creators: Andrew Polio - University of Iowa
Vincent Wagner - University of Iowa
David P. Bender - University of Iowa
Michael J. Goodheart - University of Iowa
Jesus Gonzalez Bosquet
Resource Type: Journal article
Publication Details: International journal of molecular sciences, Vol.26(15), 7432
DOI: 10.3390/ijms26157432
PMID: 40806562
PMCID: PMC12347966
NLM abbreviation: Int J Mol Sci
ISSN: 1422-0067
eISSN: 1422-0067
Publisher: MDPI
Grant note: NIHDepartment of Defense: OC190352 Research Fund of the Gynecologic Oncology Division of the University of Iowa Hospitals and ClinicsAmerican Association of Obstetricians and Gynecologists Foundation (AAOGF) Bridge Funding Award
This work was supported in part by the NIH 5R01CA99908-18 (K. Leslie PI), Department of Defense OC190352 (K. Leslie PI), and by the Research Fund of the Gynecologic Oncology Division of the University of Iowa Hospitals and Clinics. Also, it was supported in part by the American Association of Obstetricians and Gynecologists Foundation (AAOGF) Bridge Funding Award.
Language: English
Date published: 08/01/2025
Academic Unit: Obstetrics and Gynecology
Record Identifier: 9984944726502771

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