ABSTRACT
Specific genome copy number alterations, such as deletions and amplifications are an important factor in tumor development and progression, and are also associated with changes in gene expression. By combining analyses of gene expression and genome copy number we identified genes as candidate biomarkers of BC which were validated as prognostic factors of the disease progression. These results suggest that the proposed combined approach may become a valuable method for BC prognosis.
Subject(s)
Breast Neoplasms/diagnosis , Breast Neoplasms/genetics , Gene Dosage/genetics , Gene Expression Regulation, Neoplastic , Breast Neoplasms/pathology , Female , Genome, Human , Humans , Polymorphism, Single Nucleotide , Prognosis , Reproducibility of ResultsABSTRACT
Type 1 diabetes (insulin-dependent diabetes mellitus, IDDM) is an autoimmune disease affecting about 0.12% of the world's population. Diabetic nephropathy (DN) is a major long-term complication of both types of diabetes and retains a high human, social and economic cost. Thus, the identification of markers for the early detection of DN represents a relevant target of diabetic research. The present work is a pilot study focused on proteomic analysis of serum of controls (n=9), IDDM patients (n=10) and DN patients (n=4) by the ClinProt profiling technology based on mass spectrometry. This approach allowed to identify a pattern of peptides able to differentiate the studied populations with sensitivity and specificity close to 100%. Variance of the results allowed to estimate the sample size needed to keep the expected False Discovery Rate low. Moreover, three peptides differentially expressed in the serum of patients as compared to controls were identified by LC-ESI MS/MS as the whole fibrinopeptide A peptide and two of its fragments, respectively. The two fragments were under-expressed in diabetic patients, while Fibrinopeptide A was over-expressed, suggesting that anomalous turnover of Fibrinopeptide A could be involved in the pathogenesis of DN.
Subject(s)
Diabetes Mellitus, Type 1/blood , Diabetes Mellitus, Type 1/complications , Diabetic Nephropathies/blood , Fibrinopeptide A/analysis , Peptide Fragments/blood , Adult , Area Under Curve , Blood Pressure/physiology , Case-Control Studies , Chromatography, Liquid/methods , Diabetes Mellitus, Type 1/metabolism , Diabetes Mellitus, Type 1/physiopathology , Diabetic Nephropathies/metabolism , Diabetic Nephropathies/physiopathology , Female , Fibrinopeptide A/chemistry , Fibrinopeptide A/metabolism , Humans , Male , Metabolome , Middle Aged , Peptide Fragments/analysis , Peptide Fragments/metabolism , Pilot Projects , Proteome/analysis , Spectrometry, Mass, Electrospray Ionization/methods , Tandem Mass Spectrometry/methodsABSTRACT
We present a hybrid system for managing both symbolic and subsymbolic knowledge in a uniform way. Our aim is to solve problems where some gap in formal theories occurs which stops us from getting a fully symbolical solution. The idea is to use neural modules to functionally connect pieces of symbolical knowledge, such as mathematical formulas and deductive rules. The whole system is trained through a backpropagation learning algorithm where all (symbolic or subsymbolic) free parameters are updated piping back the error through each component of the system. The structure of this system is very general, possibly varying over time, possibly managing fuzzy variables and decision trees. We use as a test-bed the problem of sorting a file, where suitable suggestions on next sorting moves are supplied by the network also on the basis of hints provided by some conventional sorters. A comprehensive discussion of system performance is provided in order to understand behaviors and capabilities of the proposed hybrid system.
