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Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
10:36

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Published on: March 17, 2016

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Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using

Dinesh Kumar Barupal1,2, Bei Gao1,2, Jan Budczies3

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

Oncotarget
|June 25, 2019
PubMed

Insights

Estrogen-receptor negative breast cancer exhibits distinct metabolic profiles. Multi-omics analysis identified activated metabolic pathways and potential new drug targets for this aggressive subtype.

Area of Science:

  • Oncology
  • Metabolomics
  • Proteomics
  • Transcriptomics
  • Bioinformatics

Background:

  • Estrogen-receptor negative (ERneg) breast cancer is an aggressive subtype requiring novel therapeutic strategies.
  • Understanding the molecular differences between ERneg and ER-positive (ERpos) breast tumors is crucial for developing targeted treatments.

Purpose of the Study:

  • To comprehensively analyze the multi-omics (metabolomics, proteomics, transcriptomics) landscape of ERneg breast cancer.
  • To identify distinct metabolic pathways and potential novel drug targets associated with ERneg tumors.

Main Methods:

  • Integrated analysis of metabolomics, proteomics, and transcriptomics data from 276 breast tumors (MetaCancer study) and nine public datasets.
  • Univariate statistics, meta-analysis, Reactome pathway analysis, biochemical network mapping, and text mining of metabolic genes were employed.
  • Comparison of molecular profiles between ERneg and ERpos breast tumors.

Main Results:

  • Significant differences in metabolites, proteins, and transcripts were observed between ERneg and ERpos tumors.
  • Energy metabolism, nucleotide, amino acid, and lipid biosynthesis pathways were associated with ERneg status.
  • Text mining identified under-reported metabolic genes (e.g., PFKP, GART, ASS1) as potential drug targets.

Conclusions:

  • Multi-omics integration revealed activated TCA anaplerosis, proline biosynthesis, complex lipid synthesis, and substrate recycling mechanisms in ERneg tumors.
  • Several novel metabolic genes were identified as potential candidates for future cancer therapies.
  • The presented workflow is adaptable for analyzing other tumor types.
Keywords:
ChemRICHcandidate gene prioritizationmetabolic networksmulti-omicsset-enrichment

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