Literature DB >> 28937943

Multiomics Analysis of Tumor Microenvironment Reveals Gata2 and miRNA-124-3p as Potential Novel Biomarkers in Ovarian Cancer.

Esra Gov1,2, Medi Kori1, Kazim Yalcin Arga1.   

Abstract

Ovarian cancer is a common and, yet, one of the most deadly human cancers due to its insidious onset and the current lack of robust early diagnostic tests. Tumors are complex tissues comprised of not only malignant cells but also genetically stable stromal cells. Understanding the molecular mechanisms behind epithelial-stromal crosstalk in ovarian cancer is a great challenge in particular. In the present study, we performed comparative analyses of transcriptome data from laser microdissected epithelial, stromal, and ovarian tumor tissues, and identified common and tissue-specific reporter biomolecules-genes, receptors, membrane proteins, transcription factors (TFs), microRNAs (miRNAs), and metabolites-by integration of transcriptome data with genome-scale biomolecular networks. Tissue-specific response maps included common differentially expressed genes (DEGs) and reporter biomolecules were reconstructed and topological analyses were performed. We found that CDK2, EP300, and SRC as receptor-related functions or membrane proteins; Ets1, Ar, Gata2, and Foxp3 as TFs; and miR-16-5p and miR-124-3p as putative biomarkers and warrant further validation research. In addition, we report in this study that Gata2 and miR-124-3p are potential novel reporter biomolecules for ovarian cancer. The study of tissue-specific reporter biomolecules in epithelial cells, stroma, and tumor tissues as exemplified in the present study offers promise in biomarker discovery and diagnostics innovation for common complex human diseases such as ovarian cancer.

Entities:  

Keywords:  multiomics; network medicine; ovarian cancer; transcriptome; tumor microenvironment

Mesh:

Substances:

Year:  2017        PMID: 28937943     DOI: 10.1089/omi.2017.0115

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


  8 in total

Review 1.  The potential role of miR-124-3p in tumorigenesis and other related diseases.

Authors:  Qian Li; Shuqing Liu; Jinsong Yan; Ming-Zhong Sun; Frederick T Greenaway
Journal:  Mol Biol Rep       Date:  2021-04-20       Impact factor: 2.316

2.  Disclosing Potential Key Genes, Therapeutic Targets and Agents for Non-Small Cell Lung Cancer: Evidence from Integrative Bioinformatics Analysis.

Authors:  Md Parvez Mosharaf; Md Selim Reza; Esra Gov; Rashidul Alam Mahumud; Md Nurul Haque Mollah
Journal:  Vaccines (Basel)       Date:  2022-05-12

3.  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

4.  Circular RNA Complement Factor H (CFH) Promotes Glioma Progression by Sponging miR-149 and Regulating AKT1.

Authors:  Aimiao Bian; Yanping Wang; Ji Liu; Xiaodong Wang; Dai Liu; Jian Jiang; Lianshu Ding; Xiaobo Hui
Journal:  Med Sci Monit       Date:  2018-08-16

5.  Polymorphic variants INSIG2 rs6726538, HLA-DRB1 rs9272143, and GCNT1P5 rs7780883 contribute to the susceptibility of cervical cancer in the Bangladeshi women.

Authors:  Md Emtiaz Hasan; Maliha Matin; Md Enamul Haque; Md Abdul Aziz; Md Shalahuddin Millat; Mohammad Sarowar Uddin; Md Mizanur Rahman Moghal; Mohammad Safiqul Islam
Journal:  Cancer Med       Date:  2021-02-14       Impact factor: 4.452

6.  Targeting the tumor stroma: integrative analysis reveal GATA2 and TORYAIP1 as novel prognostic targets in breast and ovarian cancer.

Authors:  Ömer Faruk Erceylan; Ayşe Savaş; Esra Göv
Journal:  Turk J Biol       Date:  2021-04-20

7.  Characterization of the Potential Role of NTPCR in Epithelial Ovarian Cancer by Integrating Transcriptomic and Metabolomic Analysis.

Authors:  Hongkai Shang; Huizhi Zhang; Ziyao Ren; Hongjiang Zhao; Zhifen Zhang; Jinyi Tong
Journal:  Front Genet       Date:  2021-09-01       Impact factor: 4.599

Review 8.  Artificial intelligence in cancer target identification and drug discovery.

Authors:  Yujie You; Xin Lai; Yi Pan; Huiru Zheng; Julio Vera; Suran Liu; Senyi Deng; Le Zhang
Journal:  Signal Transduct Target Ther       Date:  2022-05-10
  8 in total

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