Jung, J.; Yoo, S. Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches. Genes2023, 14, 1820.
Jung, J.; Yoo, S. Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches. Genes 2023, 14, 1820.
Jung, J.; Yoo, S. Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches. Genes2023, 14, 1820.
Jung, J.; Yoo, S. Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches. Genes 2023, 14, 1820.
Abstract
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 of breast cancer (MGs), 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 AUC performance of the models. As a result, the 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. We noticed that the set of MGs with the median EP cutoff showed better performance than the other two sets, suggesting the importance of the cutoff used in determining MGs. They were also significantly enriched in biological processes associated to breast cancer metastasis. 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 are located to each other in the protein–protein interaction networks. We expect that the characterized markers will help understand and prevent breast cancer metastasis.
Biology and Life Sciences, Biochemistry and Molecular Biology
Copyright:
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