Ovarian Cancer Detection Using Plasma Metabolic Profiling
Authors: Gu Haiwei , Wei Yiping , Jasbi Paniz , Shi Xiaojian 
Published: June 2021

Ovarian cancer (OC) is a rare but potentially fatal disease, with increases in 5‐year survival rates associated with early diagnosis and timely treatment. Previous studies have shown that detectable disturbances in metabolite profiles are indicative of subtle pathophysiological or oncogenic changes. Consequently, metabolite markers may provide better diagnostic performance and earlier detection. Despite the important role of metabolism and product metabolites in the molecular pathogenesis of cancer, robust metabolic markers to enable screening, surveillance, and therapeutic monitoring of OC are still lacking. In this study, we present a mass spectrometry (MS)‐based metabolic profiling approach for identifying metabolic marker candidates that could enable highly sensitive and specific OC detection using human plasma samples.

In this approach, both aqueous metabolites as well as lipids were detected. These metabolites, which are representative of many metabolic pathways of potential biological significance, were monitored in 218 plasma samples taken from three groups of patients (78 ovarian cancer patients, 50 patients with benign disease and 90 healthy controls) using LC‐MS/MS. Univariate methods were applied for significance testing. Multivariate statistical analyses were applied to determine latent factor structure as well build a predictive model using these 218 plasma samples. Resampling was performed to validate the diagnostic model using random subsets.

Significance testing and fold change (FC) analysis were performed to determine candidate markers warranting further analysis. Of the detected compounds, a substantial portion of metabolites exhibited statistically significant differences between OC patients and healthy controls (q < 0.05), and a robust number of metabolites demonstrated both q < 0.05 and FC > 2. The latter set of metabolites comprised the biomarker panel. A partial least squares discriminant analysis (PLS‐DA) model was established using the panel metabolites. This model proved to be powerful for distinguishing OC patients from healthy controls and showed strong potential for use as an early‐stage diagnostic. A receiver operating characteristic (ROC) curve generated based on the PLS‐DA model showed high sensitivity, good specificity, and excellent area under the curve. Permutation testing (cross validation) was also applied, demonstrating the robust diagnostic power of this metabolic profiling approach. Pathway and enrichment analyses were conducted to resolve affected metabolic pathways. Results of the aforementioned analyses ubiquitously connoted alterations in pathways essential for tumor growth, metastasis, and immune escape mechanisms. The results of our pathway and enrichment analysis are highly commensurate with one another, suggesting high confidence in affected metabolic pathways. Our results indicate the effectiveness of this metabolomics approach for ovarian cancer diagnosis. Future studies should examine larger cohorts from multiple locations in addition to the altered metabolic pathways related to OC pathogenesis discovered in our study.

Support or Funding Information

Support from the College of Health Solutions at ASU is appreciated.

This abstract is from the Experimental Biology 2019 Meeting. There is no full text article associated with this abstract published in The FASEB Journal.