Literature DB >> 25611337

Triple negative breast cancer: a multi-omics network discovery strategy for candidate targets and driving pathways.

Kubra Karagoz1, Raghu Sinha, Kazim Yalcin Arga.   

Abstract

Triple negative breast cancer (TNBC) represents approximately 15% of breast cancers and is characterized by lack of expression of both estrogen receptor (ER) and progesterone receptor (PR), together with absence of human epidermal growth factor 2 (HER2). TNBC has attracted considerable attention due to its aggressiveness such as large tumor size, high proliferation rate, and metastasis. The absence of clinically efficient molecular targets is of great concern in treatment of patients with TNBC. In light of the complexity of TNBC, we applied a systematic and integrative transcriptomics and interactomics approach utilizing transcriptional regulatory and protein-protein interaction networks to discover putative transcriptional control mechanisms of TNBC. To this end, we identified TNBC-driven molecular pathways such as the Janus kinase-signal transducers, and activators of transcription (JAK-STAT) and tumor necrosis factor (TNF) signaling pathways. The multi-omics molecular target and biomarker discovery approach presented here can offer ways forward on novel diagnostics and potentially help to design personalized therapeutics for TNBC in the future.

Entities:  

Mesh:

Substances:

Year:  2015        PMID: 25611337     DOI: 10.1089/omi.2014.0135

Source DB:  PubMed          Journal:  OMICS        ISSN: 1536-2310


  21 in total

1.  Computational Systems Biology of Psoriasis: Are We Ready for the Age of Omics and Systems Biomarkers?

Authors:  Tuba Sevimoglu; Kazim Yalcin Arga
Journal:  OMICS       Date:  2015-10-19

Review 2.  Roles of miRNA and lncRNA in triple-negative breast cancer.

Authors:  Juan Xu; Kang-Jing Wu; Qiao-Jun Jia; Xian-Feng Ding
Journal:  J Zhejiang Univ Sci B       Date:  2020 Sept.       Impact factor: 3.066

3.  Interactive cooperation and hierarchical operation of microRNA and transcription factor crosstalk in human transcriptional regulatory network.

Authors:  Esra Gov; Kazim Yalcin Arga
Journal:  IET Syst Biol       Date:  2016-12       Impact factor: 1.615

4.  Predicting lncRNA-disease associations and constructing lncRNA functional similarity network based on the information of miRNA.

Authors:  Xing Chen
Journal:  Sci Rep       Date:  2015-08-17       Impact factor: 4.379

5.  Differential co-expression analysis reveals a novel prognostic gene module in ovarian cancer.

Authors:  Esra Gov; Kazim Yalcin Arga
Journal:  Sci Rep       Date:  2017-07-10       Impact factor: 4.379

6.  Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.

Authors:  Seungyoon Nam
Journal:  OMICS       Date:  2017-04

7.  A Novel Model for Predicting Associations between Diseases and LncRNA-miRNA Pairs Based on a Newly Constructed Bipartite Network.

Authors:  Shunxian Zhou; Zhanwei Xuan; Lei Wang; Pengyao Ping; Tingrui Pei
Journal:  Comput Math Methods Med       Date:  2018-05-06       Impact factor: 2.238

8.  Potential biomarkers and therapeutic targets in cervical cancer: Insights from the meta-analysis of transcriptomics data within network biomedicine perspective.

Authors:  Medi Kori; Kazim Yalcin Arga
Journal:  PLoS One       Date:  2018-07-18       Impact factor: 3.240

9.  Wnt-beta-catenin pathway signals metastasis-associated tumor cell phenotypes in triple negative breast cancers.

Authors:  Pradip De; Jennifer H Carlson; Hui Wu; Adam Marcus; Brian Leyland-Jones; Nandini Dey
Journal:  Oncotarget       Date:  2016-07-12

Review 10.  Long non-coding RNAs and complex diseases: from experimental results to computational models.

Authors:  Xing Chen; Chenggang Clarence Yan; Xu Zhang; Zhu-Hong You
Journal:  Brief Bioinform       Date:  2017-07-01       Impact factor: 11.622

View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.