Research Papers:

MALDI-imaging reveals thymosin beta-4 as an independent prognostic marker for colorectal cancer

Timo Gemoll _, Sarah Strohkamp, Katharina Schillo, Christoph Thorns and Jens K. Habermann

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Oncotarget. 2015; 6:43869-43880. https://doi.org/10.18632/oncotarget.6103

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Timo Gemoll1, Sarah Strohkamp1, Katharina Schillo1, Christoph Thorns2, Jens K. Habermann1

1Section for Translational Surgical Oncology and Biobanking, Department of Surgery, University of Lübeck and University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany

2Department of Pathology, University Medical Center Schleswig-Holstein, Campus Lübeck, Lübeck, Germany

Correspondence to:

Timo Gemoll, e-mail: [email protected]

Jens K. Habermann, e-mail: [email protected]

Keywords: mass spectrometry, genomic instability, aneuploidy, Tβ-4, prognosis

Received: June 16, 2015     Accepted: October 11, 2015     Published: November 05, 2015


DNA aneuploidy has been identified as a prognostic factor for epithelial malignancies. Matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS) is a powerful tool for direct analysis of multiple proteins in tissue sections while maintaining the cellular and molecular integrity. We compared diploid and aneuploid colon cancer tissues against normal mucosa of the colon by means of IMS.

DNA image cytometry determined the ploidy status of tissue samples that were subsequently subjected to MALDI-IMS. After obtaining protein profiles through direct analysis of tissue sections, a discovery and independent validation set were used to predict ploidy status by applying proteomic classification algorithms [Supervised Neural Network (SNN) and Receiver Operating Characteristic (ROC)]. Five peaks (m/z 2,395 and 4,977 for diploid vs. aneuploid comparison as well as m/z 3,376, 6,663, and 8,581 for normal mucosa vs. carcinoma comparison) were significant in both SNN and ROC analysis. Among these, m/z 4,977 was identified as thymosin beta 4 (Tβ-4). Tβ-4 was subsequently validated in clinical samples using a tissue microarray to predict overall survival in colon cancer patients.

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