Biomarkers that discriminate Multiple Myeloma Patients With or Without Skeletal Involvement Detected Using SELDI-TOF Mass Spectrometry and Statistical and Machine Learning Tools

Department of Orthopaedic Surgery, Center for Orthopaedic Research, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA.
Disease markers (Impact Factor: 1.56). 02/2006; 22(4):245-55. DOI: 10.1155/2006/728296
Source: PubMed


Multiple Myeloma (MM) is a severely debilitating neoplastic disease of B cell origin, with the primary source of morbidity and mortality associated with unrestrained bone destruction. Surface enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) was used to screen for potential biomarkers indicative of skeletal involvement in patients with MM. Serum samples from 48 MM patients, 24 with more than three bone lesions and 24 with no evidence of bone lesions were fractionated and analyzed in duplicate using copper ion loaded immobilized metal affinity SELDI chip arrays. The spectra obtained were compiled, normalized, and mass peaks with mass-to-charge ratios (m/z) between 2000 and 20,000 Da identified. Peak information from all fractions was combined together and analyzed using univariate statistics, as well as a linear, partial least squares discriminant analysis (PLS-DA), and a non-linear, random forest (RF), classification algorithm. The PLS-DA model resulted in prediction accuracy between 96-100%, while the RF model was able to achieve a specificity and sensitivity of 87.5% each. Both models as well as multiple comparison adjusted univariate analysis identified a set of four peaks that were the most discriminating between the two groups of patients and hold promise as potential biomarkers for future diagnostic and/or therapeutic purposes.

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Available from: Joshua Epstein, May 22, 2015
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    • "Now more and more field scientists prefer to choose machine learning techniques, especially when PLS-DA fails to give credible or good solutions (Bhattacharyyas et al. 2006; Mahadevan et al. 2008; Man et al. 2004). Chronic diseases are generally considered as systemic pathological changes, which may influence the metabonome in many aspects of different pathways. "
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    • "Another group also used mass spectrometry to identify serum biomarkers that might discriminate between patients with skeletal involvement [67]. This group screened serum samples from 48 patients either with evidence of skeletal involvement (24 patients) or without evidence of skeletal involvement (24 patients). "
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