Abstract
Background DNA aneuploidy has been identified as a prognostic factor for epithelial malignancies. In this study, we compared diploid and aneuploid colon cancer tissues against normal mucosa of the colon by means of matrix-assisted laser desorption/ionization (MALDI) imaging mass spectrometry (IMS). Material and Methods 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 a validation set were used to predict ploidy and disease status by applying proteomic classification algorithms [Supervised Neural Network (SNN) and Receiver Operating Characteristic (ROC)]. Clinical target validation was performed by immunohistochemistry using tissue microarrays (TMA) comprising healthy controls as well as diploid and aneuploid colorectal carcinomas. Results SNN algorithm categorized 99% of normal mucosa and 90% of colon carcinoma as well as 99% of diploid and 94% of aneuploid colon cancers correctly. Validation of both comparisons showed a correct classification of normal mucosa in 92%, tumors in 96%, and diploid and aneuploid colon cancers in 92% and 78%, respectively. Five peaks (m/z 2,396 and 4,977 for the diploid vs. aneuploid comparison and m/z 3,375, 6,663, 8,581 for the normal mucosa vs. carcinoma comparison) reached significance in both SNN and ROC analysis. Among these,m/z 4,977was identified as thymosin beta 4 (TYB4). TYB4 showed expression differences also in clinical samples using a tissue microarray of normal mucosa, diploid and aneuploid colorectal carcinomas and serve to predict overall survival. Conclusion Our data underscore the potential of MALDI-IMS proteomic algorithms to reveal significant molecular details from distinct tumor subtypes such as different ploidy types. Tβ-4 was validated in clinical samples using a tissue microarray to predict overall survival in colorectal cancer patients.
| Original language | German |
|---|---|
| Journal | Medizinische Genetik |
| Volume | 28 |
| Issue number | 1 |
| Pages (from-to) | 135 |
| ISSN | 0936-5931 |
| DOIs | |
| Publication status | Published - 2016 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 3 Good Health and Well-being
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SDG 9 Industry, Innovation, and Infrastructure
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