Ahn MJ, Yoo YD, Lee KH, Ahn JI, Yu DH, Lee HS, Oh HS, Choi JH, Lee YS. cDNA microarray analysis of differential gene expression in gastric cancer cells sensitive and resistant to 5-fluorouracil and cisplatin.
Cancer Res Treat 2005;
37:54-62. [PMID:
19956511 DOI:
10.4143/crt.2005.37.1.54]
[Citation(s) in RCA: 4] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2004] [Accepted: 02/08/2005] [Indexed: 11/21/2022] Open
Abstract
PURPOSE
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.
MATERIALS AND METHODS
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.
RESULTS
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 drug-resistant cell types.
CONCLUSION
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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