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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
Open accessAbstract
Validating prognostic or predictive candidate genes in appropriately powered breast cancer cohorts are of utmost interest. Our aim was to develop an online tool to draw survival plots, which can be used to assess the relevance of the expression levels of various genes on the clinical outcome both in untreated and treated breast cancer patients. A background database was established using gene expression data and survival information of 1,809 patients downloaded from GEO (Affymetrix HGU133A and HGU133+2 microarrays). The median relapse free survival is 6.43 years, 968/1,231 patients are estrogen-receptor (ER) positive, and 190/1,369 are lymph-node positive. After quality control and normalization only probes present on both Affymetrix platforms were retained (n = 22,277). In order to analyze the prognostic value of a particular gene, the cohorts are divided into two groups according to the median (or upper/lower quartile) expression of the gene. The two groups can be compared in terms of relapse free survival, overall survival, and distant metastasis free survival. A survival curve is displayed, and the hazard ratio with 95% confidence intervals and logrank P value are calculated and displayed. Additionally, three subgroups of patients can be assessed: systematically untreated patients, endocrine-treated ER positive patients, and patients with a distribution of clinical characteristics representative of those seen in general clinical practice in the US. Web address: www.kmplot.com . We used this integrative data analysis tool to confirm the prognostic power of the proliferation-related genes TOP2A and TOP2B, MKI67, CCND2, CCND3, CCNDE2, as well as CDKN1A, and TK2. We also validated the capability of microarrays to determine estrogen receptor status in 1,231 patients. The tool is highly valuable for the preliminary assessment of biomarkers, especially for research groups with limited bioinformatic resources.
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“The top 23 differentially expressed genes in CIN-high cells (referred to as CIN signature) predicted distant-metastasis-free survival (DMFS) in a meta-analysis16 as well as a validation cohort17 of patients with breast cancer, irrespective of tumor subtype, grade, or lymph node status”
From Chromosomal instability drives metastasis through a cytosolic DNA response (2018) · cites this paper as [16]
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Metadata
- Authors
- Györffy, B., Lanczky, A., Eklund, A. C., Denkert, C., Budczies, J., Li, Q., & Szallasi, Z.
- Affiliations
- Technical University of Denmark (DK)
- Charité – Universitätsmedizin Berlin (DE)
- Venue
- Breast Cancer Research and Treatment
- Published
- 2009-12-18
- Volume
- 123
- Issue
- 3
- Pages
- 725-731
- ISSN
- 0167-6806 (ISSN-L)
- 1573-7217
- OpenAlex
- W2085934079
- Cited by
- 4,169
- References
- 34
- Citations by year
- 2026: 52 · 2025: 53 · 2024: 119 · 2023: 203 · 2022: 723 · 2021: 1329 · 2020: 434 · 2019: 306 · 2018: 241 · 2017: 192 · 2016: 187 · 2015: 120 · 2014: 100 · 2013: 59 · 2012: 26
- FWCI
- 5.15
- Type
- journal article
- Language
- en
- Open access
- green
- hal.science
- Licence
- other-oa
- www.springer.com/tdm (tdm)
- Locations
- Breast Cancer Research and Treatment (publishedVersion)
- PubMed (publishedVersion)
- Repository of the Academy's Library (Library of the Hungarian Academy of Sciences) (acceptedVersion)
- HAL (submittedVersion, other-oa)
- Technical University of Denmark, DTU Orbit (Technical University of Denmark, DTU) (submittedVersion)
- Semmelweis University Repository (Semmelweis University) (submittedVersion)
- Trinity's Access to Research Output (TARA) (Trinity College Dublin) (submittedVersion)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Breast Cancer Treatment Studies
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- breast cancer prognosis, prognostic gene expression, gene expression microarrays, relapse-free survival, overall survival, distant metastasis, estrogen receptor status, endocrine therapy, TOP2A, TOP2B, MKI67, cell cycle genes, p21, thymidine kinase 2, biomarker validation
- MeSH terms
- Adult, Aged, Breast Neoplasms, Computer Graphics, Female, Genetic Markers, Humans, Lymphatic Metastasis, Middle Aged, Neoplasm Staging, Online Systems, Predictive Value of Tests, Prognosis, Receptors, Estrogen, Time Factors, Biomarkers, Tumor, Reproducibility of Results, Gene Expression Regulation, Neoplastic, Disease-Free Survival, Internet, Oligonucleotide Array Sequence Analysis, Gene Expression Profiling, Kaplan-Meier Estimate
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