Abstract
BackgroundPreoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types.MethodsIn total, 132 urine samples from 73 malignant and 59 benign cases of ovarian carcinoma were divided into C1 (training and test datasets) and C2 (validation dataset) cohorts. Mass spectrometry (MS) data of all samples were acquired in data-independent acquisition (DIA) mode with an Orbitrap mass spectrometer and analyzed using DIA-NN software. The generated classifier was trained with Random Forest algorithm from the training dataset and validated in the test and validation datasets. Serum CA125 and HE4 levels were additionally determined in all patients. Finally, classification accuracy of the classifier, serum CA125 and serum HE4 in all samples were evaluated and plotted via receiver operating characteristic (ROC) analysis.ResultsIn total, 2,199 proteins were quantified and 69 identified with differential expression in benign and malignant groups of the C1 cohort. A classifier incorporating five proteins (WFDC2, PTMA, PVRL4, FIBA, and PVRL2) was trained and validated in this study. Evaluation of the performance of the classifier revealed AUC values of 0.970 and 0.952 in the test and validation datasets, respectively. In all 132 patients, AUCs of 0.966, 0.947, and 0.979 were achieved with the classifier, serum CA125, and serum HE4, respectively. Among eight patients with early stage malignancy, 7, 6, and 4 were accurately diagnosed based on classifier, serum CA125, and serum HE4, respectively.ConclusionThe novel classifier incorporating a urinary protein panel presents a promising non-invasive diagnostic biomarker for classifying benign and malignant ovarian tumors.
Highlights
Ovarian cancer (OC) is a common malignant disease and the fifth leading cause of cancer-related mortality in women (Siegel et al, 2021)
data-independent acquisition (DIA)-Mass spectrometry (MS) analysis was performed on urine samples from 132 patients
Samples were randomly distributed into 8 batches with the aid of 120 min DIA-MS, with quality control samples included in each batch
Summary
Ovarian cancer (OC) is a common malignant disease and the fifth leading cause of cancer-related mortality in women (Siegel et al, 2021). Obvious clinical manifestations and effective diagnostic methods are lacking for early OC, making early diagnosis and discrimination from benign ovarian tumors difficult. Stage or preoperative differentiation of benign and malignant tumors is critical to improve prognosis of patients with OC. Novel effective methods and biomarkers for rapid, inexpensive and non-invasive monitoring of high-risk populations and preoperative discrimination between benign and malignant ovarian tumors are an urgent requirement. Preoperative differentiation of benign and malignant tumor types is critical for providing individualized treatment interventions to improve prognosis of patients with ovarian cancer. High-throughput proteomics analysis of urine samples was performed to identify reliable and non-invasive biomarkers that could effectively discriminate between the two ovarian tumor types
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