Cancer proteomics provide a powerful approach to identify biomarkers for personalized medicine. Particularly, biomarkers for early detection, prognosis and therapeutic intervention of bone cancers, especially osteosarcomas, are missing. Initially, we compared two-dimensional gel electrophoresis (2-DE)-based protein expression pattern between cell lines of fetal osteoblasts, osteosarcoma and pulmonary metastasis derived from osteosarcoma. Two independent statistical analyses by means of PDQuest® and SameSpot® software revealed a common set of 34 differentially expressed protein spots (p < 0.05). 17 Proteins were identified by mass spectrometry and subjected to Ingenuity Pathway Analysis resulting in one high-ranked network associated with Gene Expression, Cell Death and Cell-To-Cell Signaling and Interaction. Ran/TC4-binding protein (RANBP1) and Cathepsin D (CTSD) were further validated by Western Blot in cell lines while the latter one showed higher expression differences also in cytospins and in clinical samples using tissue microarrays comprising osteosarcomas, metastases, other bone malignancies, and control tissues. The results show that protein expression patterns distinguish fetal osteoblasts from osteosarcomas, pulmonary metastases, and other bone diseases with relevant sensitivities between 55.56% and 100% at ≥87.50% specificity. Particularly, CTSD was validated in clinical material and could thus serve as a new biomarker for bone malignancies and potentially guide individualized treatment regimes.
Pubmed ID: 26203049 RIS Download
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A web-based software application that enables users to analyze, integrate, and understand data derived from gene expression, microRNA, and SNP microarrays, metabolomics, proteomics, and RNA-Seq experiments, and small-scale experiments that generate gene and chemical lists. Users can search for targeted information on genes, proteins, chemicals, and drugs, and build interactive models of experimental systems. IPA allows exploration of molecular, chemical, gene, protein and miRNA interactions, creation of custom molecular pathways, and the ability to view and modify metabolic, signaling, and toxicological canonical pathways. In addition to the networks and pathways that can be created, IPA can provide multiple layering of additional information, such as drugs, disease genes, expression data, cellular functions and processes, or a researchers own genes or chemicals of interest.
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