Home > Publications database > Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches. > print |
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024 | 7 | _ | |a 10.3389/fonc.2024.1389066 |2 doi |
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100 | 1 | _ | |a Stawiski, Konrad |b 0 |
245 | _ | _ | |a Validation of miRNA signatures for ovarian cancer earlier detection in the pre-diagnosis setting using machine learning approaches. |
260 | _ | _ | |a Lausanne |c 2024 |b Frontiers Media |
336 | 7 | _ | |a article |2 DRIVER |
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520 | _ | _ | |a 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% 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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650 | _ | 7 | |a early detection |2 Other |
650 | _ | 7 | |a machine learning |2 Other |
650 | _ | 7 | |a microRNAs |2 Other |
650 | _ | 7 | |a ovarian cancer |2 Other |
650 | _ | 7 | |a sequencing |2 Other |
700 | 1 | _ | |a Turzanski-Fortner, Renée |0 P:(DE-He78)74a6af8347ec5cbd4b77e562e10ca1f2 |b 1 |e First author |u dkfz |
700 | 1 | _ | |a Pestarino, Luca |b 2 |
700 | 1 | _ | |a Umu, Sinan U |b 3 |
700 | 1 | _ | |a Kaaks, Rudolf |0 P:(DE-He78)4b2dc91c9d1ac33a1c0e0777d0c1697a |b 4 |u dkfz |
700 | 1 | _ | |a Rounge, Trine B |b 5 |
700 | 1 | _ | |a Elias, Kevin M |b 6 |
700 | 1 | _ | |a Fendler, Wojciech |b 7 |
700 | 1 | _ | |a Langseth, Hilde |b 8 |
773 | _ | _ | |a 10.3389/fonc.2024.1389066 |g Vol. 14, p. 1389066 |0 PERI:(DE-600)2649216-7 |p 1389066 |t Frontiers in oncology |v 14 |y 2024 |x 2234-943X |
856 | 4 | _ | |u https://inrepo02.dkfz.de/record/291564/files/fonc-14-1389066.pdf |
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