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Comprehensive Analysis of CCAAT/Enhancer Binding Protein Family in Ovarian Cancer.

Authors :
Tan, Jiahong
Wang, Daoqi
Dong, Wei
Nian, Lei
Zhang, Fen
Zhao, Han
Zhang, Jie
Feng, Yun
Source :
Cancer Informatics; 9/4/2024, p1-13, 13p
Publication Year :
2024

Abstract

Background: Ovarian cancer has brought serious threats to female health. CCAAT/enhancer binding proteins (C/EBPs) are key transcription factors involved in ovarian cancer. Therefore, comprehensive profiling C/EBPs in ovarian cancer is needed. Methods: A comprehensive analysis concerning C/EBPs in ovarian cancer was performed. Firstly, detailed expression of C/EBP family members was integrally retrieved and then confirmed using immunohistochemistry. The regulatory effects and transcription regulatory functions of C/EBPs were studied by using regulatory network analysis and enrichment analysis. Using survival analysis, receiver operating characteristic curve analysis, and target-disease association analysis, the predictive prognostic value of C/EBPs on survival and drug responsiveness was systematically evaluated. The effects of C/EBPs on tumor immune infiltration were also assessed. Results: Ovarian cancer tissues expressed increased CEBPA, CEBPB, and CEBPG but decreased CEBPD when compared with normal control tissues. The overall alteration frequency of C/EBPs in ovarian cancer was approaching 30%. C/EBP family members formed a reciprocal regulatory network involving carcinogenesis and had pivotal transcription regulatory functions. C/EBPs could affect survival of ovarian cancer and correlated with poor survival outcomes (OS: HR = 1.40, P =.0053 and PFS: HR = 1.41, P =.0036). Besides, expression of CEBPA, CEBPB, CEBPD, and CEBPE could predict platinum and taxane responsiveness of ovarian cancer. C/EBPs also affected immune infiltration of ovarian cancer. Conclusions: C/EBPs were closely involved in ovarian cancer and exerted multiple biological functions. C/EBPs could be exploited as prognostic and predictive biomarkers in ovarian cancer. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
11769351
Database :
Complementary Index
Journal :
Cancer Informatics
Publication Type :
Academic Journal
Accession number :
179485337
Full Text :
https://doi.org/10.1177/11769351241275877