@article{MTMT:3375975,
	title = {Validation of miRNA prognostic power in hepatocellular carcinoma using expression data of independent datasets},
	url = {https://m2.mtmt.hu/api/publication/3375975},
	author = {Nagy, Ádám and Lánczky, András and Menyhart, Otilia and Győrffy, Balázs},
	doi = {10.1038/s41598-018-27521-y},
	journal-iso = {SCI REP},
	journal = {SCIENTIFIC REPORTS},
	volume = {8},
	unique-id = {3375975},
	abstract = {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.},
	year = {2018},
	eissn = {2045-2322},
	orcid-numbers = {Nagy, Ádám/0000-0002-7979-7184; Menyhart, Otilia/0000-0003-4129-4589; Győrffy, Balázs/0000-0002-5772-3766}
}

@article{MTMT:31229700,
	title = {Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial},
	url = {https://m2.mtmt.hu/api/publication/31229700},
	author = {Bruix, Jordi and Qin, Shukui and Merle, Philippe and Granito, Alessandro and Huang, Yi-Hsiang and Bodoky, György and Pracht, Marc and Yokosuka, Osamu and Rosmorduc, Olivier and Breder, Valeriy and Gerolami, Rene and Masi, Gianluca and Ross, Paul J. and Song, Tianqiang and Bronowicki, Jean-Pierre and Ollivier-Hourmand, Isabelle and Kudo, Masatoshi and Cheng, Ann-Lii and Llovet, Josep M. and Finn, Richard S. and LeBerre, Marie-Aude and Baumhauer, Annette and Meinhardt, Gerold and Han, Guohong},
	doi = {10.1016/S0140-6736(16)32453-9},
	journal-iso = {LANCET},
	journal = {LANCET},
	volume = {389},
	unique-id = {31229700},
	issn = {0140-6736},
	year = {2017},
	eissn = {1474-547X},
	pages = {56-66},
	orcid-numbers = {Bodoky, György/0000-0002-5659-2020}
}

@article{MTMT:1880701,
	title = {A "multiple testing" problémája és a genomiális kísérletekre alkalmazott megoldások},
	url = {https://m2.mtmt.hu/api/publication/1880701},
	author = {Győrffy, Balázs and Gyorffy, A and Tulassay, Zsolt},
	journal-iso = {ORV HETIL},
	journal = {ORVOSI HETILAP},
	volume = {146},
	unique-id = {1880701},
	issn = {0030-6002},
	abstract = {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.},
	keywords = {Humans; *Data Interpretation, Statistical; In Situ Hybridization; Confidence Intervals; False Positive Reactions; *Models, Statistical; Research Design/*standards; *Genomics},
	year = {2005},
	eissn = {1788-6120},
	pages = {559-563},
	orcid-numbers = {Győrffy, Balázs/0000-0002-5772-3766; Tulassay, Zsolt/0000-0003-2452-6640}
}