RESUMO
Gene expression microarrays allow for the high throughput analysis of huge numbers of gene transcripts and this technology has been widely applied to the molecular and biological classification of cancer patients and in predicting clinical outcome. A potential handicap of such data intensive molecular technologies is the translation to clinical application in routine practice. In using an artificial neural network bioinformatic approach, we have reduced a 70 gene signature to just 9 genes capable of accurately predicting distant metastases in the original dataset. Upon validation in a follow-up cohort, this signature was an independent predictor of metastases free and overall survival in the presence of the 70 gene signature and other factors. Interestingly, the ANN signature and CA9 expression also split the groups defined by the 70 gene signature into prognostically distinct groups. Subsequently, the presence of protein for the principal prognosticator gene was categorically assessed in breast cancer tissue of an experimental and independent validation patient cohort, using immunohistochemistry. Importantly our principal prognosticator, CA9, showed that it is capable of selecting an aggressive subgroup of patients who are known to have poor prognosis.
Assuntos
Biomarcadores Tumorais/genética , Neoplasias da Mama/genética , Perfilação da Expressão Gênica , Metástase Neoplásica/genética , Redes Neurais de Computação , Adulto , Idoso , Antígenos de Neoplasias/biossíntese , Área Sob a Curva , Neoplasias da Mama/patologia , Anidrase Carbônica IX , Anidrases Carbônicas/biossíntese , Biologia Computacional/métodos , Feminino , Humanos , Imuno-Histoquímica , Pessoa de Meia-Idade , Prognóstico , Curva ROC , Sensibilidade e Especificidade , Análise Serial de TecidosRESUMO
A variety of prefractionation methods (including a novel reversed-phase solid-phase-extraction (RP-SPE) combined with SDS-PAGE and proteomic based approaches (e.g., 2-dimensional gel electrophoresis (2DE) and MALDI-TOF mass spectrometry combined with Artificial Neural Network (ANN) bioinformatic tools) were used to investigate the protein/peptide signatures in patients with Polycystic Ovary Syndrome (PCOS). Four potential PCOS biomarkers were identified (complement C4alpha3c and C4gamma and haptoglobin alpha and beta chains).
Assuntos
Síndrome do Ovário Policístico/metabolismo , Proteoma/metabolismo , Biomarcadores/metabolismo , Eletroforese em Gel Bidimensional/métodos , Feminino , Humanos , Espectrometria de Massas por Ionização e Dessorção a Laser Assistida por Matriz/métodosRESUMO
MOTIVATION: Robust computer algorithms are required to interpret the vast amounts of proteomic data currently being produced and to generate generalized models which are applicable to 'real world' scenarios. One such scenario is the classification of bacterial species. These vary immensely, some remaining remarkably stable whereas others are extremely labile showing rapid mutation and change. Such variation makes clinical diagnosis difficult and pathogens may be easily misidentified. RESULTS: We applied artificial neural networks (Neuroshell 2) in parallel with cluster analysis and principal components analysis to surface enhanced laser desorption/ionization (SELDI)-TOF mass spectrometry data with the aim of accurately identifying the bacterium Neisseria meningitidis from species within this genus and other closely related taxa. A subset of ions were identified that allowed for the consistent identification of species, classifying >97% of a separate validation subset of samples into their respective groups. AVAILABILITY: Neuroshell 2 is commercially available from Ward Systems.
