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000291564 1001_ $$aStawiski, Konrad$$b0
000291564 245__ $$aValidation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches.
000291564 260__ $$aLausanne$$bFrontiers Media$$c2024
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000291564 520__ $$aEffective strategies for early detection of epithelial ovarian cancer are lacking. We evaluated whether a panel of 14 previously established circulating microRNAs could discriminate between cases diagnosed <2 years after serum collection and those diagnosed 2-7 years after serum collection. miRNA sequencing data from subsequent ovarian cancer cases were obtained as part of the ongoing multi-cancer JanusRNA project, utilizing pre-diagnostic serum samples from the Janus Serum Bank and linked to the Cancer Registry of Norway for cancer outcomes.We included a total of 80 ovarian cancer cases contributing 80 serum samples and compared 40 serum samples from cases with samples collected <2 years prior to diagnosis with 40 serum samples from cases with sample collection ≥2 to 7 years. We employed the extreme gradient boosting (XGBoost) algorithm to train a binary classification model using 70% of the available data, while the model was tested on the remaining 30% of the dataset.The performance of the model was evaluated using repeated holdout validation. The previously established set of miRNAs achieved a median area under the receiver operating characteristic curve (AUC) of 0.771 in the test sets. Four out of 14 miRNAs (hsa-miR-200a-3p, hsa-miR-1246, hsa-miR-203a-3p, hsa-miR-23b-3p) exhibited higher expression levels closer to diagnosis, consistent with the previously reported upregulation in cancer cases, with statistical significance observed only for hsa-miR-200a-3p (beta=0.14; p=0.04).The discrimination potential of the selected models provides evidence of the robustness of the miRNA signature for ovarian cancer.
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000291564 650_7 $$2Other$$aearly detection
000291564 650_7 $$2Other$$amachine learning
000291564 650_7 $$2Other$$amicroRNAs
000291564 650_7 $$2Other$$aovarian cancer
000291564 650_7 $$2Other$$asequencing
000291564 7001_ $$0P:(DE-He78)74a6af8347ec5cbd4b77e562e10ca1f2$$aTurzanski-Fortner, Renée$$b1$$eFirst author$$udkfz
000291564 7001_ $$aPestarino, Luca$$b2
000291564 7001_ $$aUmu, Sinan U$$b3
000291564 7001_ $$0P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a$$aKaaks, Rudolf$$b4$$udkfz
000291564 7001_ $$aRounge, Trine B$$b5
000291564 7001_ $$aElias, Kevin M$$b6
000291564 7001_ $$aFendler, Wojciech$$b7
000291564 7001_ $$aLangseth, Hilde$$b8
000291564 773__ $$0PERI:(DE-600)2649216-7$$a10.3389/fonc.2024.1389066$$gVol. 14, p. 1389066$$p1389066$$tFrontiers in oncology$$v14$$x2234-943X$$y2024
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