Issue 7, 2015

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

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

Article information

Article type
Paper
Submitted
22 ذو الحجة 1435
Accepted
19 صفر 1436
First published
19 صفر 1436

Analyst, 2015,140, 2428-2438

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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