Our publications related to cancer biomarkers

Found 10 results
Journal Article
Győrffy B, Karn T, Sztupinszki Z, Weltz B, Müller V, Pusztai L.  2015.  Dynamic classification using case-specific training cohorts outperforms static gene expression signatures in breast cancer.. Int J Cancer. 136(9):2091-8.
Gyorffy B., Molnar B., Lage H., Szallasi Z., Eklund A.C.  2009.  Evaluation of microarray preprocessing algorithms based on concordance with RT-PCR in clinical samples. PLoS One. 4:e5645.
Pongor L, Kormos M, Hatzis C, Pusztai L, Szabó A, Győrffy B.  2015.  A genome-wide approach to link genotype to clinical outcome by utilizing next generation sequencing and gene chip data of 6,697 breast cancer patients.. Genome Med. 7(1):104.
Li Q., Birkbak N.J, Gyorffy B., Szallasi Z., Eklund A.C.  2011.  Jetset: selecting the optimal microarray probe set to represent a gene. BMC Bioinformatics. 12:474.
Mihály Z, Kormos M, Lánczky A, Dank M, Budczies J, Szász MA, Győrffy B.  2013.  A meta-analysis of gene expression-based biomarkers predicting outcome after tamoxifen treatment in breast cancer.. Breast Cancer Res Treat. 140(2):219-32.
Gyorffy B., Schafer R..  2009.  Meta-analysis of gene expression profiles related to relapse-free survival in 1,079 breast cancer patients. Breast Cancer Res Treat. 118:433-41.
Lánczky A, Nagy Á, Bottai G, Munkácsy G, Szabó A, Santarpia L, Győrffy B.  2016.  miRpower: a web-tool to validate survival-associated miRNAs utilizing expression data from 2178 breast cancer patients. Breast Cancer Res Treat. 160(3):446.
Gyorffy B., Lanczky A., Eklund A.C, Denkert C., Budczies J., Li Q., Szallasi Z..  2010.  An online survival analysis tool to rapidly assess the effect of 22,277 genes on breast cancer prognosis using microarray data of 1,809 patients. Breast Cancer Res Treat. 123:725-31.
Gyorffy B., Benke Z., Lanczky A., Balazs B., Szallasi Z., Timar J., Schafer R..  2012.  RecurrenceOnline: an online analysis tool to determine breast cancer recurrence and hormone receptor status using microarray data. Breast Cancer Res Treat. 132:1025-34.
Győrffy B, Bottai G, Lehmann-Che J, Kéri G, Orfi L, Iwamoto T, Desmedt C, Bianchini G, Turner NC, de Thè H et al..  2014.  TP53 mutation-correlated genes predict the risk of tumor relapse and identify MPS1 as a potential therapeutic kinase in TP53-mutated breast cancers.. Mol Oncol. 8(3):508-19.