The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

Sims, Andrew H.; Smethurst, Graeme J.; Hey, Yvonne; Okoniewski, Michał; Pepper, Stuart D; Howell, Anthony; Miller, Crispin; Clarke, Robert B.

doi:10.1186/1755-8794-1-42

Cited by 134 publications

(170 citation statements)

References 53 publications

(96 reference statements)

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“…Then, only probes measured on both GPL96 and GPL570 were retained (n=22,277). At this stage, we performed a second scaling normalization to set the average expression on each chip to 1000 to avoid batch effects [14].…”

Section: Methodsmentioning

confidence: 99%

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

Győrffy

Lánczky

Eklund

et al. 2009

Breast Cancer Res Treat

3,361

3,760

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Section: Methodsmentioning

confidence: 99%

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

Győrffy

Lánczky

Eklund

et al. 2009

Breast Cancer Res Treat

3,361

3,760

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“…At this stage, we performed a second scaling normalization to set the average expression on each chip to 1000. Although this technique cannot remove all, but it can significantly reduce batch effects (Sims et al 2008). We integrated the gene expression and clinical data using PostgreSQL, an open-source object-relational database system (www.postgresql.…”

Section: Setup Of Server For Online Survival Calculationmentioning

confidence: 99%

Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

Győrffy¹,

Lánczky²,

Szállási³

2012

Endocrine-Related Cancer

785

824

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The validation of prognostic biomarkers in large independent patient cohorts is a major bottleneck in ovarian cancer research. We implemented an online tool to assess the prognostic value of the expression levels of all microarray-quantified genes in ovarian cancer patients. First, a database was set up using gene expression data and survival information of 1287 ovarian cancer patients downloaded from Gene Expression Omnibus and The Cancer Genome Atlas (Affymetrix HG-U133A, HG-U133A 2.0, and HG-U133 Plus 2.0 microarrays). After quality control and normalization, only probes present on all three Affymetrix platforms were retained (nZ22 277). To analyze the prognostic value of the selected gene, we divided the patients into two groups according to various quantile expressions of the gene. These groups were then compared using progression-free survival (nZ1090) or overall survival (nZ1287). A Kaplan-Meier survival plot was generated and significance was computed. The tool can be accessed online at www.kmplot. com/ovar. We used this integrative data analysis tool to validate the prognostic power of 37 biomarkers identified in the literature. Of these, CA125 (MUC16; PZ3.7!10 K5

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“…org/packages/release/bioc/html/annotate.html), and the probe was discarded if it did not match any genes. The two expression datasets were merged and synthetically analyzed using Batch Mean-centering, a merged data method (19), following adaptation according to Support Vector Machines, through the inSilicoMerging package (20).…”

Section: Data Collection and Preprocessingmentioning

confidence: 99%

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

et al. 2017

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Abstract. The aim of the present study was to identify hub genes and signaling pathways associated with bladder cancer (BC) utilizing centrality analysis and pathway enrichment analysis. The differentially expressed genes (DEGs) were screened from the ArrayExpress database between normal subjects and BC patients. Co-expression networks of BC were constructed using differentially co-expressed genes and links, and hub genes were investigated by degree centrality analysis of co-expression networks in BC. The enriched signaling pathways were investigated by Kyoto Encyclopedia of Genes and Genomes database analysis based on the DEGs. The hub gene expression in BC tissues was validated using reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and western blotting. A total of 329 DEGs were screened, including 147 upregulated and 182 downregulated genes. The co-expression network constructed between BC and normal controls consisted of 182 nodes and 434 edges, and the two genes in each gene pair were differentially co-expressed genes. Centrality analysis of co-expression networks suggested that the top 5 hub genes with high degree included lectin, galactoside-binding, soluble, 4 (LGALS4), protein tyrosine phosphatase, receptor type N2 (PTPRN2), transmembrane protease, serine 11E (TMPRSS11E), tripartite motif containing 31 (TRIM31) and potassium voltage-gated channel subfamily D member 3 (KCND3). Pathway analysisrevealed that the 329 DEGs were significantly enriched in 5 terms (cell cycle, DNA replication, oocyte meiosis, p53 signaling pathway and peroxisome proliferator-activated receptor signaling pathway). According to RT-qPCR and western blot analysis, 4/5 hub genes were significantly expressed, including LGALS4, PTPRN2, TMPRSS11E, TRIM31; however, KCND3 was not significantly expressed. In the present study, 5 hub genes were successfully identified (LGALS4, PTPRN2, TMPRSS11E, TRIM31 and KCND3) and 5 biological pathways that may be underlying biomarkers for early diagnosis and treatment associated with bladder cancer were revealed.

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The removal of multiplicative, systematic bias allows integration of breast cancer gene expression datasets – improving meta-analysis and prediction of prognosis

Cited by 134 publications

References 53 publications

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

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

Implementing an online tool for genome-wide validation of survival-associated biomarkers in ovarian-cancer using microarray data from 1287 patients

Identification of hub genes and pathways associated with bladder cancer based on co-expression network analysis

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