Identification of Gastric Cancer Patients by Serum Protein Profiling

Ebert, Matthias P.; Meuer, Jörn; Wiemer, J.; Schulz, Hans–Ulrich; Reymond, Marc A.; Traugott, U.; Malfertheiner, Peter; Röcken, Christoph

doi:10.1021/pr049865s

Cited by 93 publications

(101 citation statements)

References 18 publications

(68 reference statements)

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“…(35) They found three potential biomarkers (3503, 3946 and 15 958 m/z) that were obviously different to our result. This difference was possibly caused by the patients' racial differences, cancer types and the different pH value of the binding buffer (we used binding buffer of pH 8.0, compare with pH 8.5 used by Ebert et al (35) ), which could result in the SAX2 ProteinChip capturing different proteins.…”

Section: Discussioncontrasting

confidence: 99%

Diagnosis of gastric cancer using decision tree classification of mass spectral data

Shen

Qian

et al. 2006

Cancer Science

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Although gastric cancer is the second leading cause of cancer death worldwide, specific and sensitive biomarkers that can be used for its diagnosis are still unavailable. Attempting to improve on current approaches to the serological diagnosis of gastric cancer, we subjected serum samples from 245 individuals (including 127 gastric cancer patients, 100 age-and sex-matched healthy individuals, nine benign gastric lesion patients and nine colorectal cancer patients) for analysis by surface-enhanced laser desorption/ionization (SELDI) mass spectrometry. Peaks were detected with Ciphergen SELDI software version 3.1.1 and analyzed with Biomarker Patterns' software 5.0. We developed a classifier for separating the gastric cancer groups from the healthy groups. Three protein masses with 1468, 3935 and 7560 m/z were selected as a potential 'fingerprint' for the detection of gastric cancer. It was able to distinguish the gastric cancer patients from the health volunteers with a sensitivity of 95.6% and a specificity of 92.0% in the training set. In the blinding set, it was capable of differentiating the gastric cancer samples from the others with a specificity of 88.0%, a sensitivity of 85.3%, and an accuracy of 86.4%. These values were all higher than those achieved in a parallel analysis by measuring serum carcinoembryonic antigen (CEA) and carbohydrate antigen (CA)19-9 together. Therefore, the decision tree analysis of serum proteomic patterns has the potential to be used in gastric cancer diagnosis. (Cancer Sci 2007; 98: 37-43)

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Section: Discussioncontrasting

confidence: 99%

Diagnosis of gastric cancer using decision tree classification of mass spectral data

Shen

Qian

et al. 2006

Cancer Science

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“…[4][5][6][7] With regard to tissue samples, proteomic analysis usually necessitates prefractionation in order to separate tumor from nontumor, or neoplastic cells from non-neoplastic cells either by macrodissection, laser capture-microdissection, or homogenization and, for example, subsequent fractionation using antibody-coupled magnetic beads. [4][5][6] While these technologies have demonstrated their suitability, they still provide only limited information about the spatial variability of the cancer proteome. The protein composition of a malignant tumor varies qualitatively and quantitatively.…”

Section: Introductionmentioning

confidence: 99%

MALDI Imaging Combined with Hierarchical Clustering as a New Tool for the Interpretation of Complex Human Cancers

et al. 2008

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Proteomics analyses have been exploited for the discovery of novel biomarkers for the early recognition and prognostic stratification of cancer patients. These analyses have now been extended to whole tissue sections by using a new tool, that is, MALDI imaging. This allows the spatial resolution of protein and peptides and their allocation to histoanatomical structures. Each MALDI imaging data set contains a large number of proteins and peptides, and their analysis can be quite tedious. We report here a new approach for the analysis of MALDI imaging results. Mass spectra are classified by hierarchical clustering by similarity and the resulting tissue classes are compared with the histology. The same approach is used to compare data sets of different patients. Tissue sections of gastric cancer and non-neoplastic mucosa obtained from 10 patients were forwarded to MALDI-Imaging. The in situ proteome expression was analyzed by hierarchical clustering and by principal component analysis (PCA). The reconstruction of images based on principal component scores allowed an unsupervised feature extraction of the data set. Generally, these images were in good agreement with the histology of the samples. The hierarchical clustering allowed a quick and intuitive access to the multidimensional information in the data set. It allowed a quick selection of spectra classes representative for different tissue features. The use of PCA for the comparison of MALDI spectra from different patients showed that the tumor and non-neoplastic mucosa are separated in the first three principal components. MALDI imaging in combination with hierarchical clustering allows the comprehensive analysis of the in situ cancer proteome in complex human cancers. On the basis of this cluster analysis, classification of complex human tissues is possible and opens the way for specific and cancer-related in situ biomarker analysis and identification.

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“…[6][7][8][9] Proteome analysis of childhood leukemia subtypes may yield similar results and further aide the diagnostic process, [10][11][12] as well as increase the precision of epidemiologic studies to detect associations between subtypes of leukemia with environmental exposures. Surface-enhanced laser desorption/ionization time-of-flight mass spectrometry (SELDI-TOF MS) has successfully been used to analyze differentially expressed proteins in multiple studies of malignancies such as breast cancer [13][14][15][16][17][18] and to distinguish between rat prostate cancer cell lines of variable metastatic potential. 19 Here, we have applied SELDI-TOF MS proteomic technology based on ProteinChip s Arrays from Ciphergen Biosystems to analyze the proteomes of four childhood leukemia cell lines (two ALL cell lines harboring t(12;21) and t(1;19), a MLL cell line harboring t(4;11), and an AML line harboring t(8;21)), as well as 20 childhood leukemia bone marrow samples of different subtypes.…”

Section: Introductionmentioning

confidence: 99%

Proteomic analysis of childhood leukemia

et al. 2005

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Childhood acute lymphoblastic and myeloid leukemias are stratified into molecular and cytogenetic subgroups important for prognosis and therapy. Studies have shown that gene expression profiles can discriminate between leukemia subtypes. Thus, proteome analysis similarly holds the potential for characterizing different subtypes of childhood leukemia. We used surface-enhanced laser desorption/ionization time-offlight mass spectrometry to analyze cell lysates from childhood leukemia cell lines as well as pretreatment leukemic bone marrow derived from childhood leukemia cases. Comparison of the acute myeloid leukemia (AML) cell line, Kasumi, and the biphenotypic myelomonocytic cell line, MV4;11, with the acute lymphoblastic leukemia (ALL) cell lines, 697 and REH, revealed many differentially expressed proteins. In particular, one 8.3 kDa protein has been identified as a C-terminal truncated ubiquitin. Analysis of childhood leukemia bone marrow showed differentially expressed proteins between AML and ALL, including a similar peak at 8.3 kDa, as well as several proteins that differentiate between the ALL t(12;21) and hyperdiploid subtypes. These results demonstrate the potential for proteome analysis to distinguish between various forms of childhood leukemia. Future analyses are warranted to validate these findings and to investigate the role of the C-terminal truncated ubiquitin in the etiology of ALL.

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Identification of Gastric Cancer Patients by Serum Protein Profiling

Cited by 93 publications

References 18 publications

Diagnosis of gastric cancer using decision tree classification of mass spectral data

Diagnosis of gastric cancer using decision tree classification of mass spectral data

MALDI Imaging Combined with Hierarchical Clustering as a New Tool for the Interpretation of Complex Human Cancers

Proteomic analysis of childhood leukemia

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