Lymph-node metastasis is a main factor causing poor prognosis of patients with gastric cancer (GC). In order to determine the genes involved in lymph-node metastasis, we compared primary tumors with their synchronous lymph-node metastases for DNA sequence copy number aberrations (DSCNAs) in 20 patients diagnosed as having intestinal-type GC using comparative genomic hybridization (CGH). The results showed that some DSCNAs (gains at 8q, 13q, 5p, 7 and X, and losses at 1p, 17p, 19, 21q and 22q) were frequently found in both primary tumors and their metastases. However, metastases often contained DSCNAs that were not found in corresponding primary tumors, and gain at 20q12-13 and losses at 21qcen-21, 4q and 14q22-ter were significantly more frequently observed in metastatic lesions than in their primary tumors (10:2, 9:0, 6:0, and 7:0 between metastases and corresponding primary tumors, respectively). Our data indicate that gain at 20q12-13 and losses at 21qcen-21, 4q, and 14q22-ter are involved in lymph-node metastases, and that these chromosomal regions may contain the genes related to lymph-node metastases in intestinal-type GC.
"Investigation of the molecular background of tumor metastasis through " omics " studies revealed that multiple genes aberrations were contributed to the tumor metastasis  . Therefore, we tried to examine the overall features of the expressed proteins, and identified 109 aberrantly expressed proteins. "
[Show abstract][Hide abstract] ABSTRACT: To examine the proteomic background of lymph node metastasis (LNM) in gastric cancer, we performed protein expression profiling of paired non-tumor, primary tumor, and LNM tissues. Using a label-free proteomic approach, we generated protein expression profiles of 3894 unique proteins and identified 109 differentially expressed proteins. Functional pathway analysis of the differentially expressed proteins showed that members of the beta-3 integrin (ITGB3) pathway were significantly enriched. Aberrations of ITGB3 were reported in various malignancies; however, ITGB3 in LNM tissues has not been examined to date. Different level of ITGB3 expression was confirmed in 20 gastric cancer cases by Western blotting. We analyzed the mRNA levels of the differentially expressed proteins by using a public mRNA expression database; 38.8% of the proteins examined, including those involved in oxidation and reduction, showed correlation between protein and mRNA levels. Proteins without such correlation included factors related to cell adhesion. Our study suggests a novel role for the integrin pathway in the development of LNM in gastric cancer and indicated possible benefits of observational transcriptomic analysis for proteomic studies.
EuPA Open Proteomics 06/2014; 3. DOI:10.1016/j.euprot.2014.03.001
[Show abstract][Hide abstract] ABSTRACT: Deletions of 8p and gains of 8q belong to the most frequent cytogenetic alterations in prostate cancer. The target genes of these alterations and their biological significance are unknown.
To determine the relationship between chromosome 8 changes, and prostate cancer phenotype and prognosis, a set of 1.954 fully annotated prostate cancers were analyzed in a tissue microarray format by fluorescence in situ hybridization.
Both 8p deletions and 8q gains increased in number during different stages of prostate cancer progression. 8p deletions/8q gains were found in 26.1%/4.8% of 1,239 pT(2) cancers, 38.5%/9.8% of 379 pT(3a) cancers, 43.5%/8.9% of 237 pT(3b) cancers, 40.7%/14.8% of 27 pT(4) cancers, 39.1%/34.8% of 23 nodal metastases, 51.9%/33.3% of 27 bone metastases, and 45.5%/59.9% of 22 hormone refractory cancers (P < 0.0001 each). Both 8p deletions and 8q gains were also significantly associated with high Gleason grade and with each other (P < 0.0001 each). In primary tumors, 8p deletions were seen in only 27.3% of 1,882 cancers without 8q gain but in 57.4% of 122 tumors with 8q gain (P < 0.0001). Among cancers treated with radical prostatectomy, 8p deletions (P = 0.003) and 8q gains (P = 0.02) were associated with biochemical tumor recurrence. However, multivariate analysis (including prostate-specific antigen, pT/pN stage, Gleason score, and surgical margin status) did not reveal any statistically independent effect of 8p or 8q alterations on biochemical tumor recurrence.
8p deletions and 8q gains are relatively rare in early stage prostate cancer but often develop during tumor progression. The prognostic effect does not seem to be strong enough to warrant clinical application.
Clinical Cancer Research 12/2009; 16(1):56-64. DOI:10.1158/1078-0432.CCR-09-1423 · 8.72 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: Array-based Comparative Genomic Hybridization (aCGH) is a microarray-based technology that assists in identification of DNA sequence copy number changes across the genome. Examination of differences in instability phenotype, or pattern of copy number alterations, between cancer subtypes can aid in classification of cancers and lead to better understanding of the underlying cytogenic mechanism. Instability phenotypes are composed of a variety of copy number alteration features including height or magnitude of copy number alteration level, frequency of transition between copy number states such as gain and loss, and total number of altered clones or probes. That is, instability phenotype is multivariate in nature. Current methods of instability phenotype assessment, however, are limited to univariate measures and are therefore limited in both sensitivity and interpretability. In this paper, a novel method of instability assessment is presented that is based on the Engler et al. (2006) pseudolikelhood approach for aCGH data analysis. Through use of a pseudolikelihood ratio test (PLRT), more sensitive assessment of instability phenotype differences between cancer subtypes is possible. Evaluation of the PLRT method is conducted through analysis of a meningioma data set and through simulation studies. Results are shown to be more accurate and more easily interpretable than current measures of instability assessment.
Statistical Applications in Genetics and Molecular Biology 01/2011; 10(1):31-31. DOI:10.2202/1544-6115.1407 · 1.13 Impact Factor
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