Article

Neuroblastoma detection using serum proteomic profiling: a novel mining technique for cancer?

Department of Surgery, Indiana University School of Medicine and Riley Children's Hospital, Indianapolis, IN 46202, USA.
Journal of Pediatric Surgery (Impact Factor: 1.38). 04/2006; 41(4):639-46; discussion 639-46. DOI: 10.1016/j.jpedsurg.2005.12.037
Source: PubMed

ABSTRACT Serum proteins in neuroblastoma (NB), such as neuron-specific enolase and lactate dehydrogenase, are used as nonspecific markers of disease severity. In this study, we have generated serum protein profiles that correlate with NB by applying proteomic technologies to uncover, at the protein level, serum polypeptide expression patterns in patients with NB.
Surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry was used to generate protein expression spectra in human NB (group I, n = 18) and healthy children (group II, n = 17) sera. Groups I and II mass spectral data were compared after baseline subtraction. Peaks with high signal-to-noise ratios were selected and grouped into bins with various intervals along mass-to-charge axis. Two-sample t test and 3-fold cross validation were used to identify differential biomarkers between groups I and II.
Significant differentially expressed proteins were identified between groups I and II (P < or = .05). The discriminatory features (proteomic patterns) of cancer from normal sera were successfully identified using the classification algorithm. The average classification performance after 3-fold cross validation was 87.26%.
Surface-enhanced laser desorption/ionization-time-of-flight mass spectrometry is suitable for preliminary assessment of NB and could potentially provide a noninvasive diagnosis of NB. We propose that surface-enhanced laser desorption/ionization provides a novel method for NB diagnosis because direct observations of spectral differences between normal and NB sera are possible.

0 Bookmarks
 · 
66 Views
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Soft tissue sarcomas are rare and account for less than 1% of all malignant cancers. Other than development of intensive therapies, the clinical outcome of patients with soft tissue sarcoma remains very poor, particularly when diagnosed at a late stage. Unique mutations have been associated with certain soft tissue sarcomas, but their etiologies remain unknown. The proteome is a functional translation of a genome, which directly regulates the malignant features of tumors. Thus, proteomics is a promising approach for investigating soft tissue sarcomas. Various proteomic approaches and clinical materials have been used to address clinical and biological issues, including biomarker development, molecular target identification, and study of disease mechanisms. Several cancer-associated proteins have been identified using conventional technologies such as 2D-PAGE, mass spectrometry, and array technology. The functional backgrounds of proteins identified were assessed extensively using in vitro experiments, thus supporting expression analysis. These observations demonstrate the applicability of proteomics to soft tissue sarcoma studies. However, the sample size in each study was insufficient to allow conclusive results. Given the low frequency of soft tissue sarcomas, multi-institutional collaborations are required to validate the results of proteomic approaches.
    International journal of proteomics. 01/2012; 2012:876401.
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: High-mobility group proteins are a superfamily of DNA-binding proteins that bind to the DNA minor groove and bend it, whereas most of the transcription factors such as centromere protein B (CENP-B), octamer (Oct)-1, growth factor independence 1 (Gfi-1), and WRKY bind to the major groove of DNA. Classification of proteins using their DNA-binding features is the aim of this study. Nuclear localization signals play more important roles in entering DNA-binding proteins to nucleus and doing their functions; therefore, they have been considered as a feature which is important for DNA-binding manner in proteins. Nuclear localization signals (NLSs) were predicted by two prediction web servers, and then, their sequence ordered features were extracted by Chou's pseudo amino acid composition (PseAAC) and ProtParam. Multilayer perceptron was used as an artificial neural network for analyzing the features by calculating the correlation coefficient and 30-fold cross-validation. Another used data-analyzing program was principal component analysis of the Minitab software. By calculating the eigenvalues and considering five principal components, the sequence length of NLSs was known as the best feature for classifying DNA-binding proteins. Minimum mean squared error (MSE) (0.1098) and the highest R (2) (0.963) mean that there is a significant difference between the NLS length of the DNA major groove and minor groove binder proteins. Results showed that it is possible to classify DNA major groove and minor groove binder proteins by their NLS sequences as a feature.
    Applied Biochemistry and Biotechnology 05/2014; · 1.89 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: High-risk neuroblastoma (NB) represents a problematic tumor phenotype associated with a dreary outlook. Modern molecular achievements over the last decade have seen the increase and implementation of 'omics technologies in oncology that promises to provide for a deeper comprehension of complex tumor pathways. The emerging concept of analyzing NB-specific 'omics profiles to better understand and define the behavior of advanced-stage tumors along with providing direct and targeted therapy may ultimately translate into improved outcomes for high-risk NB. Knowledge of NB proteomics has gradually become available, but the challenge remains to integrate data obtained from different levels of biological organization. In this review, we provide an overview of the proteomics-based techniques that can be used to advance and accelerate the discovery of novel molecular biomarkers for NB. By citing specific examples, we discuss how proteomics has contributed to the early detection of advanced-stage NB and minimal residual disease. We end by contemplating the emerging technologies that are likely to have a high impact on the field of NB in the near future.
    Current Proteomics 03/2010; 7(1):1-14. · 0.83 Impact Factor

Full-text (2 Sources)

Download
48 Downloads
Available from
Jun 5, 2014