271 related articles for article (PubMed ID: 33650093)
1. 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]
2. 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]
3. Identification of a five genes prognosis signature for triple-negative breast cancer using multi-omics methods and bioinformatics analysis.
Ma J; Chen C; Liu S; Ji J; Wu D; Huang P; Wei D; Fan Z; Ren L
Cancer Gene Ther; 2022 Nov; 29(11):1578-1589. PubMed ID: 35474355
[TBL] [Abstract][Full Text] [Related]
4. 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]
5. 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]
6. 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]
7. Overexpression of CCNE1 confers a poorer prognosis in triple-negative breast cancer identified by bioinformatic analysis.
Yuan Q; Zheng L; Liao Y; Wu G
World J Surg Oncol; 2021 Mar; 19(1):86. PubMed ID: 33757543
[TBL] [Abstract][Full Text] [Related]
8. 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]
9. 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]
10. 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]
11. 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]
12. A Machine Learning Model to Predict the Triple Negative Breast Cancer Immune Subtype.
Chen Z; Wang M; De Wilde RL; Feng R; Su M; Torres-de la Roche LA; Shi W
Front Immunol; 2021; 12():749459. PubMed ID: 34603338
[TBL] [Abstract][Full Text] [Related]
13. Identification of new subtypes and potential genetic signatures in triple-negative breast cancer using weighted gene co-expression network analysis.
Huang J; Liao LY; Jiang WJ; Li YH; Lu BM; Wen ZP; Li FJ; Fang DL; Lu GM
Eur Rev Med Pharmacol Sci; 2024 Jan; 28(2):603-614. PubMed ID: 38305604
[TBL] [Abstract][Full Text] [Related]
14. Identification of Key Genes and Pathways in Triple-Negative Breast Cancer by Integrated Bioinformatics Analysis.
Dong P; Yu B; Pan L; Tian X; Liu F
Biomed Res Int; 2018; 2018():2760918. PubMed ID: 30175120
[TBL] [Abstract][Full Text] [Related]
15. Landscape of toll-like receptors expression in tumor microenvironment of triple negative breast cancer (TNBC): Distinct roles of TLR4 and TLR8.
Roychowdhury A; Jondhale M; Saldanha E; Ghosh D; Kumar Panda C; Chandrani P; Mukherjee N
Gene; 2021 Aug; 792():145728. PubMed ID: 34022297
[TBL] [Abstract][Full Text] [Related]
16. Comprehensive Analysis of Differentially Expressed Profiles of lncRNAs/mRNAs and miRNAs with Associated ceRNA Networks in Triple-Negative Breast Cancer.
Yang R; Xing L; Wang M; Chi H; Zhang L; Chen J
Cell Physiol Biochem; 2018; 50(2):473-488. PubMed ID: 30308479
[TBL] [Abstract][Full Text] [Related]
17. Identification of a Group of Therapeutic Targets and Prognostic Biomarker for Triple Negative Breast Cancer.
Li Y; Yang S; Qi L; Li Y; Wang X
Adv Ther; 2024 Apr; 41(4):1621-1636. PubMed ID: 38421558
[TBL] [Abstract][Full Text] [Related]
18. The role of FOXC1/FOXCUT/DANCR axis in triple negative breast cancer: a bioinformatics and experimental approach.
Kamaliyan Z; Mirfakhraie R; Azizi-Tabesh G; Darbeheshti F; Omranipour R; Ahmadinejad N; Zokaei E; Yassaee VR
Mol Biol Rep; 2022 Apr; 49(4):2821-2829. PubMed ID: 35066769
[TBL] [Abstract][Full Text] [Related]
19. 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]
20. Selected ideal natural ligand against TNBC by inhibiting CDC20, using bioinformatics and molecular biology.
Liu N; Wang X; Zhu Z; Li D; Lv X; Chen Y; Xie H; Guo Z; Song D
Aging (Albany NY); 2021 Oct; 13(20):23702-23725. PubMed ID: 34686627
[TBL] [Abstract][Full Text] [Related]
[Next] [New Search]
