IPO-38 is identified as a novel serum biomarker of gastric cancer based on clinical proteomics technology.
ABSTRACT Gastric cancer is one of the most common malignancies in China. So far, there are few reliable serum biomarkers for diagnosis. The available biomarkers of CEA, CA19-9 and CA72-4 are not sufficiently sensitive and specific for gastric cancer. In this study, a high density antibody microarray was used for identifying new biomarkers from serum samples of gastric cancer. Serum samples from colorectal cancer, pancreatic cancer, hepatocellular cancer, and breast cancer were also screened for comparative study. As result, some candidate biomarkers were identified. IPO-38, an up-regulated serum protein in gastric cancer was selected for subsequent validation including serum IPO-38 expression by ELISA and IPO-38 protein expression by immunohistochemistry. The immunoprecipitation by IPO-38 for gastric cancer cell line and MALDI-TOF/TOF mass spectrometer suggested that pull-down of IPO-38 belongs to H2B histone, which was supported by co-localization study of laser scanning confocal microscope. A follow-up study showed that the survival rate of IPO-38 negative group was better than that in IPO-38 positive group. The study first clarified the property of IPO-38 proliferating marker, and proposed that IPO-38 protein is a promising biomarker both for diagnosis and for predicting prognosis of gastric cancer.
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ABSTRACT: To gain new insights into tumor metabolism and to identify possible biomarkers with potential diagnostic values to predict tumor metastasis. Human gastric cancer SGC-7901 cells were implanted into 24 severe combined immune deficiency (SCID) mice, which were randomly divided into metastasis group (n = 8), non-metastasis group (n = 8), and normal group (n = 8). Urinary metabolomic information was obtained by gas chromatography/mass spectrometry (GC/MS). There were significant metabolic differences among the three groups (t test, P < 0.05). Ten selected metabolites were different between normal and cancer groups (non-metastasis and metastasis groups), and seven metabolites were also different between non-metastasis and metastasis groups. Two diagnostic models for gastric cancer and metastasis were constructed respectively by the principal component analysis (PCA). These PCA models were confirmed by corresponding receiver operating characteristic analysis (area under the curve = 1.00). The urinary metabolomic profile is different, and the selected metabolites might be instructive to clinical diagnosis or screening metastasis for gastric cancer.World Journal of Gastroenterology 02/2011; 17(6):727-34. · 2.55 Impact Factor
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ABSTRACT: L: -plastin, an actin-binding protein, is upregulated in many tumours, including colorectal carcinoma. This study evaluated the expression of L: -plastin in plasma and colorectal tumour tissue and analysed the correlation between clinicopathological staging and prognosis. Enzyme-linked immunosorbent assay was used to detect L: -plastin in the plasma of 120 colorectal carcinoma patients and 40 control subjects. Immunohistochemistry analyses were also used. The rate of positive L: -plastin expression was significantly higher in colorectal carcinoma patients than in control subjects, and was significantly higher in tumour tissues than in the tissues surrounding the tumour. L: -Plastin expression also is correlated with tumour grade and size, and lymph node metastasis. However, there was no correlation with the extent of tumour invasion or distant metastasis. L: -Plastin may be a useful marker for screening colorectal carcinoma and determining the prognosis of patients with colorectal carcinoma, and for genetic therapy and targeted therapy of colorectal carcinoma.Journal of Gastrointestinal Surgery 09/2011; 15(11):1982-8. · 2.36 Impact Factor
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ABSTRACT: The proteins secreted by prostate cancer cells (PC3(AR)6) were separated by strong anion exchange chromatography, digested with trypsin and analyzed by unbiased liquid chromatography tandem mass spectrometry with an ion trap. The spectra were matched to peptides within proteins using a goodness of fit algorithm that showed a low false positive rate. The parent ions for MS/MS were randomly and independently sampled from a log-normal population and therefore could be analyzed by ANOVA. Normal distribution analysis confirmed that the parent and fragment ion intensity distributions were sampled over 99.9% of their range that was above the background noise. Arranging the ion intensity data with the identified peptide and protein sequences in structured query language (SQL) permitted the quantification of ion intensity across treatments, proteins and peptides. The intensity of 101,905 fragment ions from 1421 peptide precursors of 583 peptides from 233 proteins separated over 11 sample treatments were computed together in one ANOVA model using the statistical analysis system (SAS) prior to Tukey-Kramer honestly significant difference (HSD) testing. Thus complex mixtures of proteins were identified and quantified with a high degree of confidence using an ion trap without isotopic labels, multivariate analysis or comparing chromatographic retention times.Journal of proteomics 11/2011; 75(4):1303-17. · 5.07 Impact Factor