cDNA microarray analysis of the effect of cantharidin on DNA damage, cell cycle and apoptosis-associated gene expression in NCI-H460 human lung cancer cells in vitro
- Authors:
- Published online on: March 24, 2015 https://doi.org/10.3892/mmr.2015.3538
- Pages: 1030-1042
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Copyright: © Hsia et al. This is an open access article distributed under the terms of Creative Commons Attribution License [CC BY_NC 3.0].
Abstract
Introduction
Lung cancer accounts for ~28% of cancer-associated mortali-ties (1), the occurrence of which is increasing worldwide. There are ~1.2 million novel cases of lung cancer and ~1 million mortalities from lung cancer each year (2). Lung cancer may be subdivided into small cell lung carcinoma and non-small cell lung carcinoma (NSCLC). The majority of lung cancer diagnoses are NSCLC (3,4), which has a five-year survival rate of ~33% (5). At present, the standard treatment for patients with resectable stage I to IIIA NSCLC is surgical excision; however, the prognosis remains poor (6). In addition, chemotherapy with or without surgery is not effective in the majority of cases; therefore, it is essential to identify novel compounds, including natural products, which may be employed for the treatment of lung cancer.
Cantharidin (CTD) is a component of mylabris (blister beetle), which has previously been used as a Traditional Chinese Medicine (7). Previous studies have reported that CTD induced cytotoxic effects in leukemia stem cells (8) as well as U937 (9), pancreatic cancer (10), hepatocellular carcinoma (11,12), colon cancer (13) and human lung cancer A549 (14) cells. In addition, CTD was found to inhibit the activity of protein phosphatase 2A (PP2A) (9) and heat shock factor 1 (HSF1) (15). Furthermore, it was shown that CTD induced cell death in human colorectal cancer cells, which was suggested to proceed through inhibiting the binding of heat shock protein 70 (HSP70), B cell lymphoma 2-associated athanogene domain 3 (BAG3) and HSF1 to promoters (15).
Genetic mutations in oncogenes and tumor suppressor genes are present in cancer cells (16,17). The development of cancer cells is well-known to be dependent on oncogenes for tumor initiation and progression; this concept has therefore been named oncogene addiction (18). Oncogenes are commonly used as targets for drug-screening programs (19); however, other signaling pathways have also been examined, such as the molecular chaperone pathway (20). The present study aimed to investigate the effect of CTD on the expression of key genes and functional pathways of human H460 lung cancer cells using complementary DNA microarray analysis. The results of the present study showed that CTD affected DNA damage, the cell cycle and the expression of apoptosis-associated genes in vitro. Differentially expressed genes were then used to generate interaction maps of signaling pathways. The epidermal growth factor and vascular endothelial growth factor receptor pathways, provided by the present study may be useful for the development of novel molecular targeted therapies against lung cancer (21).
Materials and methods
Chemicals and reagents
Cantharidin (CTD), propidium iodide and dimethyl sulfoxide (DMSO) were purchased from Sigma-Aldrich (St. Louis, MO, USA). Minimum essential medium (MEM), fetal bovine serum (FBS), L-glutamine and penicillin-streptomycin were purchased from Gibco-BRL (Carlsbad, CA, USA). CTD was dissolved in DMSO and stored at 20°C.
Lung cancer cell culture
The NCI-H460 human lung cancer cell line was purchased from the Food Industry Research and Development Institute (Hsinchu, Taiwan). Cells were grown in MEM containing 10% (v/v) FBS as well as 100 U/ml penicillin and 100 μg/ml streptomycin in a 37°C humidified incubator with 5% CO2. Cells were then subcultured once they reached 80–90% confluence, as previously described (22).
Complementary (c)DNA microarray assay
H460 cells were placed on 12-well plates at a density of 5×105 cells/well in 2 ml MEM with 10% (v/v) FBS and 2 mM L-glutamine, as well as 100 U/ml penicillin and 100 μg/ml streptomycin for 24 h. Subsequently, cells were treated with or without 10 μM CTD for a further 24 h. Cells (3×106) were then harvested and washed twice with phosphate-buffered saline (Gibco-BRL). Cells were lysed in TRIzol® (Invitrogen Life Technologies, Carlsbad, CA, USA) and total RNA was extracted using a Qiagen RNeasy Mini kit (Qiagen, Valencia, CA, USA). RNA concentrations were determined using a Qubit™ Fluorocytometer (Invitrogen Life Technologies).
Total RNA of CTD-treated and untreated H460 cells was used for cDNA synthesis. Samples were hybridized using an Affymetrix GeneChip Human Gene 1.0 ST array (Affymetrix, Santa Clara, CA, USA) according to the manufacturer’s instructions. Sample fluorescence was quantified by Asia BioInnovations Corp. (Taipei, Taiwan), while data were analyzed using the Transcriptome Analysis Console™ 2.0 Version 2.0.0.9. (Affymetrix) with default robust multichip analysis parameters. A 2-fold change in gene expression was used as the threshold to indicate an effect on expression (7–10). An Oligo(dT) Maxime RT PreMix kit (iNtRON Biotechnology, Gyeonggi-do, South Korea) was used to reverse transcribe RNA into cDNA. The Affymetrix GeneChip® Whole Transcript Sense Target (ST) Labeling (cat. no. 900673; 30 Rxn; Affymetrix, Santa Clara, CA, USA) assay is designed to generate amplified and biotinylated sense-strand DNA targets from the entire expressed genome without bias. This assay and associated reagents have been optimized specifically for use with the GeneChip® ST arrays, and the probes on the arrays have been selected to be distributed throughout the entire length of each transcript. The gene list complete with Affymetrix transcript identifiers, was uploaded from a spreadsheet onto Metacore 5.0 software (GeneGo pathways analysis; http://www.genego.com). GeneGo recognizes the Affymetrix identifiers and maps the gene to the MetaCore™ data analysis suite, generating maps to describe common pathways or molecular connections between genes in the list. Graphical representations of the molecular associations between the genes were generated using the GeneGo pathway analysis, based upon processes exhibiting a significant association (P<0.05).
Gene ontology analysis
For detection of significantly over-represented GO biological processes, the DAVID functional annotation clustering tool (http://david.abcc.ncifcrf.gov) was used (DAVID Bioinformatics Resources 6.7). Enrichment was determined at the DAVID calculated Benjamini value <0.05. The significance of the overexpression of individual genes was determined using Student’s t-test.
Statistical analysis
Values are representative of three independent experiments. Differences between control and CTD-experimental groups are presented which >2-fold, where positive numbers represent upregulation and negative numbers represent downregulation.
Results
Upregulated and downregulated gene expression in H460 cells exposed to CTD
H460 cells were incubated in the presence or absence of 10 μM CTD in a 12-well plate for 24 h. Cells were then harvested and following the extraction of total RNA, RNA concentrations were determined and cDNA microarray analysis was performed in order to determine the expression of genes. The calculated upregulation and downregulation of gene expression, as determined by the microarray, are shown in Tables I and II, respectively. As shown in Table I, the results indicated that in CTD-treated H460 cells, 8 genes were upregulated >4-fold, 29 genes were upregulated >3–4-fold and 156 genes were upregulated >2–3-fold compared with expression levels in the untreated control cells. In addition, Table II indicated that one gene was downregulated >4 fold, 14 genes were downregulated >3–4 fold and 150 genes were downregulated >2–3 fold in H460 cells following exposure to CTD compared with those in the untreated control cells. The results presented in Table I demonstrated that genes associated with DNA damage, including DN1T3 and GADD45A were upregulated by 2.26-and 2.60-fold, respectively; in addition, the expression of genes associated with the cell cycle progression (check point proteins) were upregulated, including CCND2, CDKL3 and RASA4, which were upregulated 2.72-, 2.19- and 2.72-fold, respectively. Furthermore, the expression of apoptosis-associated genes was upregulated, such as CARD6, which was upregulated 3.54-fold (Table I). By contrast, the results presented in Table II demonstrated that genes associated with DNA damage, cell cycle progression and apoptosis were also downregulated, including DdiT4, CDC42EP3 and STAT2, respectively. These genes were found to be downregulated 3.14-, 2.16 and 2.04-fold, respectively (Table II). Overall, cDNA microarray analysis of H460 cells following treatment with CTD demonstrated that CTD induced the differential expression of numerous genes associated with DNA damage, cell cycle progression and apoptosis.
GeneGo analysis
A GeneGo analysis program was used to analyze the CTD-treated NCI-H460 cells in order to determine the top scoring genes which were differentially expressed, as determined by the number of pathway networks involved. The results of the GeneGo analyses are shown in Figs. 1Figure 23, which reveal the top, second and third scored genes by the number of pathways, respectively. Experimental data were used to generate maps of the pathway interactions and genes which were upregulated (indicated by red circles) and down-regulated (indicated by blue circles) in H460 cells following treatment with CTD. It was indicated that these genes may also be involved in DNA damage, cell cycle arrest and apop-tosis-associated responses in CTD-treated H460 cells.
Discussion
CTD has been reported to have cytotoxic effects in numerous different types of cancer cell (8–15). The results of previous studies have also demonstrated that CTD-induced cell death occurred due to the induction of apoptosis in human lung cancer cells (data not shown) (23). However, the effects of CTD on gene expression in cancer cells have remained to be elucdiated. To the best of our knowledge, the present study was the first to report on the effects of CTD on gene expression in H460 cells. Therefore, the present study not only advanced the understanding of the differential gene expression following treatment with CTD in lung cancer cells, but may additionally provide several potential biomarkers for use as future therapeutic clinical targets for the treatment of lung cancer.
It has been well documented that the tumor microenvironment, which contains matrix proteins, stromal cells and associated secreted molecules, including cytokines and associated genes, which may be used as targets of cancer therapeutic drugs (24–26). Therefore, an increasing number of studies focus on elucidating the tumor microenvironment and associated gene expression in order to determine potential novel therapeutic agents for treating cancer patients (27). Over the past decade, there have been numerous clinical trials of treatments for lung cancer patients, including adjuvant chemotherapy trials and neo-adjuvant chemotherapy trials (28–30); however, the results of these trials have not yet provided a successful, effective treatment for lung cancer. Numerous studies have demonstrated that chemotherapeutics may result in cell death through DNA damage, cell cycle arrest and the induction of apoptosis (31,32). In the present study, H460 cells were treated with CTD and incubated in 12-well plates, and their RNA was then isolated in order to determine which genes exhibited altered expression following treatment with CTD. The results revealed that CTD effected the upregulation and downregulation, respectively, of the expression of certain genes which are known to be associated with DNA damage, cell cycle progression and apoptosis in H460 cells.
In order to further elucidate the molecular signaling pathways associated with altered gene expression in H460 cells following exposure to CTD, GeneGo Process Networks were used in the present study in order to analyze the altered gene expression results of the microarray, in order to determine the possible signaling pathways involved. Based on GeneGo pathway and canonical pathway maps, which represent a set of ~650 signaling and metabolic maps covering human biology (signaling and metabolism) in a comprehensive way. A preset network of protein interaction characteristics for the process was used for each process, and the experimental data were mapped regarding the specific process. The obtained hypothetical molecular signaling pathways indicated that CTD affects numerous associated signaling pathways, indicated by the involvement of the differentially expressed genes in the network of the respective the signaling pathways. The gene content of the uploaded files was used as the input list for the generation of biological networks using the Analyze Networks algorithm with default settings. This is a variant of the shortest paths algorithm, with main parameters of relative enrichment with the uploaded data, and relative saturation of the networks with canonical pathways. The network provides data listing interacting proteins. In this workflow the network is prioritized based on the number of fragments of canonical pathways on the network.
In conclusion, the results of the present study revealed that treatment with CTD induced the upregulation and downregulation of numerous genes in H460 cells. In addition, these differentially expressed genes were associated with DNA damage, cell cycle progression and apoptotic cell death in human lung cancer H460 cells. The present study also revealed possible signaling pathways, which may provide more information on the possible mechanism of CTD in H460 cells; however, further studies are required.
Acknowledgments
The present study was supported by a grant from China Medical University [grant no. MU 101-AWARD-03(1/2); Taichung, Taiwan].
References
|
Bilello KS, Murin S and Matthay RA: Epidemiology, etiology, and prevention of lung cancer. Clin Chest Med. 23:1–25. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Parkin DM, Bray F, Ferlay J and Pisani P: Global cancer statistics, 2002. CA Cancer J Clin. 55:74–108. 2005. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang P, Gao WY, Turner S and Ducatman BS: Gleevec (STI-571) inhibits lung cancer cell growth (A549) and potentiates the cisplatin effect in vitro. Mol Cancer. 2:12003. View Article : Google Scholar : PubMed/NCBI | |
|
Midthun DE and Jett JR: Chemotherapy for advanced lung cancer. When to expect a response. Postgrad Med. 101:187–194. 1997. View Article : Google Scholar : PubMed/NCBI | |
|
Stewart DJ: Tumor and host factors that may limit efficacy of chemotherapy in non-small cell and small cell lung cancer. Crit Rev Oncol Hematol. 75:173–234. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Chansky K, Sculier JP, Crowley JJ, et al: International Staging Committee and Participating Institutions: The International Association for the Study of Lung Cancer Staging Project: prognostic factors and pathologic TNM stage in surgically managed non-small cell lung cancer. J Thorac Oncol. 4:792–801. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Zhang W, Ma YZ, Song L, Wang CH, Qi TG and Shao GR: Effect of cantharidins in chemotherapy for hepatoma: A retrospective cohort study. Am J Chin Med. 42:561–567. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Dorn DC, Kou CA, Png KJ and Moore MA: The effect of cantharidins on leukemic stem cells. Int J Cancer. 124:2186–2199. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Huh JE, Kang KS, Chae C, Kim HM, Ahn KS and Kim SH: Roles of p38 and JNK mitogen-activated protein kinase pathways during cantharidin-induced apoptosis in U937 cells. Biochem Pharmacol. 67:1811–1818. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Li W, Xie L, Chen Z, et al: Cantharidin, a potent and selective PP2A inhibitor, induces an oxidative stress-independent growth inhibition of pancreatic cancer cells through G2/M cell-cycle arrest and apoptosis. Cancer Sci. 101:1226–1233. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Yeh CB, Su CJ, Hwang JM and Chou MC: Therapeutic effects of cantharidin analogues without bridging ether oxygen on human hepatocellular carcinoma cells. Eur J Med Chem. 45:3981–3985. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Chang C, Zhu YQ, Mei JJ, Liu SQ and Luo J: Involvement of mitochondrial pathway in NCTD-induced cytotoxicity in human hepG2 cells. J Exp Clin Cancer Res. 29:1452010. View Article : Google Scholar : PubMed/NCBI | |
|
Huang WW, Ko SW, Tsai HY, et al: Cantharidin induces G2/M phase arrest and apoptosis in human colorectal cancer colo 205 cells through inhibition of CDK1 activity and caspase-dependent signaling pathways. Int J Oncol. 38:1067–1073. 2011.PubMed/NCBI | |
|
Zhang WD, Zhao HR, Yan Y, Wang XH, Zong ZH and Liu Y: Apoptosis induced by cantharidin in human pulmonary carcinoma cells A549 and its molecular mechanisms. Zhonghua Zhong Liu Za Zhi. 27:330–334. 2005.In Chinese. PubMed/NCBI | |
|
Kim JA, Kim Y, Kwon BM and Han DC: The natural compound cantharidin induces cancer cell death through inhibition of heat shock protein 70 (HSP70) and Bcl-2-associated athanogene domain 3 (BAG3) expression by blocking heat shock factor 1 (HSF1) binding to promoters. J Biol Chem. 288:28713–28726. 2013. View Article : Google Scholar : PubMed/NCBI | |
|
Osborne C, Wilson P and Tripathy D: Oncogenes and tumor suppressor genes in breast cancer: potential diagnostic and therapeutic applications. Oncologist. 9:361–377. 2004. View Article : Google Scholar : PubMed/NCBI | |
|
Heneghan HM, Miller N and Kerin MJ: MiRNAs as biomarkers and therapeutic targets in cancer. Curr Opin Pharmacol. 10:543–550. 2010. View Article : Google Scholar : PubMed/NCBI | |
|
Weinstein IB: Cancer. Addiction to oncogenes – the Achilles heal of cancer. Science. 297:63–64. 2002. View Article : Google Scholar : PubMed/NCBI | |
|
Luo J, Solimini NL and Elledge SJ: Principles of cancer therapy: oncogene and non-oncogene addiction. Cell. 136:823–837. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Solimini NL, Luo J and Elledge SJ: Non-oncogene addiction and the stress phenotype of cancer cells. Cell. 130:986–988. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Méndez M, Custodio A and Provencio M: New molecular targeted therapies for advanced non-small cell lung cancer. J Thorac Dis. 3:30–56. 2011. | |
|
Chang YM, Velmurugan BK, Kuo WW, et al: Inhibitory effect of alpinate Oxyphyllae fructus extracts on Ang II-induced cardiac pathological remodeling-related pathways in H9c2 cardio-myoblast cells. BioMedicine. 3:148–152. 2013. View Article : Google Scholar | |
|
Hsia TC, Yu CC, Hsu SC, et al: Cantharidin induces apoptosis of H460 human lung cancer cells through mitochondria-dependent pathways. Int J Oncol. 45:245–254. 2014.PubMed/NCBI | |
|
Ayala F, Dewar R, Kieran M and Kalluri R: Contribution of bone microenvironment to leukemogenesis and leukemia progression. Leukemia. 23:2233–2241. 2009. View Article : Google Scholar : PubMed/NCBI | |
|
Straussman R, Morikawa T, Shee K, et al: Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion. Nature. 487:500–504. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Wilson TR, Fridlyand J, Yan Y, et al: Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature. 487:505–509. 2012. View Article : Google Scholar : PubMed/NCBI | |
|
Moniri MR, Dai LJ and Warnock GL: The challenge of pancreatic cancer therapy and novel treatment strategy using engineered mesenchynmal stem cells. Cancer Gene Ther. 21:12–23. 2014. View Article : Google Scholar : PubMed/NCBI | |
|
Pignon JP, Tribodet H, Sagliotti GV, et al: LACE Collaborative Group: Lung adjuvant cisplatin evaluation: A pooled analysis by the LACE Collaborative Group. J Clin Oncol. 26:3552–3559. 2008. View Article : Google Scholar : PubMed/NCBI | |
|
Pepe C, Hasan B, Winton TL, et al National Cancer Institute of Canada and Intergroup Study FBR.10: Adjuvant vinorelbine and cisplatin in elderly patients: National Cancer Institute of Canada and Intergroup Study JBR.10. J Clin Oncol. 25:1553–1561. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Glynne-Jones R and Hoskin P: Neoadjuvant cisplatin chemotherapy before chemoradiation: A flawed paradigm? J Clin Oncol. 25:5281–5286. 2007. View Article : Google Scholar : PubMed/NCBI | |
|
Ricci MS and Zong WX: Chemotherapeutic approaches for targeting cell death pathways. Oncologist. 11:342–357. 2006. View Article : Google Scholar : PubMed/NCBI | |
|
Tan TT and White E: Therapeutic targeting of death pathways in cancer: Mechanisms for activating cell death in cancer cells. Adv Exp Med Biol. 615:81–104. 2008.PubMed/NCBI |
