Bioinformatics analysis of prognostic value of PITX1 gene in breast cancer. Issue 9 (10th September 2020)

• Record Type: Journal Article
• Authors: Wang, Qiaoyun; Zhao, Shuai; Gan, Lei; Zhuang, Zhixiang
• Is Part Of: Bioscience reports. Volume 40:Issue 9(2020)
• Publication Date: 2020-09-10
• Subjects: bioinformation -- breast cancer -- PITX1 -- prognosis; Molecular biology -- Periodicals; Cytology -- Periodicals; 572.8
• DOI: 10.1042/BSR20202537
• Languages: English
• ISSNs: 0144-8463

Abstract

Abstract: Background: Paired-like homeodomain transcription factor 1 ( PITX1 ) participates in miscellaneous biological processes including cell growth, development, progression and invasion in various malignant tumors. However, the analysis of the association between PITX1 expression and the survival in breast cancer remains unclear. Methods: Clinical prognostic parameters and survival data related to PITX1 in breast cancer patients were performed using the bioinformatic analysis including Oncomine, Bc-GenExMiner v4.3, PrognoScan and UCSC Xena. Results: We found that PITX1 gene expression was significantly higher in different histological classification of breast cancer . The Scarff–Bloom–Richardson (SBR) grade, Nottingham prognostic index (NPI), estrogen receptor (ER) negative, epidermal growth factor receptor-2 (HER2) positive, lymph node positive, triple-negative status and basal-like status were positively correlated with PITX1 level, except for patients' age and the progesterone receptor (PR) status. We have found that the increased PITX1 expression correlated with worse relapse-free survival, disease specific survival and overall survival. PITX1 was positively correlated with metastatic relapse-free survival and distant metastasis-free survival. We also confirmed positive correlation between PITX1 and the nucleotide-binding oligomerization domain 2 ( NOD2 ) . Conclusion: The lower expression of PITX1 was associated with better clinical prognostic parameters and clinical survival in breast cancer according to the bioinformatic analysis.