In silico analysis of protein neoplastic biomarkers for cervix and uterine cancer

Protein Information and Knowledge Extractor: Discovering biological information from proteomics data

One of the main goals in proteomics is to solve biological and molecular questions regarding a set of identified proteins. In order to achieve this goal, one has to extract and collect the existing biological data from public repositories for every protein and afterward, analyze and organize the collected data. Due to the complexity of this task and the huge amount of data available, it is not possible to gather this information by hand, making it necessary to find automatic methods of data collection. Within a proteomic context, we have developed Protein Information and Knowledge Extractor (PIKE) which solves this problem by automatically accessing several public information systems and databases across the Internet. PIKE bioinformatics tool starts with a set of identified proteins, listed as the most common protein databases accession codes, and retrieves all relevant and updated information from the most relevant databases. Once the search is complete, PIKE summarizes the information for every single protein using several file formats that share and exchange the information with other software tools. It is our opinion that PIKE represents a great step forward for information procurement and drastically reduces manual database validation for large proteomic studies. It is available at http://proteo.cnb.csic.es/pike.

Software tool for researching annotations of proteins: open-source protein annotation software with data visualization

In order that biological meaning may be derived and testable hypotheses may be built from proteomics experiments, assignments of proteins identified by mass spectrometry or other techniques must be supplemented with additional notation, such as information on known protein functions, protein-protein interactions, or biological pathway associations. Collecting, organizing, and interpreting this data often requires the input of experts in the biological field of study, in addition to the time-consuming search for and compilation of information from online protein databases. Furthermore, visualizing this bulk of information can be challenging due to the limited availability of easy-to-use and freely available tools for this process. In response to these constraints, we have undertaken the design of software to automate annotation and visualization of proteomics data in order to accelerate the pace of research. Here we present the Software Tool for Researching Annotations of Proteins (STRAP), a user-friendly, open-source C# application. STRAP automatically obtains gene ontology (GO) terms associated with proteins in a proteomics results ID list using the freely accessible UniProtKB and EBI GOA databases. Summarized in an easy-to-navigate tabular format, STRAP results include meta-information on the protein in addition to complementary GO terminology. Additionally, this information can be edited by the user so that in-house expertise on particular proteins may be integrated into the larger data set. STRAP provides a sortable tabular view for all terms, as well as graphical representations of GO-term association data in pie charts (biological process, cellular component, and molecular function) and bar charts (cross comparison of sample sets) to aid in the interpretation of large data sets and differential analyses experiments. Furthermore, proteins of interest may be exported as a unique FASTA-formatted file to allow for customizable re-searching of mass spectrometry data, and gene names corresponding to the proteins in the lists may be encoded in the Gaggle microformat for further characterization, including pathway analysis. STRAP, a tutorial, and the C# source code are freely available from http://cpctools.sourceforge.net.

Diagnostic protein marker patterns in squamous cervical cancer

PURPOSE

Cervical cancer is the second most prevalent malignancy of women. Our aim was to identify additional marker protein patterns for objective diagnosis of squamous cervical cancer (SCC).

EXPERIMENTAL DESIGN

Collected tissue biopsies of SCC, squamous vaginal cancer (SVC), normal cervical and vaginal mucosa were subjected to 2-DE, SameSpot analysis, MALDI-TOF-MS protein identification, and analysis of the expression of selected proteins by immunohistochemistry.

RESULTS

In 148 protein spots selected by the difference in expression 99 proteins were identified. A differential protein pattern for SCC was, e.g. over-expressed (OE) eukaryotic translation initiation factor 3-2β, neutrophil cytosolic factor 2, annexin A6 (ANXA6), for SVC it was OE cathepsin D, γ-catenin, RAB2A, for both cancers it was OE apolipoprotein E, tropomyosin 3, HSPA8, and underexpressed cytokeratin 13, osteoglycin. In SCC nuclear expression of neutrophil cytosolic factor 2, PRDX2, HSP27 (nine of ten cases), ANXA6 (nine of ten cases) was observed while tropomyosin 4 was expressed only in two of ten cases. There was 81.1% (43/53) agreement between the expression of protein spots and the immune expression of proteins (www.proteinatlas.org).

CONCLUSIONS AND CLINICAL RELEVANCE

SCC is characterized by specific tissue marker protein patterns that allow objective detection of the disease. They can become a basis for objective automated cytology-based screening and improve current diagnostics of SCC.