Proteomics for biomarker discovery in malignant melanoma

Findeisen, Peter; Peccerella, Teresa; Neumaier, Michael; Schadendorf, Dirk

doi:10.1586/17469872.3.2.209

Cited by 6 publications

(7 citation statements)

References 107 publications

(107 reference statements)

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“…Regardless of this controversy, we chose this fractionation procedure as a convenient method to produce a cell fraction amenable to proteomics approaches. Although the nuclear matrix protein composition had been examined both in normal human tissue [19][20][21] and in melanoma cells [22], the nuclear matrix composition had not been examined in either of these groups.…”

Section: Discussionmentioning

confidence: 99%

“…[19][20][21]] and of melanocytes and melanoma cells has been reported [reviewed in reference [22]], the protein composition of the nuclear matrixintermediate filament subproteome from neither the skin epidermis nor melanoma cells has not been reported. Thus we decided that an examination of the protein content of the melanoma nuclear matrix-intermediate filament system might be a productive avenue for identifying melanoma biomarkers.…”

Section: Introductionmentioning

confidence: 99%

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The nuclear matrix shell proteome of human epidermis

Warters

Cassidy

Sunseri

et al. 2010

Journal of Dermatological Science

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The nuclear matrix shell proteome of human epidermis

Warters

Cassidy

Sunseri

et al. 2010

Journal of Dermatological Science

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“…This approximate location (11728) is identified in our analysis as one where the two groups differ in mean. In follow‐up work Findeisen et al (2008) reported that this signal is stably overexpressed in late stage melanoma (stages III and IV), even when using matrix‐assisted laser desorption–ionization rather than SELDI. This signal has been identified as serum amyloid A, which has also be seen in other malignant diseases (Hortin, 2006).…”

Section: Resultsmentioning

confidence: 98%

Mixed Effect Modelling of Proteomic Mass Spectrometry Data by Using Gaussian Mixtures

Browne

Dryden

Handley

et al. 2010

Journal of the Royal Statistical Society Series C: Applied Statistics

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Statistical methodology for the analysis of proteomic mass spectrometry data is proposed using mixed effects models. Each high dimensional spectrum is represented by using a near orthogonal low dimensional representation with a basis of Gaussian mixture functions. Linear mixed effect models are proposed in the lower dimensional space. In particular, differences between groups are investigated by using fixed effect parameters, and individual variability of spectra is modelled by using random effects. A deterministic peak fitting algorithm provides estimates of the near orthogonal Gaussian basis. The mixed effects model is fitted by using restricted maximum likelihood, and a parallel fitting procedure is used for computational convenience. The methodology is applied to proteomic mass spectrometry data from serum samples from melanoma patients who were categorized as stage I or stage IV, and significant locations of peaks are identified. Copyright (c) 2010 Royal Statistical Society.

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“…Proteomic studies of melanoma using mass spectrometry (MS)‐based strategies have generally been limited to cultured melanoma cell lines and serum samples of patients with melanoma 4,5 . In only a handful of cases have solid tumor melanoma specimens been studied by MS‐based approaches 6–12 .…”

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confidence: 99%

“…Proteomic studies of melanoma using mass spectrometry (MS)-based strategies have generally been limited to cultured melanoma cell lines and serum samples of patients with melanoma. 4,5 In only a handful of cases have solid tumor melanoma specimens been studied by MS-based approaches. 6 -12 Of these, two studies have utilized xenografts from melanoma cell lines or solid murine melanoma, 6,7 one study employed laser microdissected melanoma cells from skin organ cultures 8 and four studies analyzed solid tumor samples from patients with melanoma.…”

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confidence: 99%