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

By Balázs Győrffy, A. Lánczky, A. Eklund, Carsten Denkert, Jan Budczies, Qiyuan Li, Zoltan Szallasi. Breast Cancer Research and Treatment, 2010

2,609 citations recorded by the source corpus.

Subfields: Cancer Research, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/fd77586f5ee88ce7c69634f5c2095bd2a950bae2

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Citations in the corpus

Cutoff Finder: A Comprehensive and Straightforward Web Application Enabling Rapid Biomarker Cutoff Optimization (2012, methodology): “For example, percentile based cutoffs were also implemented in our previous project where biomarkers can be assessed for their association to survival [33].”
Integrated analysis of public datasets for the discovery and validation of survival-associated genes in solid tumors (2024, methodology): “4 In this platform, the patient samples are divided into two groups conferring to various quantile expressions of the investigated gene, and the two cohorts are compared by a Cox regression and a KM survival plot.”
Most Random Gene Expression Signatures Are Significantly Associated with Breast Cancer Outcome (2011, background): “Servers resting on these data [41,42] make step #3 accessible to anyone with an Internet connection.”
Survival analysis across the entire transcriptome identifies biomarkers with the highest prognostic power in breast cancer (2021, methodology): “Our database was initially established in 2010 with 1809 patients and at that time we also established an online survival analysis platform to enable the investigation of the assembled dataset by independent researchers [18].”
Direct targeting of Sec23a by miR-200s influences cancer cell secretome and promotes metastatic colonization (2011, background): “Indeed, evaluation of SEC23A expression as a primary tumor prognostic marker in a large public clinical database of breast cance”
Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients. (2012, methodology): “We recently developed an online biomarker validation tool using microarray data of 2000 breast cancer patients (Gyorffy et al. 2010).”
Distinct EMT programs control normal mammary stem cells and tumour-initiating cells (2015, neutral): “Since the knockdown of SNAIL, but not SLUG, resulted in down-regulation of ZEB1 in MDA-MB-231 cells (Fig.”