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Items: 1 to 20 of 109

1.

Identification of a transcription factor-microRNA network in esophageal adenocarcinoma through bioinformatics analysis and validation through qRT-PCR.

Chen D, Lu T, Tan J, Zhao K, Li Y, Zhao W, Li H, Wang Q, Wang Y, Wei L.

Cancer Manag Res. 2019 Apr 18;11:3315-3326. doi: 10.2147/CMAR.S201274. eCollection 2019.

2.

Identification of prognostic risk factors for esophageal adenocarcinoma using bioinformatics analysis.

Dong Z, Wang J, Zhan T, Xu S.

Onco Targets Ther. 2018 Jul 25;11:4327-4337. doi: 10.2147/OTT.S156716. eCollection 2018.

3.

MicroRNA-related transcription factor regulatory networks in human colorectal cancer.

Hao S, Huo S, Du Z, Yang Q, Ren M, Liu S, Liu T, Zhang G.

Medicine (Baltimore). 2019 Apr;98(15):e15158. doi: 10.1097/MD.0000000000015158.

4.

Key elements involved in Epstein-Barr virus-associated gastric cancer and their network regulation.

Jing JJ, Wang ZY, Li H, Sun LP, Yuan Y.

Cancer Cell Int. 2018 Sep 21;18:146. doi: 10.1186/s12935-018-0637-5. eCollection 2018.

5.

Biomarker identification and trans-regulatory network analyses in esophageal adenocarcinoma and Barrett's esophagus.

Lv J, Guo L, Wang JH, Yan YZ, Zhang J, Wang YY, Yu Y, Huang YF, Zhao HP.

World J Gastroenterol. 2019 Jan 14;25(2):233-244. doi: 10.3748/wjg.v25.i2.233.

6.

Bioinformatics method to predict two regulation mechanism: TF-miRNA-mRNA and lncRNA-miRNA-mRNA in pancreatic cancer.

Ye S, Yang L, Zhao X, Song W, Wang W, Zheng S.

Cell Biochem Biophys. 2014 Dec;70(3):1849-58. doi: 10.1007/s12013-014-0142-y.

PMID:
25087086
7.

Identification of hub genes with prognostic values in gastric cancer by bioinformatics analysis.

Li T, Gao X, Han L, Yu J, Li H.

World J Surg Oncol. 2018 Jun 19;16(1):114. doi: 10.1186/s12957-018-1409-3.

8.

Dysregulation of NCAPG, KNL1, miR-148a-3p, miR-193b-3p, and miR-1179 may contribute to the progression of gastric cancer.

Song B, Du J, Song DF, Ren JC, Feng Y.

Biol Res. 2018 Nov 3;51(1):44. doi: 10.1186/s40659-018-0192-5.

9.
10.

Identification of potential pathogenic biomarkers in clear cell renal cell carcinoma.

Wang Z, Zhang Z, Zhang C, Xu Y.

Oncol Lett. 2018 Jun;15(6):8491-8499. doi: 10.3892/ol.2018.8398. Epub 2018 Mar 30.

11.

Identification of key miRNAs and genes associated with stomach adenocarcinoma from The Cancer Genome Atlas database.

Liu J, Liu F, Shi Y, Tan H, Zhou L.

FEBS Open Bio. 2018 Jan 2;8(2):279-294. doi: 10.1002/2211-5463.12365. eCollection 2018 Feb.

12.

Identification of lymph node metastasis-related microRNAs in lung adenocarcinoma and analysis of the underlying mechanisms using a bioinformatics approach.

Yan L, Jiao D, Hu H, Wang J, Tang X, Chen J, Chen Q.

Exp Biol Med (Maywood). 2017 Apr;242(7):709-717. doi: 10.1177/1535370216677353. Epub 2016 Nov 14.

13.

Identification of key genes and miRNAs markers of papillary thyroid cancer.

Qiu J, Zhang W, Zang C, Liu X, Liu F, Ge R, Sun Y, Xia Q.

Biol Res. 2018 Nov 10;51(1):45. doi: 10.1186/s40659-018-0188-1.

14.

Identification of key microRNAs associated with diffuse large B-cell lymphoma by analyzing serum microRNA expressions.

Meng Y, Quan L, Liu A.

Gene. 2018 Feb 5;642:205-211. doi: 10.1016/j.gene.2017.11.022. Epub 2017 Nov 8.

PMID:
29128636
15.

Identification of potential transcription factors, long noncoding RNAs, and microRNAs associated with hepatocellular carcinoma.

Yan H, Wang Q, Shen Q, Li Z, Tian J, Jiang Q, Gao L.

J Cancer Res Ther. 2018 Sep;14(Supplement):S622-S627. doi: 10.4103/0973-1482.204846.

16.

Integrated microRNA-mRNA analyses of distinct expression profiles in follicular thyroid tumors.

Chi J, Zheng X, Gao M, Zhao J, Li D, Li J, Dong L, Ruan X.

Oncol Lett. 2017 Dec;14(6):7153-7160. doi: 10.3892/ol.2017.7146. Epub 2017 Oct 6.

17.

Differential expression profiles of microRNAs as potential biomarkers for the early diagnosis of lung cancer.

Zhang Y, Sui J, Shen X, Li C, Yao W, Hong W, Peng H, Pu Y, Yin L, Liang G.

Oncol Rep. 2017 Jun;37(6):3543-3553. doi: 10.3892/or.2017.5612. Epub 2017 Apr 28.

PMID:
28498428
18.

Normalization matters: tracking the best strategy for sperm miRNA quantification.

Corral-Vazquez C, Blanco J, Salas-Huetos A, Vidal F, Anton E.

Mol Hum Reprod. 2017 Jan;23(1):45-53. doi: 10.1093/molehr/gaw072. Epub 2016 Dec 8.

PMID:
27932553
19.

Integrated analysis of microRNA-mRNA expression in A549 cells infected with influenza A viruses (IAVs) from different host species.

Gao J, Gao L, Li R, Lai Z, Zhang Z, Fan X.

Virus Res. 2019 Apr 2;263:34-46. doi: 10.1016/j.virusres.2018.12.016. Epub 2018 Dec 31.

PMID:
30605755
20.

Construction and analysis of mRNA, miRNA, lncRNA, and TF regulatory networks reveal the key genes associated with prostate cancer.

Ye Y, Li SL, Wang SY.

PLoS One. 2018 Aug 23;13(8):e0198055. doi: 10.1371/journal.pone.0198055. eCollection 2018.

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