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An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy
This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surg...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6801627/ https://www.ncbi.nlm.nih.gov/pubmed/31590408 http://dx.doi.org/10.3390/ijms20194938 |
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author | Lee, Shin-Wha Lee, Ha-Young Bang, Hyo Joo Song, Hye-Jeong Kong, Sek Won Kim, Yong-Man |
author_facet | Lee, Shin-Wha Lee, Ha-Young Bang, Hyo Joo Song, Hye-Jeong Kong, Sek Won Kim, Yong-Man |
author_sort | Lee, Shin-Wha |
collection | PubMed |
description | This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples with OC (mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772–0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714–0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool. |
format | Online Article Text |
id | pubmed-6801627 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-68016272019-10-31 An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy Lee, Shin-Wha Lee, Ha-Young Bang, Hyo Joo Song, Hye-Jeong Kong, Sek Won Kim, Yong-Man Int J Mol Sci Article This study was designed to analyze urinary proteins associated with ovarian cancer (OC) and investigate the potential urinary biomarker panel to predict malignancy in women with pelvic masses. We analyzed 23 biomarkers in urine samples obtained from 295 patients with pelvic masses scheduled for surgery. The concentration of urinary biomarkers was quantitatively assessed by the xMAP bead-based multiplexed immunoassay. To identify the performance of each biomarker in predicting cancer over benign tumors, we used a repeated leave-group-out cross-validation strategy. The prediction models using multimarkers were evaluated to develop a urinary ovarian cancer panel. After the exclusion of 12 borderline tumors, the urinary concentration of 17 biomarkers exhibited significant differences between 158 OCs and 125 benign tumors. Human epididymis protein 4 (HE4), vascular cell adhesion molecule (VCAM), and transthyretin (TTR) were the top three biomarkers representing a higher concentration in OC. HE4 demonstrated the highest performance in all samples with OC (mean area under the receiver operating characteristic curve (AUC) 0.822, 95% CI: 0.772–0.869), whereas TTR showed the highest efficacy in early-stage OC (AUC 0.789, 95% CI: 0.714–0.856). Overall, HE4 was the most informative biomarker, followed by creatinine, carcinoembryonic antigen (CEA), neural cell adhesion molecule (NCAM), and TTR using the least absolute shrinkage and selection operator (LASSO) regression models. A multimarker panel consisting of HE4, creatinine, CEA, and TTR presented the best performance with 93.7% sensitivity (SN) at 70.6% specificity (SP) to predict OC over the benign tumor. This panel performed well regardless of disease status and demonstrated an improved performance by including menopausal status. In conclusion, the urinary biomarker panel with HE4, creatinine, CEA, and TTR provided promising efficacy in predicting OC over benign tumors in women with pelvic masses. It was also a non-invasive and easily available diagnostic tool. MDPI 2019-10-05 /pmc/articles/PMC6801627/ /pubmed/31590408 http://dx.doi.org/10.3390/ijms20194938 Text en © 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Lee, Shin-Wha Lee, Ha-Young Bang, Hyo Joo Song, Hye-Jeong Kong, Sek Won Kim, Yong-Man An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy |
title | An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy |
title_full | An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy |
title_fullStr | An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy |
title_full_unstemmed | An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy |
title_short | An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy |
title_sort | improved prediction model for ovarian cancer using urinary biomarkers and a novel validation strategy |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6801627/ https://www.ncbi.nlm.nih.gov/pubmed/31590408 http://dx.doi.org/10.3390/ijms20194938 |
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