{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T07:45:30Z","timestamp":1777448730410,"version":"3.51.4"},"reference-count":50,"publisher":"MDPI AG","issue":"9","license":[{"start":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T00:00:00Z","timestamp":1695168000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT)","doi-asserted-by":"publisher","award":["NRF-2022R1C1C1008823"],"award-info":[{"award-number":["NRF-2022R1C1C1008823"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"a National Research Foundation of Korea (NRF) grant funded by the Korean government (MSIT)","doi-asserted-by":"publisher","award":["21162MFDS045"],"award-info":[{"award-number":["21162MFDS045"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","award":["NRF-2022R1C1C1008823"],"award-info":[{"award-number":["NRF-2022R1C1C1008823"]}],"id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003569","name":"Ministry of Food and Drug Safety","doi-asserted-by":"publisher","award":["21162MFDS045"],"award-info":[{"award-number":["21162MFDS045"]}],"id":[{"id":"10.13039\/501100003569","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Genes"],"abstract":"<jats:p>Cancer metastasis accounts for approximately 90% of cancer deaths, and elucidating markers in metastasis is the first step in its prevention. To characterize metastasis marker genes (MGs) of breast cancer, XGBoost models that classify metastasis status were trained with gene expression profiles from TCGA. Then, a metastasis score (MS) was assigned to each gene by calculating the inner product between the feature importance and the AUC performance of the models. As a result, 54, 202, and 357 genes with the highest MS were characterized as MGs by empirical p-value cutoffs of 0.001, 0.005, and 0.01, respectively. The three sets of MGs were compared with those from existing metastasis marker databases, which provided significant results in most comparisons (p-value &lt; 0.05). They were also significantly enriched in biological processes associated with breast cancer metastasis. The three MGs, SPPL2C, KRT23, and RGS7, showed highly significant results (p-value &lt; 0.01) in the survival analysis. The MGs that could not be identified by statistical analysis (e.g., GOLM1, ELAVL1, UBP1, and AZGP1), as well as the MGs with the highest MS (e.g., ZNF676, FAM163B, LDOC2, IRF1, and STK40), were verified via the literature. Additionally, we checked how close the MGs were to each other in the protein\u2013protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.<\/jats:p>","DOI":"10.3390\/genes14091820","type":"journal-article","created":{"date-parts":[[2023,9,20]],"date-time":"2023-09-20T21:26:32Z","timestamp":1695245192000},"page":"1820","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":7,"title":["Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4962-8347","authenticated-orcid":false,"given":"Jinmyung","family":"Jung","sequence":"first","affiliation":[{"name":"Division of Data Science, College of Information and Communication Technology, The University of Suwon, Hwaseong 18323, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0925-1853","authenticated-orcid":false,"given":"Sunyong","family":"Yoo","sequence":"additional","affiliation":[{"name":"Department of ICT Convergence System Engineering, Chonnam National University, Gwangju 61005, Republic of Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,9,20]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5574","DOI":"10.1002\/cam4.2474","article-title":"Are 90% of deaths from cancer caused by metastases?","volume":"8","author":"Dillekas","year":"2019","journal-title":"Cancer Med."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.apsb.2015.07.005","article-title":"Cancer metastases: Challenges and opportunities","volume":"5","author":"Guan","year":"2015","journal-title":"Acta Pharm. 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