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. 2022 Jul;65(4):346-354.
doi: 10.5468/ogs.22017. Epub 2022 Apr 21.

Multiple biomarkers are more accurate than a combination of carbohydrate antigen 125 and human epididymis protein 4 for ovarian cancer screening

Kyung Nam Kang  1 , Eun Young Koh  1 , Ji Young Jang  1 , Chul Woo Kim  1
Affiliations

Multiple biomarkers are more accurate than a combination of carbohydrate antigen 125 and human epididymis protein 4 for ovarian cancer screening

Kyung Nam Kang et al. Obstet Gynecol Sci. 2022 Jul.

Abstract

Objective: The objective of this study was to compare and evaluate the diagnostic value of serum carbohydrate antigen 125 (CA125) and/or human epididymis protein 4 (HE4) and a panel of novel multiple biomarkers in patients with ovarian tumors to identify more accurate and effective markers for screening ovarian cancer.

Methods: Candidate ovarian cancer biomarkers were selected based on a literature search. Dozens of candidate biomarkers were examined using 143 serum samples from patients with ovarian cancer and 157 healthy serum samples as noncancer controls. To select the optimal marker panel for an ovarian cancer classification model, a set of biomarker panels was created with the number of possible combinations of eight biomarkers. Using the set of biomarkers as an input variable, the optimal biomarker panel was selected by examining the performance of the biomarker panel set using the Random Forest algorithm as a non-linear classification method and a 10-fold cross-validation technique.

Results: The final selected optimal combination of five biomarkers (CA125, HE4, cancer antigen 15-3, apolipoprotein [Apo] A1, and ApoA2) exhibited a sensitivity of 93.71% and specificity of 93.63% for ovarian cancer detection during validation.

Conclusion: Combining multiple biomarkers is a valid strategy for ovarian cancer diagnosis and can be used as a minimally invasive screening method for early ovarian cancer. A panel of five optimal biomarkers, including CA125 and HE4, was verified in this study. These can potentially be used as clinical biomarkers for early detection of ovarian cancer.

Keywords: Algorithm; Biomarkers; Ovarian cancer; Screening.

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Conflict of interest statement

Conflicts of interest

No potential conflict of interest relevant to this article was reported.

Figures

Fig. 1
Fig. 1
Box plots and Mann-Whitney U-test of single biomarker. Seven out of the eight biomarker candidates were found to be effective in distinguishing healthy controls from patients with ovarian cancer in Mann-Whitney U-test and box plots. CA125, carbohydrate antigen 125; OC, ovarian cancer; HC, healthy control; HE4, human epididymis protein 4; CA15.3, cancer antigen 15-3; ApoA2, apolipoprotein A2; TTR, transthyretin; ApoA1, apolipoprotein A1; CYFRA21.1, cytokeratin fragment 21-1; CEA, carcinoembryonic antigen.
Fig. 2
Fig. 2
Two receiver operating characteristic (ROC) curves of 10-fold cross-validation for four classification methods for optimal biomarker sets. All four methods showed similar Random Forest performances. The X-axis represents the 1-specificity and the Y-axis represents sensitivity. The set of multiple biomarkers exhibited the highest performance in early diagnosis of ovarian cancer. AUC, area under the curve; GLM, general linear model; XGBoost, extreme gradient boosting; GLMRF, generalized linear model random forest.
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