Cervical Fluids Are a Source of Protein Biomarkers for Early, Non-Invasive Endometrial Cancer Diagnosis

Cancers (Basel). 2023 Jan 31;15(3):911. doi: 10.3390/cancers15030911.

Abstract

Background: Abnormal uterine bleeding is the main symptom of endometrial cancer (EC), but it is highly nonspecific. This represents a huge burden for women's health since all women presenting with bleeding will undergo sequential invasive tests, which are avoidable for 90-95% of those women who do not have EC.

Methods: This study aimed to evaluate the potential of cervical samples collected with five different devices as a source of protein biomarkers to diagnose EC. We evaluated the protein quantity and the proteome composition of five cervical sampling methods.

Results: Samples collected with a Rovers Cervex Brush® and the HC2 DNA collection device, Digene, were the most suitable samples for EC proteomic studies. Most proteins found in uterine fluids were also detected in both cervical samples. We then conducted a clinical retrospective study to assess the expression of 52 EC-related proteins in 41 patients (22 EC; 19 non-EC), using targeted proteomics. We identified SERPINH1, VIM, TAGLN, PPIA, CSE1L, and CTNNB1 as potential protein biomarkers to discriminate between EC and symptomatic non-EC women with abnormal uterine bleeding in cervical fluids (AUC > 0.8).

Conclusions: This study opens an avenue for developing non-invasive protein-based EC diagnostic tests, which will improve the standard of care for gynecological patients.

Keywords: Carcinoma of the endometrium; biomarker; cervical sample; diagnosis; endometrial cancer; endometrial sampling; gynecology; non-invasive; protein; proteomics; uterine cancer.

Grants and funding

This research was funded by grants from the Instituto de Salud Carlos III (ISCIII) grant number PI17/02155, PI20/00644 to E.C. and S.C., and the IFI19/00029 to E.C.-d.l.-R.; from Fundación Científica Asociación Española Contra el Cáncer (AECC) grant number GCTRA1804MATI; and the INVES20051COLA to E.C.; the CIBERONC network grant number CB16/12/00328; and the ERA PerMed ERA-NET grant (PERME212443COLA funded by AECC and AEC21_2/00030 funded by ISCIII); and 2021 SGR 1157 by AGAUR. The present work has been also supported by a Televie grant 5/20/5—TLV/DD to G.D.