Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer
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...
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| creator | Miao, Ruizhong Badger, Taylor C Groesch, Kathleen Diaz-Sylvester, Paula L Wilson, Teresa Ghareeb, Allen Martin, Jongjin Anne Cregger, Melissa Welge, Michael Bushell, Colleen Auvil, Loretta Zhu, Ruoqing Brard, Laurent Braundmeier-Fleming, Andrea |
| description | 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. |
| doi_str_mv | 10.1371/journal.pone.0227707 |
| format | Article |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0227707</identifier><identifier>PMID: 31917801</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>60 APPLIED LIFE SCIENCES ; Aged ; Antigens ; Ascites ; Ascitic Fluid - microbiology ; Bioinformatics ; Biology ; Biology and Life Sciences ; Biomarkers ; Blood ; CA-125 Antigen - blood ; Cancer ; Cancer detection and diagnosis ; Cancer research ; Carcinoma, Ovarian Epithelial - blood ; Carcinoma, Ovarian Epithelial - diagnosis ; Carcinoma, Ovarian Epithelial - microbiology ; Carcinoma, Ovarian Epithelial - surgery ; Clostridium ; Computational biology ; Computer and Information Sciences ; Cross-Sectional Studies ; Development and progression ; Diagnostic imaging ; Diagnostic medicine ; Diagnostic software ; Diagnostic systems ; Disease ; DNA, Bacterial - genetics ; DNA, Bacterial - isolation & purification ; Epididymis ; Female ; Fungi ; Gynecology ; Health screening ; Humans ; Hysterectomy ; Immunology ; Laparoscopy ; Learning algorithms ; Machine Learning ; Markers ; Medical diagnosis ; Medical schools ; Medicine ; Medicine and Health Sciences ; Membrane Proteins - blood ; Microbiome ; Microbiota - genetics ; Microorganisms ; Middle Aged ; Model accuracy ; Models, Biological ; Mucosa ; Next-generation sequencing ; Obstetrics ; Ovarian cancer ; Ovarian carcinoma ; Ovarian Neoplasms - blood ; Ovarian Neoplasms - diagnosis ; Ovarian Neoplasms - microbiology ; Ovarian Neoplasms - surgery ; Ovariectomy ; Pathogenesis ; Patients ; Peritoneal fluid ; Peritoneum ; Pilot Projects ; Preoperative Period ; Prognosis ; Reproductive system ; RNA, Ribosomal, 16S - genetics ; Serum levels ; Surgery ; Surgical oncology ; Tumor markers ; Tumors ; Ultrasonic imaging ; Ultrasound imaging ; WAP Four-Disulfide Core Domain Protein 2 - analysis ; Womens health</subject><ispartof>PloS one, 2020-01, Vol.15 (1), p.e0227707-e0227707</ispartof><rights>COPYRIGHT 2020 Public Library of Science</rights><rights>This is an open access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication: https://creativecommons.org/publicdomain/zero/1.0/ (the “License”). 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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.</description><subject>60 APPLIED LIFE SCIENCES</subject><subject>Aged</subject><subject>Antigens</subject><subject>Ascites</subject><subject>Ascitic Fluid - microbiology</subject><subject>Bioinformatics</subject><subject>Biology</subject><subject>Biology and Life Sciences</subject><subject>Biomarkers</subject><subject>Blood</subject><subject>CA-125 Antigen - blood</subject><subject>Cancer</subject><subject>Cancer detection and diagnosis</subject><subject>Cancer research</subject><subject>Carcinoma, Ovarian Epithelial - blood</subject><subject>Carcinoma, Ovarian Epithelial - diagnosis</subject><subject>Carcinoma, Ovarian Epithelial - microbiology</subject><subject>Carcinoma, Ovarian Epithelial - surgery</subject><subject>Clostridium</subject><subject>Computational biology</subject><subject>Computer and Information Sciences</subject><subject>Cross-Sectional Studies</subject><subject>Development and progression</subject><subject>Diagnostic imaging</subject><subject>Diagnostic medicine</subject><subject>Diagnostic software</subject><subject>Diagnostic systems</subject><subject>Disease</subject><subject>DNA, Bacterial - genetics</subject><subject>DNA, Bacterial - isolation & purification</subject><subject>Epididymis</subject><subject>Female</subject><subject>Fungi</subject><subject>Gynecology</subject><subject>Health screening</subject><subject>Humans</subject><subject>Hysterectomy</subject><subject>Immunology</subject><subject>Laparoscopy</subject><subject>Learning algorithms</subject><subject>Machine Learning</subject><subject>Markers</subject><subject>Medical diagnosis</subject><subject>Medical schools</subject><subject>Medicine</subject><subject>Medicine and Health Sciences</subject><subject>Membrane Proteins - blood</subject><subject>Microbiome</subject><subject>Microbiota - genetics</subject><subject>Microorganisms</subject><subject>Middle Aged</subject><subject>Model accuracy</subject><subject>Models, Biological</subject><subject>Mucosa</subject><subject>Next-generation sequencing</subject><subject>Obstetrics</subject><subject>Ovarian cancer</subject><subject>Ovarian carcinoma</subject><subject>Ovarian Neoplasms - blood</subject><subject>Ovarian Neoplasms - diagnosis</subject><subject>Ovarian Neoplasms - microbiology</subject><subject>Ovarian Neoplasms - surgery</subject><subject>Ovariectomy</subject><subject>Pathogenesis</subject><subject>Patients</subject><subject>Peritoneal fluid</subject><subject>Peritoneum</subject><subject>Pilot Projects</subject><subject>Preoperative Period</subject><subject>Prognosis</subject><subject>Reproductive system</subject><subject>RNA, Ribosomal, 16S - genetics</subject><subject>Serum levels</subject><subject>Surgery</subject><subject>Surgical oncology</subject><subject>Tumor markers</subject><subject>Tumors</subject><subject>Ultrasonic imaging</subject><subject>Ultrasound imaging</subject><subject>WAP Four-Disulfide Core Domain Protein 2 - analysis</subject><subject>Womens health</subject><issn>1932-6203</issn><issn>1932-6203</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2020</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><sourceid>BENPR</sourceid><sourceid>DOA</sourceid><recordid>eNqNk99rFDEQxxdRbK3-B6KLgujDnfmxm01ehFL8cVAo-Os1ZLOzd6l7yTXJFv3vne1dy530QfKQMPnMdzKTmaJ4Tsmc8oa-vwxj9GaYb4KHOWGsaUjzoDimirOZYIQ_3DsfFU9SuiSk5lKIx8URp4o2ktDjIp-mBCmtwecy9OUGossoaIZy7WwMrcNTDyaPEVJpfFfmcR1iuTbxF8RygGsY0J7KTcgoMdGdM0sfUna2zCHgbY98uDbRGV9a4y3Ep8Wj3gwJnu32k-LHp4_fz77Mzi8-L85Oz2e2oSrPjCCks5ZDL62RjHZWUlNXLbNdU1lMAYSQRnIFlpqqo0CBcJCg2kaCYC0_KV5udTdDSHpXsKQZ5zVpGlEpJBZbogvmUm-iw8T-6GCcvjGEuNQmYioDaNariqm2s40SGEyo2tREMGrbVihSGdT6sIs2tmvoLNYjmuFA9PDGu5VehmuNUoxIgQKvtgJT9XSyLoNd2eA92KxpLakUFKG3uygxXI2Qsl67ZGEYjIcw3iQnWF0JNaGv_0HvL8GOWhrM0vk-4OPsJKpPBSVSVbya3ja_h8LVATYKdkzv0H7g8O7AAZkMv_PSjCnpxbev_89e_Dxk3-yxK-zUvEphGLMLPh2C1RbENk4pQn_3E5ToaYJuq6GnCdK7CUK3F_u_eOd0OzL8Ly_LF3E</recordid><startdate>20200109</startdate><enddate>20200109</enddate><creator>Miao, 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of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer</title><author>Miao, Ruizhong ; Badger, Taylor C ; Groesch, Kathleen ; Diaz-Sylvester, Paula L ; Wilson, Teresa ; Ghareeb, Allen ; Martin, Jongjin Anne ; Cregger, Melissa ; Welge, Michael ; Bushell, Colleen ; Auvil, Loretta ; Zhu, Ruoqing ; Brard, Laurent ; Braundmeier-Fleming, Andrea</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c719t-a600dcc3ef8ca821dc81a54b2cd74c191e668a839ec1a4d1e1e03e8e9b78e62b3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2020</creationdate><topic>60 APPLIED LIFE SCIENCES</topic><topic>Aged</topic><topic>Antigens</topic><topic>Ascites</topic><topic>Ascitic Fluid - microbiology</topic><topic>Bioinformatics</topic><topic>Biology</topic><topic>Biology and Life Sciences</topic><topic>Biomarkers</topic><topic>Blood</topic><topic>CA-125 Antigen - blood</topic><topic>Cancer</topic><topic>Cancer detection and diagnosis</topic><topic>Cancer research</topic><topic>Carcinoma, Ovarian Epithelial - blood</topic><topic>Carcinoma, Ovarian Epithelial - diagnosis</topic><topic>Carcinoma, Ovarian Epithelial - microbiology</topic><topic>Carcinoma, Ovarian Epithelial - surgery</topic><topic>Clostridium</topic><topic>Computational biology</topic><topic>Computer and Information Sciences</topic><topic>Cross-Sectional Studies</topic><topic>Development and progression</topic><topic>Diagnostic imaging</topic><topic>Diagnostic medicine</topic><topic>Diagnostic software</topic><topic>Diagnostic systems</topic><topic>Disease</topic><topic>DNA, Bacterial - genetics</topic><topic>DNA, Bacterial - isolation & purification</topic><topic>Epididymis</topic><topic>Female</topic><topic>Fungi</topic><topic>Gynecology</topic><topic>Health screening</topic><topic>Humans</topic><topic>Hysterectomy</topic><topic>Immunology</topic><topic>Laparoscopy</topic><topic>Learning algorithms</topic><topic>Machine Learning</topic><topic>Markers</topic><topic>Medical diagnosis</topic><topic>Medical schools</topic><topic>Medicine</topic><topic>Medicine and Health Sciences</topic><topic>Membrane Proteins - blood</topic><topic>Microbiome</topic><topic>Microbiota - genetics</topic><topic>Microorganisms</topic><topic>Middle Aged</topic><topic>Model accuracy</topic><topic>Models, Biological</topic><topic>Mucosa</topic><topic>Next-generation sequencing</topic><topic>Obstetrics</topic><topic>Ovarian cancer</topic><topic>Ovarian carcinoma</topic><topic>Ovarian Neoplasms - blood</topic><topic>Ovarian Neoplasms - diagnosis</topic><topic>Ovarian Neoplasms - microbiology</topic><topic>Ovarian Neoplasms - surgery</topic><topic>Ovariectomy</topic><topic>Pathogenesis</topic><topic>Patients</topic><topic>Peritoneal 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C)</collection><collection>Biological Science Database</collection><collection>Engineering Database</collection><collection>Nursing & Allied Health Premium</collection><collection>ProQuest advanced technologies & aerospace journals</collection><collection>ProQuest Advanced Technologies & Aerospace Collection</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Environmental Science Database</collection><collection>Materials Science Collection</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>Engineering collection</collection><collection>Environmental Science Collection</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><collection>OSTI.GOV</collection><collection>PubMed Central (Full Participant titles)</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>PloS one</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Miao, Ruizhong</au><au>Badger, Taylor C</au><au>Groesch, Kathleen</au><au>Diaz-Sylvester, Paula L</au><au>Wilson, Teresa</au><au>Ghareeb, Allen</au><au>Martin, Jongjin Anne</au><au>Cregger, Melissa</au><au>Welge, Michael</au><au>Bushell, Colleen</au><au>Auvil, Loretta</au><au>Zhu, Ruoqing</au><au>Brard, Laurent</au><au>Braundmeier-Fleming, Andrea</au><aucorp>Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)</aucorp><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer</atitle><jtitle>PloS one</jtitle><addtitle>PLoS One</addtitle><date>2020-01-09</date><risdate>2020</risdate><volume>15</volume><issue>1</issue><spage>e0227707</spage><epage>e0227707</epage><pages>e0227707-e0227707</pages><issn>1932-6203</issn><eissn>1932-6203</eissn><abstract>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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>31917801</pmid><doi>10.1371/journal.pone.0227707</doi><tpages>e0227707</tpages><orcidid>https://orcid.org/0000-0002-5268-1915</orcidid><orcidid>https://orcid.org/0000-0002-1531-1958</orcidid><orcidid>https://orcid.org/0000000252681915</orcidid><orcidid>https://orcid.org/0000000215311958</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 1932-6203 |
| ispartof | PloS one, 2020-01, Vol.15 (1), p.e0227707-e0227707 |
| issn | 1932-6203 1932-6203 |
| language | eng |
| recordid | cdi_plos_journals_2335077649 |
| source | PubMed Central (Open Access); Public Library of Science (PLoS) Journals Open Access; MEDLINE; Full-Text Journals in Chemistry (Open access); DOAJ Directory of Open Access Journals; EZB Electronic Journals Library |
| subjects | 60 APPLIED LIFE SCIENCES Aged Antigens Ascites Ascitic Fluid - microbiology Bioinformatics Biology Biology and Life Sciences Biomarkers Blood CA-125 Antigen - blood Cancer Cancer detection and diagnosis Cancer research Carcinoma, Ovarian Epithelial - blood Carcinoma, Ovarian Epithelial - diagnosis Carcinoma, Ovarian Epithelial - microbiology Carcinoma, Ovarian Epithelial - surgery Clostridium Computational biology Computer and Information Sciences Cross-Sectional Studies Development and progression Diagnostic imaging Diagnostic medicine Diagnostic software Diagnostic systems Disease DNA, Bacterial - genetics DNA, Bacterial - isolation & purification Epididymis Female Fungi Gynecology Health screening Humans Hysterectomy Immunology Laparoscopy Learning algorithms Machine Learning Markers Medical diagnosis Medical schools Medicine Medicine and Health Sciences Membrane Proteins - blood Microbiome Microbiota - genetics Microorganisms Middle Aged Model accuracy Models, Biological Mucosa Next-generation sequencing Obstetrics Ovarian cancer Ovarian carcinoma Ovarian Neoplasms - blood Ovarian Neoplasms - diagnosis Ovarian Neoplasms - microbiology Ovarian Neoplasms - surgery Ovariectomy Pathogenesis Patients Peritoneal fluid Peritoneum Pilot Projects Preoperative Period Prognosis Reproductive system RNA, Ribosomal, 16S - genetics Serum levels Surgery Surgical oncology Tumor markers Tumors Ultrasonic imaging Ultrasound imaging WAP Four-Disulfide Core Domain Protein 2 - analysis Womens health |
| title | Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer |
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