Identification of two new serum protein profiles for renal cell carcinoma

J. Engwegen; N. Mehra; J. Haanen; J.H.M. Schellens; E.E. Voest; J.H. Beijnen

Identification of two new serum protein profiles for renal cell carcinoma

J. Engwegen, N. Mehra, J. Haanen, J.H.M. Schellens, E.E. Voest, J.H. Beijnen

Dept of Pharmaceutical Sciences

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Research output: Contribution to journal › Article › Academic › peer-review

Abstract

Renal cell carcinoma (RCC) is the most lethal urological cancer, and survival greatly depends on early diagnosis. Therefore, reliable, new biomarkers for detection of RCC are required. We assessed serum protein profiles of samples from two institutes with SELDI-TOF MS in duplicate on CM 10 chips at pH 6.0 (set 1:37 RCC + 32 healthy controls (HC), set 2: 20 RCC + 25 HC). Mean peak intensities of detected proteins were compared between RCC and HC with non-parametric testing. Classification trees were built with discriminating peaks using one sample set as training set and the other as independent validation set. We found 15 peaks significantly different (p

Original language	Undefined/Unknown
Pages (from-to)	401-408
Number of pages	8
Journal	Oncology Reports
Volume	22
Issue number	2
Publication status	Published - 2009

Bibliographical note

ISI:000267858200024

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Cite this

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AU - Engwegen, J.

AU - Mehra, N.

AU - Haanen, J.

AU - Schellens, J.H.M.

AU - Voest, E.E.

AU - Beijnen, J.H.

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AB - Renal cell carcinoma (RCC) is the most lethal urological cancer, and survival greatly depends on early diagnosis. Therefore, reliable, new biomarkers for detection of RCC are required. We assessed serum protein profiles of samples from two institutes with SELDI-TOF MS in duplicate on CM 10 chips at pH 6.0 (set 1:37 RCC + 32 healthy controls (HC), set 2: 20 RCC + 25 HC). Mean peak intensities of detected proteins were compared between RCC and HC with non-parametric testing. Classification trees were built with discriminating peaks using one sample set as training set and the other as independent validation set. We found 15 peaks significantly different (p

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