495 related articles for article (PubMed ID: 30302816)
1. 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]
2. Integrated analysis of differentially expressed genes and pathways in triple‑negative breast cancer.
Peng C; Ma W; Xia W; Zheng W
Mol Med Rep; 2017 Mar; 15(3):1087-1094. PubMed ID: 28075450
[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. Screening and Identification of Key Biomarkers in Inflammatory Breast Cancer Through Integrated Bioinformatic Analyses.
Wu J; Lv Q; Huang H; Zhu M; Meng D
Genet Test Mol Biomarkers; 2020 Aug; 24(8):484-491. PubMed ID: 32598242
[No Abstract] [Full Text] [Related]
5. Molecular Features of Triple Negative Breast Cancer: Microarray Evidence and Further Integrated Analysis.
He J; Yang J; Chen W; Wu H; Yuan Z; Wang K; Li G; Sun J; Yu L
PLoS One; 2015; 10(6):e0129842. PubMed ID: 26103053
[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. 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]
8. 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]
9. 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]
10. 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]
11. 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]
12. Identification and Integrated Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer.
Liu X; Liu X; Li J; Ren F
Med Sci Monit; 2019 Dec; 25():9280-9289. PubMed ID: 31805030
[TBL] [Abstract][Full Text] [Related]
13. 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]
14. KEGG-expressed genes and pathways in triple negative breast cancer: Protocol for a systematic review and data mining.
Chen J; Liu C; Cen J; Liang T; Xue J; Zeng H; Zhang Z; Xu G; Yu C; Lu Z; Wang Z; Jiang J; Zhan X; Zeng J
Medicine (Baltimore); 2020 May; 99(18):e19986. PubMed ID: 32358373
[TBL] [Abstract][Full Text] [Related]
15. Identification of Key Pathways and Genes in Anaplastic Thyroid Carcinoma via Integrated Bioinformatics Analysis.
Hu S; Liao Y; Chen L
Med Sci Monit; 2018 Sep; 24():6438-6448. PubMed ID: 30213925
[TBL] [Abstract][Full Text] [Related]
16. 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]
17. Hsa-mir-3163 and CCNB1 may be potential biomarkers and therapeutic targets for androgen receptor positive triple-negative breast cancer.
Qiu P; Guo Q; Yao Q; Chen J; Lin J
PLoS One; 2021; 16(11):e0254283. PubMed ID: 34797837
[TBL] [Abstract][Full Text] [Related]
18. 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]
19. Clinical Identification of Dysregulated Circulating microRNAs and Their Implication in Drug Response in Triple Negative Breast Cancer (TNBC) by Target Gene Network and Meta-Analysis.
Qattan A; Al-Tweigeri T; Alkhayal W; Suleman K; Tulbah A; Amer S
Genes (Basel); 2021 Apr; 12(4):. PubMed ID: 33918859
[TBL] [Abstract][Full Text] [Related]
20. 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]
[Next] [New Search]