Research Papers:

Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining

Dinesh Kumar Barupal, Bei Gao, Jan Budczies, Brett S. Phinney, Bertrand Perroud, Carsten Denkert and Oliver Fiehn _

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Oncotarget. 2019; 10:3894-3909. https://doi.org/10.18632/oncotarget.26995

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Dinesh Kumar Barupal1,*, Bei Gao1,*, Jan Budczies2, Brett S. Phinney4, Bertrand Perroud4, Carsten Denkert2,3 and Oliver Fiehn1

1 West Coast Metabolomics Center, University of California, Davis, CA, USA

2 Institute of Pathology, Charité University Hospital, Berlin, Germany

3 German Institute of Pathology, Philipps-University Marburg, Marburg, Germany

4 UC Davis Genome Center, University of California, Davis, CA, USA

* Co-first authors and contributed equally to this work

Correspondence to:

Oliver Fiehn,email: ofiehn@ucdavis.edu

Keywords: set-enrichment; ChemRICH; multi-omics; metabolic networks; candidate gene prioritization

Received: March 12, 2019     Accepted: May 13, 2019     Published: June 11, 2019


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. In the nine public transcriptomics datasets, on average 23% of all genes were significantly different (raw p <0.05). Specifically, up to 60% of the metabolic genes were significantly different (meta-analysis raw p <0.05) across the transcriptomics datasets. Reactome pathway analysis of all omics showed that energy metabolism, and biosynthesis of nucleotides, amino acids, and lipids were associated with ERneg status. Text mining revealed that several significant metabolic genes and enzymes have been rarely reported to date, including PFKP, GART, PLOD1, ASS1, NUDT12, FAR1, PDE7A, FAHD1, ITPK1, SORD, HACD3, CDS2 and PDSS1. Metabolic processes associated with ERneg tumors were identified by multi-omics integration analysis of metabolomics, proteomics and transcriptomics data. Overall results suggested that TCA anaplerosis, proline biosynthesis, synthesis of complex lipids and mechanisms for recycling substrates were activated in ERneg tumors. Under-reported genes were revealed by text mining which may serve as novel candidates for drug targets in cancer therapies. The workflow presented here can also be used for other tumor types.

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