Genes, Vol. 14, Pages 1820: Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches

Genes, Vol. 14, Pages 1820: Identification of Breast Cancer Metastasis Markers from Gene Expression Profiles Using Machine Learning Approaches Genes doi: 10.3390/genes14091820 Authors: Jinmyung Jung Sunyong Yoo 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 < 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 < 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 clo...
Source: Genes - Category: Genetics & Stem Cells Authors: Tags: Article Source Type: research