One of the most aggressive forms of breast cancer is inflammatory breast cancer (IBC), whose lack of tumour mass also makes a prompt diagnosis difficult. Moreover, genomic differences between common breast cancers and IBC have not been completely assessed, thus substantially limiting the identification of biomarkers unique to IBC. Here, we developed a novel statistical analysis of gene expression profiles corresponding to microdissected IBC, non-IBC (nIBC) and normal samples that enabled us to identify a set of genes significantly associated with a specific disease state. Second, by using advanced methods based on controllability network theory, we identified a set of critical control proteins that uniquely and structurally control the entire proteome. By mapping high change variance genes in protein interaction networks, we found that a large statistically significant fraction of genes whose variance changed significantly between normal and IBC and nIBC disease states were among the set of critical control proteins. Moreover, this analysis identified the overlapping genes with the highest statistical significance; these genes may assist in developing future biomarkers and determining drug targets to disrupt the molecular pathways driving carcinogenesis in IBC.