TY  - JOUR
AU  - Stawiski, Konrad
AU  - Turzanski-Fortner, Renée
AU  - Pestarino, Luca
AU  - Umu, Sinan U
AU  - Kaaks, Rudolf
AU  - Rounge, Trine B
AU  - Elias, Kevin M
AU  - Fendler, Wojciech
AU  - Langseth, Hilde
TI  - Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.
JO  - Frontiers in oncology
VL  - 14
SN  - 2234-943X
CY  - Lausanne
PB  - Frontiers Media
M1  - DKFZ-2024-01449
SP  - 1389066
PY  - 2024
N1  - #EA:C020#
AB  - 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
KW  - early detection (Other)
KW  - machine learning (Other)
KW  - microRNAs (Other)
KW  - ovarian cancer (Other)
KW  - sequencing (Other)
LB  - PUB:(DE-HGF)16
C6  - pmid:38983926
C2  - pmc:PMC11231195
DO  - DOI:10.3389/fonc.2024.1389066
UR  - https://inrepo02.dkfz.de/record/291564
ER  -