www.oncotarget.com Oncotarget, 2019, Vol. 10, (No. 39), pp: 3894-3909 Research Paper Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining Dinesh Kumar Barupal1,*, Bei Gao1,*, Jan Budczies2, Brett S. Phinney4, Bertrand Perroud4, Carsten Denkert2,3 and Oliver Fiehn1 1West Coast Metabolomics Center, University of California, Davis, CA, USA 2Institute of Pathology, Charité University Hospital, Berlin, Germany 3German Institute of Pathology, Philipps-University Marburg, Marburg, Germany 4UC Davis Genome Center, University of California, Davis, CA, USA *Co-first authors and contributed equally to this work Correspondence to: Oliver Fiehn, email:
[email protected] Keywords: set-enrichment; ChemRICH; multi-omics; metabolic networks; candidate gene prioritization Received: March 12, 2019 Accepted: May 13, 2019 Published: June 11, 2019 Copyright: Barupal et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License 3.0 (CC BY 3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. ABSTRACT Estrogen-receptor negative (ERneg) breast cancer is an aggressive breast cancer subtype in the need for new therapeutic options. We have analyzed metabolomics, proteomics and transcriptomics data for a cohort of 276 breast tumors (MetaCancer study) and nine public transcriptomics datasets using univariate statistics, meta- analysis, Reactome pathway analysis, biochemical network mapping and text mining of metabolic genes. In the MetaCancer cohort, a total of 29% metabolites, 21% proteins and 33% transcripts were significantly different (raw p <0.05) between ERneg and ERpos breast tumors.