Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer


Autoři: Ruizhong Miao aff001;  Taylor C. Badger aff002;  Kathleen Groesch aff003;  Paula L. Diaz-Sylvester aff003;  Teresa Wilson aff003;  Allen Ghareeb aff003;  Jongjin Anne Martin aff004;  Melissa Cregger aff005;  Michael Welge aff007;  Colleen Bushell aff008;  Loretta Auvil aff007;  Ruoqing Zhu aff009;  Laurent Brard aff004;  Andrea Braundmeier-Fleming aff002
Působiště autorů: Department of Statistics, University of Virginia, Charlottesville, Virginia, United States of America aff001;  Department of Medical Microbiology, Immunology and Cell Biology, SIU School of Medicine, Springfield, Illinois, United States of America aff002;  Center for Clinical Research, SIU School of Medicine, Springfield, Illinois, United States of America aff003;  Department of Obstetrics & Gynecology, SIU School of Medicine, Springfield, Illinois, United States of America aff004;  Oak Ridge National Laboratory, Oak Ridge, Tennessee, United States of America aff005;  Department of Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee, United States of America aff006;  National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America aff007;  Applied Research Institute, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America aff008;  Department of Statistics, University of Illinois at Urbana-Champaign, Champaign, Illinois, United States of America aff009;  Simmons Cancer Institute at SIU, Springfield, Illinois, United States of America aff010
Vyšlo v časopise: PLoS ONE 15(1)
Kategorie: Research Article
doi: 10.1371/journal.pone.0227707

Souhrn

Epithelial ovarian cancer (OC) is the most deadly cancer of the female reproductive system. To date, there is no effective screening method for early detection of OC and current diagnostic armamentarium may include sonographic grading of the tumor and analyzing serum levels of tumor markers, Cancer Antigen 125 (CA-125) and Human epididymis protein 4 (HE4). Microorganisms (bacterial, archaeal, and fungal cells) residing in mucosal tissues including the gastrointestinal and urogenital tracts can be altered by different disease states, and these shifts in microbial dynamics may help to diagnose disease states. We hypothesized that the peritoneal microbial environment was altered in patients with OC and that inclusion of selected peritoneal microbial features with current clinical features into prediction analyses will improve detection accuracy of patients with OC. Blood and peritoneal fluid were collected from consented patients that had sonography confirmed adnexal masses and were being seen at SIU School of Medicine Simmons Cancer Institute. Blood was processed and serum HE4 and CA-125 were measured. Peritoneal fluid was collected at the time of surgery and processed for Next Generation Sequencing (NGS) using 16S V4 exon bacterial primers and bioinformatics analyses. We found that patients with OC had a unique peritoneal microbial profile compared to patients with a benign mass. Using ensemble modeling and machine learning pathways, we identified 18 microbial features that were highly specific to OC pathology. Prediction analyses confirmed that inclusion of microbial features with serum tumor marker levels and control features (patient age and BMI) improved diagnostic accuracy compared to currently used models. We conclude that OC pathogenesis alters the peritoneal microbial environment and that these unique microbial features are important for accurate diagnosis of OC. Our study warrants further analyses of the importance of microbial features in regards to oncological diagnostics and possible prognostic and interventional medicine.

Klíčová slova:

Ascites – Cancer detection and diagnosis – Clostridium – Diagnostic medicine – Machine learning – Microbiome – Ovarian cancer – Surgical oncology


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2020 Číslo 1