## 4 Discussion  
In this study, we applied the hub gene set from RNA-seq and machine learning algorithms for the first time to construct predictive models for breast cancer metastasis with high accuracy, contrasting with most of the previous studies that have focused on nuclear imaging data. Although the results of analyzing imaging factors, such as computerized tomography and magnetic resonance imaging were applied to machine learning to estimate the accuracy of the predictive model, it is still difficult to apply in the clinical setting. Genetic data obtained by next generation sequencing can describe the patient clinical condition more accurately than data obtained solely by image analysis. Therefore, we expected that in the construction of this prediction model, higher accuracy than the existing image analysis-based prediction model could be estimated. RNA-sequencing techniques and machine learning algorithms was used to build a model to predict breast cancer metastasis, which is promoted by genetic changes within cancer cells. Such a model can be used as the basis for diagnosis and treatment of breast cancer by analyzing changes in genetic events. In the past, only a limited number of genes could be analyzed to profile cancer cells, but advances in technology have enhanced the analytical methods, allowing the assessment of the genetic landscape of cells at once. RNA-seq is believed to hold the power to explain the behavior of cancer by identifying key genes. However, the behavior of cancer cells is difficult to explain based solely on the gene expression of one gene. To overcome this limitation, herein we used the concept of a functional group gene and a hub gene. The expression of a hub gene can accurately determine the function of a cell rather than using a single gene since it is closely related to the function of the cell. Several genes were found to be involved when the cancer cell function was addressed. Hence, genes involved in the same function were defined as the functional group genes, among which genes with the most essential functions were selected and defined as the hub genes. WGCNA, a bioinformatics analysis method, was used to identify the functional group gene and the hub gene [35]. This approach allows to evaluate the expression pattern of a gene and construct a module of genes with similar expression patterns, which is generally used for gene analysis of cancer cells [36]. This method has the advantage of being able to search for a function related to a highly expressed gene among the functions of a cell, as it analyzes only the expression pattern of the gene. Gene modules were defined as functional groups, and hub genes were retrieved from within. The hub gene was selected as the gene with the most connections after confirming the association between the genes in the analyzed module using CHAT app in Cytoscape [37]. Therefore, the identified functional group and three hub genes could represent a more detailed and reliable indicator of lymph node metastasis in breast cancer. A detailed analysis of the functional group genes revealed that they were mainly involved in cell division and proliferation (Figure 6). Epithelial mesenchymal transition (EMT) is the most frequently expressed function in tumor metastases, and cell division and