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Multi-omics inference of differential breast cancer-related transcriptional regulatory network gene hubs between young Black and White patients

      Highlights

      • Among Breast cancer, Black young women have a higher incidence than White women.
      • Our goal is to identify omics differences between White and Black young patients.
      • We found master regulators differentiated between White and Black young patients.

      Abstract

      Objective

      Breast cancers (BrCA) are a leading cause of illness and mortality worldwide. Black women have a higher incidence rate relative to white women prior to age 40 years, and a lower incidence rate after 50 years. The objective of this study is to identify -omics differences between the two breast cancer cohorts to better understand the disparities observed in patient outcomes.

      Materials and Methods

      Using Standard SQL, we queried ISB-CGC hosted Google BigQuery tables storing TCGA BrCA gene expression, methylation, and somatic mutation data and analyzed the combined multi-omics results using a variety of methods.

      Results

      Among Stage II patients 50 years or younger, genes PIK3CA and CDH1 are more frequently mutated in White (W50) than in Black or African American patients (BAA50), while HUWE1, HYDIN, and FBXW7 mutations are more frequent in BAA50. Over-representation analysis (ORA) and Gene Set Enrichment Analysis (GSEA) results indicate that, among others, the Reactome Signaling by ROBO Receptors gene set is enriched in BAA50. Using the Virtual Inference of Protein-activity by Enriched Regulon analysis (VIPER) algorithm, putative top 20 master regulators identified include NUPR1, NFKBIL1, ZBTB17, TEAD1, EP300, TRAF6, CACTIN, and MID2. CACTIN and MID2 are of prognostic value. We identified driver genes, such as OTUB1, with suppressed expression whose DNA methylation status were inversely correlated with gene expression. Networks capturing microRNA and gene expression correlations identified notable microRNA hubs, such as miR-93 and miR-92a-2, expressed at higher levels in BAA50 than in W50.

      Discussion/Conclusion

      The results point to several driver genes as being involved in the observed differences between the cohorts. The findings here form the basis for further mechanistic exploration.

      Graphical abstract

      Keywords

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