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...
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MDPI AG
2019-10-01
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| Series: | International Journal of Molecular Sciences |
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| Online Access: | https://www.mdpi.com/1422-0067/20/19/4938 |
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| author | Shin-Wha Lee Ha-Young Lee Hyo Joo Bang Hye-Jeong Song Sek Won Kong Yong-Man Kim |
| author_facet | Shin-Wha Lee Ha-Young Lee Hyo Joo Bang Hye-Jeong Song Sek Won Kong Yong-Man Kim |
| author_sort | Shin-Wha Lee |
| collection | DOAJ |
| 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 withOC(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. |
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| language | English |
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| publishDate | 2019-10-01 |
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| spelling | doaj.art-f2f76d1b6aef4aa4a28e131448ee12de2022-12-22T03:09:18ZengMDPI AGInternational Journal of Molecular Sciences1422-00672019-10-012019493810.3390/ijms20194938ijms20194938An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation StrategyShin-Wha Lee0Ha-Young Lee1Hyo Joo Bang2Hye-Jeong Song3Sek Won Kong4Yong-Man Kim5Department of Obstetrics & Gynecology, University of Ulsan, ASAN Medical Center, Seoul 05505, KoreaASAN Institute for Life Science, ASAN Medical Center, Seoul 05505, KoreaAhngook Pharmaceutical Co., Ltd., Seoul 07445, KoreaBio-IT Research Center, Hallym University, Chuncheon, Gangwon-do 24252, KoreaComputational Health Informatics Program, Boston Children’s Hospital, Boston, MA 02115, USADepartment of Obstetrics & Gynecology, University of Ulsan, ASAN Medical Center, Seoul 05505, KoreaThis 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 withOC(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.https://www.mdpi.com/1422-0067/20/19/4938ovarian cancerprediction modelurinary biomarker |
| spellingShingle | Shin-Wha Lee Ha-Young Lee Hyo Joo Bang Hye-Jeong Song Sek Won Kong Yong-Man Kim An Improved Prediction Model for Ovarian Cancer Using Urinary Biomarkers and a Novel Validation Strategy International Journal of Molecular Sciences ovarian cancer prediction model urinary biomarker |
| 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 | ovarian cancer prediction model urinary biomarker |
| url | https://www.mdpi.com/1422-0067/20/19/4938 |
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