Identification of candidate biomarkers and pathways in breast cancer by differential network analysis

被引:0
作者
Mendi, Onur [1 ]
Karahoca, Adem [2 ]
机构
[1] Demiroglu Bilim Univ, Dept Bioinformat, Fac Med, Istanbul, Turkey
[2] MEF Univ, Dept Comp Engn, Fac Engn, Istanbul, Turkey
关键词
breast cancer; differential network analysis; bioinformatics; microarray; POOR-PROGNOSIS; EXPRESSION; PROLIFERATION; FOXM1; MECHANISMS; REVEAL; GROWTH; STAT4;
D O I
暂无
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Breast cancer is one of the most malignant cancers in women worldwide. The aim of the present study was to explore the underlying biological mechanisms of breast cancer. For this purpose, we propose a novel framework to reveal mechanisms that drive disease progression in breast cancer by combining prior knowledge in the literature with differential networking methodology. Our integration framework has resulted in the most important genes and interactions by allowing ranking the breast cancer-specific gene network. YY1, SMARCA5, FOXM1, STAT4 and PTTG1 were found to be the most important genes in breast cancer. Functional and pathway enrichment analyses identified numerous pathways that may play a critical role in disease progression. Considering the success of the comparison of the results with the literature, the systemic lupus erythematosus pathway may be a potential target of breast cancer.
引用
收藏
页码:344 / 367
页数:24
相关论文
共 68 条
  • [1] A study on the most common algorithms implemented for cancer gene search and classifications
    Al-Rajab, Murad M.
    Lu, Joan
    [J]. INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2016, 14 (02) : 159 - 176
  • [2] Differential C3NET reveals disease networks of direct physical interactions
    Altay, Goekmen
    Asim, Mohammad
    Markowetz, Florian
    Neal, David E.
    [J]. BMC BIOINFORMATICS, 2011, 12
  • [3] Inferring the conservative causal core of gene regulatory networks
    Altay, Goekmen
    Emmert-Streib, Frank
    [J]. BMC SYSTEMS BIOLOGY, 2010, 4
  • [4] Global assessment of network inference algorithms based on available literature of gene/protein interactions
    Altay, Gokmen
    Altay, Nejla
    Neal, David
    [J]. TURKISH JOURNAL OF BIOLOGY, 2013, 37 (05) : 547 - 555
  • [5] [Anonymous], 2019, EGAS00000000083
  • [6] [Anonymous], 2019, R: The R Project for Statistical Computing
  • [7] Ovarian Cancer Differential Interactome and Network Entropy Analysis Reveal New Candidate Biomarkers
    Ayyildiz, Dilara
    Gov, Esra
    Sinha, Raghu
    Arga, Kazim Yalcin
    [J]. OMICS-A JOURNAL OF INTEGRATIVE BIOLOGY, 2017, 21 (05) : 285 - 294
  • [8] Azamjah Nasrindokht, 2019, Asian Pac J Cancer Prev, V20, P2015, DOI 10.31557/APJCP.2019.20.7.2015
  • [9] Bioconductor, 2019, **DATA OBJECT**
  • [10] A comparative review of recent bioinformatics tools for inferring gene regulatory networks using time-series expression data
    Byron, Kevin
    Wang, Jason T. L.
    [J]. INTERNATIONAL JOURNAL OF DATA MINING AND BIOINFORMATICS, 2018, 20 (04) : 320 - 340