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@ARTICLE{Stawiski:291564,
      author       = {K. Stawiski and R. Turzanski-Fortner$^*$ and L. Pestarino
                      and S. U. Umu and R. Kaaks$^*$ and T. B. Rounge and K. M.
                      Elias and W. Fendler and H. Langseth},
      title        = {{V}alidation of mi{RNA} signatures for ovarian cancer
                      earlier detection in the pre-diagnosis setting using machine
                      learning approaches.},
      journal      = {Frontiers in oncology},
      volume       = {14},
      issn         = {2234-943X},
      address      = {Lausanne},
      publisher    = {Frontiers Media},
      reportid     = {DKFZ-2024-01449},
      pages        = {1389066},
      year         = {2024},
      note         = {#EA:C020#},
      abstract     = {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.},
      keywords     = {early detection (Other) / machine learning (Other) /
                      microRNAs (Other) / ovarian cancer (Other) / sequencing
                      (Other)},
      cin          = {C020},
      ddc          = {610},
      cid          = {I:(DE-He78)C020-20160331},
      pnm          = {313 - Krebsrisikofaktoren und Prävention (POF4-313)},
      pid          = {G:(DE-HGF)POF4-313},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:38983926},
      pmc          = {pmc:PMC11231195},
      doi          = {10.3389/fonc.2024.1389066},
      url          = {https://inrepo02.dkfz.de/record/291564},
}