%0 Journal Article
%A Stawiski, Konrad
%A Turzanski-Fortner, Renée
%A Pestarino, Luca
%A Umu, Sinan U
%A Kaaks, Rudolf
%A Rounge, Trine B
%A Elias, Kevin M
%A Fendler, Wojciech
%A Langseth, Hilde
%T Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.
%J Frontiers in oncology
%V 14
%@ 2234-943X
%C Lausanne
%I Frontiers Media
%M DKFZ-2024-01449
%P 1389066
%D 2024
%Z #EA:C020#
%X Effective strategies for early detection of epithelial ovarian cancer are lacking. We evaluated whether a panel of 14 previously established circulating microRNAs could discriminate between cases diagnosed <2 years after serum collection and those diagnosed 2-7 years after serum collection. miRNA sequencing data from subsequent ovarian cancer cases were obtained as part of the ongoing multi-cancer JanusRNA project, utilizing pre-diagnostic serum samples from the Janus Serum Bank and linked to the Cancer Registry of Norway for cancer outcomes.We included a total of 80 ovarian cancer cases contributing 80 serum samples and compared 40 serum samples from cases with samples collected <2 years prior to diagnosis with 40 serum samples from cases with sample collection ≥2 to 7 years. We employed the extreme gradient boosting (XGBoost) algorithm to train a binary classification model using 70
%K early detection (Other)
%K machine learning (Other)
%K microRNAs (Other)
%K ovarian cancer (Other)
%K sequencing (Other)
%F PUB:(DE-HGF)16
%9 Journal Article
%$ pmid:38983926
%2 pmc:PMC11231195
%R 10.3389/fonc.2024.1389066
%U https://inrepo02.dkfz.de/record/291564