Antibody Arrays Identified Cycle-Dependent Plasma Biomarker Candidates of Peritoneal Endometriosis

Maja Pušić, Teja Klančič, Tamara Knific, Andrej Vogler, Ronny Schmidt, Christoph Schröder, Tea Lanišnik Rižner
Journal of personalized medicine · 2022 · vol. 12(6) , pp. 852 · doi:10.3390/jpm12060852 · PMID:35743637 · W4281492738
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Abstract

Endometriosis is an estrogen-dependent inflammatory disease affecting women in their reproductive age. Due to non-specific symptoms, women with endometriosis are often misdiagnosed or are accurately diagnosed only after several years. Diagnosis of peritoneal endometriosis is especially challenging and relies only on laparoscopic surgery. To date, different molecules have been proposed as potential non-invasive biomarkers of endometriosis; however, none have been confirmed as clinically useful. Therefore, this study aimed to discover novel plasma biomarker candidates for peritoneal endometriosis using an antibody array platform. This study included patients with endometriosis-like symptoms characterized by the absence (controls) or presence of peritoneal endometriosis (cases) after laparoscopic surgery and histological evaluation. Patients were further divided into secretory and proliferative groups, according to the phase of their menstrual cycle. Their plasma samples were collected and analyzed on an antibody array platform targeting more than 1350 proteins with over 1820 antibodies. In the proliferative group, the analysis revealed three differential proteins between cases and controls: ITB3, ITA2B2, and ACVL-1. In the secretory group, none of the examined proteins reached the log-fold change (logFC) and significance thresholds simultaneously. The potential of the identified differential proteins as plasma biomarker candidates for peritoneal endometriosis should be evaluated on a larger cohort, and their role in endometriosis should be investigated in further studies.

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endometriosis

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[{'doi': '10.13039/501100004329', 'name': 'Slovenian Research Agency', 'awards': ['J3-1755', '101008193']}, {'doi': None, 'name': 'EU H2020-MSCA-RISE project TRENDO', 'awards': ['J3-1755', '101008193']}]

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