Current Molecular Medicine 2010, 10, 249-257 249 Tumor Markers: The Potential of “Omics” Approach M. Sikaroodi1, Y. Galachiantz2 and A. Baranova*,1,3 1 Molecular & Microbiology Department, College of Science, George Mason University, Fairfax, VA 22030 2 3 USA; Biomedical Center, St. Petersburg, Russian Federation; Research Center for Medical Genetics, RAMS, Moskvorechie Str., 1, Moscow, Russian Federation Abstract: Tumor markers are the molecules that indicate the presence or prognosis of malignancy. Most often, tumor markers are produced by the cancer tissue itself. Many of them could be secreted into the body fluids in small quantities. Thus, tumor markers could be useful for early diagnostics of primary tumors and relapsed disease, as well as for determining tumor prognosis and predicting likely response of the tumor to therapy. Tumor markers are part of the clinical routine. Nevertheless, lack of sensitivity and specificity precludes routine usage of single tumor markers in population-based screening. Shortcomings of single tumor markers could be solved by parallel evaluation of multiple tumor markers that can perform with required certainty. Genome and proteome-wide approaches currently lead to identification and initial characterization of hundreds new tumor marker candidates. Most prominent of such methods are serological analyses of recombinant cDNA expression libraries (SEREX), 2-dimensional polyacrylamide gel electrophoresis, mass spectrometry, as well as protein and DNA microarrays. Last but not the least is a computational approach allowing high-throughput detection of tumor marker candidate genes in publicly available datasets. Listed approaches are critically discussed in this review as well as the most crucial tumor-related findings. Finally, a perspective on the future of tumor markers in the tailored medicine is given. Keywords: Tumor markers, marker panels, mass spectrometry, 2D–PAGE, SEREX, bioinformatics, differential display. INTRODUCTION Early detection of malignancy is the key to effective treatment of cancer. With the recent advances in cellular and molecular biology, it seems possible to identify markers that will permit diagnosis of cancer in its early, non-symptomatic stages when complete eradication of malignancy is still possible . Patients with advanced stages of cancer also benefit from the introduction of tumor markers into the standard clinical routine as such markers allowing direct monitoring of disease progression and therefore a more effective treatment and individualized therapy . Although, available tumor markers are not sensitive or specific enough for population-based screenings for the presence of malignancy, they still hold the promise of early detection and potential aid for the treatment. Consequently, the need for discovery of more specific biomarkers, and/or the creation of tumor marker panels to improve the detection and clinical management of the disease is an important avenue in cancer research. WHAT ARE TUMOR MARKERS? Tumor markers refer to substances, usually proteins or parts of proteins, which are produced by the body in response to cancer growth or by the tumor itself, and are detectable in serum, urine or other body fluids and tissues. The best way to detect a tumor in its early *Address correspondence to this author at the Molecular Biology and Microbiology, David King Hall, MSN 3E1, George Mason University, Fairfax, VA, 22030, USA; Tel: 703-993-42-93; E-mail: [email protected] 1566-5240/10 $55.00+.00 stages is by the application of a simple, minimally invasive test, avoiding unnecessary biopsies, as is common in current practices of oncology. Table 1 shows examples of tumor markers currently being used in clinical practice. Table 1. Some Tumor Markers Currently Being Used in Clinical Routine (Combined According to www.cancer.org) Tumor Marker Description Cancer Type PSA Prostate-Specific Antigen Prostate PAP Prostatic Acid Phosphatase Prostate CA 125 Cancer Antigen Ovarian CEA Carcinoembryonic Antigen Colorectal AFP Alpha-Fetoprotein Liver/germ cell HCG Human Chorionic Gonadotropin Uterine CA 19-9 Cancer Antigen Colorectal/Pancreatic CA 15-3 Cancer Antigen Breast/ovary/lung/prostate CA 27-29 Cancer Antigen Breast LDH Lactate Dehydrogenase Most tumor types NSE Neuron Specific Enolase Neuroblastoma/lung (SC) The majority of these markers are not specific to a particular type of cancer, with the exception of PSA. This marker allows for the detection of early stages of prostatic carcinoma, but is also elevated in benign inflammatory diseases of the prostate. This example © 2010 Bentham Science Publishers Ltd. 250 Current Molecular Medicine, 2010, Vol. 10, No. 2 Sikaroodi et al. illustrates that tumor markers can be produced by normal tissue, thus malignancy detection is usually based on the concentration of the marker exceeding a defined threshold. Unfortunately, the process of transition between normal and malignant states is not clear as tissue exposure to an inflammatory environment may serve as a promoter of proliferation for dormant pre-malignant cells  as well as for de novo “field cancerization” . Consequently, tumor markers’ elevation often accompanies common inflammatory processes such as prostatitis , pancreatitis , urogenital infections , and bronchopulmonary injury  are common in normal pregnancy . The relative magnitude in marker elevation cannot serve as solid ground for differential diagnosis, as “normal” levels of tumor marker expression are subject to polymorphism. For example, serum PSA levels are associated with a G/A polymorphism at androgen responsive element 1 (ARE1) of PSA and/or the CAG repeats in exon 1 of the androgen receptor (AR) gene [10, 11]. Thus, it is unlikely that sensitivity and specificity of tumor detection could be improved by the finding of a solitary “ideal” marker, or by collecting more data about existing ones. Most likely, the future is in tumor detection panels encompassing from tens to hundreds of marker molecules. In the recent “proof-of-theprinciple” studies, the multimarker panels comprised of 25 and 31 analytes offered the highest power for the prognostic discrimination between patients with squamous cell carcinoma of the head and neck and diagnostic discrimination between pancreatic cancer and chronic pancreatitis, respectively [12, 13]. as well. Difficulties in cross-platform and crosstechnology proteomic data transfers and analyses rival those common for gene expression microarray experiments. As a switch to the marker panels is anticipated, high-throughput attempts to the discovery of new tumor markers en mass are under way in many labs involved in translational research. One group of methods is aimed at revealing protein markers such as, often as molecules that have undergone post-translational modifications (a proteomics/immunomics approach), and another group focuses on revealing mRNA sequences overrepresented or exclusive to tumor samples (a genomics/transcriptomics approach). The most pronounced methods of the proteomics/ immunomics group are serological identification of antigens by recombinant expression cloning (SEREX), protein microarrays, two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) and variety of mass spectrometry based approaches. The advantage of the proteomics/immunomics approach is its relative straightforwardness: profiling is performed directly on the serum or urine samples and leads to isolation of potentially immunogenic epitopes. These epitopes could be useful not only for easy detection of tumors, but also for cancer immunotherapy. Drawbacks of proteomics/immunomics include the difficulties in postscreening phases of peak/spot identification. Additionally, serum profiles of oncology patients are usually compared only to serum profiles of healthy subjects, while should be compared to profiles characteristic for multiple chronic inflammatory states Serological identification of antigens by recombinant expression cloning (SEREX) is a strategy for identification of tumor antigens based on an existing antibody repertoire of cancer patients . SEREX was developed by implementing molecular cloning techniques into the original strategy of autologous typing . In this approach, a cDNA library is constructed by using mRNA from fresh tumor specimens as a template, then packaged into lambdaphage vectors and recombinant clones are expressed in E. coli . Recombinant proteins produced by bacterial cells are transferred onto nitrocellulose membranes, and identified as antigens by their reactivity with high titer IgG antibodies of patient’s serum. Nucleotide sequences of cDNA inserts in the plasmids contained in positive clones are determined in order to identify corresponding human antigens. Genomics/transcriptomics methods identify molecules of interest without hesitation, but in this case the molecule is usually mRNA. Nucleic acids are attractive as molecular markers easily detectable by PCR, including quantitative PCR. On the other hand, differential or even exclusive tumor–specific expression of mRNA does not guarantee that corresponding protein product could be useful as a serum tumor marker. The most widely employed methods of genomics/transcriptomics are expression microarrays [14, 15], and SAGE . Both methods are very well established, and will not be reviewed here, except a relatively new approach of direct mRNA profiling in the cell-free fractions of the body fluids. Recently, a bioinformatics approach demonstrated unexpected relevance to tumor marker discovery: computer-based methods are attractively cheap, but require extensive follow-up studies. The latter is true for any other highthroughput method of marker discovery as well. In this review, several methods of measuring tumor markers will be discussed, as well as advantages and limitations associated with each technique. PROTEOMICS/IMMUNOMICS APPROACH TO TUMOR MARKER DISCOVERY Serological Identification of Antigens Recombinant Expression Cloning (SEREX) by In SEREX researchers test potential antigens by serum of the cancer patients, thus the experimental outcome is limited to the antigens present in the tumor in vivo, and cell culture related artifacts are avoided. Although, SEREX allows an unbiased search for antigenic proteins, all epitopes that fail to undergo proper post-translational or conformational changes when expressed in bacteria will escape the detection . There have been several modifications to the originally described method to improve identification of tumor specific antigens. After initial identification of Tumor Markers prospective biomarker with autologous (or pooled) sera tumor specificity of antigen needs to be proved by allogenic screening. At least 3 modification of allogenic screening were suggested: SADA [20, 21], SMARTA  and SeroGRID . In the original study that introduced SEREX, five different antigens were identified including NY-ESO-1 . Lately, this list was expanded to more than 100 well-described different antigens, and many more contained in the publicly available Cancer Immunome Database [www2.licr.org/CancerImmunomeDB]. These studies revealed that human tumors express multiple antigens causing immune responses in the host. Some of them are known and common for many individual tumors, and some are less obvious and have not been known to cause immune responses in humans previously. The spectrum of tumor antigen specificities supports the hypothesis that the immunogenicity of a given molecule depends more on the context in which it is presented than on its more or less restricted expression in certain tissues . The clinical significance of anti-tumoral antibodies in cancer patients is not known, although the presence of some antibodies is associated with a poor prognosis in various cancers . The expression of a particular antigen is not a clear indication of the production of corresponding antibodies, and there is no correlation between the antibody production and the clinical stage of the tumor. There is only a limited diagnostic use for any single serum antibody assay, as most of these antibodies are present in only a small percentage of the patients. The development of the autoantibodies-based diagnostic panels is a clear way to go. For example, in one of the diagnostic attempts of this kind, a panel of 22 peptides expressed by prostate cancer tissue allowed detection of the tumor with a specificity of 88.2% and a sensitivity of 81.6%. These results were significantly better than PSA screening, especially among men with a PSA between 4 and 10 ng/ml . TWO-DIMENSIONAL POLYACRYLAMIDE GEL ELECTROPHORESIS Two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) followed by Mass Spectrometry (MS) for protein analysis and identification is a powerful tool and a primary technique for discovering disease biomarkers. Expression patterns revealed by 2D-PAGE gels are compared after simultaneous separation of proteins in immobilized pH gradient by their charge (pI) and by gel mobility that depends on the molecular weight of the protein [26, 27]. Because a protein’s charge and molecular weight are unrelated, protein spots are distributed across the gel in a relatively uniform way. Spots that correspond to individual proteins can be detected by staining with Coomassie blue or with fluorescent dyes, such as SYPRO RUBY . Identification of proteins of interest is performed after spot excision, followed by proteolytic or chemical digestion and MS analyses. Current Molecular Medicine, 2010, Vol. 10, No. 2 251 Generally speaking, 2D-PAGE is a reliable separation technique that provides excellent resolution of complex protein mixtures, even allowing identification of the proteins changed by individual missense mutations . However, there are several disadvantages of the method: a large amount of starting material is required, it is time-consuming, has low sensitivity with conventional stains and relatively poor resolution [26, 29]. In the past, tumor markers revealed by 2D-PAGE usually were not very specific, but that is explained by heterogeneity of the starting material. The invention of laser capture microdissection (LCM)  improved specificity of 2D-PAGE biomarkers as well as markers revealed by other methods. MASS SPECTROMETRIC METHODS Mass spectrometry (MS) has become one of the most important tools for disease marker discovery. This is especially due to the introduction of electrospray ionization (ESI)  and matrix-assisted laser desorption ionization (MALDI) [32,33] to the field. MSbased methods of peptide fragment separations include quadruple ion-trap-MS (e.g., LCQ-MS), matrixassisted laser desorption ionization (MALDI), surfaceenhanced laser desorption/ionization (SELDI), and Fourier transform ion cyclotron resonance (FTICR)-MS. MALDI-MS is a laser-based soft ionization method, first introduced by Karas and Hillenkamp in 1988 [32-34]. A matrix is used in MALDI –MS to protect the sample from being destroyed by a direct laser beam. The laser radiates this matrix-analyte mixture and results in vaporization of the matrix along with the sample. This directed energy transfer event provides high ion yields of the intact analyte and allows for the measurement of compounds with high accuracy and sensitivity, which can be very useful for protein identification and characterization , especially for protein spots extracted from 2D gels. A common type of MALDI spectrometer is the MALDI-TOF (time-of-flight) [34, 35]. TOF analysis is based on accelerating a set of protein-derived ions to a detector. As all of the ions are given the same amount of energy, but have a different mass, the ions reach the detector at different times. An ion’s mass is determined by its time of flight. The arrival time at the detector is dependent upon the mass, charge, and kinetic energy (KE) of the ion. Although, MALDI mass spectrometry is a powerful tool for accurate mass determination of the peptide mixtures, it is highly dependent on the sample and the sample preparation. Obtaining meaningful MALDI data, depends on the choice of suitable matrices and solvents, the functional and structural properties of the analyte, sample purity, and sample preparation . This is a major limitation of MALDITOF, as the presence of buffer components, lipids and carbohydrates prevent efficient ionization of the proteins. Because of this, samples of body fluids must be pre-separated prior to MS analyses by other techniques, for example by 2D-PAGE. 252 Current Molecular Medicine, 2010, Vol. 10, No. 2 Surface-enhanced laser desorption/ionization (SELDI) is an affinity-based mass spectrometric method in which proteins of interest are selectively absorbed in a chemically modified surface on a biochip. This approach was introduced in 1993 by Hutchens and Yip [36, 37]. SELDI-TOF MS combines retention with mass spectrometry, unlike other methods based on elution, such as HPLC/MS. The principle of this approach is that proteins are captured by adsorption, partition, electrostatic interaction, or affinity chromatography on a solid-phase protein chip surface. Chromatographic surfaces of SELDI protein chips are uniquely designed to retain proteins from complex mixtures according to their specific properties . Because proteins are retained on the surface, contaminants can easily be washed away prior to MS analyses, thus eliminating the need for preparation of techniques . Another advantage of SELDI is the small sample requirements (typically 1–2 l per analysis), which is ideal for small biopsies or microdissected tissue samples common in cancer research. Apart from the sample preparation, SELDI technology is similar to MALDI MS. After adding a matrix solution proteins are ionized with a nitrogen laser and their molecular masses measured by TOF MS . SELDI-MS can be used in a high-throughput setting that allows processing of hundreds of low volume specimens. This explains its growing popularity as a primary tool for differentiation of disease from normal states, thereby illuminating possible protein markers. SELDI-TOF MS is most effective at profiling lowmolecular-weight (<20-kDa) proteins and peptides. In this range, SELDI-TOF detection sensitivity exceeds the Western blot technique, while at higher molecular weights (>30 kDa) the two methods are comparable . On the other hand, SELDI does not allow direct identification of resolved proteins. Analyses of complex protein mixes, e.g. specimens of the serum, urine, needle aspirates or cell lysates generate mass spectra represented by peak profiles that in some cases may be diagnostically useful. For example, SELDI-based serum proteomic patterns discriminated men with elevated PSA levels due to benign processes from men with carcinoma within the diagnostic gray zone of PSA levels . Nevertheless, “classical” SELDI based TOF approach for detection cannot separate ions that are close in mass/charge, so some discrete ions can coalesce into single peak. To enhance the resolution of SELDI technology, it could be interfaced to a SELDI hybrid quadrupole TOF (QqTOF) mass spectrometer [43, 44]. Some isobaric fragment ions would remain unresolved even with the QqTOF instrument and would require Fourier transform ion cyclotron resonance (FTICR) MS. The very high accuracy measurements offered by FTICR-MS are used to generate ‘accurate mass tags’ (AMTs) suitable for protein identification [45, 46]. The vast majority of SELDI-MS studies of human tumors still use spectral signatures from sera to differentiate normal and diseased states without actual Sikaroodi et al. protein identification. Reproducibility of the proteomic profiling approach was recently questioned, as two independent studies have reported the same pattern to date . The most disturbing observation comes from the study of Hu and co-authors, who demonstrated that changes in the profiling protocol result in the reproducible shift in protein profiles masking more subtle changes corresponding to different histological subtypes of cancer . Potential solutions to these problems, including adherence to the minimal requirements for marker confirmation and validation are reviewed elsewhere [47, 49, 50]. The confirmation of the diagnostic value of a protein spectrum ultimately relies on the biological validation and identification of the underlying proteins. Therefore, the most valuable analytical methods are those that allow profiling in parallel with protein identification. Labeling proteins with isotope-coded affinity tags (ICAT) or isobaric tags for relative and absolute quantitation (iTRAQ) followed by liquid chromatography/tandem mass spectrometry (LCMS/MS) are promising analytical methods for just this reason [51, 52]. Recently, iTRAQ approach was employed for searches of tumor markers of endometrial carcinoma and lead to the development of the panel of markers including pyruvate kinase, chaperonin 10, and alpha(1)-antitrypsin discriminating endometrial cancer from a number of types of benign endometrium with both sensitivity and specificity of 0.95 . ICAD allowed identification and quantification of proteins in the pancreatic juice samples as compared to normal controls, revealing insulin-like growth factor binding protein-2 as novel tumor marker . Similarly, ICAT revealed overexpresion of vitamin D-binding protein in nipple aspirate fluid of early stage breast tumor-bearing breasts . In classical profiling studies, e.g. SELDI-based projects, relatively few have reported the identities of proteins allowing for discrimination of tumor-bearing and healthy signatures. One such study reported the alpha subunit of haptoglobin, an acute-phase protein produced by liver cells as a potential marker for ovarian cancer . Another study of the same tumor type revealed a diagnostic value of down-regulation of apolipoprotein A1 and a truncated form of transthyretin in combination with up-regulation of a cleavage fragment of inter-alpha-trypsin inhibitor heavy chain H4 . The latter study is especially prominent, as a combination of all three new markers with CA125 greatly improved specificity and sensitivity of the diagnostic assay. SELDI analysis led to the identification of 8.9-kDa isoform of ApoA-II as a tumor marker superior to PSA in diagnosis of indolent prostatic carcinoma . Guo and co-authors established calgranulin A and chaperonin 10 as protein markers for endometrial carcinoma . Unfortunately, both markers are non-exclusive to tumor cells, suggesting that although these proteins could not be used for diagnostic purposes independently, they may prove useful when combined with other tumor markers [44, 59]. Potential cancer-related biomarkers often appear to be fragments or isoforms of major serum Tumor Markers components and house-keeping proteins. For example, one of the studies identified down-regulation of - and - subunits of hemoglobin as a serum indicator of ovarian carcinoma . Other examples include a serum amyloid A that could serve as a biomarker to distinguish prostate cancer patients with bone lesions  and the C-terminal part of the V10 fragment of vitronectin discriminating hepatocellular carcinoma from chronic liver disease . Actual tumor-derived molecules may escape detection in serum due to their low molecular mass or low concentration. Recent observations suggest that many potential serum biomarkers are bound to carrier proteins, such as albumin and usually become depleted during sample preparation . As the accumulation of biomarkers on circulating carrier proteins greatly amplifies the total serum/plasma concentration of the measurable biomarker, examination of the carrier-bound molecules represents an important future avenue in tumor marker discovery. Recently, the harvesting of biomarkers bound to the carrier proteins followed by high-resolution MALDI orthogonal TOF (OTOF) MS was successfully employed for discriminant analysis of the ovarian cancer serum samples . Importantly, a number of discriminating protein fragments, particularly transthyretin, has been reported as putative ovarian carcinoma biomarkers earlier . A number of recent publications reported proteomics signatures that classify cancer patients according to their likely outcomes rather than differentiate tumor-bearing and non-tumor-bearing individuals. For example, Taguchi and colleagues  developed a MALDI-based profiling approach for preselection of the patients with advanced non–smallcell lung cancer (NSCLC), who will be most likely beneficial from expensive targeted therapy with smallmolecule inhibitors of the EGF receptor gefitinib and erlotinib. SELDI-TOF based profiles were shown to capture the signature of biochemical relapse in prostatic adenocarcinomas, independent of their clinical PSA status . Unfortunately, in both of these studies diagnostic peaks were not tracked down to the particular proteins or protein fragments. Another interesting field for application of MS-based techniques to a search for cancer biomarkers is the shift of the discovery focus from the proteins, such as, to their posttranslationally modified components. For example, glycome profiles acquired by using MALDIMS of permethylated N-glycans released from serum samples showed that fucosylation of glycan structures is generally higher in prostate cancer and revealed a number of cancer-specific glycans . Whether tumor glycomics will develop into specific area of biomarker research and rival more traditional protein biomarkers remains to be seen. PROTEIN MICROARRAYS Unlike DNA microarrays, which quantify mRNA messages possibly correlated in functionality with Current Molecular Medicine, 2010, Vol. 10, No. 2 253 proteins encoded by them, protein microarrays measure activity and/or abundance directly reflecting the functional aspects of these molecules. In brief, protein microarrays are composed of immobilized homogeneous or heterogeneous spots representing antibodies, DNA or RNA fragments, small molecular compounds, peptides, recombinant proteins or total cell lysates. The spot array could be probed by tagged antibodies, ligands, total serum, or cell lysates to analyze the signal intensity, which is proportional to the quantity of the tagged molecules [67, 68]. The protein microarrays offer high-throughput utilization, and also require a low volume of sample material. Experiments by using protein microarrays are either function-based or abundance-based [67, 68]. Functionbased microarrays are useful for the measurement of enzyme activity or substrate specificity of arrayed proteins, and are used mostly in pharmacological research. Abundance-based arrays allow for more focused proteomics research and are supported by forward or reverse phase spotting. In forward phase arrays, labeled bait molecules, which are typically antibodies, are immobilized on the surface. The arrays are then incubated with the test sample that may contain various analytes . In reverse-phase arrays, which are more applicable in tumor marker identification, multiple analytes represented by tested samples are immobilized within the same spot, but the spot array is probed with a single detection molecule [67, 68]. Therefore this method allows for detection of a single analyte in a number of samples under the same conditions . This is useful in the detection of a specific protein or a specific (e.g. phosphorylated) state of the protein in the process of marker validation. Reverse protein arrays detect analytes in very small volume of sample, e.g. in 30-60 μl of whole cell lysate or in core needle biopsies obtained with a 16 or 18 gauge needle [67, 68]. Protein microarray printers use the same technology as DNA microarray printers, but the layout is different, as protein array surfaces should not change the protein structure . Silanized silica, nylon and nitrocellulose may be used as substratum, and the latter is more common . Printing different sample dilutions on the same array allows quantitative matching of the antibody to the analyte concentration, which helps to analyze each analyte in a linear fashion, thereby improving the sensitivity and specificity of the technique. Like other techniques in their infancy, protein microarray technology has its own limitations, including its unavailability of specific and high affinity antibodies for a range of potential diagnostic targets [67, 69]. Unlike DNA probes that allow for affinity customization, probes for protein arrays usually represent a “black box”, so multiple probes needs to be pre-tested in order to find the most reliable molecule that allow high fidelity quantification of analytes. Normalization techniques for antibody microarrays still have room for improvement, with IgM-ELISA-based normalization currently established as the best performing procedure . 254 Current Molecular Medicine, 2010, Vol. 10, No. 2 PCR-like amplification methods are unavailable for proteins, so low abundance molecules may not be detected. Pre-fractionation of the proteins has been suggested to solve this problem . Current methods of protein extraction and purification cannot guarantee the integrity of the proteins, thus introducing an extra level of inter-experimental variability . These limiting factors must be overcome in order to enhance the performance of protein microarray technology. Currently, this technology has been applied infrequently to tumor marker studies. Sreekumar and co-authors monitored levels for 146 human proteins in LoVo colon carcinoma cells treated with ionizing radiation with forward phase arrays of corresponding antibodies, and demonstrated radiation-induced downregulation of carcinoembryonic antigen . Forward phase attempts to evaluate the proteome in clinical samples were performed for malignant and adjacent normal tissue in a primary breast cancer specimen  and for serum samples obtained from 33 prostate carcinoma patients and 20 controls . Reverse phase protein microarrays were used in the analysis of the lysates of follicle center cells isolated by laser capture microdissection from three different types of follicular lymphomas  and in NCI-60 cancer cell lines . Reverse phase proteomic mapping of phosphorylation end points is also under development and has been tested in prostate, breast and ovarian carcinoma studies [71-80]. Recently, phosphoproteomic network analyses of microdissected tumor cells from childhood rhabdomyosarcoma patients showed that this approach can be used as a mean to select patients to receive mTOR/IRS pathway inhibitors before administration of chemotherapy . Currently, it is unclear whether this type of analysis is applicable to serum samples. An interesting approach has been developed by Qui and co-authors, who explored the use of microarrays spotted with tumor proteins as an alternative to Western blots for tumor antigen profiling. In this study, lung tumor cell line protein lysate was separated into 1840 fractions and spotted on nitrocellulose slides, then probed by sera from lung carcinoma patients and healthy controls. Less than four percent of arrayed fractions demonstrated increased reactivity in cancer patients, indicative in the presence of potentially immunogenic tumor marker yet to be purified from given tumor lysate . In contrast to other protein arrays primarily aimed for marker validation studies, this method allows for the discovery of new tumor markers. GENOMICS/TRANSCRIPTOMICS AND BIOINFORMATICS APPROACH TO TUMOR MARKER DISCOVERY Direct mRNA Profiling in the Body Fluids PCR-based quantification of tumor-specific mRNA in the blood is a valuable tool for registering circulating tumor cells representing seeds for micrometastases Sikaroodi et al. [83, 84]. For example, molecular detection of cytokeratin and AFP mRNAs in the nuclear cell component of peripheral blood was employed for the assessment of microscopic lymphatic spread of nonsmall cell lung carcinoma  and hepatocellular carcinoma . Unfortunately, cell-containing fractions of body fluids are unsuitable for tumor marker discovery by gene expression profiling, as any cancer specific molecular signature will be buried under overwhelming amount of lymphocytic transcripts. Luckily, the analyses of cell-free fraction of body fluids indicate the substantial presence of free circulating RNA that could be extracted and amplified for diagnostic purposes. The origin of this RNA is unclear; most probably it is derived for apoptotic bodies  and stays protected from degradation via its association with proteins or lipid vesicles . Consistent with this hypothesis, serum RNA concentration in cancer patients is higher than healthy individuals . Profiling of these circulating RNA molecules by expression microarrays or by Real-Time PCR may lead to the developments of new classes of tumor markers possibly suitable for diagnostics of primary tumors. The feasibility of this approach has been proven recently by fetal mRNA profiling in amniotic fluid  and in sera samples collected from patients with gastric cancer  and oral squamous cell carcinoma . Bioinformatics Discovery Approach to Tumor Marker The completion of the human genome sequence and the impressive repertoire of the Expressed Sequence Tags (ESTs) representing a wide spectrum of tissue types both provide a powerful source of data to identify molecular targets and diagnostic markers for a broad range of diseases, including cancer. Modern bioinformatics approaches allow automation of sequence annotation and provide tools for highthroughput analyses of accumulated sequences. Every newly sequenced cDNA clone is routinely evaluated for similarity to known genes and other ESTs, mapped to the corresponding completed genome and placed in UniGene and other databases. These databases and tools are extremely useful for mining functional information about human transcripts and thus helping uncover candidates for subsequent experimental studies. However, even if the function of mRNA or its fragment remains unknown, this mRNA may still have medical utility as potential tumor marker could rely solely on its expression pattern as a diagnostic test. Moreover, as in this case the diagnostic molecule is an mRNA itself, there is no restriction on its proteinencoding ability. Some recent studies point at expression levels of human antisense RNAs, particularly of intronic antisenses, as a novel source for tumor-specific molecules [92, 93]. One of the possible explanations of the overall cancer-specific increase in the production of non-coding RNA is a well-known epigenetic phenomenon of the chromatin phenotype disruption commonly found in human tumors. Tumor Markers As every EST represents a random sample, multiple copies of the same sequence reflect the relative abundance of the corresponding transcript, and thus provide assessment of expression profiles for particular tissues in normal and malignant states. Automated mining of potential tumor markers in EST databases seeks to identify UniGene clusters predominantly containing ESTs obtained by sequencing of tumorderived libraries. Mining could be performed either as pairwise comparisons of normal tissues with corresponding tumors [94-96], or as global subtraction procedures discriminating tumor-specific clusters without any regard to particular tissue of origin . The latter approach is better designed for tumor marker discovery, as the resulting candidate RNAs are characterized by minimal presence in normal cells could be useful in serum-based diagnostics. Both methods are also known as CDD (Computer-based Differential Display) and are employed by on-line expression visualization tools, e.g. Virtual Northern (vNorthern) and SAGE digital gene expression displayer (DGED). Currently, there is no public EST database interface capable of sorting cDNA libraries according to their preparation techniques or to perform data threshold adjustments. Therefore, many researchers use in-house computational tools that allow variation of identification criteria. Experimental evaluation of computationally predicted tumor markers often confirms their tumorspecific or tumor-prevalent expression patterns [98, 99]. In one of the recent studies, CDD technique led to identification of novel tumor marker, Brachyury, and isolation of its HLA-A0201 Brachyury epitope that promotes expansion of T lymphocytes with the ability to lyse Brachyury-expressing tumor cells . It is interesting that tumor markers previously characterized by traditional methods (e.g. PSA and CEA) are independently identified by many CDD algorithms . Many tumor-specific mRNAs appear to represent new isoforms of known human genes, thus indicating that tumor-specific alternative splicing is a widespread phenomenon [99, 101, 102]. However, sets of candidate molecules identified in silico usually contain a larger proportion of false positives than experimentally obtained ones, therefore requiring more extensive marker validation procedures. Current Molecular Medicine, 2010, Vol. 10, No. 2 Regardless of acknowledged pitfalls, it is unquestionable that the immense opportunities of “omics” will surely advance our understanding of human tumorigenesis and lead to early and efficient diagnoses of primary malignancies. ACKNOWLEDGEMENTS The authors are extremely grateful to Prof. A.P. Kozlov for sharing a new look on tumor markers and for valuable discussions, to Dr. Yury Shebzukhov for sharing a point of view on SEREX approaches and to Prof. A. Christensen and Dr. K. Schlauch for their help with MS proofreading and helpful suggestions. This work was partially supported by NIH grant 1 R15 CA113331-01 (AB). REFERENCES               CONCLUSION High-throughput “omics” approaches to identify tumor markers are widely employed for the creation of “treasure troves” of candidate molecules. No initial methods of candidate selection remain free of false positives, thus indicating extreme importance for validation of reported discoveries. To reduce the lists of available targets, in addition to traditional comparisons of tumor and normal specimens, extra controls representing common chronic inflammatory and premalignant states must be included in the initial study. Integrative and cross-platform approaches may also be necessary, to identify the most reliable candidates. 255        Wulfkuhle, J.D., Liotta, L.A. and Petricoin, E.F. (2003) Nat. Rev. Cancer, 3(4), 267-275. Schmid, H.P., Adolfsson, J. and Aus, G. (2001) Eur. Urol., 40(5), 488-494. Okada, F. (2002) Redox Rep., 7(6), 357-368. Braakhuis, B.J., Tabor, M.P., Kummer, J.A., Leemans, C.R. and Brakenhoff, R.H. (2003) Cancer Res., 63(8), 1727-1730. Gumus, B.H., Nese, N., Gunduz, M.I., Kandiloglu, A.R., Ceylan, Y. and Buyuksu, C. (2004) Int. Urol. Nephrol., 36(4), 549-553. Chen, R., Brentnall, T.A., Pan, S., Cooke, K., Moyes, W.K., Crispin, D.A., Goodlett, D.R., Aebersold, R. And Bronner, M.P. (2007) Mol. Cell Proteomics, 6(8), 1331-1342. Konety, B.R. and Getzenberg, R.H. (2001) J. Urol., 165(2), 600-611. Cremades, M.J., Menendez, R., Pastor, A., Llopis, R. and Aznar, J. (1998) Respir. Med., 92(5), 766-771. Sarandakou, A., Protonotariou, E. and Rizos, D. (2007) Crit. Rev. Clin. Lab. Sci., 44(2), 151-178. Xue, W., Irvine, R.A., Yu, M.C., Ross, R.K., Coetzee, G.A. and Ingles, S.A. (2000) Cancer Res., 60(4), 839-841. Xu, J., Meyers, D.A., Sterling, D.A., Zheng, S.L., Catalona, W.J., Cramer, S.D., Bleecker, E.R. and Ohar, J. (2002) Cancer Epidemiol. Biomarkers Prev., 11(7), 664-669. Linkov, F., Lisovich, A., Yurkovetsky, Z., Marrangoni, A., Velikokhatnaya, L., Nolen, B., Winans, M., Bigbee, W., Siegfried, J., Lokshin, A., Ferris, R.L. (2007) Cancer Epidemiol. Biomarkers Prev., 16(1), 102-107. Zeh, H.J., Winikoff, S., Landsittel, D.P., Gorelik, E., Marrangoni, A.M., Velikokhatnaya, L., Winans, M.T., Lee, K., Moser, A., Bartlett, D., Lotze, M.T., Siegfried, J.M., Whitcomb, D., Papacristou, G., Slivka, A., Bigbee, W.L. and Lokshin A.E. (2005) Cancer Biomark., 1(6), 259-269. Michiels, S, Koscielny, S. and Hill, C. (2007) Br. J. Cancer, 96(8), 1155-1158. Pollack, JR. (2007) Am. J. Pathol., 171(2), 375-385. Porter, D., Yao, J. and Polyak, K. (2006) Drug Discov. Today, 11(3-4), 110-118. Sahin, U., Tureci, O. and Pfreundschuh, M. (1997) Curr. Opin. Immunol., 9, 709-716. Old, L.J. (1981) Cancer Res., 41(2), 361-375. Sahin, U., Tureci, O., Schmitt, H., Cochlovius, B., Johannes, T., Schmits, R., Stenner, F., Luo, G., Schobert, I. and Pfreundschuh, M. (1995) Proc. Natl. Acad. Sci. USA, 92, 11810-11813. Scanlan, M.J., Gout, I., Gordon, C.M., Williamson, B., Stockert, E., Gure, A.O., Jager, D., Chen, Y.T., Mackay, A., O'Hare, M.J. and Old, L.J. (2001) Cancer Immun., 1(4), 1-17. Scanlan, M.J., Welt, S., Gordon, C.M., Chen , Y.T., Gure, A.O., Stockert, E., Jungbluth, A.A., Ritter, G., Jager, D., 256                               Current Molecular Medicine, 2010, Vol. 10, No. 2 Jager, E., Knuth, A. and Old, L.J. (2002) Cancer Res., 62(14), 4041-4047. Lagarkova, M.A., Koroleva, E.P., Kuprash, D.V., Boitchenko, V.E., Kashkarova, U.A., Nedospasov, S.A. and Shebzukhov, Y.V. (2003) Immunol. Lett., 85(1), 71-74. Krause, P., Tureci, O., Micke, P., Buhl, R., Huber, C. and Sahin, U. (2003). J. Immunol. Methods, 283(1-2), 261-267. Chen, Y.T., Scanlan, M.J., Sahin, U., Tureci, O., Gure, A.O., Tsang, S., Williamson, B., Stockert, E., Pfreundschuh, M. and Old, L.J. (1997) Proc. Natl. Acad. Sci. USA, 94(5), 19141918. Bradford, T.J., Wang X. and Chinnaiyan A.M. (2006) Urol. Oncol., 24(3), 237-242. O’Farrells, P.H. (1975) J. Biol. Chem., 250(10), 4007-4021. Craven, R.A., Selby, P.J. and Banks, R.A. (2003) in Principles of Molecular Oncology, Humana Press, Totowa, NJ, pp. 247-264. Berggren, K., Chernokalskaya, E., Steinberg, T.H., Kemper, C., Lopez, M.F., Diwu, Z., Haugland, R.P. and Patton, W.F. (2000) Electrophoresis, 21, 2509-2521. Monteoliva, L. and Albar, J.P. (2004) Brief Funct. Genomic Proteomic, 3(3), 220-239. Espina, V., Milia, J., Wu, G., Cowherd, S. and Liotta, L.A. (2006) Methods Mol. Biol., 319, 213-229. Fenn, J.B., Mann, M., Mann, M., Meng, C.K., Wong, S.F. and Whitehouse, C.M. (1989) Science, 246(4926), 64-71. Karas, M. and Hillenkamp, F. (1988) Anal. Chem., 60(20), 2299-2301. Hillenkamp, F. and Karas, M. (2000) Int. J. Mass Spectrom., 200, 71-77. Lewis, J.K. and Wei, J. (2000) in Encyclopedia of Analytical Chemistry. Meyers, R. A., Ed. Chichester, John Wiley & Sons Ltd, pp. 5880-5894. Siuzdak, G. (1996) Mass Spectrometry for Biotechnology, Academic Press, New York. Hutchens, T.W. and Yip, T.T. (1993) Rapid Commun. Mass Spectrom., 7, 576-580. Yip, T.T. and Hutchens, T.W. (1994) Mol. Biotechnol., 1(2), 151-164. Issaq, H.J., Conrads, T.P., Prieto, D.A., Tirumalai. R. and Veenstra, TD. (2003) Anal. Chem., 75(7), 148A-155A. von Eggeling, F., Junker, K., Fiedle, W., Wollscheid, V., Durst, M., Claussen, U. and Ernst, G. (2001) Electrophoresis, 22(14), 2898-2902. Issaq, H.J., Veenstra, T.D., Conrads, T.P. and Felschow, D. (2002) Biochem. Biophys. Res. Commun., 292(3), 587-592. Grus, F.H., Joachim, S.C. and Pfeiffer, N. (2003) Proteomics, 3(6), 957-961. Ornstein, D.K., Rayford, W., Fusaro, V.A., Conrads, T.P., Ross, S.J., Hitt, B.A., Wiggins, W.W., Veenstra, T.D., Liotta, L.A. and Petricoin, E.F. 3rd. (2004) J. Urol., 172(4), 13021305. Petricoin, E.F. and Liotta, L.A. (2004) Curr. Opin. Biotechnol., 15(1), 24-30. Guo, J., Yang, E.C., Desouza, L., Diehl, G., Rodrigues, M.J., Romaschin, A.D., Colgan, T.J. and Siu, K.W. (2005) Proteomics, 3(6), 957-961. Bogdanov, B. and Smith, R.D. (2005) Mass Spectrom. Rev., 24(2), 168-200. Qian W.J., Monroe M.E., Liu, T., Jacobs, J.M., Anderson, G.A., Shen, Y., Moore, R.J., Anderson, D.J., Zhang, R., Calvano, S.E., Lowry, S.F., Xiao, W., Moldawer, L.L., Davis, R.W., Tompkins, R.G., Camp, D.G. 2nd and Smith, R.D. (2005) Mol. Cell Proteomics, 4(5), 700-709. Coombes, K.R., Morris, J.C., Hu, J., Edmonson, S.R. and Baggerly, K.A. (2005) Nat. Biotechnol., 23, 291-292. Hu, J., Coombes, K.R., Morris, J.S. and Baggerly, K.A. (2005) Brief. Funct. Genomics Proteomics, 3(4), 322-331. Liggett, W.S., Barker, P.E., Semmes, O.J., and Cazares, L.H. (2004) Dis. Markers, 20(6), 295-307. Diamandis, E.P. (2006) Cancer Res., 66, 5540-5541. Shiio, Y. and Aebersold, R. (2006) Nat. Protoc., 1(1), 139145. Sikaroodi et al.                       Wiese, S., Reidegeld, K.A., Meyer, H.E. and Warscheid, B. (2007) Proteomics, 7(3), 340-350. Desouza, L.V., Grigull, J., Ghanny, S., Dube, V., Romaschin, A.D., Colgan, T.J. and Siu, K.W. (2007) Mol. Cell Proteomics, 6(7), 1170-1182. Chen, R., Pan, S., Yi, E.C., Donohoe, S., Bronner, M.P., Potter, J.D., Goodlett, D.R., Aebersold, R. and Brentnall, T.A. (2006) Proteomics, 6(13), 3871-3879. Pawlik, T.M., Hawke, D.H., Liu, Y., Krishnamurthy, S., Fritsche, H., Hunt, K.K. and Kuerer, H.M. (2006) BMC Cancer, 6, 68. Ye, B., Cramer, D.W., Skates, S.J., Gygi, S.P., Pratomo, V., Fu, L., Horick, N.K., Licklider, L.J., Schorge, J.O., Berkowitz, R.S. and Mok, S.C. (2003) Clin. Cancer Res., 9(8), 29042911. Zhang, Z., Bast, R.C. Jr., Yu, Y., Li, J., Sokoll, L.J., Rai, A.J., Rosenzweig, J.M., Cameron, B., Wang, Y.Y., Meng, X.Y., Berchuck, A., Van Haaften-Day, C., Hacker, N.F., de Bruijn, H.W., van der Zee, A.G., Jacobs, I.J., Fung, E.T. and Chan, D.W. (2004) Cancer Res., 64(16), 5882-5890. Malik, G., Ward, M.D., Gupta, S.K., Trosset, M.W., Grizzle, W.E., Adam, B.L., Diaz, J.I. and Semmes, O.J. (2005) Clin. Cancer Res., 11(3), 1073-1085. Yang, E.C., Guo, J., Diehl, G., DeSouza, L., Rodrigues, M.J., Romaschin, A.D., Colgan, T.J. and Siu, K.W. (2004) J. Proteome Res., 3(3), 636-643. Woong-Shick, A., Sung-Pil, P., Su-M, B., Joon-Mo, L., SungEun, N., Gye-Hyun, N., Young-Lae, C., Ho-Sun, C., HeungJae, J., Chong-Kook, K., Young-Wan, K., Byoung-Don, H. and Hyun-Sun J. (2005) Cancer Sci., 96(3), 197-201. Le, L., Chi, K., Tyldesley, S., Flibotte, S., Diamond, D.L., Kuzyk, M.A. and Sadar, M.D. (2005) Clin. Chem., 51(4), 695707. Paradis, V., Degos, F., Dargere, D., Pham, N., Belghiti, J., Degott, C., Janeau, J.L., Bezeaud, A., Delforge, D., Cubizolles, M., Laurendeau, I. and Bedossa, P. (2005) Hepatology, 41(1), 40-47. Lopez, M.F., Patton, W.F., Whiteley, G.R., Rosenblatt, K., Gurnani, P., Nandi, A., Neill, S., Cullen, S., O'Gorman, M., Sarracino, D., Lynch, C., Johnson, A., Mckenzie, W. and Fishman, D. (2007) Clin. Chem., 53(6), 1067-1074. Taguchi, F., Solomon, B., Gregorc, V., Roder, H., Gray, R., Kasahara, K., Nishio, M., Brahmer, J., Spreafico, A., Ludovini, V., Massion, P.P., Dziadziuszko, R., Schiller, J., Grigorieva, J., Tsypin, M., Hunsucker, S.W., Caprioli, R., Duncan, M.W., Hirsch, F.R., Bunn, P.A. Jr. and Carbone, D.P. (2007) J. Natl. Cancer Inst., 99, 838-846. Kohli, M., Siegel, E., Bhattacharya, S., Khan, M.A., Shah, R. and Suva, L.J. (2006) Cancer Biomark., 2(6), 249-258. Kyselova, Z., Mechref, Y., Al Bataineh, M.M., Dobrolecki, L.E., Hickey, R.J., Vinson, J., Sweeney, C.J. and Novotny, M.V. (2007) J. Proteome Res. 6(5), 1822-1832. Espina, V., Mehta, A.I., Winters, M.E., Calvert, V., Wulfkuhle, J., Petricoin, E.F. 3rd and Liotta, L.A. (2003) Proteomics, 3(11), 2091-2100. rd Liotta, L. and Petricoin, E.F. 3 (2000) Nat. Rev. Genet., 1(1), 48-56. Rapkiewicz, A., Espina, V., Zujewski, J.A., Lebowitz, P.F., rd Filie, A., Wulfkuhle, J., Camphausen, K., Petricoin, E.F. 3 , Liotta, L.A. and Abati, A. (2007) Cancer, 111(3),173-184. Poetz, O., Schwenk, J.M., Kramer, S., Stoll, D., Templin, M.F. and Joos, T.O. (2005) Mech. Ageing Dev., 126, 161170. Hamelinck, D., Zhou, H., Li, L., Verweij, C., Dillon, D., Feng, Z., Costa, J. and Haab, B.B. (2005) Mol. Cell Proteomics, 4(6), 773-784. Sreekumar, A., Nyati, M.K., Varambally, S., Barrette, T.R., Ghosh, D., Lawrence, T.S. and Chinnaiyan, A.M. (2001) Cancer Res., 61(20), 7585-7593. Hudelist, G., Pacher-Zavisin, M., Singer, C.F., Holper, T., Kubista, E., Schreiber, M., Manavi, M., Bilban, M. and Czerwenka, K. (2004) Breast Cancer Res. Treat., 86(3), 281291. Tumor Markers                Miller, J.C., Zhou,H., Kwekel, J., Cavallo, R., Burke, J., Butler, E.B., Teh, B.S. and Haab, B.B. (2003) Proteomics, 3(1), 56-63. Zha, H., Raffeld, M., Charboneau, L., Pittaluga, S., Kwak, rd L.W., Petricoin, E. 3 , Liotta, L.A. and Jaffe, E.S. (2004) Lab. Invest. 84(2), 235-244. Shankavaram, U.T., Reinhold, W.C., Nishizuka, S., Major, S., Morita, D., Chary, K.K., Reimers, M.A., Scherf, U., Kahn, A., Dolginow, D., Cossman, J., Kaldjian, E.P., Scudiero, D.A., Petricoin, E., Liotta, L., Lee, J.K. and Weinstein, JN. (2007) Mol. Cancer Ther., 6(3), 820-832. Wulfkuhle, J.D., Aquino, J.A., Calvert, V.S., Fishman, D.A., Coukos, G., Liotta, L.A. and Petricoin, E.F. 3rd. (2003) Proteomics, 3(11), 2085-2090. Sheehan, K.M., Calvert, V.S., Kay, E.W., Lu, Y., Fishman, D., Espina, V., Aquino, J., Speer, R., Araujo, R., Mills, G.B., Liotta, L.A., Petricoin, E.F. 3rd and Wulfkuhle, J.D. (2005) Mol. Cell Proteomics, 4(4), 346-355. Grubb, R.L., Calvert, V.S., Wulkuhle, J.D., Paweletz, C.P., Linehan, W.M., Phillips, J.L., Chuaqui, R., Valasco, A., Gillespie, J., Emmert-Buck, M., Liotta, L.A. and Petricoin, E.F. (2003) Proteomics, 3(11), 2142-2126. Cowherd, S.M., Espina, V.A., Petricoin, E. 3rd and Liotta, L.A. (2004) Clin. Breast Cancer, 5(5), 385-392. Petricoin, E.F. 3rd, Espina, V., Araujo, R.P., Midura, B., Yeung, C., Wan, X., Eichler, G.S., Johann, D.J. Jr, Qualman, S., Tsokos, M., Krishnan, K., Helman, L.J. and Liotta, LA.(2007) Cancer Res. 67(7), 3431-3440. Qiu, J., Madoz-Gurpide, J., Misek, D.E., Kuick, R., Brenner, D.E., Michailidis, G., Haab, B.B., Omenn, G.S. and Hanash, S. (2004) J. Proteome Res., 3(2), 261-267. Miura, M., Ichikawa, Y., Tanaka, K., Kamiyama, M., Hamaguchi, Y., Ishikawa, T., Yamaguchi, S., Togo, S., Ike, H., Ooki, S. and Shimada, H. (2003) Anticancer Res., 23(2B), 1271-1276. Saintigny, P., Coulon, S., Kambouchner, M., Ricci, S., Martinot, E., Danel, C., Breau, J.L. and Bernaudin, J.F. (2005) Int. J. Cancer, 115(5), 777-782. Liu, Y., Wu, M.C., Qian, G.X. and Zhang, B.H. (2002) Hepatobiliary Pancreat. Dis. Int., 1(1), 72-76. Biggiogera, M., Bottone M.G. and Pellicciari C. (1998) J. Histochem. Cytochem., 46(9), 999-1005. El-Hefnawy, T., Raja, S., Kelly, L., Bigbee, W.L., Kirkwood, J.M., Luketich, J.D. and Godfrey T.E. (2004) Clin. Chem., 50(3), 564-573. Ng, E.K., Tsui, N.B., Lam, N.Y., Chiu, R.W., Yu, S.C., Wong, S.C., Lo, E.S., Rainer, T.H., Johnson, P.J. and Lo, Y.M. (2002). Clin. Chem., 48(8), 1212-1217. Received: June 05, 2009 Current Molecular Medicine, 2010, Vol. 10, No. 2 Revised: June 19, 2009 Accepted: June 19, 2009               257 Larrabee, P.B., Johnson, K.L., Lai, C., Ordovas, J., Cowan, J.M., Tantravahi, U. and Bianchi, D.W. (2005) JAMA, 293(7), 836-842. Tani, N., Ichikawa, D., Ikoma, D., Tomita, H., Sai, S., Ikoma, H., Okamoto, K., Ochiai, T., Ueda, Y., Otsuji, E., Yamagishi, H., Miura, N. and Shiota, G. (2007) Anticancer Res., 27(2), 1207-1212. Li, Y., Elashoff, D., Oh, M., Sinha, U., St John, M.A., Zhou, X., Abemayor E. and Wong D.T. (2006) J. Clin. Oncol., 24(11), 1754-1760. Reis, E.M., Nakaya, H.I., Louro, R., Canavez, F.C., Flatschart, A.V., Almeida, G.T., Egidio, C.M., Paquola, A.C., Machado, A.A., Festa, F., Yamamoto, D., Alvarenga, R., da Silva, C.C., Brito, G.C., Simon, S.D., Moreira-Filho, C.A., Leite, K.R., Camara-Lopes, L.H., Campos, F.S., Gimba, E., Vignal, G.M., El-Dorry, H., Sogayar, M.C., Barcinski, M.A., da Silva, A.M., Verjovski-Almeida, S. (2004) Oncogene, 23(39), 6684-6692. Klimov, D., Skoblov, M., Ryazantzev, A., Tyazhelova, T. and Baranova, A. (2006) J. Bioinform. Comput. Biol. 4(2), 515521. Schmitt, A.O., Specht, T., Beckmann, G., Dahl, E., Pilarsky, C.P., Hinzmann, B. and Rosenthal, A. (1999) Nucleic Acids Res., 27(21), 4251-4260. Brett, D., Kemmner, W., Koch, G., Roefzaad, C., Gross, S. and Schlag, P.M. (2001) Oncogene, 20(33), 4581-4585. Jin, L., Wang, H., Narita, T., Kikuno, R., Ohara, O., Shihara, N., Nishigori, T., Horikawa, Y. and Takeda, J. (2003) J. Mol. Endocrinol., 31(3), 519-528. Baranova, A.V., Lobashev, A.V., Ivanov, D.V., Krukovskaya, L.L., Yankovsky, N.K. and Kozlov, A.P. (2001) FEBS Lett., 508(1), 143-148. Krukovskaja, L.L., Baranova, A., Tyezelova, T., Polev, D. and Kozlov, A.P. (2005) Tumour Biol., 26(1), 17-24. Shen, D., He, J. and Chang H.R. (2005) Int. J. Mol. Med., 15(2), 205-212. Palena, C., Polev, D.E., Tsang, K.Y., Fernando, R.I., Litzinger, M., Krukovskaya, L.L., Baranova, A.V., Kozlov, A.P. and Schlom, J. (2007) Clin. Cancer Res. 13(8), 24712478. Hui, L., Zhang, X., Wu, X., Lin, Z., Wang, Q., Li, Y. and Hu, G. (2004) Oncogene, 23(17), 3013-3023. Xu, Q. and Lee, C. (2003) Nucleic Acids Res., 31(19), 56355643.
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