TY  - JOUR
AU  - Nagy, Ádám
AU  - Lánczky, András
AU  - Menyhart, Otilia
AU  - Győrffy, Balázs
TI  - Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets
JF  - SCIENTIFIC REPORTS
J2  - SCI REP
VL  - 8
PY  - 2018
IS  - 1
PG  - 9
SN  - 2045-2322
DO  - 10.1038/s41598-018-27521-y
UR  - https://m2.mtmt.hu/api/publication/3375975
ID  - 3375975
N1  - Export Date: 30 April 2026; Cited By: 1047; Correspondence Address: B. Gyorffy; MTA TTK Lendület Cancer Biomarker Research Group, Institute of Enzymology, Budapest, Magyar Tudósok körútja 2, 1117, Hungary; email: gyorffy.balazs@ttk.mta.hu;
AB  - Multiple studies suggested using different miRNAs as biomarkers for prognosis of hepatocellular carcinoma (HCC). We aimed to assemble a miRNA expression database from independent datasets to enable an independent validation of previously published prognostic biomarkers of HCC. A miRNA expression database was established by searching the TCGA (RNA-seq) and GEO (microarray) repositories to identify miRNA datasets with available expression and clinical data. A PubMed search was performed to identify prognostic miRNAs for HCC. We performed a uni- and multivariate Cox regression analysis to validate the prognostic significance of these miRNAs. The Limma R package was applied to compare the expression of miRNAs between tumor and normal tissues. We uncovered 214 publications containing 223 miRNAs identified as potential prognostic biomarkers for HCC. In the survival analysis, the expression levels of 55 and 84 miRNAs were significantly correlated with overall survival in RNA-seq and gene chip datasets, respectively. The most significant miRNAs were hsa-miR-149, hsa-miR-139, and hsa-miR-3677 in the RNA-seq and hsa-miR-146b-3p, hsa-miR-584, and hsa-miR-31 in the microarray dataset. Of the 223 miRNAs studied, the expression was significantly altered in 102 miRNAs in tumors compared to normal liver tissues. In summary, we set up an integrated miRNA expression database and validated prognostic miRNAs in HCC.
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Bruix, Jordi
AU  - Qin, Shukui
AU  - Merle, Philippe
AU  - Granito, Alessandro
AU  - Huang, Yi-Hsiang
AU  - Bodoky, György
AU  - Pracht, Marc
AU  - Yokosuka, Osamu
AU  - Rosmorduc, Olivier
AU  - Breder, Valeriy
AU  - Gerolami, Rene
AU  - Masi, Gianluca
AU  - Ross, Paul J.
AU  - Song, Tianqiang
AU  - Bronowicki, Jean-Pierre
AU  - Ollivier-Hourmand, Isabelle
AU  - Kudo, Masatoshi
AU  - Cheng, Ann-Lii
AU  - Llovet, Josep M.
AU  - Finn, Richard S.
AU  - LeBerre, Marie-Aude
AU  - Baumhauer, Annette
AU  - Meinhardt, Gerold
AU  - Han, Guohong
TI  - Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial
JF  - LANCET
J2  - LANCET
VL  - 389
PY  - 2017
IS  - 10064
SP  - 56
EP  - 66
PG  - 11
SN  - 0140-6736
DO  - 10.1016/S0140-6736(16)32453-9
UR  - https://m2.mtmt.hu/api/publication/31229700
ID  - 31229700
LA  - English
DB  - MTMT
ER  - 

TY  - JOUR
AU  - Győrffy, Balázs
AU  - Gyorffy, A
AU  - Tulassay, Zsolt
TI  - A "multiple testing" problémája és a genomiális kísérletekre alkalmazott megoldások
JF  - ORVOSI HETILAP
J2  - ORV HETIL
VL  - 146
PY  - 2005
IS  - 12
SP  - 559
EP  - 563
PG  - 5
SN  - 0030-6002
UR  - https://m2.mtmt.hu/api/publication/1880701
ID  - 1880701
AB  - The problem of multiple testing and its solutions for genome-wide studies. Even if there is no real change, the traditional p = 0.05 can cause 5% of the investigated tests being reported significant. Multiple testing corrections have been developed to solve this problem. Here the authors describe the one-step (Bonferroni), multi-step (step-down and step-up) and graphical methods. However, sometimes a correction for multiple testing creates more problems, than it solves: the universal null hypothesis is of little interest, the exact number of investigations to be adjusted for can not determined and the probability of type II error increases. For these reasons the authors suggest not to perform multiple testing corrections routinely. The calculation of the false discovery rate is a new method for genome-wide studies. Here the p value is substituted by the q value, which also shows the level of significance. The q value belonging to a measurement is the proportion of false positive measurements when we accept it as significant. The authors propose using the q value instead of the p value in genome-wide studies.
LA  - Hungarian
DB  - MTMT
ER  -