Abstract
The altered glucose metabolism characterising cancer cells determines an increased amount of methylglyoxal in their secretome. Previous studies have demonstrated that the methylglyoxal, in turn, modifies the protonation state (PS) of soluble proteins contained in the secretomes of cultivated circulating tumour cells (CTCs). In this study, we describe a method to assess the content of methylglyoxal adducts (MAs) in the secretome by near-infrared (NIR) portable handheld spectroscopy and the extreme learning machine (ELM) algorithm. By measuring the vibration absorption functional groups containing hydrogen, such as C-H, O-H and N-H, NIR generates specific spectra. These spectra reflect alterations of the energy frequency of a sample bringing information about its MAs concentration levels. The algorithm deciphers the information encoded in the spectra and yields a quantitative estimate of the concentration of MAs in the sample. This procedure was used for the comparative analysis of different biological fluids extracted from patients suspected of having cancer (secretome, plasma, serum, interstitial fluid and whole blood) measured directly on the solute left on a surface upon a sample-drop cast and evaporation, without any sample pretreatment. Qualitative and quantitative regression models were built and tested to characterise the different levels of MAs by ELM. The final model we selected was able to automatically segregate tumour from non-tumour patients. The method is simple, rapid and repeatable; moreover, it can be integrated in portable electronic devices for point-of-care and remote testing of patients.
Highlights
Evaluation of the NIR Spectroscopic Response of Different Biological Matrices. It is well-known that the performances of the machine learning procedure are strictly. It is well-known that the performances of the machine learning procedure are strictly related to to the the nature nature(chemical
In order to assess the reliability of the matrix chosen for our study and toand set to set theanalytical best analytical condition we evaluated the the best condition we evaluated the specific specific spectroscopic behaviour of the secretome in comparison with biological other biospectroscopic behaviour of the secretome samplessamples in comparison with other logical matrices usually exploited in the laboratory cancer procedures
NIRresponse responseclose closetotothat that plasma, interFigure ofof plasma, interstistitial fluid and serum, suggesting the possibility to apply to our matrix the same chemotial fluid and serum, suggesting the possibility to apply to our matrix the same chemometric metric calibration procedures established forbiological other biological calibration procedures alreadyalready established for other sourcessources of interstitial fluid, plasma, serum, secretome and undiluted blood biological matrices
Summary
Several different approaches have been developed for fast, efficient and reliable early cancer detection. Separation techniques including 2D gel electrophoresis, liquid chromatography (LC) hyphenated to electrospray ionization mass spectrometry (ESI-MS) [1], capillary electrophoresis [2], enrichment techniques and MALDI-imaging MS techniques [3], have been shown to be too time-consuming for fast high-throughput online analysis. Plasmonic biosensors and fluorescence coupled vibrational spectroscopy techniques can provide nondestructive, rapid, clinically relevant diagnostic information but are not affordable due to the need for highly sophisticated laboratories [4,5]. In the last decade, near-infrared spectroscopy (NIRS) has gained importance for non-invasive or minimally
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