Mass spectrometry-based proteomics techniques and their application in ovarian cancer research

Abstract

Ovarian cancer has emerged as one of the leading cause of gynecological malignancies. So far, the measurement of CA125 and HE4 concentrations in blood and transvaginal ultrasound examination are essential ovarian cancer diagnostic methods. However, their sensitivity and specificity are still not sufficient to detect disease at the early stage. Moreover, applied treatment may appear to be ineffective due to drug-resistance. Because of a high mortality rate of ovarian cancer, there is a pressing need to develop innovative strategies leading to a full understanding of complicated molecular pathways related to cancerogenesis. Recent studies have shown the great potential of clinical proteomics in the characterization of many diseases, including ovarian cancer. Therefore, in this review, we summarized achievements of proteomics in ovarian cancer management. Since the development of mass spectrometry has caused a breakthrough in systems biology, we decided to focus on studies based on this technique. According to PubMed engine, in the years 2008–2010 the number of studies concerning OC proteomics was increasing, and since 2010 it has reached a plateau. Proteomics as a rapidly evolving branch of science may be essential in novel biomarkers discovery, therapy decisions, progression predication, monitoring of drug response or resistance. Despite the fact that proteomics has many to offer, we also discussed some limitations occur in ovarian cancer studies. Main difficulties concern both complexity and heterogeneity of ovarian cancer and drawbacks of the mass spectrometry strategies. This review summarizes challenges, capabilities, and promises of the mass spectrometry-based proteomics techniques in ovarian cancer management.

Definition and Independent Validation of a Proteomic-Classifier in Ovarian Cancer

“…Concordance of 89% was seen between test classifications generated from serum and plasma samples obtained simultaneously from a subset of patients in this study, indicating that the proposed procedure might be feasible in both serum and plasma samples. As a patient's proteomic signature reflects a specific health condition at a given time point, it may be essential for therapy guidance [47]. We used mass-spectral data and machine learning to develop a classifier that stratifies patients into two proteomic phenotypes.…”

Proteome-transcriptome alignment of molecular portraits achieved by self-contained gene set analysis: Consensus colon cancer subtypes case study

“…There are many excellent bioinformatics methods developed for proteomics data, including the techniques for normalization and preprocessing (e.g. spectral counts modeling with edgeR [70], MetaMass [71], reviewed in [72]), detecting and quantifying protein complexes (CCprofiler [73]), protein-protein interaction networks analysis and visualization (Cytoscape [74]) as well as dedicated software platforms with a set of statistical tools for high-dimensional proteomics data analysis (e.g. Perseus [75]).…”

Mapping human N-linked glycoproteins and glycosylation sites using mass spectrometry

“…Ovary.-Till now, the glycoprotein Cancer Antigen 125 (CA125) is the most frequently used biomarker for ovarian cancer detection and different glycoforms of CA125 have been investigated to improve its sensitivity and specificity [84,85]. In 2011, our team analyzed three cases of normal ovary and ovarian cancer tissues and identified 368 N-glycosylation sites in 286 glycoproteins using a combined method of solid-phase extraction and iTRAQ labeled glycoproteins [86].…”