1759 related articles for article (PubMed ID: 31926587)
41. Identification of breast cancer hub genes and analysis of prognostic values using integrated bioinformatics analysis.
Fang E; Zhang X
Cancer Biomark; 2017 Dec; 21(1):373-381. PubMed ID: 29081411
[TBL] [Abstract][Full Text] [Related]
42. Identification of Key Biomarkers and Potential Molecular Mechanisms in Oral Squamous Cell Carcinoma by Bioinformatics Analysis.
Yang B; Dong K; Guo P; Guo P; Jie G; Zhang G; Li T
J Comput Biol; 2020 Jan; 27(1):40-54. PubMed ID: 31424263
[TBL] [Abstract][Full Text] [Related]
43. Bioinformatics analyses of significant genes, related pathways and candidate prognostic biomarkers in glioblastoma.
Zhou L; Tang H; Wang F; Chen L; Ou S; Wu T; Xu J; Guo K
Mol Med Rep; 2018 Nov; 18(5):4185-4196. PubMed ID: 30132538
[TBL] [Abstract][Full Text] [Related]
44. Identifying the mRNAs associated with Bladder cancer recurrence.
Cao H; Cheng L; Yu J; Zhang Z; Luo Z; Chen D
Cancer Biomark; 2020; 28(4):429-437. PubMed ID: 32390597
[TBL] [Abstract][Full Text] [Related]
45. Possible Molecular Markers for the Diagnosis of Pancreatic Ductal Adenocarcinoma.
Shen Q; Yu M; Jia JK; Li WX; Tian YW; Xue HZ
Med Sci Monit; 2018 Apr; 24():2368-2376. PubMed ID: 29671412
[TBL] [Abstract][Full Text] [Related]
46. ITGA2, LAMB3, and LAMC2 may be the potential therapeutic targets in pancreatic ductal adenocarcinoma: an integrated bioinformatics analysis.
Islam S; Kitagawa T; Baron B; Abiko Y; Chiba I; Kuramitsu Y
Sci Rep; 2021 May; 11(1):10563. PubMed ID: 34007003
[TBL] [Abstract][Full Text] [Related]
47. Identification of candidate aberrantly methylated and differentially expressed genes in Esophageal squamous cell carcinoma.
Han BA; Yang XP; Hosseini DK; Zhang P; Zhang Y; Yu JT; Chen S; Zhang F; Zhou T; Sun HY
Sci Rep; 2020 Jun; 10(1):9735. PubMed ID: 32546690
[TBL] [Abstract][Full Text] [Related]
48. SOX9 and IL1A as the Potential Gene Biomarkers of the Oral Cancer.
Li T; Cheng D; Guo J; Chen H; Zhang S; Bao Y
Comb Chem High Throughput Screen; 2023; 26(8):1461-1479. PubMed ID: 35762542
[TBL] [Abstract][Full Text] [Related]
49. Identification of Key Genes and Pathways in Myeloma side population cells by Bioinformatics Analysis.
Yang Q; Li K; Li X; Liu J
Int J Med Sci; 2020; 17(14):2063-2076. PubMed ID: 32922167
[No Abstract] [Full Text] [Related]
50. Identification of prognostic risk factors for pancreatic cancer using bioinformatics analysis.
Jin D; Jiao Y; Ji J; Jiang W; Ni W; Wu Y; Ni R; Lu C; Qu L; Ni H; Liu J; Xu W; Xiao M
PeerJ; 2020; 8():e9301. PubMed ID: 32587798
[TBL] [Abstract][Full Text] [Related]
51. Bioinformatics analysis of potential core genes for glioblastoma.
Zhang Y; Yang X; Zhu XL; Hao JQ; Bai H; Xiao YC; Wang ZZ; Hao CY; Duan HB
Biosci Rep; 2020 Jul; 40(7):. PubMed ID: 32667033
[TBL] [Abstract][Full Text] [Related]
52. Construction of a circRNA-miRNA-mRNA network to explore the pathogenesis and treatment of pancreatic ductal adenocarcinoma.
Xiao Y
J Cell Biochem; 2020 Jan; 121(1):394-406. PubMed ID: 31232492
[TBL] [Abstract][Full Text] [Related]
53. Integrated bioinformatics analysis reveals novel key biomarkers and potential candidate small molecule drugs in gastric cancer.
Wu Q; Zhang B; Wang Z; Hu X; Sun Y; Xu R; Chen X; Wang Q; Ju F; Ren S; Zhang C; Qi F; Ma Q; Xue Q; Zhou YL
Pathol Res Pract; 2019 May; 215(5):1038-1048. PubMed ID: 30975489
[TBL] [Abstract][Full Text] [Related]
54. Identification of Core Genes and Pathways in Melanoma Metastasis via Bioinformatics Analysis.
Xie R; Li B; Jia L; Li Y
Int J Mol Sci; 2022 Jan; 23(2):. PubMed ID: 35054979
[TBL] [Abstract][Full Text] [Related]
55. Analysis of genes associated with prognosis of lung adenocarcinoma based on GEO and TCGA databases.
Yu Y; Tian X
Medicine (Baltimore); 2020 May; 99(19):e20183. PubMed ID: 32384511
[TBL] [Abstract][Full Text] [Related]
56. The identification of liver metastasis- and prognosis-associated genes in pancreatic ductal adenocarcinoma.
Luan H; He Y; Zhang T; Su Y; Zhou L
BMC Cancer; 2022 Apr; 22(1):463. PubMed ID: 35477379
[TBL] [Abstract][Full Text] [Related]
57. Exploring prognostic genes in ovarian cancer stage-related coexpression network modules.
Yang L; Jing J; Sun L; Yue Y
Medicine (Baltimore); 2018 Aug; 97(34):e11895. PubMed ID: 30142790
[TBL] [Abstract][Full Text] [Related]
58. Identification of Hub Gene GRIN1 Correlated with Histological Grade and Prognosis of Glioma by Weighted Gene Coexpression Network Analysis.
Yang A; Wang X; Hu Y; Shang C; Hong Y
Biomed Res Int; 2021; 2021():4542995. PubMed ID: 34840971
[TBL] [Abstract][Full Text] [Related]
59. Bioinformatics analysis of aberrantly methylated-differentially expressed genes and pathways in hepatocellular carcinoma.
Sang L; Wang XM; Xu DY; Zhao WJ
World J Gastroenterol; 2018 Jun; 24(24):2605-2616. PubMed ID: 29962817
[TBL] [Abstract][Full Text] [Related]
60. Identification of key genes and associated pathways in KIT/PDGFRA wild‑type gastrointestinal stromal tumors through bioinformatics analysis.
Wang WJ; Li HT; Yu JP; Li YM; Han XP; Chen P; Yu WW; Chen WK; Jiao ZY; Liu HB
Mol Med Rep; 2018 Nov; 18(5):4499-4515. PubMed ID: 30221743
[TBL] [Abstract][Full Text] [Related]
[Previous] [Next] [New Search]