Tumor size, as indicated by the T-category, is known as a strong prognostic indicator for breast cancer. It is\ncommon practice to distinguish the T1 and T2 groups at a tumor size of 2.0 cm. We investigated the 2.0-cm rule\nfrom a new point of view. Here, we try to find the optimal threshold based on the differences between the gene\nexpression profiles of the T1 and T2 groups (as defined by the threshold). We developed a numerical algorithm to\nmeasure the overall differential gene expression between patients with smaller tumors and those with larger\ntumors among multiple expression datasets from different studies. We confirmed the performance of the proposed\nalgorithm by a simulation study and then applied it to three different studies conducted at two Norwegian\nhospitals. We found that the maximum difference in gene expression is obtained at a threshold of 2.2ââ?¬â??2.4 cm, and\nwe confirmed that the optimum threshold was over 2.0 cm, as indicated by a validation study using five publicly\navailable expression datasets. Furthermore, we observed a significant differentiation between the two threshold\ngroups in terms of time to local recurrence for the Norwegian datasets. In addition, we performed an associated\nnetwork and canonical pathway analyses for the genes differentially expressed between tumors below and above\nthe given thresholds, 2.0 and 2.4 cm, using the Norwegian datasets. The associated network function illustrated a\ncellular assembly of the genes for the 2.0-cm threshold: an energy production for the 2.4-cm threshold and an\nenrichment in lipid metabolism based on the genes in the intersection for the 2.0- and 2.4-cm thresholds
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