Open Access

Bioinformatic identification of candidate genes induced by trichostatin A in BGC-823 gastric cancer cells

  • Authors:
    • Yunlong Li
    • Lisha Zhang
    • Chunfa Yang
    • Riheng Li
    • Longbin Shang
    • Xiaoming Zou
  • View Affiliations

  • Published online on: December 12, 2016     https://doi.org/10.3892/ol.2016.5485
  • Pages: 777-783
  • Copyright: © Li et al. This is an open access article distributed under the terms of Creative Commons Attribution License.

Metrics: Total Views: 0 (Spandidos Publications: | PMC Statistics: )
Total PDF Downloads: 0 (Spandidos Publications: | PMC Statistics: )


Abstract

The aim of the present study was to identify the candidate genes induced by trichostatin A (TSA) in BGC-823 gastric cancer (GC) cells and to explore the possible inhibition mechanism of TSA in GC. Gene expression data were obtained through chip detection, and differentially expressed genes (DEGs) between GC cells treated with TSA and untreated GC cells (control group) were identified. Gene ontology analysis of the DEGs was performed using the database for annotation, visualization and integrated discovery. Then sub‑pathway enrichment analysis was performed and a microRNA (miRNA) regulatory network was constructed. We selected 76 DEGs, among which 43 were downregulated genes and 33 were upregulated genes. By sub‑pathway enrichment analysis of the DEGs, the propanoate metabolism pathway was selected as the sub‑pathway. By constructing a miRNA regulatory network, we identified that DKK1 and KLF13 were the top hub nodes. The propanoate metabolism pathway and the genes DKK1 and KLF13 may play significant roles in the inhibition of GC induced by TSA. These genes may be potential therapeutic targets for GC. However, further experiments are still required to confirm our results.

Introduction

Gastric cancer (GC) is one of the most common malignancies worldwide and a leading cause of cancer-related mortality (1,2). Little is known about GC-associated genes or its diverse clinical properties, including metastatic status, invasiveness, histological type and responsiveness to chemotherapy (3). The carcinogenic mechanism of GC is still not fully understood, although previous studies have demonstrated a number of genetic alterations in this disease (3).

Studies have revealed that trichostatin A (TSA) has an inhibitory role in GC (47). In eukaryotic transcription, chromatin modifications by histone acetyltransferase or histone deacetyltransferase (HDAC) represent a fundamental mechanism of transcriptional regulation (8). Evidence from further studies suggests that histone acetylation alters nucleosomal structures and facilitates the accessibility of transcription-associated factors to chromatin DNA through the disruption of interactions between histones and DNA (911). As an HDAC inhibitor, TSA has been known to cause a variety of phenotypic changes, including cell-cycle arrest in the G1/G2 phase, apoptosis and differentiation in cultured transformed cells (1214). Although significant efforts have been made, the candidate genes and inhibition mechanism of TSA in GC remain unclear.

In the present study, microarray data were obtained through chip detection, and the differentially expressed genes (DEGs) between TSA-treated GC cells and untreated GC cells were identified. In addition, gene ontology (GO) analysis of the DEGs was performed. Then sub-pathway enrichment analysis was performed, and a microRNA (miRNA) regulatory network was constructed. Through the identification of GC-associated genes and biological changes, possible molecular mechanisms and potential therapeutic targets for GC were explored.

Materials and methods

Affymetrix microarray data

The GC cell line, BGC-823, was grown in RPMI-1640 supplemented with 10% fetal bovine serum and antibiotics in 5% CO2. The cells were grown to ~70–80% confluence and treated with 330 nM TSA (Sigma-Aldrich, St. Louis, MO, USA) for 24 h. Then total RNA was isolated from TSA-treated cells using TRIzol (Invitrogen Life Technologies, Gaithersburg, MD, USA). The construction of a fluorescence probe and hybridization were performed using a 3DNA array detection kit (Genisphere, Hatfield, PA, USA). cDNA microarrays were performed on a cDNA chip containing 21,522 cDNA clones selected from the Human Genome Oligo set version 2.1 (Operon, Ebersberg, Germany). Each slide contained 12 control genes to normalize the signal intensities of the different fluorescent dyes. Each hybridization array was scanned on a LuxScan 10KA (CapitalBio, Beijing, China). The intensity of each hybridization signal was calculated by the GenePix Pro 4.0 program (Axon, Schaumburg, IL, USA). Three repeats were included in this study, and each repeat contained TSA treated GC cells and untreated cells.

Data processing and DEG analysis

The signal intensities of TSA treated GC cells and untreated GC cells in each repeat were obtained through chip detection and the gene expression ratio (TSA-treated GC cells / untreated GC cells) in each repeat was calculated. Then, the DEGs between the TSA-treated group and the control group were analyzed, and the score was calculated by the significant analysis of microarray (SAM) algorithm (15). Each gene was assigned a difference score (defined as “d”) based on the significance of its gene expression changes between TSA treated GC cells and untreated GC cells. The multiple testing correction was performed using a false discovery rate (FDR) (16). The fold change of the expression of individual genes was also observed for the differential expression test. DEGs with an FDR<0.05 were considered to be significant. DEGs with a score of (d) ≥1.25 and a ratio (TSA-treated GC cells / untreated GC cells) >1.5 in at least two groups were defined as upregulated DEGs, and DEGs with a score of (d) ≤-1.25 and a ratio (TSA-treated GC cells / untreated GC cells) <0.66 in at least two groups were defined as downregulated DEGs.

GO analysis

GO analysis has become a commonly used approach for functional studies of large-scale genomic or transcriptomic data (17). The database for annotation, visualization and integrated discovery (DAVID) (18) consists of an integrated biological knowledge base and analytic tools aimed at systematically extracting biological meaning from large gene or protein lists. The GO function of DEGs was analyzed using DAVID. P<0.05 was considered to indicate a statistically significant difference.

Sub-pathway enrichment analysis

The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database (19) contains information on how molecules or genes are networked, and is complementary to most of the existing molecular biological databases that contain information on individual molecules or genes. The KEGG pathways of the DEGs were analyzed using DAVID. P<0.05 was considered to indicate a statistically significant difference. The closer the components within the metabolic pathways, the greater the similarity in their biological functions. Therefore, the identification of the sub-pathway of disease is crucial. K-clique was used to divide the metabolic pathway into sub-pathways through the iSubpathwayMiner package in R (https://www.r-project.org/). The sub-pathway with P<0.05 was considered to be significant.

miRNA regulatory network analysis

miRNAs are a type of endogenous non-coding RNA with regulatory functions, and their size is ~20–25 nucleotides. Mature miRNAs are assembled into RNA-induced silencing complexes, then target genes are identified and regulated by them. Thus, the identification of the miRNA regulatory network is essential. We predicted the interactions between target genes and miRNAs based on the TarBase (http://microrna.gr/tarbase) (20), TargetScan (www.targetscan.org) (21) and miRecord (http://mire-cords.biolead.org) (22) databases. The miRNA regulatory network was constructed using Cytoscape (http://cytoscape.org/) (23).

Literature mining analysis of key genes

The online tool GenCLip2.0 was used to mine DEGs in literature (24). A list of DEG names was input into GenCLip2.0, then the Gene Cluster with Literature Profiles module was used to cluster based on the documented frequency of the input genes. GenCLip2.0 also provided GO and pathway enrichment analysis of genes. The GC research-related DEGs were mined from the literature and these DEGs were submitted to GO and pathway enrichment analyses.

Results

Screening and GO analysis of DEGs

Three groups of gene expression ratio (TSA-treated GC cells / untreated GC cells) about TSA-treated group and control group were obtained through chip detection. A total of 76 DEGs (43 downregulated genes and 33 upregulated genes) were obtained. The results of GO analysis revealed that the upregulated DEGs were mainly enriched in biological processes including skeletal muscle contraction, response to organic substance and regulation of cell adhesion, and the downregulated DEGs were mainly enriched in processes including the DNA catabolic process, DNA metabolic process and response to the tumor necrosis factor (Table I).

Table I.

Gene ontology analysis results of differentially expressed genes.

Table I.

Gene ontology analysis results of differentially expressed genes.

Gene typeCategoryTermCountP-value
UpregulatedGOTERM_BP_FAT GO:0003010~voluntary skeletal muscle contraction  20.004135
GOTERM_BP_FATGO:0014721~twitch skeletal muscle contraction  20.004135
GOTERM_BP_FAT GO:0031444~slow-twitch skeletal muscle fiber contraction  20.004135
GOTERM_BP_FATGO:0010033~response to organic substance  60.015009
GOTERM_BP_FATGO:0003009~skeletal muscle contraction  20.024566
GOTERM_BP_FAT GO:0030155~regulation of cell adhesion  30.032410
GOTERM_BP_FAT GO:0050881~musculoskeletal movement  20.038627
GOTERM_BP_FAT GO:0050879~multicellular organismal movement  20.038627
GOTERM_BP_FATGO:0009725~response to hormone stimulus  40.039305
GOTERM_BP_FATGO:0050873~brown fat cell differentiation  20.048550
GOTERM_CC_FAT GO:0005576~extracellular region100.010773
GOTERM_CC_FAT GO:0005615~extracellular space  50.042191
GOTERM_MF_FAT GO:0003735~structural constituent of ribosome  30.041104
DownregulatedGOTERM_BP_FATGO:0006308~DNA catabolic process  30.004610
GOTERM_BP_FATGO:0006259~DNA metabolic process  50.009728
GOTERM_BP_FAT GO:0006301~post-replication repair  20.013524
GOTERM_BP_FATGO:0034612~response to tumor necrosis factor  20.021888
GOTERM_BP_FATGO:0051129~negative regulation of cellular component organization  30.023964
GOTERM_BP_FAT GO:0065003~macromolecular complex assembly  50.024316
GOTERM_BP_FATGO:0006974~response to DNA damage stimulus  40.024466
GOTERM_BP_FATGO:0070647~protein modification by small protein conjugation or removal  30.029893
GOTERM_BP_FAT GO:0043933~macromolecular complex subunit organization  50.030046
GOTERM_BP_FATGO:0006309~DNA fragmentation involved in apoptosis  20.031835
GOTERM_BP_FATGO:0044265~cellular macromolecule catabolic process  50.032124
GOTERM_BP_FATGO:0006921~cell structure disassembly during apoptosis  20.038412
GOTERM_BP_FAT GO:0009057~macromolecule catabolic process  50.040630
GOTERM_BP_FATGO:0000737~DNA catabolic process, endonucleolytic  20.043317
GOTERM_BP_FAT GO:0030262~apoptotic nuclear changes  20.043317
GOTERM_BP_FATGO:0009314~response to radiation  30.044895
GOTERM_CC_FAT GO:0043232~intracellular non-membrane-bounded organelle  90.025314
GOTERM_CC_FAT GO:0043228~non-membrane-bounded organelle  90.025314
GOTERM_CC_FAT GO:0070013~intracellular organelle lumen  70.038954
GOTERM_CC_FAT GO:0043233~organelle lumen  70.042966
GOTERM_CC_FAT GO:0031974~membrane-enclosed lumen  70.046708

[i] BP, biological process; CC, biological process; MF, molecular function.

Sub-pathway enrichment analysis

The sub-pathway of disease was screened. The propanoate metabolism pathway was the sub-pathway selected in our study. Propionyl-coenzyme A carboxylase, beta polypeptide (PCCB) was the DEG enriched in this sub-pathway (Fig. 1).

miRNA regulatory network analysis

According to the miRNA regulatory network, the upregulated genes dickkopf wnt signaling pathway inhibitor 1 (DKK1), follistatin (FST), clusterin (CLU), inhibitor of DNA binding 3, dominant negative helix-loop-helix protein (ID3), cysteine-rich, angiogenic inducer 61 (CYR61), x-box binding protein 1 (XBP1) and small nuclear ribonucleoprotein polypeptide N (SNRPN) were the hub nodes, and among them DKK1 was the top hub node (Fig. 2A). From the miRNA regulatory network, the downregulated genes kruppel-like factor 13 (KLF13), breast cancer 1, early onset (BRCA1), caspase 3, apoptosis-related cysteine peptidase (CASP3), 2′-5′-oligoadenylate synthetase-like (OASL) and histone cluster 1, H2ac (HIST1H2AC) were the hub nodes, and among them KLF13 was the top hub node (Fig. 2B).

Literature mining analysis of key genes

A heatmap of DEGs was constructed (Fig. 3). From the heatmap, the upregulated genes were mainly enriched in functions including epidermal growth factor, inflammation and tumor necrosis factor; the downregulated genes were mainly enriched in biological processes including squamous cell carcinoma entries, apoptosis and the DNA replication process. The results of GO analysis revealed that the upregulated genes were mainly enriched in functions including extracellular space and response to organic substance (Table II); the downregulated genes were mainly enriched in the DNA metabolic process (Table II).

Table II.

Gene ontology analysis results of the key genes.

Table II.

Gene ontology analysis results of the key genes.

Gene typePathwayHit enrichment scoreP-valueQ-valueGO ID
UpregulatedCluster14.97
Extracellular space8.004.72E-060.002702511GO:0005615
Extracellular region13.002.42E-050.004620421GO:0005576
Single14.90
Response to organic substance13.001.27E-050.003630953GO:0010033
Single24.13
BMP signaling pathway4.007.42E-050.010635069GO:0030509
DownregulatedSingle16.14
DNA metabolic process8.007.25E-070.000332722GO:0006259

[i] GO, gene ontology.

Discussion

GC is one of the most common malignant diseases and a leading cause of cancer-related mortality worldwide (1,2). The mechanism of TSA inhibition in GC is unclear. In the present study, we used bioinformatics approaches to explore the candidate genes induced by TSA in GC, and identified 76 DEGs between TSA-treated GC cells and untreated GC cells. Among these DEGs, 43 genes were upregulated and 33 were downregulated. In addition, sub-pathway enrichment analysis was performed, and the propanoate metabolism sub-pathway was identified. Then the miRNA regulatory network was constructed; from this, DKK1 was identified as the top hub node among the upregulated DEGs, while KLF13 was the top hub node among the downregulated DEGs.

The propanoate metabolism pathway was the sub-pathway enriched in our study. To date, there have been no studies on the correlation between the propanoate metabolism pathway and GC. However, certain studies have reported that this sub-pathway is associated with other cancer types. Zhu et al observed that bladder cancer-related long noncoding RNAs (lncRNAs) demonstrated a significant association with the propanoate metabolism pathway (25). Furthermore, certain studies have reported that dysregulation of lncRNAs is a primary feature of several human cancers, including prostate cancer, breast cancer, GC, bladder cancer and hepatocellular carcinoma (2632). The kidney cancer-related proteins were demonstrated to be involved with a high degree of confidence in propanoate metabolism, pyruvate metabolism, the urea cycle and arginine pathways (33). Gmeiner et al observed that the downregulated DEGs in colon cancer were involved with propanoate metabolism and the isoleucine degradation pathway (34). Yang et al revealed that valine relating to lung cancer was involved in a number of metabolic processes including the propanoate metabolism process (35). Gonzalez-Angulo et al demonstrated that the metabolic pathways (glutamate metabolism, chondroitin sulfate biosynthesis and propanoate metabolism) were the most consistently upregulated in basal-like residual cancers (36). Considering the studies above, the propanoate metabolism pathway may play a significant role in TSA inhibition in GC, and may be a therapeutic target for GC.

DKK1 and KLF13 were observed to be hub nodes in our miRNA regulatory networks. We revealed that DKK1 was an upregulated DEG. Hirata et al suggested that DKK1 induced cell apoptosis and inhibited cell growth in renal cell carcinoma (RCC) (37). The levels of DKK1 were decreased in RCC cell lines, but they increased following treatment with TSA. These authors also revealed that TSA induced histone acetylation at the DKK1 promoter, which resulted in the reactivation of DKK1 expression in human RCC. This result demonstrated that the silencing of DKK1 is caused by histone modification (37). Lee et al demonstrated that DKK1 was repressed by histone deacetylation in cervical cancer cells (38). The DKK family was demonstrated to suppress cell growth and induce cell apoptosis in gastrointestinal and colon cancer cells (39,40). Studies have revealed that DKK1 is preferentially expressed in lung cancer, and it was identified as a novel prognostic biomarker as well as a therapeutic target for lung cancers (41,42). Certain studies demonstrated that DKK1 significantly reduced tumor growth (39,43). DKK1 levels may aid in diagnosing GC, and the gene may be a novel prognostic marker for GC (35). Our results revealed that DKK1 was an upregulated DEG in GC cells treated with TSA. Furthermore, the literature mining results from our study revealed that DKK1 was mainly enriched in functions including cell growth, cell differentiation and tumor necrosis factor; these functions have been demonstrated to be correlated with certain cancers (44,45). In our study, DKK1 was overexpressed in GC cells induced by TSA. Considering the studies above, one of the possible inhibition mechanisms of TSA in GC may be that TSA induces histone acetylation at the DKK1 promoter, which results in the reactivation of DKK1 expression (37,38). In other words, DKK1 may be a significant therapeutic target for GC.

KLF13 is a zinc finger transcription factor known to play a role in proliferation, differentiation, cell cycle progression and apoptosis (4648). KLF13 is required for the expression of several oncogenes including cyclin D1, which is a known oncogene in oral squamous cell carcinoma (47,49). Furthermore, KLF13 is believed to play a role in cancer, and altered expression of it contributes to tumorigenesis (5053). Henson et al revealed that KLF13 was overexpressed in oral cancer cells, and artificially reducing its cellular levels decreases cell proliferation and malignancy; therefore, KLF13 may be a useful biomarker for early detection and a possible target for therapy (49). KLF13 is also involved in the proliferation and differentiation of the heart (47,54). KLF13 was downregulated in our study. All of the studies above indicated that decreased expression of KLF13 may be a possible inhibition mechanism of GC induced by TSA. In summary, KLF13 may be utilized as a biomarker for detection and a new therapeutic target for GC.

In conclusion, the propanoate metabolism pathway and the associated genes DKK1 and KLF13 may play significant roles in the inhibition of GC induced by TSA. They may be possible therapeutic targets for GC. However, further studies are necessary to verify the clinical applications of these genes as biological targets for GC treatment.

References

1 

Parkin DM, Pisani P and Ferlay J: Estimates of the worldwide incidence of 25 major cancers in 1990. Int J Cancer. 80:827–841. 1999. View Article : Google Scholar : PubMed/NCBI

2 

Neugut AI, Hayek M and Howe G: Epidemiology of gastric cancer. Semin Oncol. 23:281–291. 1996.PubMed/NCBI

3 

Hippo Y, Taniguchi H, Tsutsumi S, Machida N, Chong JM, Fukayama M, Kodama T and Aburatani H: Global gene expression analysis of gastric cancer by oligonucleotide microarrays. Cancer Res. 62:233–240. 2002.PubMed/NCBI

4 

Zhang X, Yashiro M, Ren J and Hirakawa K: Histone deacetylase inhibitor, trichostatin A, increases the chemosensitivity of anticancer drugs in gastric cancer cell lines. Oncol Rep. 16:563–568. 2006.PubMed/NCBI

5 

Wu ZQ, Zhang R, Chao C, Zhang JF and Zhang YQ: Histone deacetylase inhibitor trichostatin A induced caspase-independent apoptosis in human gastric cancer cell. Chin Med J (Engl). 120:2112–2118. 2007.PubMed/NCBI

6 

Lee HS, Park MH, Yang SJ, Jung HY, Byun SS, Lee DS, Yoo HS, Yeom YI and Seo SB: Gene expression analysis in human gastric cancer cell line treated with trichostatin A and S-adenosyl-L-homocysteine using cDNA microarray. Biol Pharm Bull. 27:1497–1503. 2004. View Article : Google Scholar : PubMed/NCBI

7 

Zou XM, Li YL, Wang H, Cui W, Li XL, Fu SB and Jiang HC: Gastric cancer cell lines induced by trichostatin A. World J Gastroenterol. 14:4810–4815. 2008. View Article : Google Scholar : PubMed/NCBI

8 

Friedgen B, Wölfel R, Russ H, Schömig E and Graefe KH: The role of extraneuronal amine transport systems for the removal of extracellular catecholamines in the rabbit. Naunyn Schmiedebergs Arch Pharmacol. 354:275–286. 1996. View Article : Google Scholar : PubMed/NCBI

9 

Chen H, Tini M and Evans RM: HATs on and beyond chromatin. Curr Opin Cell Biol. 13:218–224. 2001. View Article : Google Scholar : PubMed/NCBI

10 

Strahl BD, Briggs SD, Brame CJ, Caldwell JA, Koh SS, Ma H, Cook RG, Shabanowitz J, Hunt DF, Stallcup MR and Allis CD: Methylation of histone H4 at arginine 3 occurs in vivo and is mediated by the nuclear receptor coactivator PRMT1. Curr Biol. 11:996–1000. 2001. View Article : Google Scholar : PubMed/NCBI

11 

Cheung WL, Briggs SD and Allis CD: Acetylation and chromosomal functions. Curr Opin Cell Biol. 12:326–333. 2000. View Article : Google Scholar : PubMed/NCBI

12 

Yoshida M, Kijima M, Akita M and Beppu T: Potent and specific inhibition of mammalian histone deacetylase both in vivo and in vitro by trichostatin A. J Biol Chem. 265:17174–17179. 1990.PubMed/NCBI

13 

Finnin MS, Donigian JR, Cohen A, Richon VM, Rifkind RA, Marks PA, Breslow R and Pavletich NP: Structures of a histone deacetylase homologue bound to the TSA and SAHA inhibitors. Nature. 401:188–193. 1999. View Article : Google Scholar : PubMed/NCBI

14 

Kosugi H, Towatari M, Hatano S, Kitamura K, Kiyoi H, Kinoshita T, Tanimoto M, Murate T, Kawashima K, Saito H and Naoe T: Histone deacetylase inhibitors are the potent inducer/enhancer of differentiation in acute myeloid leukemia: a new approach to anti-leukemia therapy. Leukemia. 13:1316–1324. 1999. View Article : Google Scholar : PubMed/NCBI

15 

Tusher VG, Tibshirani R and Chu G: Significance analysis of microarrays applied to the ionizing radiation response. Proc Natl Acad Sci USA. 98:5116–5121. 2001. View Article : Google Scholar : PubMed/NCBI

16 

Yekutieli D and Benjamini Y: Resampling-based false discovery rate controlling multiple test procedures for correlated test statistics. J Stat Plan Infer. 82:171–196. 1999. View Article : Google Scholar

17 

Hulsegge I, Kommadath A and Smits MA: Globaltest and GOEAST: two different approaches for gene ontology analysis. BMC Proc. 3:(Suppl 4). S102009. View Article : Google Scholar : PubMed/NCBI

18 

da W Huang, Sherman BT and Lempicki RA: Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat Protoc. 4:44–57. 2009.PubMed/NCBI

19 

Ogata H, Goto S, Sato K, Fujibuchi W, Bono H and Kanehisa M: KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 27:29–34. 1999. View Article : Google Scholar : PubMed/NCBI

20 

Papadopoulos GL, Reczko M, Simossis VA, Sethupathy P and Hatzigeorgiou AG: The database of experimentally supported targets: a functional update of TarBase. Nucleic Acids Res. 37:(Database issue). D155–D158. 2009. View Article : Google Scholar : PubMed/NCBI

21 

Garcia DM, Baek D, Shin C, Bell GW, Grimson A and Bartel DP: Weak seed-pairing stability and high target-site abundance decrease the proficiency of lsy-6 and other microRNAs. Nat Struct Mol Biol. 18:1139–1146. 2011. View Article : Google Scholar : PubMed/NCBI

22 

Xiao F, Zuo Z, Cai G, Kang S, Gao X and Li T: miRecords: an integrated resource for microRNA-target interactions. Nucleic Acids Res. 37:(Database issue). D105–D110. 2009. View Article : Google Scholar : PubMed/NCBI

23 

Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B and Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13:2498–2504. 2003. View Article : Google Scholar : PubMed/NCBI

24 

Wang JH, Zhao LF, Lin P, Su XR, Chen SJ, Huang LQ, Wang HF, Zhang H, Hu ZF, Yao KT and Huang ZX: GenCLiP 2.0: a web server for functional clustering of genes and construction of molecular networks based on free terms. Bioinformatics. 30:2534–2536. 2014. View Article : Google Scholar : PubMed/NCBI

25 

Zhu YP, Bian XJ, Ye DW, Yao XD, Zhang SL, Dai B, Zhang HL and Shen YJ: Long noncoding RNA expression signatures of bladder cancer revealed by microarray. Oncol Lett. 7:1197–1202. 2014.PubMed/NCBI

26 

Gupta RA, Shah N, Wang KC, Kim J, Horlings HM, Wong DJ, Tsai MC, Hung T, Argani P, Rinn JL, et al: Long non-coding RNA HOTAIR reprograms chromatin state to promote cancer metastasis. Nature. 464:1071–1076. 2010. View Article : Google Scholar : PubMed/NCBI

27 

Yang F, Bi J, Xue X, Zheng L, Zhi K, Hua J and Fang G: Up-regulated long non-coding RNA H19 contributes to proliferation of gastric cancer cells. FEBS J. 279:3159–3165. 2012. View Article : Google Scholar : PubMed/NCBI

28 

Lai MC, Yang Z, Zhou L, Zhu QQ, Xie HY, Zhang F, Wu LM, Chen LM and Zheng SS: Long non-coding RNA MALAT-1 overexpression predicts tumor recurrence of hepatocellular carcinoma after liver transplantation. Med Oncol. 29:1810–1816. 2012. View Article : Google Scholar : PubMed/NCBI

29 

Cao DL, Ye DW, Zhang HL, Zhu Y, Wang YX and Yao XD: A multiplex model of combining gene-based, protein-based, and metabolite-based with positive and negative markers in urine for the early diagnosis of prostate cancer. Prostate. 71:700–710. 2011. View Article : Google Scholar : PubMed/NCBI

30 

Yang C, Li X, Wang Y, Zhao L and Chen W: Long non-coding RNA UCA1 regulated cell cycle distribution via CREB through PI3-K dependent pathway in bladder carcinoma cells. Gene. 496:8–16. 2012. View Article : Google Scholar : PubMed/NCBI

31 

Ying L, Chen Q, Wang Y, Zhou Z, Huang Y and Qiu F: Upregulated MALAT-1 contributes to bladder cancer cell migration by inducing epithelial-to-mesenchymal transition. Mol Biosyst. 8:2289–2294. 2012. View Article : Google Scholar : PubMed/NCBI

32 

Zhu Y, Yu M and Li Z, Kong C, Bi J, Li J, Gao Z and Li Z: ncRAN, a newly identified long noncoding RNA, enhances human bladder tumor growth, invasion, and survival. Urology. 77(510): e1–e5. 2011.PubMed/NCBI

33 

Perroud B, Lee J, Valkova N, Dhirapong A, Lin PY, Fiehn O, Kültz D and Weiss RH: Pathway analysis of kidney cancer using proteomics and metabolic profiling. Mol Cancer. 5:642006. View Article : Google Scholar : PubMed/NCBI

34 

Gmeiner WH, Hellmann GM and Shen P: Tissue-dependent and independent gene expression changes in metastatic colon cancer. Oncol Rep. 19:245–251. 2008.PubMed/NCBI

35 

Yang Q, Shen SS, Zhou S, Ni J, Chern D, Wang G and Li Y: STAT3 activation and aberrant ligand-dependent sonic hedgehog signaling in human pulmonary adenocarcinoma. Exp Mol Pathol. 93:227–236. 2012. View Article : Google Scholar : PubMed/NCBI

36 

Gonzalez-Angulo AM, Iwamoto T, Liu S, Chen H, Do KA, Hortobagyi GN, Mills GB, Meric-Bernstam F, Symmans WF and Pusztai L: Gene expression, molecular class changes, and pathway analysis after neoadjuvant systemic therapy for breast cancer. Clin Cancer Res. 18:1109–1119. 2012. View Article : Google Scholar : PubMed/NCBI

37 

Hirata H, Hinoda Y, Nakajima K, Kawamoto K, Kikuno N, Ueno K, Yamamura S, Zaman MS, Khatri G, Chen Y, et al: Wnt antagonist DKK1 acts as a tumor suppressor gene that induces apoptosis and inhibits proliferation in human renal cell carcinoma. Int J Cancer. 128:1793–1803. 2011. View Article : Google Scholar : PubMed/NCBI

38 

Lee J, Yoon YS and Chung JH: Epigenetic silencing of the WNT antagonist DICKKOPF-1 in cervical cancer cell lines. Gynecol Oncol. 109:270–274. 2008. View Article : Google Scholar : PubMed/NCBI

39 

Sato H, Suzuki H, Toyota M, Nojima M, Maruyama R, Sasaki S, Takagi H, Sogabe Y, Sasaki Y, Idogawa M, et al: Frequent epigenetic inactivation of DICKKOPF family genes in human gastrointestinal tumors. Carcinogenesis. 28:2459–2466. 2007. View Article : Google Scholar : PubMed/NCBI

40 

Wang H, Li Q and Chen H: Genistein affects histone modifications on Dickkopf-related protein 1 (DKK1) gene in SW480 human colon cancer cell line. PLoS One. 7:e409552012. View Article : Google Scholar : PubMed/NCBI

41 

Yamabuki T, Takano A, Hayama S, Ishikawa N, Kato T, Miyamoto M, Ito T, Ito H, Miyagi Y, Nakayama H, et al: Dikkopf-1 as a novel serologic and prognostic biomarker for lung and esophageal carcinomas. Cancer Res. 67:2517–2525. 2007. View Article : Google Scholar : PubMed/NCBI

42 

Sheng SL, Huang G, Yu B and Qin WX: Clinical significance and prognostic value of serum Dickkopf-1 concentrations in patients with lung cancer. Clin Chem. 55:1656–1664. 2009. View Article : Google Scholar : PubMed/NCBI

43 

Park H, Jung HY, Choi HJ, Kim DY, Yoo JY, Yun CO, Min JK, Kim YM and Kwon YG: Distinct roles of DKK1 and DKK2 in tumor angiogenesis. Angiogenesis. 17:221–234. 2014. View Article : Google Scholar : PubMed/NCBI

44 

Katzenellenbogen BS: Estrogen receptors: bioactivities and interactions with cell signaling pathways. Biol Reprod. 54:287–293. 1996. View Article : Google Scholar : PubMed/NCBI

45 

Katzenellenbogen BS, Montano MM, Ekena K, Herman ME and McInerney EM: William L. McGuire memorial lecture. Antiestrogens: mechanisms of action and resistance in breast cancer. Breast Cancer Res Treat. 44:23–38. 1997. View Article : Google Scholar : PubMed/NCBI

46 

Chen X, Johns DC, Geiman DE, Marban E, Dang DT, Hamlin G, Sun R and Yang VW: Krüppel-like factor 4 (gut-enriched Krüppel-like factor) inhibits cell proliferation by blocking G1/S progression of the cell cycle. J Biol Chem. 276:30423–30428. 2001. View Article : Google Scholar : PubMed/NCBI

47 

Nemer M and Horb ME: The KLF family of transcriptional regulators in cardiomyocyte proliferation and differentiation. Cell Cycle. 6:117–121. 2007. View Article : Google Scholar : PubMed/NCBI

48 

Kaczynski J, Cook T and Urrutia R: Sp1- and Krüppel-like transcription factors. Genome Biol. 4:2062003. View Article : Google Scholar : PubMed/NCBI

49 

Henson BJ and Gollin SM: Overexpression of KLF13 and FGFR3 in oral cancer cells. Cytogenet Genome Res. 128:192–198. 2010. View Article : Google Scholar : PubMed/NCBI

50 

Black AR, Black JD and Azizkhan-Clifford J: Sp1 and krüppel-like factor family of transcription factors in cell growth regulation and cancer. J Cell Physiol. 188:143–160. 2001. View Article : Google Scholar : PubMed/NCBI

51 

Dang DT, Mahatan CS, Dang LH, Agboola IA and Yang VW: Expression of the gut-enriched Krüppel-like factor (Krüppel-like factor 4) gene in the human colon cancer cell line RKO is dependent on CDX2. Oncogene. 20:4884–4890. 2001. View Article : Google Scholar : PubMed/NCBI

52 

Miller KA, Eklund EA, Peddinghaus ML, Cao Z, Fernandes N, Turk PW, Thimmapaya B and Weitzman SA: Kruppel-like factor 4 regulates laminin alpha 3A expression in mammary epithelial cells. J Biol Chem. 276:42863–42868. 2001. View Article : Google Scholar : PubMed/NCBI

53 

Martin KM, Ellis PD, Metcalfe JC and Kemp PR: Selective modulation of the SM22alpha promoter by the binding of BTEB3 (basal transcription element-binding protein 3) to TGGG repeats. Biochem J. 375:457–463. 2003. View Article : Google Scholar : PubMed/NCBI

54 

Lavallée G, Andelfinger G, Nadeau M, Lefebvre C, Nemer G, Horb ME and Nemer M: The Kruppel-like transcription factor KLF13 is a novel regulator of heart development. EMBO J. 25:5201–5213. 2006. View Article : Google Scholar : PubMed/NCBI

Related Articles

Journal Cover

February-2017
Volume 13 Issue 2

Print ISSN: 1792-1074
Online ISSN:1792-1082

Sign up for eToc alerts

Recommend to Library

Copy and paste a formatted citation
x
Spandidos Publications style
Li Y, Zhang L, Yang C, Li R, Shang L and Zou X: Bioinformatic identification of candidate genes induced by trichostatin A in BGC-823 gastric cancer cells. Oncol Lett 13: 777-783, 2017.
APA
Li, Y., Zhang, L., Yang, C., Li, R., Shang, L., & Zou, X. (2017). Bioinformatic identification of candidate genes induced by trichostatin A in BGC-823 gastric cancer cells. Oncology Letters, 13, 777-783. https://doi.org/10.3892/ol.2016.5485
MLA
Li, Y., Zhang, L., Yang, C., Li, R., Shang, L., Zou, X."Bioinformatic identification of candidate genes induced by trichostatin A in BGC-823 gastric cancer cells". Oncology Letters 13.2 (2017): 777-783.
Chicago
Li, Y., Zhang, L., Yang, C., Li, R., Shang, L., Zou, X."Bioinformatic identification of candidate genes induced by trichostatin A in BGC-823 gastric cancer cells". Oncology Letters 13, no. 2 (2017): 777-783. https://doi.org/10.3892/ol.2016.5485