BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

149 related articles for article (PubMed ID: 32544280)

  • 1. ABCC9, NKAPL, and TMEM132C are potential diagnostic and prognostic markers in triple-negative breast cancer.
    Zhang X; Kang X; Jin L; Bai J; Zhang H; Liu W; Wang Z
    Cell Biol Int; 2020 Oct; 44(10):2002-2010. PubMed ID: 32544280
    [TBL] [Abstract][Full Text] [Related]  

  • 2. Highly heterogeneous-related genes of triple-negative breast cancer: potential diagnostic and prognostic biomarkers.
    Liu Y; Teng L; Fu S; Wang G; Li Z; Ding C; Wang H; Bi L
    BMC Cancer; 2021 May; 21(1):644. PubMed ID: 34053447
    [TBL] [Abstract][Full Text] [Related]  

  • 3. Novel biomarkers identified in triple-negative breast cancer through RNA-sequencing.
    Chen YL; Wang K; Xie F; Zhuo ZL; Liu C; Yang Y; Wang S; Zhao XT
    Clin Chim Acta; 2022 Jun; 531():302-308. PubMed ID: 35504321
    [TBL] [Abstract][Full Text] [Related]  

  • 4. Identification of key genes as potential biomarkers for triple‑negative breast cancer using integrating genomics analysis.
    Zhong G; Lou W; Shen Q; Yu K; Zheng Y
    Mol Med Rep; 2020 Feb; 21(2):557-566. PubMed ID: 31974598
    [TBL] [Abstract][Full Text] [Related]  

  • 5. Roadmap of DNA methylation in breast cancer identifies novel prognostic biomarkers.
    de Almeida BP; Apolónio JD; Binnie A; Castelo-Branco P
    BMC Cancer; 2019 Mar; 19(1):219. PubMed ID: 30866861
    [TBL] [Abstract][Full Text] [Related]  

  • 6. Identification of differentially expressed genes between triple and non-triple-negative breast cancer using bioinformatics analysis.
    Zhai Q; Li H; Sun L; Yuan Y; Wang X
    Breast Cancer; 2019 Nov; 26(6):784-791. PubMed ID: 31197620
    [TBL] [Abstract][Full Text] [Related]  

  • 7. Bioinformatics Analysis Identifies IL6ST as a Potential Tumor Suppressor Gene for Triple-Negative Breast Cancer.
    Jia R; Weng Y; Li Z; Liang W; Ji Y; Liang Y; Ning P
    Reprod Sci; 2021 Aug; 28(8):2331-2341. PubMed ID: 33650093
    [TBL] [Abstract][Full Text] [Related]  

  • 8. Integrated network analysis and machine learning approach for the identification of key genes of triple-negative breast cancer.
    Naorem LD; Muthaiyan M; Venkatesan A
    J Cell Biochem; 2019 Apr; 120(4):6154-6167. PubMed ID: 30302816
    [TBL] [Abstract][Full Text] [Related]  

  • 9. Novel key genes in triple-negative breast cancer identified by weighted gene co-expression network analysis.
    Chen J; Qian X; He Y; Han X; Pan Y
    J Cell Biochem; 2019 Oct; 120(10):16900-16912. PubMed ID: 31081967
    [TBL] [Abstract][Full Text] [Related]  

  • 10. Screening and identification of potential biomarkers in triple-negative breast cancer by integrated analysis.
    Guo J; Gong G; Zhang B
    Oncol Rep; 2017 Oct; 38(4):2219-2228. PubMed ID: 28849078
    [TBL] [Abstract][Full Text] [Related]  

  • 11. Identification of a DNA Methylation-Based Prognostic Signature for Patients with Triple-Negative Breast Cancer.
    Gao Y; Wang X; Li S; Zhang Z; Li X; Lin F
    Med Sci Monit; 2021 May; 27():e930025. PubMed ID: 34003815
    [TBL] [Abstract][Full Text] [Related]  

  • 12. Comprehensive analysis of novel three-long noncoding RNA signatures as a diagnostic and prognostic biomarkers of human triple-negative breast cancer.
    Fan CN; Ma L; Liu N
    J Cell Biochem; 2019 Mar; 120(3):3185-3196. PubMed ID: 30203490
    [TBL] [Abstract][Full Text] [Related]  

  • 13. Upregulated cyclins may be novel genes for triple-negative breast cancer based on bioinformatic analysis.
    Lu Y; Yang G; Xiao Y; Zhang T; Su F; Chang R; Ling X; Bai Y
    Breast Cancer; 2020 Sep; 27(5):903-911. PubMed ID: 32338339
    [TBL] [Abstract][Full Text] [Related]  

  • 14. Identification of CXCR4 and CXCL10 as Potential Predictive Biomarkers in Triple Negative Breast Cancer (TNBC).
    Chuan T; Li T; Yi C
    Med Sci Monit; 2020 Jan; 26():e918281. PubMed ID: 31924747
    [TBL] [Abstract][Full Text] [Related]  

  • 15. Risk stratification of triple-negative breast cancer with core gene signatures associated with chemoresponse and prognosis.
    Kim EK; Park AK; Ko E; Park WY; Lee KM; Noh DY; Han W
    Breast Cancer Res Treat; 2019 Nov; 178(1):185-197. PubMed ID: 31342312
    [TBL] [Abstract][Full Text] [Related]  

  • 16. Investigation of Candidate Genes and Pathways in Basal/TNBC Patients by Integrated Analysis.
    Liu Q; Song X; Liu Z; Yu Z
    Technol Cancer Res Treat; 2021; 20():15330338211019506. PubMed ID: 34184566
    [TBL] [Abstract][Full Text] [Related]  

  • 17. Exploring specific prognostic biomarkers in triple-negative breast cancer.
    Bao C; Lu Y; Chen J; Chen D; Lou W; Ding B; Xu L; Fan W
    Cell Death Dis; 2019 Oct; 10(11):807. PubMed ID: 31649243
    [TBL] [Abstract][Full Text] [Related]  

  • 18. Identification of Key Prognostic Genes of Triple Negative Breast Cancer by LASSO-Based Machine Learning and Bioinformatics Analysis.
    Chen DL; Cai JH; Wang CCN
    Genes (Basel); 2022 May; 13(5):. PubMed ID: 35627287
    [TBL] [Abstract][Full Text] [Related]  

  • 19. Screening of DNA Damage Repair Genes Involved in the Prognosis of Triple-Negative Breast Cancer Patients Based on Bioinformatics.
    Wang N; Gu Y; Chi J; Liu X; Xiong Y; Zhong C; Wang F; Wang X; Li L
    Front Genet; 2021; 12():721873. PubMed ID: 34408776
    [No Abstract]   [Full Text] [Related]  

  • 20. DNA methylation biomarkers for noninvasive detection of triple-negative breast cancer using liquid biopsy.
    Manoochehri M; Borhani N; Gerhäuser C; Assenov Y; Schönung M; Hielscher T; Christensen BC; Lee MK; Gröne HJ; Lipka DB; Brüning T; Brauch H; Ko YD; Hamann U
    Int J Cancer; 2023 Mar; 152(5):1025-1035. PubMed ID: 36305646
    [TBL] [Abstract][Full Text] [Related]  

    [Next]    [New Search]
    of 8.