4.7 Article

FOXA1 is Prognostic of Triple Negative Breast Cancers by Transcriptionally Suppressing SOD2 and IL6

期刊

INTERNATIONAL JOURNAL OF BIOLOGICAL SCIENCES
卷 15, 期 5, 页码 1030-1041

出版社

IVYSPRING INT PUBL
DOI: 10.7150/ijbs.31009

关键词

FOXA1; SOD2; IL6; breast cancer; molecular subtyping; triple negative subtype

资金

  1. National Natural Science Foundation of China [31471251, 81672328]
  2. Natural Science Foundation of Jiangsu Province [BK20161130, BK20150004]
  3. Six Talent Peaks Project in Jiangsu Province [SWYY-128]
  4. National Science and Technology Major project [2018ZX103022 05-004-002]
  5. Research Funds for the Medical School of Jiangnan University ESI special cultivation project [1286010241170320]
  6. Medical Key Professionals Program of Jiangsu Province [AF052141]
  7. Medical Innovation Team Program of Wuxi [CXTP003]
  8. Hospital Management Centre of Wuxi [YGZXZ1401]
  9. Postgraduate Education Reform Project of Jiangsu Province

向作者/读者索取更多资源

Having markers feasible for breast cancer subtyping, especially for triple negative breast cancer identification is crucial for improving the treatment outcome of such cancers. Here we explore the role of FOXA1 in characterizing triple negative breast cancers and the driving mechanisms. Through in vitro examination of the expression pattern at both transcriptional and translational levels, patient relapse-free survival analysis, immunohistochemistry staining and prediction power assessment using clinical samples, as well as functional studies, we systematically compared the role of FOXA1 in identifying triple negative and luminal type of breast cancers and explored the mechanisms driving such functionalities. We report that FOXA1 under-expression can lead to increased malignancy and cancer stemness, and is a subtyping marker identifying triple negative breast cancers rather than the luminal subtype by transcriptionally suppressing the expression of SOD2 and IL6. We are the first to systematically address the significance of FOXA1 in triple negative breast cancer identification as a biomarker and elucidate the mechanism at the molecular level, through a sequential bioinformatics analysis and experimental validations both in vitro and in clinics. Our discoveries compliment the current biomarker modalities once verified using larger clinical cohorts and improve the precision on characterizing breast cancer heterogeneity.

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