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Issue 7, 2015
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The combination of artificial neural networks and synchrotron radiation-based infrared micro-spectroscopy for a study on the protein composition of human glial tumors

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Abstract

Protein-related changes associated with the development of human brain gliomas are of increasing interest in modern neuro-oncology. It is due to the fact that they might make some of these tumors highly aggressive and difficult to treat. This paper presents a methodology for protein-based analysis of human brain gliomas using synchrotron radiation based Fourier transform infrared spectroscopy (SRFTIR) coupled with artificial neural networks (ANNs). The main goal of this study was to optimize a set of ANNs to predict the secondary structure of proteins (alpha-helices, beta-sheets, beta-turns, bends, random coils) in brain gliomas, based on the amide I–II spectral range. All networks were tested and optimized to reach the standard error of prediction (SEP) lower than 5%. The results indicate that protein-related changes are associated with a tumor's malignancy grade. Particularly, the content of alpha helices increases with increasing malignancy grade, while the content of beta sheets decreases. We also found that proteomic information could be a useful marker to distinguish either between low and high grade tumors or between oligodendroglial- and astrocyte-derived ones. This demonstrates the applicability of FTIR coupled with ANNs to provide clinically relevant information.

Graphical abstract: The combination of artificial neural networks and synchrotron radiation-based infrared micro-spectroscopy for a study on the protein composition of human glial tumors

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Publication details

The article was received on 16 Oct 2014, accepted on 11 Dec 2014 and first published on 11 Dec 2014


Article type: Paper
DOI: 10.1039/C4AN01867B
Citation: Analyst, 2015,140, 2428-2438

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    The combination of artificial neural networks and synchrotron radiation-based infrared micro-spectroscopy for a study on the protein composition of human glial tumors

    A. D. Surowka, D. Adamek and M. Szczerbowska-Boruchowska, Analyst, 2015, 140, 2428
    DOI: 10.1039/C4AN01867B

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