{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,7]],"date-time":"2025-10-07T11:45:01Z","timestamp":1759837501093,"version":"3.44.0"},"reference-count":116,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T00:00:00Z","timestamp":1758153600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Bioinform."],"abstract":"<jats:sec><jats:title>Introduction<\/jats:title><jats:p>Disease heterogeneity is the hallmark of breast cancer, which is the most common female malignancy. With a disturbing increase in mortality and disease burden, there remains a need for effective early-stage theragnostic and prognostic biomarkers. In this work, we improved on BrcaDx (<jats:ext-link>https:\/\/apalania.shinyapps.io\/brcadx\/<\/jats:ext-link>) for cancer vs control screening and examined a cluster of adjoining learning problems in breast cancer heterogeneity: (i) identification of metastatic cancers; (ii) molecular subtyping (TNBC, HER2, or luminal); and (iii) histological subtyping (invasive ductal or invasive lobular).<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>We analyzed the transcriptomic profiles of breast cancer patients from public-domain databases such as the TCGA using stage-encoded problem-specific statistical models of gene expression and unveiled stage-salient and progression-significant genes. Using a consensus approach, we identified potential machine learning features, and considered six model classes for each learning problem, with hyperparameter optimization on a training dataset and evaluation on a holdout test dataset. A nested approach enabled us to identify the best model class for each learning problem.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>External validation of the best models yielded balanced accuracies of 97.42% for cancer vs normal; 88.22% for metastatic v\/s non metastatic; 88.79% for ternary molecular subtyping; and ensemble accuracy of 94.23% for histological subtyping. The model for molecular subtyping was validated on a 26-sample TNBC-only out-of-distribution cohort, yielding 25 correct predictions. We performed a late integration of multi-omics datasets by validating the feature space used in each problem with miRNA profiles, methylation profiles, and commercial breast cancer panels.<\/jats:p><\/jats:sec><jats:sec><jats:title>Discussion<\/jats:title><jats:p>Pending prospective studies, we have translated the models into BC-Predict that forks the best models developed for each problem in a unified interface and provides a complete readout for input instances of expression data, including uncertainty estimates. BC-Predict is freely available for non-commercial purposes at: <jats:ext-link>https:\/\/apalania.shinyapps.io\/BC-Predict<\/jats:ext-link>.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fbinf.2025.1644695","type":"journal-article","created":{"date-parts":[[2025,9,18]],"date-time":"2025-09-18T05:30:21Z","timestamp":1758173421000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["BC-predict: mining of signal biomarkers and production of models for early-stage breast cancer subtyping and prognosis"],"prefix":"10.3389","volume":"5","author":[{"given":"Sangeetha","family":"Muthamilselvan","sequence":"first","affiliation":[]},{"given":"Natarajan","family":"Vaithilingam","sequence":"additional","affiliation":[]},{"given":"Ashok","family":"Palaniappan","sequence":"additional","affiliation":[]}],"member":"1965","published-online":{"date-parts":[[2025,9,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"e05005","DOI":"10.7554\/elife.05005","article-title":"Predicting effective microRNA target sites in mammalian mRNAs","volume":"4","author":"Agarwal","year":"2015","journal-title":"eLife"},{"key":"B2","doi-asserted-by":"publisher","first-page":"16042-","DOI":"10.1038\/sigtrans.2016.42","article-title":"The paradoxical functions of EGFR during breast cancer progression","volume":"2","author":"Ali","year":"2017","journal-title":"Signal Transduct. 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