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. 2018 Jul 31;19(8):2240.
doi: 10.3390/ijms19082240.

Understanding Ovarian Cancer: iTRAQ-Based Proteomics for Biomarker Discovery

Agata Swiatly  1 , Agnieszka Horala  2 , Jan Matysiak  3 , Joanna Hajduk  4 , Ewa Nowak-Markwitz  5 , Zenon J Kokot  6
Affiliations

Understanding Ovarian Cancer: iTRAQ-Based Proteomics for Biomarker Discovery

Agata Swiatly et al. Int J Mol Sci. .

Abstract

Despite many years of studies, ovarian cancer remains one of the top ten cancers worldwide. Its high mortality rate is mainly due to lack of sufficient diagnostic methods. For this reason, our research focused on the identification of blood markers whose appearance would precede the clinical manifestation of the disease. ITRAQ-tagging (isobaric Tags for Relative and Absolute Quantification) coupled with mass spectrometry technology was applied. Three groups of samples derived from patients with: ovarian cancer, benign ovarian tumor, and healthy controls, were examined. Mass spectrometry analysis allowed for highlighting the dysregulation of several proteins associated with ovarian cancer. Further validation of the obtained results indicated that five proteins (Serotransferrin, Amyloid A1, Hemopexin, C-reactive protein, Albumin) were differentially expressed in ovarian cancer group. Interestingly, the addition of Albumin, Serotransferrin, and Amyloid A1 to CA125 (cancer antigen 125) and HE4 (human epididymis protein4) improved the diagnostic performance of the model discriminating between benign and malignant tumors. Identified proteins shed light on the molecular signaling pathways that are associated with ovarian cancer development and should be further investigated in future studies. Our findings indicate five proteins with a strong potential to use in a multimarker test for screening and detection of ovarian cancer.

Keywords: MALDI-TOF MS; carcinogenesis; isobaric tags; ovarian cancer; proteomics.

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Conflict of interest statement

The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, and in the decision to publish the results.

Figures

Figure 1
Figure 1
Schematic workflow of the study. Three groups of samples were analyzed: OC ovarian cancer; BOT benign ovarian tumor; HC healthy controls.
Figure 2
Figure 2
Distribution of all identified proteins in mass spectrometry analysis based on (A) Sequence Coverage (B) Molecular Weight.
Figure 3
Figure 3
Gene ontology analysis of the most discriminative 11 proteins identified in iTRAQ analysis based on PANTHER database, including: (A) molecular function; (B) biological process; and, (C) cell component.
Figure 4
Figure 4
Interaction network of proteins identified as differentially expressed using iTRAQ analysis (source: https://string-db.org): C-reactive protein (CRP), Fibrinogen alpha chain (FGA), Apolipoprotein C-II (APOC2), Apolipoprotein IV (APOC4), Serum amyloid A1 protein (SAA1), Alpha-1-acid glycoprotein 1 (ORM1), Alpha-1-antitrypsin (SERPINA1), Hemopexin (HPX), Serotransferrin (TF), Protein Z-dependent protease inhibitor (SERPINA10) and Serum albumin (ALB).
See this image and copyright information in PMC

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