Understanding the mechanisms of gene regulation during breast cancer is one of the most difficult problems among oncologists because this regulation is likely comprised of complex genetic interactions. Given this complexity, a computational study using the Bayesian network technique has been employed to construct a gene regulatory network from microarray data. Although the Bayesian network has been notified as a prominent method to infer gene regulatory processes, learning the Bayesian network structure is NP hard and computationally intricate. Therefore, we propose a novel inference method based on low-order conditional independence that extends to the case of the Bayesian network to deal with a large number of genes and an insufficient sample size. This method has been evaluated and compared with full-order conditional independence and different prognostic indices on a publicly available breast cancer data set. Our results suggest that the low-order conditional independence method will be able to handle a large number of genes in a small sample size with the least mean square error. In addition, this proposed method performs significantly better than other methods, including the full-order conditional independence and the St. Gallen consensus criteria. The proposed method achieved an area under the ROC curve of 0.79203, whereas the full-order conditional independence and the St. Gallen consensus criteria obtained 0.76438 and 0.73810, respectively. Furthermore, our empirical evaluation using the low-order conditional independence method has demonstrated a promising relationship between six gene regulators and two regulated genes and will be further investigated as potential breast cancer metastasis prognostic markers.