Integrated Transcriptomic Profiling of Metabolic Alterations in Breast and Ovarian Cancer

Extensive metabolic reprogramming that supports increased energy demand, biosynthesis, and tumor cell survival is closely linked to the progression of cancer. Key dysregulated metabolic pathways and genes across breast and ovarian cancers were identified in this study using a metabolism-centric integrative bioinformatics approach. First, a 434-node protein-protein interaction (PPI) network was created using dysregulated metabolic pathways found in cancer. The top 10 hub genes were selected based on degree centrality after network topological analysis was carried out using CytoHubba, indicating their possible regulatory significance within metabolic networks. The biological significance of the chosen hub genes was confirmed by functional enrichment analysis using g: Profiler, which showed a significant enrichment of pathways linked to energy metabolism, oxidative phosphorylation, and mitochondrial function. Expression profiling of the shortlisted genes was carried out across six separate GEO datasets that included tumor and normal samples, including two ovarian cancer datasets and three breast cancer datasets, in order to further validate these findings. Hub genes were uniformly dysregulated in all types of cancers, and the tumor samples exhibited varying expression patterns to the normal tissues. The significant dysregulation of the hub genes in tumor conditions was verified by the differential expression analysis and the high correlation among them in normal samples and dysregulated pattern in tumor samples by the co-expression analysis. Principal component analysis (PCA) also showed strong separation between tumor and normal samples as a result of the expression of these genes and the combined effect of these genes in cancer-related metabolic alterations in patient samples of ovarian and breast cancer. The whole scenario is that this integrative analysis has significant metabolic hub genes that are systematically dysregulated in both the ovarian and breast cancers, which provides mechanistic understanding of cancer metabolism and a viable platform upon which to conduct further functional and therapeutic studies.