Ronald Rapberger | PubMed Health

PubMed2006

Identification of a melanoma marker derived from melanoma-associated endogenous retroviruses.

We previously described the expression of melanoma-associated endogenous retrovirus (MERV) proteins and viral particles in human melanomas and metastases. The objective of the present study was to determine whether a humoral immune response to MERV proteins occurs in melanoma. Candidate B-cell epitopes on MERV proteins were predicted using bioinformatic screening. The reactivity of MERV peptides corresponding to the predicted epitopes with antibodies prevalent in sera of melanoma patients was analyzed. An immunodominant peptide located in the env protein of MERV was identified. Subsequent analyzes using 81 samples from stage I to stage IV melanoma patients and 95 sera from healthy subjects revealed statistically significant differences in seroprevalence of antibodies in melanoma sera samples when compared with reference samples from healthy subjects. The prevalence of anti-MERV antibodies in melanoma patient sera was confirmed by immunofluorescence on env-transfected cells. These data indicate the potential of this candidate peptide as target for diagnosis and immunotherapy.

Antibodies, Neoplasm↗

PubMed2006

Transforming omics data into context: bioinformatics on genomics and proteomics raw data.

Differential gene expression analysis and proteomics have exerted significant impact on the elucidation of concerted cellular processes, as simultaneous measurement of hundreds to thousands of individual objects on the level of RNA and protein ensembles became technically feasible. The availability of such data sets has promised a profound understanding of phenomena on an aggregate level, expressed as the phenotypic response (observables) of cells, e.g., in the presence of drugs, or characterization of cells and tissue displaying distinct patho-physiological states. However, the step of transforming these data into context, i.e., linking distinct expression or abundance patterns with phenotypic observables - and furthermore enabling a sound biological interpretation on the level of reaction networks and concerted pathways, is still a major shortcoming. This finding is certainly based on the enormous complexity embedded in cellular reaction networks, but a variety of computational approaches have been developed over the last few years to overcome these issues. This review provides an overview on computational procedures for analysis of genomic and proteomic data introducing a sequential analysis workflow: Explorative statistics for deriving a first, from the purely statistical viewpoint, relevant candidate gene/protein list, followed by co-regulation and network analysis to biologically expand this core list toward functional networks and pathways. The review on these procedures is complemented by example applications tailored at identification of disease-associated proteins. Optimization of computational procedures involved, in conjunction with the continuous increase in additional biological data, clearly has the potential of boosting our understanding of processes on a cell-wide level.

Animals↗