Breast cancer has become a leading cause of death for women as the economy has grown and the number of women in the labor force has increased. Several biomarkers with diagnostic, prognostic, and therapeutic implications for breast cancer have been identified in studies, leading to therapeutic advances. Resistance, on the other hand, is one of clinical practice's limitations. In this paper, we use Nonnegative Matrix Factorization to automatically extract two gene signatures from gene expression profiles of wild-type and resistance MCF-7 cells, which were then investigated further using pathways analysis and proved useful in relating resistance pathways to breast cancer regardless of the stimulus that caused it. A few extracted genes (including MAOA, IL4I1, RRM2, DUT, NME4, and SUMO3) represent new elements in the functional network for resistance in MCF-7 ER+ breast cancer. As a result of this research, a better understanding of how resistance occurs or the pathways that contribute to it may allow more effective therapies to be developed.
Keywords: Biomarkers; Breast cancer; Mechanisms of resistance.
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