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.