Person:

Kassis, Amin

Background: With the arrival of the postgenomic era, there is increasing interest in the discovery of biomarkers for the accurate diagnosis, prognosis, and early detection of cancer. Blood-borne cancer markers are favored by clinicians, because blood samples can be obtained and analyzed with relative ease. We have used a combined mining strategy based on an integrated cancer microarray platform, Oncomine, and the biomarker module of the Ingenuity Pathways Analysis (IPA) program to identify potential blood-based markers for six common human cancer types. Methodology/Principal Findings: In the Oncomine platform, the genes overexpressed in cancer tissues relative to their corresponding normal tissues were filtered by Gene Ontology keywords, with the extracellular environment stipulated and a corrected Q value (false discovery rate) cut-off implemented. The identified genes were imported to the IPA biomarker module to separate out those genes encoding putative secreted or cell-surface proteins as blood-borne (blood/serum/plasma) cancer markers. The filtered potential indicators were ranked and prioritized according to normalized absolute Student t values. The retrieval of numerous marker genes that are already clinically useful or under active investigation confirmed the effectiveness of our mining strategy. To identify the biomarkers that are unique for each cancer type, the upregulated marker genes that are in common between each two tumor types across the six human tumors were also analyzed by the IPA biomarker comparison function. Conclusion/Significance: The upregulated marker genes shared among the six cancer types may serve as a molecular tool to complement histopathologic examination, and the combination of the commonly upregulated and unique biomarkers may serve as differentiating markers for a specific cancer. This approach will be increasingly useful to discover diagnostic signatures as the mass of microarray data continues to grow in the ‘omics’ era.

A high-yield radioiodination method for various types of molecules is described. The approach employs DMSO as precursor solvent, a reaction ratio of 2-5 precursor molecules per iodine atom, 5-10 mu g oxidant, and a 10-25 mu l reaction volume. The solution is vortexed at room temperature for 1-5 min and progress of the reaction is assessed by HPLC. Radioiodinated products are obtained in >= 95% yield and meet the requirements for radiotracer imaging, biodistribution studies, and molecular and cellular biology research.

The widely used agarose gel electrophoresis method for assessing radiation-induced single-strand-break (SSB) yield in plasmid DNA involves measurement of the fraction of relaxed-circular (C) form that migrates independently from the intact supercoiled (SC) form. We rationalized that this method may underestimate the SSB yield since the position of the relaxed-circular form is not altered when the number of SSB per DNA molecule is >1. To overcome this limitation, we have developed a novel method that directly probes and quantifies SSBs. Supercoiled (3)H-pUC19 plasmid samples were irradiated with gamma-rays, alkali-denatured, dephosphorylated, and kinated with gamma-[(32)P]ATP, and the DNA-incorporated (33)P activities were used to quantify the SSB yields per DNA molecule, employing a standard curve generated using DNA molecules containing a known number of SSBs. The same irradiated samples were analyzed by agarose gel and SSB yields were determined by conventional methods. Comparison of the data demonstrated that the mean SSB yield per plasmid DNA molecule of [21.2 +/- 0.59] x 10(-2) Gy(-1) as measured by direct probing is similar to 10-fold higher than that obtained from conventional gel-based methods. These findings imply that the SSB yields inferred from agarose gels need reevaluation, especially when they were utilized in the determination of radiation risk.

Background: We present an effective, rapid, systematic data mining approach for identifying genes or proteins related to a particular interest. A selected combination of programs exploring PubMed abstracts, universal gene/protein knowledge bases (LSGraph and Ingenuity Pathway Analysis) was assembled to distinguish enzymes with hydrolytic activities that are expressed in the extracellular space of cancer cells. Proteins were identified with respect to six types of cancer occurring in the prostate, breast, lung, colon, ovary, and pancreas.Results: The data mining method identified previously undetected targets. Our combined strategy applied to each cancer type identified a minimum of 375 proteins expressed within the extracellular space and/or attached to the plasma membrane. The method led to the recognition of human cancer-related hydrolases (on average, similar to 35 per cancer type), among which were prostatic acid phosphatase, prostate-specific antigen, and sulfatase 1.Conclusion: The combined data mining of several databases overcame many of the limitations of querying a single database and enabled the facile identification of gene products. In the case of cancer-related targets, it produced a list of putative extracellular, hydrolytic enzymes that merit additional study as candidates for cancer radioimaging and radiotherapy. The proposed data mining strategy is of a general nature and can be applied to other biological databases for understanding biological functions and diseases.