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Maspin is a tumor suppressor protein that has been reported to stimulate the cell death of cancer and inhibit the metastasis of cancer. The present study aimed to explore the survival pathway by which maspin modulates the resistance of human lung cancer cells to chemotherapeutic drugs, and the consequences of maspin gene therapy in an animal model.
NCI-H157 and A549 cells were transfected with either a mock vector (pCMVTaq4C), maspin (pCMV-maspin), siControl or siMaspin. RT-PCR and Western blot analysis were performed to study the expressions of survival proteins in lung cancer. cDNA microarray analysis was carried out to compare the maspin-modulated gene expression between the xenograft tumors derived from the lung cancer cells that were stably transfected with pCMVTaq4C or pCMV-maspin. Maspin gene therapy was performed by intra-tumoral injections of pCMVTaq4C or pCMV-maspin into the pre-established subcutaneous tumors in nude mice.
Maspin significantly decreased the survival to doxorubicin and etoposide, whereas did not affect the survival to cisplatin in the NCI-H157 cells. Interestingly, transfection with a maspin plasmid resulted in a significant reduction of the phosphorylation of Akt in the NCI-H157 cells, whereas knockdown of maspin increased the phosphorylation of Akt in the A549 cells. Microarray analysis of the xenograft tumors revealed a specific gene expression profile, demonstrating that maspin is associated with the differential expressions of PTEN and IGF2R. Direct transfer of pCMV-maspin into the tumor significantly retarded the tumor growth in the animal experiments (p=0.0048).
Lung cancer cells lacking maspin could be resistant to chemotherapeutic drugs such as doxorubicin or etoposide, at least in part by maintaining Akt phosphorylation.
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The diverse experimental environments in microarray technology, such as the different platforms or different RNA sources, can cause biases in the analysis of multiple microarrays. These systematic effects present a substantial obstacle for the analysis of microarray data, and the resulting information may be inconsistent and unreliable. Therefore, we introduced a simple integration method for combining microaray data sets that are derived from different experimental conditions, and we expected that more reliable information can be detected from the combined data set rather than from the separated data sets.
This method is based on the distributions of the gene expression ratios among the different microarray data sets and it transforms, gene by gene, the gene expression ratios into the form of the reference data set. The efficiency of the proposed integration method was evaluated using two microarray data sets, which were derived from different RNA sources, and a newly defined measure, the
The proposed integration method intermixed the two data sets that were obtained from different RNA sources, which in turn reduced the experimental bias between the two data sets, and the
The proposed method worked well in adjusting systematic biases, including the source effect. The ability to use an effectively integrated microarray data set yields more reliable results due to the larger sample size and this also decreases the chance of false negatives.
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RhoA is a critical transducer of extracellular signals, which leads to organization of actin cytoskeleton, motility, adhesion and gene regulation. The present study aimed to explore whether RhoA influences the susceptibility of gastric cancer cells to chemotherapeutic drugs.
SNU638 cells were transfected with a mock vector (pcDNA3.1), RhoA (pcDNA/RhoA), or constitutively active RhoA (pcDNA/caRhoA). MTT assay and Western blot analysis were performed to study the growth response to several chemotherapeutic drugs in the gastric cancer cell line, SNU638, with different RhoA levels.
RhoA significantly enhanced the resistance to lovastatin, 5-FU, taxol and vincristine, but did not affect the sensitivity to cisplatin or etoposide in SNU638. In the Western blot analysis, RhoA decreased the PARP cleavage, which was accompanied by a concurrent reductionin cell death. The gene expression profile after a cDNA microarray analysis demonstrated that RhoA was associated with the differential expression of 19 genes, including those involved in anti-oxidant defense, glucose metabolism, anti-apoptosis and protein turnover.
Gastric cancer cells with a high expression of RhoA could be resistant to chemotherapeutic drugs, such as taxol or vincristine, implying that treatment strategies aimed at inactivation of RhoA might be promising for improving the efficacy of these chemotherapeutic drugs.
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Gastric cancer is one of the most prevalent cancers worldwide. 5-fluorouracil (5-FU) and cisplatin are the most commonly used drugs for the treatment of gastric cancer. However, a significant number of tumors often fail to respond to chemotherapy.
To better understand the molecular mechanisms underlying drug resistance in gastric cancer the gene expression in gastric cancer cells, which were either sensitive or resistant to 5-FU and cisplatin, were examined using cDNA microarray analysis. To confirm the differential gene expression, as determined using the microarray, semiquantitative RT-PCR was performed on a subset of differentially expressed cDNAs.
69 and 45 genes, which were either up-regulated (9 and 22 genes) or down-regulated (60 and 25 genes), were identified in 5-FU- and cisplatin-resistant cells, respectively. Several genes, such as adaptor-related protein complex 1 and baculoviral IAP repeat-containing 3, were up-regulated in both drug-resistant cell types. Several genes, such as the ras homolog gene family, tropomyosin, tumor rejection antigen, protein disulfide isomerase-related protein, melanocortin 1 receptor, defensin, cyclophilin B, dual specificity phosphatase 8 and hepatocyte nuclear factor 3, were down-regulated in both drugresistant cell types.
These findings show that cDNA microarray analysis can be used to obtain gene expression profiles that reflect the effect of anticancer drugs on gastric cancer cells. Such data may lead to the assigning of signature expression profiles of drug-resistant tumors, which may help predict responses to drugs and assist in the design of tailored therapeutic regimens to overcome drug resistance.
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DNA microarray technology permits simultaneous analysis of thousands of DNA sequences for genomic research and diagnostics applications. Microarray technology represents the most recent and exciting advance in the application of hybridization-based technology for biological sciences analysis. This review focuses on the classification (oligonucleotide vs. cDNA) and application (mutation-genotyping vs. gene expression) of microarrays. Oligonucleotide microarrays can be used both in mutation-genotyping and gene expression analysis, while cDNA microarrays can only be used in gene expression analysis. We review microarray mutation analysis, including examining the use of three oligonucleotide microarrays developed in our laboratory to determine mutations in
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Adriamycin® is one of the most commonly used drugs in the treatment of breast cancer. This study was performed to understand the molecular mechanisms of drug resistance in breast cancer cells.
We have analyzed the MCF-7 breast cell line and its adriamycin-resistant variants, MCF-7/ADR using human 10 K element cDNA microarrays.
We defined 68 genes that were up-regulated (14 genes) or down-regulated (54 genes) in adriamycin resistant breast cancer cells. Several genes, such as G protein-coupled receptor kinase 5, phospholipase A2, guanylate cyclase 1, vimentin, matrix metalloproteinase 1 are up-regulated in drug resistant cells. Several genes, such as interferon, alpha-inducible protein 27, forkhead box M1, mitogen-activated protein kinase 6, regulator of mitotic spindle assembly 1 and tumor necrosis factor superfamily are down-regulated in adriamycin resistant cells. The altered expression of genes observed in microarray was verified by RT-PCR.
These findings show that cDNA microarray analysis can be used to obtain gene expression profiles reflecting the effect of anticancer drugs on breast cancer cells. Such data may lead to the assigning of signature expression profiles of drug-resistant tumors which may help predict responses to drugs and assist in the design of tailored therapeutic regimens to overcome drug resistance.
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This study utilized both cDNA microarray and 2D protein gel electrophoresis technology to investigate the multiple interactions of the genes and proteins involved in the pathophysiology of uterine leiomyomas. Also, Gene Ontology (GO) analysis was used to systematically characterize the global expression profiles, which were found to correlate with the leiomyosarcomas.
The uterine leiomyoma biopsies were obtained from patients in the Department of Obstetrics and Gynecology, The Catholic University of Korea. Differentially expressed transcriptome and proteome, in 6 paired leiomyoma and normal myometrium, were profiled. The total RNAs from the leiomyoma and normal myometrium were labeled with Cy5 and Cy3. All specimens were punch-biopsy-obtained, and frozen in liquid nitrogen.
Screening of up to 17,000 genes identified 71 that were either up-regulated or down-regulated (21 and 50, respectively). The gene expression profiles were classified into 420 mutually dependent functional sets, resulting in 611 cellular processes, according to the gene ontology. Also, the protein analysis, using 2D gel electrophoresis, identified 33 proteins (17 up-regulated and 16 down-regulated) with more than 500 total spots, which were classified into 302 cellular processes. O f these functional profilings, transcriptomes and proteoms down-regulations were shown in the cell adhesion, cell m otility, organogenesis, enzyme regulator, structural molecule activity and responses to external stimulus functional activities, which are supposed to play important roles in the pathophysiology. In contrast, up-regulation was only shown in the nucleic acid binding activity. The CDKN2A, ADH1A, DCX, IGF2, CRABP2 and KIF5C were found to increase the reliability of this study, and correlate with the leiomyosarcomas.
Potentially significant pathogenetic cellular processes showed that down-regulated functional profiling has an important impact on the discovery of the pathogenic pathways in leiomyomas and leiomyosarcomas. GO analysis can also overcome the complexity of the expression profiles of cDNA microarrays and 2D protein analyses, via a cellular process level approach. Thereby, a valuable prognostic candidate gene, with real relevance to disease-specific pathogenesis, can be found at cellular process levels.
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