ANALYSIS OF CORRELATION STRUCTURES IN RENAL CELL CARCINOMA PATIENT DATA

Italo Zoppis, Massimiliano Borsani, Erica Gianazza, Clizia Chinello, Francesco Rocco, Giancarlo Albo, André M. Deelder, Yuri E. M. van der Burgt, Fulvio Magni, Marco Antoniotti, Giancarlo Mauri

Abstract

Mass Spectrometry (MS)-based technologies represent a promising area of research in clinical analysis. They are primarily concerned with measuring the relative intensity (abundance) of many protein/peptide molecules associated with their mass-to-charge ratios over a particular range of molecular masses. These measurements (generally referred as proteomic signals or spectra) constitute a huge amount of information which requires adequate tools to be investigated and interpreted. Following the methodology for testing hypotheses, we investigate the proteomic signals of the most common type of Renal Cell Carcinoma, the Clear Cell variant (ccRCC). Specifically, the aim of our investigation is to detect changes of the signal correlations from control to case group (ccRCC or non–ccRCC). To this end, we sample and represent each population group through a graph providing, as it will be defined below, the observed signal correlation structure. This way, graphs establish abstract frames of reference in our analysis giving the opportunity to test hypotheses over their properties. In other terms, changes are detected by testing graph property modifications from group to group. We show the results by reporting the mass-to-charge values which identify bounded regions where changes have been detected. The main interest in handling these regions is to perceive which signal ranges are associated with some specific factors of interest (e.g., studying differentially expressed peaks between case and control groups) and thus, to suggest potential biomarkers for future analysis or for clinical monitoring. Data were collected, from patients and healthy volunteers at the Ospedale Maggiore Policlinico Foundation (Milano, Italy).

References

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Paper Citation


in Harvard Style

Zoppis I., Borsani M., Gianazza E., Chinello C., Rocco F., Albo G., M. Deelder A., E. M. van der Burgt Y., Antoniotti M., Magni F. and Mauri G. (2012). ANALYSIS OF CORRELATION STRUCTURES IN RENAL CELL CARCINOMA PATIENT DATA . In Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012) ISBN 978-989-8425-90-4, pages 251-256. DOI: 10.5220/0003856702510256


in Bibtex Style

@conference{bioinformatics12,
author={Italo Zoppis and Massimiliano Borsani and Erica Gianazza and Clizia Chinello and Francesco Rocco and Giancarlo Albo and André M. Deelder and Yuri E. M. van der Burgt and Marco Antoniotti and Fulvio Magni and Giancarlo Mauri},
title={ANALYSIS OF CORRELATION STRUCTURES IN RENAL CELL CARCINOMA PATIENT DATA},
booktitle={Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)},
year={2012},
pages={251-256},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0003856702510256},
isbn={978-989-8425-90-4},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Bioinformatics Models, Methods and Algorithms - Volume 1: BIOINFORMATICS, (BIOSTEC 2012)
TI - ANALYSIS OF CORRELATION STRUCTURES IN RENAL CELL CARCINOMA PATIENT DATA
SN - 978-989-8425-90-4
AU - Zoppis I.
AU - Borsani M.
AU - Gianazza E.
AU - Chinello C.
AU - Rocco F.
AU - Albo G.
AU - M. Deelder A.
AU - E. M. van der Burgt Y.
AU - Antoniotti M.
AU - Magni F.
AU - Mauri G.
PY - 2012
SP - 251
EP - 256
DO - 10.5220/0003856702510256