GATA3 somatic mutations are associated with clinicopathological features and expression profile in TCGA breast cancer patients

Fahimeh Afzaljavan, Ayeh Sadat Sadr, Sevtap Savas, Alireza Pasdar*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

The effect of somatic mutations and the gene expression profiles on the prognosis is well documented in cancer research. This study was conducted to evaluate the association of GATA3 somatic mutations with tumor features, survival, and expression profiles in breast cancer. Clinicopathological information was compared between TCGA-BRCA patients with GATA3-mutant and non-mutant tumors in all patients as well as in ER-positive subgroup. Cox-regression method was used to evaluate the association of the GATA3 mutation status with overall survival time. Differential gene expression, functional annotation, and protein–protein interaction analyses were performed using edgeR, Metascape, DAVID, STRING and CytoNCA. GATA3-mutant and non-mutant samples had significantly different clinicopathological features (p < 0.05). While GATA3 mutation status was not associated with the overall survival in the entire cohort (padj = 0.52), the GATA3-wild type ER-positive cases had a better prognosis than mutant ones (padj = 0.04). GATA3 expression was higher in tumors than normal tissues. Several pathways were different between mutant and non-mutant groups (p < 0.05). Interleukin-6 was found as the highest scored gene in both comparisons (normal vs. mutant and normal vs. non-mutant groups) in the entire patient and in the ER-positive subgroup, suggesting the association of IL6 with breast tumorigenesis. These findings suggest that GATA3 mutations can be associated with several tumor characteristics and influence the pattern of gene expression. However, GATA3 mutation status seems to be a prognostic factor for the disease only in ER-positive patients.

Original languageEnglish
Article number1679
Number of pages13
JournalScientific Reports
Volume11
Issue number1
DOIs
Publication statusPublished - 18 Jan 2021

Keywords

  • Biomarkers
  • Cancer
  • Computational biology and bioinformatics
  • Genetics
  • Molecular biology
  • Oncology
  • Risk factors

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