Targeting FOS attenuates malignant phenotypes of breast cancer: Evidence from in silico and in vitro studies

被引:5
作者
Chang, Defeng [1 ]
Li, Lanlan [2 ]
Xu, Zhongqing [1 ]
Chen, Xiaohong [1 ,3 ]
机构
[1] Heilongjiang Prov Hosp, Dept Gen Surg 2, Harbin, Peoples R China
[2] Heilongjiang Prov Hosp, Dept Anesthesiol, Harbin, Peoples R China
[3] Heilongjiang Prov Hosp, Dept Gen Surg 2, 82 Zhongshan Rd, Harbin 150036, Heilongjiang, Peoples R China
关键词
bioinformatics-based analysis; breast cancer; difference analysis; functional prediction; in vitro experiments; malignant phenotypes; protein-protein interaction network; GENE-EXPRESSION; C-FOS; POOR-PROGNOSIS; PHOSPHORYLATION; IDENTIFICATION; PROGRESSION; INTEGRATION; EPITHELIUM; SIGNATURES; PATHWAY;
D O I
10.1002/jbt.23358
中图分类号
Q5 [生物化学]; Q7 [分子生物学];
学科分类号
071010 ; 081704 ;
摘要
Data retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases can reveal important information behind molecular biomarkers and their associated oncogenesis. Therefore, this study was based on in silico predictions and in vitro experiments to explore regulatory network associated with breast carcinogenesis. The breast cancer (BC)-related data sets were retrieved from GEO database, followed by differential analysis and protein-protein interaction (PPI) analysis. Then, Fos proto-oncogene, AP-1 transcription factor subunit (FOS)-associated gene network was constructed, and the key gene-related genes in BC were screened by LinkedOmics. Finally, FOS expression was determined in BC tissues and cells, and gain-of-function assays were performed to define the role of FOS in BC cells. It was noted that seven differentially expressed genes (EGR1, RASSF9, FOSB, CDC20, KLF4, PTGS2, and FOS) were obtained from BC microarray data sets. FOS was the gene with the most nodes in PPI analysis. Poor FOS mRNA expression was identified in BC patients. Furthermore, FOS was mainly located in the extracellular matrix and was involved in cell processes. FOS was downregulated in BC tissues and cells, and FOS overexpression restrained the malignant phenotypes of BC cells. Collectively, ectopic expression of FOS curtails the development of BC.
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页数:13
相关论文
共 44 条
[1]   Increased Expression of MicroRNA 551a by c-Fos Reduces Focal Adhesion Kinase Levels and Blocks Tumorigenesis [J].
Anuj ;
Arivazhagan, Lakshmi ;
Venkatraman, Ganesh ;
Rayala, Suresh K. .
MOLECULAR AND CELLULAR BIOLOGY, 2019, 39 (07)
[2]  
Ashouri S., 2016, CELL MOL NEUROBIOL, V5, P172
[3]   Molecular profiling of inflammatory breast cancer:: Identification of a poor-prognosis gene expression signature [J].
Bièche, I ;
Lerebours, F ;
Tozlu, S ;
Espie, M ;
Marty, M ;
Lidereau, R .
CLINICAL CANCER RESEARCH, 2004, 10 (20) :6789-6795
[4]   Identification of an AP1-ZFP36 Regulatory Network Associated with Breast Cancer Prognosis [J].
Canzoneri, R. ;
Naipauer, J. ;
Stedile, M. ;
Rodriguez Pena, A. ;
Lacunza, E. ;
Gandini, N. A. ;
Curino, A. C. ;
Facchinetti, M. M. ;
Coso, O. A. ;
Kordon, E. ;
Abba, M. C. .
JOURNAL OF MAMMARY GLAND BIOLOGY AND NEOPLASIA, 2020, 25 (02) :163-172
[5]   UALCAN: A Portal for Facilitating Tumor Subgroup Gene Expression and Survival Analyses [J].
Chandrashekar, Darshan S. ;
Bashel, Bhuwan ;
Balasubramanya, Sai Akshaya Hodigere ;
Creighton, Chad J. ;
Ponce-Rodriguez, Israel ;
Chakravarthi, Balabhadrapatruni V. S. K. ;
Varambally, Sooryanarayana .
NEOPLASIA, 2017, 19 (08) :649-658
[6]   Egr-1 is activated by 17β-estradiol in MCF-7 cells by mitogen-activated protein kinase-dependent phosphorylation of ELK-1 [J].
Chen, CC ;
Lee, WR ;
Safe, S .
JOURNAL OF CELLULAR BIOCHEMISTRY, 2004, 93 (05) :1063-1074
[7]   Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis [J].
Chen, Gang ;
Yu, Mingwei ;
Cao, Jianqiao ;
Zhao, Huishan ;
Dai, Yuanping ;
Cong, Yizi ;
Qiao, Guangdong .
BIOENGINEERED, 2021, 12 (01) :5149-5161
[8]   Up-Regulation of c-Fos Associated with Neuronal Apoptosis Following Intracerebral Hemorrhage [J].
Chen, Xiaomei ;
Shen, Jiabing ;
Wang, Yang ;
Chen, Xiaojing ;
Yu, Shi ;
Shi, Huili ;
Huo, Keke .
CELLULAR AND MOLECULAR NEUROBIOLOGY, 2015, 35 (03) :363-376
[9]   Bioinformatics analysis of potential prognostic biomarkers among Kruppel-like transcription Factors (KLFs) in breast cancer [J].
Cheng, Lin ;
Shi, Liang ;
Dai, Hong .
CANCER BIOMARKERS, 2019, 26 (04) :411-420
[10]   Evaluation of FGFR targeting in breast cancer through interrogation of patient-derived models [J].
Chew, Nicole J. ;
Lim Kam Sian, Terry C. C. ;
Nguyen, Elizabeth V. ;
Shin, Sung-Young ;
Yang, Jessica ;
Hui, Mun N. ;
Deng, Niantao ;
McLean, Catriona A. ;
Welm, Alana L. ;
Lim, Elgene ;
Gregory, Peter ;
Nottle, Tim ;
Lang, Tali ;
Vereker, Melissa ;
Richardson, Gary ;
Kerr, Genevieve ;
Micati, Diana ;
Jarde, Thierry ;
Abud, Helen E. ;
Lee, Rachel S. ;
Swarbrick, Alex ;
Daly, Roger J. .
BREAST CANCER RESEARCH, 2021, 23 (01)