Proteomic datasets for the identification of endometrial cancer in minimally invasive samples (cervico-vaginal fluid and blood plasma).

The aim of the underlying study was to identify protein signatures for the detection of endometrial cancer in minimally invasive samples such as cervico-vaginal fluid and blood plasma. Plasma and Delphi Screener-collected cervico-vaginal fluid samples were acquired from post-menopausal women who were symptomatic with (n=53)and without(n=65)endometrial cancer. Digitised proteomic maps were developed for each sample by sequential window acquisition of all theoretical mass spectra (SWATH-MS). Machine learning was employed to identify the most discriminatory proteins and a set of high-perfoming biomarker signatures obtained.