Are data from different gene expression microarray platforms comparable?

Järvinen, Anna-Kaarina; Hautaniemi, Sampsa; Edgren, Henrik; Auvinen, Petri; Saarela, Janna; Kallioniemi, Olli; Monni, Outi

doi:10.1016/j.ygeno.2004.01.004

Cited by 171 publications

(129 citation statements)

References 26 publications

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“…For example, the reported Pearson correlation coefficient between technical replicates can range between the disappointing level of 0.5 and the more reassuring level of 0.95. [148][149][150] Cross-platform reproducibility studies undertaken so far 148,149,151 have identified two main problems. First, microarrays are not able to accurately measure genes expressed at low levels.…”

Section: Reproducibilitymentioning

confidence: 99%

Analysis of microarray experiments of gene expression profiling

Tarca

Romero

Drăghici³

2006

American Journal of Obstetrics and Gynecology

263

150

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The study of gene expression profiling of cells and tissue has become a major tool for discovery in medicine. Microarray experiments allow description of genome-wide expression changes in health and disease. The results of such experiments are expected to change the methods employed in the diagnosis and prognosis of disease in obstetrics and gynecology. Moreover, an unbiased and systematic study of gene expression profiling should allow the establishment of a new taxonomy of disease for obstetric and gynecologic syndromes. Thus, a new era is emerging in which reproductive processes and disorders could be characterized using molecular tools and fingerprinting. The design, analysis, and interpretation of microarray experiments require specialized knowledge that is not part of the standard curriculum of our discipline. This article describes the types of studies that can be conducted with microarray experiments (class comparison, class prediction, class discovery). We discuss key issues pertaining to experimental design, data preprocessing, and gene selection methods. Common types of data representation are illustrated. Potential pitfalls in the interpretation of microarray experiments, as well as the strengths and limitations of this technology, are highlighted. This article is intended to assist clinicians in appraising the quality of the scientific evidence now reported in the obstetric and gynecologic literature.

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Section: Reproducibilitymentioning

confidence: 99%

Analysis of microarray experiments of gene expression profiling

Tarca

Romero

Drăghici³

2006

American Journal of Obstetrics and Gynecology

263

150

View full text Add to dashboard Cite

show abstract

“…However, in most studies little overlap was found between differentially expressed genes, which may be due to a variety of methodological issues (18). Explanations that have been debated extensively in the literature are the use of different microarray platforms (18,19) and the restricted number of samples used to select genes from a large pool of probes (20). Therefore, comparing gene expression profiles with gene sets that represent unique pathways may provide more insight into the mechanism of lymph node metastasis.…”

Section: Introductionmentioning

confidence: 99%

Involvement of the TGF-β and β-Catenin Pathways in Pelvic Lymph Node Metastasis in Early-Stage Cervical Cancer

Noordhuis

Fehrmann²,

Wisman

et al. 2011

Clinical Cancer Research

116

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Purpose: Presence of pelvic lymph node metastases is the main prognostic factor in early-stage cervical cancer patients, primarily treated with surgery. Aim of this study was to identify cellular tumor pathways associated with pelvic lymph node metastasis in early-stage cervical cancer.Experimental Design: Gene expression profiles (Affymetrix U133 plus 2.0) of 20 patients with negative (N 0 ) and 19 with positive lymph nodes (N þ ), were compared with gene sets that represent all 285 presently available pathway signatures. Validation immunostaining of tumors of 274 consecutive early-stage cervical cancer patients was performed for representatives of the identified pathways.Results: Analysis of 285 pathways resulted in identification of five pathways (TGF-b, NFAT, ALK, BAD, and PAR1) that were dysregulated in the N 0 , and two pathways (b-catenin and Glycosphingolipid Biosynthesis Neo Lactoseries) in the N þ group. Class comparison analysis revealed that five of 149 genes that were most significantly differentially expressed between N 0 and N þ tumors (P < 0.001) were involved in b-catenin signaling (TCF4, CTNNAL1, CTNND1/p120, DKK3, and WNT5a). Immunohistochemical validation of two well-known cellular tumor pathways (TGF-b and b-catenin) confirmed that the TGF-b pathway (positivity of Smad4) was related to N 0 (OR: 0.20, 95% CI: 0.06-0.66) and the b-catenin pathway (p120 positivity) to N þ (OR: 1.79, 95%CI: 1.05-3.05).Conclusions: Our study provides new, validated insights in the molecular mechanism of lymph node metastasis in cervical cancer. Pathway analysis of the microarray expression profile suggested that the TGF-b and p120-associated noncanonical b-catenin pathways are important in pelvic lymph node metastasis in early-stage cervical cancer.

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“…This is similar to the situation in cDNA microarray data [62]. The hope is to avoid repetition and to allow for better comparisons between studies for more definitive answers.…”

Section: Data Reporting Standardizationmentioning

confidence: 64%

Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer

et al. 2006

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The human proteome, due to the enormity of post-translational permutations that result in large numbers of isoforms, is much more complex than the genome and alterations in cancer can occur in ways that are not predictable by translational analysis alone. Proteomic analysis therefore represents a more direct way of investigating disease at the individual patient level. Furthermore, since most novel therapeutic targets are proteins, proteomic analysis potentially has a central role in patient care. At the same time, it is becoming clear that mapping entire networks rather than individual markers may be necessary for robust diagnostics as well as tailoring of therapy. Consequently, there is a need for high-throughput multiplexed proteomic techniques, with the capability of scanning multiple cases and analysing large numbers of endpoints. New types of protein arrays combined with advanced bioinformatics are currently being used to identify molecular signatures of individual tumours based on protein pathways and signalling cascades. It is envisaged that analysing the cellular 'circuitry' of ongoing molecular networks will become a powerful clinical tool in patient management.

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Are data from different gene expression microarray platforms comparable?

Cited by 171 publications

References 26 publications

Analysis of microarray experiments of gene expression profiling

Analysis of microarray experiments of gene expression profiling

Involvement of the TGF-β and β-Catenin Pathways in Pelvic Lymph Node Metastasis in Early-Stage Cervical Cancer

Array-based proteomics: mapping of protein circuitries for diagnostics, prognostics, and therapy guidance in cancer

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