Dataset Information

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

ABSTRACT: 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.

INSTRUMENT(S): TripleTOF 5600

ORGANISM(S): Homo Sapiens (human)

TISSUE(S): Vaginal Fluid

DISEASE(S): Endometrial Cancer

SUBMITTER: Kelechi Njoku

LAB HEAD: Prof Emma J Crosbie

PROVIDER: PXD050276 | Pride | 2024-06-23

REPOSITORIES: pride

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Publications

Detection of endometrial cancer in cervico-vaginal fluid and blood plasma: leveraging proteomics and machine learning for biomarker discovery.

Njoku Kelechi K Pierce Andrew A Chiasserini Davide D Geary Bethany B Campbell Amy E AE Kelsall Janet J Reed Rachel R Geifman Nophar N Whetton Anthony D AD Crosbie Emma J EJ

EBioMedicine 20240320

<h4>Background</h4>The anatomical continuity between the uterine cavity and the lower genital tract allows for the exploitation of uterine-derived biomaterial in cervico-vaginal fluid for endometrial cancer detection based on non-invasive sampling methodologies. Plasma is an attractive biofluid for cancer detection due to its simplicity and ease of collection. In this biomarker discovery study, we aimed to identify proteomic signatures that accurately discriminate endometrial cancer from control ...[more]

PMID: 38513301

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