434 related articles for article (PubMed ID: 34797837)
21. 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]
22. Identification of candidate biomarkers correlated with poor prognosis of breast cancer based on bioinformatics analysis.
Chen G; Yu M; Cao J; Zhao H; Dai Y; Cong Y; Qiao G
Bioengineered; 2021 Dec; 12(1):5149-5161. PubMed ID: 34384030
[TBL] [Abstract][Full Text] [Related]
23. Coexpression Network Analysis of Genes Related to the Characteristics of Tumor Stemness in Triple-Negative Breast Cancer.
Suo HD; Tao Z; Zhang L; Jin ZN; Li XY; Ma W; Wang Z; Qiu Y; Jin F; Chen B; Cao Y
Biomed Res Int; 2020; 2020():7575862. PubMed ID: 32766313
[TBL] [Abstract][Full Text] [Related]
24. Identification of hub genes associated with bladder cancer using bioinformatic analyses.
Zheng W; Zhao Y; Wang T; Zhao X; Tan Z
Transl Cancer Res; 2022 May; 11(5):1330-1343. PubMed ID: 35706790
[TBL] [Abstract][Full Text] [Related]
25. [Bioinformatics analysis of core differentially expressed genes in hepatitis B virus-related hepatocellular carcinoma].
Yu Y; Cheng J; Mei CZ; Dai YZ
Zhongguo Xue Xi Chong Bing Fang Zhi Za Zhi; 2022 Nov; 34(5):507-513. PubMed ID: 36464255
[TBL] [Abstract][Full Text] [Related]
26. Identification of key molecular markers in epithelial ovarian cancer by integrated bioinformatics analysis.
Qin W; Yuan Q; Liu Y; Zeng Y; Ke D; Dai X; Shuai Y; Hu J; Shi H
Taiwan J Obstet Gynecol; 2021 Nov; 60(6):983-994. PubMed ID: 34794761
[TBL] [Abstract][Full Text] [Related]
27. 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]
28. In silico analysis revealed the potential circRNA-miRNA-mRNA regulative network of non-small cell lung cancer (NSCLC).
Balasundaram A; C GPD
Comput Biol Med; 2023 Jan; 152():106315. PubMed ID: 36495751
[TBL] [Abstract][Full Text] [Related]
29. 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]
30. Identification and Integrate Analysis of Key Biomarkers for Diagnosis and Prognosis of Non-Small Cell Lung Cancer Based on Bioinformatics Analysis.
Gong K; Zhou H; Liu H; Xie T; Luo Y; Guo H; Chen J; Tan Z; Yang Y; Xie L
Technol Cancer Res Treat; 2021; 20():15330338211060202. PubMed ID: 34825846
[No Abstract] [Full Text] [Related]
31. Bioinformatics Analysis Identifies the Estrogen Receptor 1 (ESR1) Gene and hsa-miR-26a-5p as Potential Prognostic Biomarkers in Patients with Intrahepatic Cholangiocarcinoma.
Qin X; Song Y
Med Sci Monit; 2020 May; 26():e921815. PubMed ID: 32435051
[TBL] [Abstract][Full Text] [Related]
32. Screening of potential hub genes and key pathways associated with breast cancer by bioinformatics tools.
Oumeddour A
Medicine (Baltimore); 2023 Mar; 102(11):e33291. PubMed ID: 36930083
[TBL] [Abstract][Full Text] [Related]
33. Identification of novel genes associated with a poor prognosis in pancreatic ductal adenocarcinoma via a bioinformatics analysis.
Zhou J; Hui X; Mao Y; Fan L
Biosci Rep; 2019 Aug; 39(8):. PubMed ID: 31311829
[TBL] [Abstract][Full Text] [Related]
34. Identification of critical genes in gastric cancer to predict prognosis using bioinformatics analysis methods.
Liu J; Ma L; Chen Z; Song Y; Gu T; Liu X; Zhao H; Yao N
Ann Transl Med; 2020 Jul; 8(14):884. PubMed ID: 32793728
[TBL] [Abstract][Full Text] [Related]
35. Comprehensive Analysis of Candidate Diagnostic and Prognostic Biomarkers Associated with Lung Adenocarcinoma.
Li J; Liu X; Cui Z; Han G
Med Sci Monit; 2020 Jun; 26():e922070. PubMed ID: 32578582
[TBL] [Abstract][Full Text] [Related]
36. 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]
37. Identification of potential core genes in triple negative breast cancer using bioinformatics analysis.
Li MX; Jin LT; Wang TJ; Feng YJ; Pan CP; Zhao DM; Shao J
Onco Targets Ther; 2018; 11():4105-4112. PubMed ID: 30140156
[TBL] [Abstract][Full Text] [Related]
38. Integrated bioinformatics analysis of potential biomarkers for pancreatic cancer.
Shi H; Xu H; Chai C; Qin Z; Zhou W
J Clin Lab Anal; 2022 May; 36(5):e24381. PubMed ID: 35403252
[TBL] [Abstract][Full Text] [Related]
39. 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]
40. Secreted breast tumor interstitial fluid microRNAs and their target genes are associated with triple-negative breast cancer, tumor grade, and immune infiltration.
Terkelsen T; Russo F; Gromov P; Haakensen VD; Brunak S; Gromova I; Krogh A; Papaleo E
Breast Cancer Res; 2020 Jun; 22(1):73. PubMed ID: 32605588
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]
