Systematic identification of drug-based prognostic biomarkersby integrating multiomics  in breast cancer

Background Drug repositioning plays an important role in the current drug research and development field, as the high cost and long period required to bring drugs into commerce are driving efforts to repurpose drugs approved by the U.S. Food and Drug Administration for new uses. Methods Here, we used breast cancer as a disease model and systematically proposed a new strategy based on multidimensional omics data (e.g., mRNA expression, DNA methylation, DNA copy number, and mutational profiles) to reconstruct the human functional linkage network. In addition, it was used to systematically interpret the functional relationship between human genes and to establish a measure of the relationship between drugs and diseases to reset drugs and achieve drug-based prognostic biomarkers. Results We found that differentially expressed genes driven by DNA methylation, DNA copy number, and somatic mutation were significantly enriched in cancer-related biological pathways and functions. Survival analysis showed that multiomics-driven target genes were repositioned, which significantly distinguished between high-risk and low-risk groups in breast cancer patients, such as the methylation-driven gene glycoprotein M6B (P = 0.00141), copy number-driven gene acyl-CoA synthetase long chain family member 1 (P = 0.0582), mutation-driven gene minichromosome maintenance 10 replication initiation factor(P = 0.0303), and multiomics synergy driven gene catechol-O-methyltransferase domain containing 1 (P = 0.0071). Conclusions Our approach provides a basis for identifying novel candidate drug and drug-based prognostic biomarkers.