- Research article
- Open Access
Global analysis of gene expression changes during retinoic acid-induced growth arrest and differentiation of melanoma: comparison to differentially expressed genes in melanocytes vs melanoma
© Estler et al; licensee BioMed Central Ltd. 2008
Received: 11 June 2008
Accepted: 11 October 2008
Published: 11 October 2008
The incidence of malignant melanoma has significantly increased over the last decade. Some of these malignancies are susceptible to the growth inhibitory and pro-differentiating effects of all-trans-retinoic acid (RA). The molecular changes responsible for the biological activity of RA in melanoma are not well understood.
In an analysis of sequential global gene expression changes during a 4–48 h RA treatment of B16 mouse melanoma cells, we found that RA increased the expression of 757 genes and decreased the expression of 737 genes. We also compared the gene expression profile (no RA treatment) between non-malignant melan-a mouse melanocytes and B16 melanoma cells. Using the same statistical test, we found 1495 genes whose expression was significantly higher in melan-a than in B16 cells and 2054 genes whose expression was significantly lower in melan-a than in B16 cells. By intersecting these two gene sets, we discovered a common set of 233 genes whose RNA levels were significantly different between B16 and melan-a cells and whose expression was altered by RA treatment. Within this set, RA treatment altered the expression of 203 (87%) genes toward the melan-a expression level. In addition, hierarchical clustering showed that after 48 h of RA treatment expression of the 203 genes was more closely related to the melan-a gene set than any other RA treatment time point. Functional analysis of the 203 gene set indicated that RA decreased expression of mRNAs that encode proteins involved in cell division/cell cycle, DNA replication, recombination and repair, and transcription regulation. Conversely, it stimulated genes involved in cell-cell signaling, cell adhesion and cell differentiation/embryonic development. Pathway analysis of the 203 gene set revealed four major hubs of connectivity: CDC2, CHEK1, CDC45L and MCM6.
Our analysis of common genes in the 48 h RA-treatment of B16 melanoma cells and untreated B16 vs. melan-a data set show that RA "normalized" the expression of genes involved in energy metabolism, DNA replication, DNA repair and differentiation. These results are compatible with the known growth inhibitory and pro-differentiating effects of RA. Pathway analysis suggests that CDC2, CHEK1, CDC45L and MCM6 are key players in mediating the biological activity of RA in B16 melanoma cells.
Malignant melanoma is one of the fastest rising cancers in the US population and its yearly incidence is estimated at 60,000 . If it is diagnosed early and is restricted to the epidermis, surgical resection can yield high cure rates. However, once the melanoma invades through the basement membrane into the dermis, surgical resection is less successful and there is no effective radio/chemotherapy for patients with metastatic melanoma.
Vitamin A (retinol) is an essential nutrient that is required for night vision, reproduction, embryogenesis and differentiation . Retinol is metabolized by cells into a number of compounds, the most biologically active being all-trans-retinoic acid (RA). The biological changes induced by RA are mediated by binding and activation of nuclear receptors [3, 4]. There are three subtypes of retinoic acid receptors (RAR); RAR-α, β and γ, which bind RA. Another vitamin A metabolite, 9-cis RA, also binds these receptors but in addition serves as the ligand for closely related nuclear receptors, the retinoid × receptors (RXRs) [5, 6]. Analogous to the RARs, there are three RXR subtypes, α, β and γ . RXRs form heterodimers with the RARs as well as with several other members of the nuclear hormone receptor family, such as Peroxisome Proliferator Activated Receptor (PPAR) , vitamin D3  and thyroid hormone receptors . The RXR:RAR heterodimer appears to be the physiologically relevant dimer for stimulating target gene expression . This receptor heterodimer binds to a DNA element in the 5' flanking region of target genes and usually consists of a direct repeat of the sequence 5'-(A/G)G(G/T)TCA-3' separated by 5 bp [12, 13]. These receptors are also regulated by co-repressors and co-activators. Several of the co-activators contain histone acetyltransferase activity, while co-repressor complexes contain histone deacetylase activity, suggesting that localized chromatin remodeling modulates receptor activity .
RA inhibits the proliferation and stimulates the differentiation of many different tumor cell lines, including murine and human melanoma cells . We have previously used B16 mouse melanoma as a model for our retinoid studies. This cell line is very responsive to the growth inhibitory and pro-differentiating effects of retinoic acid . The B16 cell line also has major deletions in the p16INK 4aand p19 QRF genes with consequent loss of expression of these proteins, but it lacks an activating mutation in the BRAF gene . Loss of p16INK 4aand p19 ORF contributes to melanomagenesis by overcoming senescence . In addition, activating BRAF mutations have been found in up to 60% of sporadic human melanomas . We have found that our strain of B16 melanoma has an activating N-ras mutation (Niles et al., unpublished). Thus B16 cells have a number of the genetic/protein changes found in human melanoma cell lines and clinical melanoma specimens.
Melan-a cells are nonmalignant mouse melanocytes derived from C57BL/6 mice (the same origin as B16 melanoma) that have been partially immortalized. They have diploid chromosomes and do not form tumors in syngeneic or athymic mice . Therefore melan-a cells serve as a good model to compare gene expression profiles to B16 melanoma cells with the aim of identifying genes whose altered expression might contribute to malignancy. In the study reported here, we used DNA microarrays to compare gene expression patterns between B16 and melan-a cells. We also used DNA microarrays to identify time-dependent changes in gene expression during RA-induced growth arrest and differentiation of B16 melanoma cells. From these two data sets, we then performed an analysis to identify a unique gene set, whose expression was altered in B16 compared to melan-a and whose expression was restored toward the melan-a level in RA-treated B16 melanoma cells.
Time-dependent regulation of gene expression by RA
Numbers of genes significantly differentially expressed relative to untreated B16 cellsa
Retinoic Acid Treatment
melan-a vs. B16
Expression profiling of untreated melan-a nonmalignant mouse melanocytes vs. B16 mouse melanoma cells
Melan-a and B16 cells are both derived from the C57BL/6 strain of mouse. The melan-a cells are partially immortalized, but they are diploid, lack transformed cell phenotypes and do not form tumors in syngeneic mice . A comparison of global gene expression between these two melanocyte/melanoma cell lines has not been reported. Our analyses show that 1495 genes had expression levels significantly higher in melan-a vs. B16 cells, i.e. their expression was downregulated in the malignant melanoma cells relative to the non-malignant cells (Table 1). In addition, there were 2054 genes whose expression levels were significantly lower in melan-a cells vs. B16 cells, i.e. their expression was upregulated in the malignant melanoma cells. The identity of these differentially regulated genes can be found in Additional file 2.
Gene expression changes induced by RA treatment of B16 cells that mimic expression in Melan-a cells: the intersecting gene set
Numbers of genes in the intersections of gene sets generated by the two experiments
Over-expressed in melan-a v B16
Under-expressed in melan-a v B16
Upregulated by RA treatment
Downregulated by RA treatment
We determined if members of the 203 gene set were enriched in known biological processes listed in the Gene Ontology database by using the Fisher Exact Test in Ariadne Genomics Pathway Studio. Additional file 4 provides a list of these processes ordered by statistical significance. The five biological processes which were most significant are cell division, cell cycle, cell cycle regulation, DNA replication and mitosis. In these processes, all genes were expressed higher in B16 than in melan-a cells. Examination of members within the processes reveals gene products that are clearly involved in promoting the cell division and DNA replication. For example, in the DNA replication set, 12 of the 13 genes encode proteins known to function directly in DNA synthesis or nucleotide metabolism (TOP2A, MCM2, MCM4, MCM6, CDC6, POLA, POLE, CDC45L, DUT, RNASEH2A, RPA1 and ZRF1). The identity of each gene within the five processes is given in Additional file 5. Several significant biological processes (protein phosphorylation, proteolysis, apoptosis induction cell-cell signaling) contained genes that were overexpressed in melan-a cells. These findings are consistent with the more rapid doubling of B16 (16 hours in log phase) in contrast to melan-a cells (28 hours in log phase). The total number of genes in both columns is greater than 203 because several genes are present in more than one process.
Validation of microarray-based mRNA levels by qRT-PCR
B16 48 h RA vs. control
melan-a vs. B16
B16 48 h RA vs. control
melan-a vs. B16
Clustering and pathway analysis
Number of interactions among genes in the 203 gene set
Number of Interactions
1.25 × 10-08
1.05 × 10-07
1.03 × 10-06
1.98 × 10-05
2.66 × 10-05
6.96 × 10-05
1.70 × 10-04
3.44 × 10-04
5.55 × 10-04
6.17 × 10-04
1.50 × 10-03
3.11 × 10-03
8.50 × 10-03
RA has the ability to induce differentiation and/or growth arrest in a variety of cancer cells . However, the genes and pathways that mediate the biological effects of RA have not been fully elucidated. B16 mouse melanoma cells are very sensitive to RA treatment and respond by undergoing growth arrest and differentiation . In this study we used mouse DNA microarrays to determine the time-dependent changes in gene expression in control vs. RA-treated B16 mouse melanoma cells. Our results show a small number of changes, mostly increases, in gene expression at early time points (4–10 h).
There was a major increase in the number of genes whose expression changed at 48 h of RA treatment and at this time point genes that were downregulated outnumbered those that were upregulated. To our knowledge, this is the first reported study of time-dependent changes in gene expression in RA-treated melanoma cells. Other studies have examined time-dependent gene expression changes in human embryonal carcinoma cells , F9 teratocarcinoma cells , and mouse skin  treated with RA. Similar to our results, a much larger number of genes had altered expression at 24–48 h of RA treatment. Comparison of the identity of RA-regulated genes in these studies with those reported here reveal some commonality of genes such as RAR-β2, PKCα and insulin-like growth factor binding protein 6, but also a number of different gene expression changes. Possible explanations for these differences are analysis of different cell types, use of different microarray platforms and different methods of statistical analysis.
In the hierarchical clustering analysis (Fig. 1) the 4 and 10 h RA-treated B16 cell-gene expression changes formed a tight cluster as do the 48 h RA-treated B16 cell gene changes. However, the changes in gene expression in 24 h RA-treated B16 cells did not form a cluster. This implies more variability in gene expression around this time of RA treatment, possibly because there are dramatic changes in gene expression around this time point.
The second microarray data set is a comparison between gene expression in untreated B16 mouse melanoma cells and melan-a mouse melanocytes. Both of these cell lines are derived from the same C57BL/6 inbred mouse strain. The melan-a cells have a longer replicative life-span than primary mouse melanocytes before they enter senescence . Significant differences in expression are found in genes encoding cell cycle regulatory proteins, DNA metabolism, and DNA repair enzymes. There have been a number of studies comparing gene expression profiles in human melanocytes to human melanoma cell lines [30, 31] and human nevus tissue to various stages of human melanoma tumors from patients [32, 33], but very little information is available comparing expression profiles in mouse melanocytes with mouse melanoma cells. Despite the species difference, a number of genes which had altered expression in human melanocytes vs. human melanoma cells, such as CDC2, C-MYC, DNA repair enzymes and differentiation/embryonic markers, were found to be altered in the expression profiles between melan-a and B16 cells. Our findings suggest that B16 melanoma cells serve as a valid model for studying human melanoma
We sought to determine whether there were common genes between the microarray data set generated from RA treatment of B16 melanoma cells and the microarray data set comparing the gene expression of melan-a to B16 cells. We found that there were 233 genes in common between these two data sets. Within this group, the large majority, 203 genes, (87%) had their expression altered by RA treatment of B16 melanoma cells toward the levels found in melan-a cells. In general, RA treatment decreases the expression of genes involved in cell division/cell cycle, DNA replication and repair, and transcription regulation. It also decreases expression of the genes involved in DNA recombination and protein folding. In contrast, RA increases the expression of genes that regulate cell-cell signaling, cell adhesion and cell differentiation and development. Considering that RA inhibits cell replication and stimulates differentiation, these RA-induced changes in gene expression reflect the reprogramming of the B16 cells toward the melan-a (normal) phenotype. The reversion is not complete since some genes whose expression is decreased in B16 melanoma cells relative to melan-a cells (e.g. cell adhesion and extracellular matrix proteins) are not regulated by RA. Finally, we note that only three genes within the 203 gene set were differentially regulated by RA at 4 h. (1) DHRS3, a member of SDR family, encodes an enzyme which catalyzes the reduction of all trans-retinal to all-trans-retinol in the presence of NADPH and is highly expressed in the retina . (2) Mouse CAMK2Nl mRNA is induced 2.2 fold by RA at 4 h and is downregulated 3.8 fold in B16 relative to melan-a cells. This protein exhibits strong similarity (97.44%) to the human CAMKIINα which has been shown to inhibit cell cycle progression in S phase in human colon adenocarcinoma cells . (3) Although RIKEN cDNA 3110001A13 had significant homology to human, chimp, macaque and rat cDNAs (Blast search results not shown), no function has been ascribed to its predicted protein.
To determine the pathway(s) that mediate the effect of RA inhibition of cell proliferation and stimulation of differentiation, we used Pathway Studio v5.0 software to discover relationships between the proteins encoded by the 203 gene set. We found 74 genes (36%) whose encoded proteins have known connections either through physical interaction, regulation of gene expression or protein modification (e.g. phosphorylation). These interactions clustered around four major "hub" proteins: CDC2, CHEK1, CDC45L and MCM6. We note that even though these four proteins had small numbers of partners, by using the Fisher Exact Test we found that these hubs were statistically enriched for molecular interactions. In addition, three other proteins p53, p21 and CMYC appear to be important since they have large numbers of known interactions. Zhang and Rosdahl  found that RA treatment of human melanoma cells increased the expression of p53, while Vertuani et al.  observed p53 to be increased in RA-treated human uveal melanoma cells. Likewise there are numerous reports that RA alters the expression of p21WAF-1/CIP-1 in a variety of tumor cells [38, 39]. Gompel  found that RA decreases the expression and activity of CDK1 (CDC2) and there are a variety of studies showing that RA inhibits the expression of C-MYC [41, 42]. In contrast, no evidence has been published that RA regulates CHEK1 gene expression. CHEK1 is involved in stopping cells from progressing through the cell cycle while DNA damage is repaired. Increased expression of this gene is frequently associated with resistance to chemotherapy . A search of the PubMed database revealed no publications linking retinoic acid to the regulation of MCM6 or CDC45L. Further analysis of the time-dependent changes in RA-induced gene expression is needed to define the sequence of pathways that lead to RNA expression levels found in the 203 gene set.
Our study has shown that RA regulates the expression of 4.8% of the total known mouse genes in B16 mouse melanoma cells over the course of 48 h of treatment. Alteration of the expression of these genes is associated with RA-induced growth arrest and differentiation of B16 mouse melanoma cells. We also found that ~13.7% of the total known mouse genes were differentially expressed in melan-a mouse melanocytes vs. B16 mouse melanoma cells. In comparing these two data sets, we found 233 common genes. Within this set, 203 genes in B16 melanoma cells had their RNA expression "normalized" toward the melan-a level by treatment with RA. Pathway analysis of the proteins encoded by this 203 data set revealed that 32% are involved in common interactions. The major hubs of connectivity are centered around CDC2, CHEK1, CDC45L and MCM6. It is likely that these four genes and their encoded proteins play a major role in RA-induced growth arrest and differentiation of B16 mouse melanoma cells.
B16 mouse melanoma cells obtained from the American Type Culture Collection were grown in DMEM (Invitrogen, Carlsbad, CA), supplemented with 10% bovine calf serum (Hyclone Laboratories, Logan, UT) at 37°C in a 5% CO2/95% air humidified atmosphere. Cells were treated for 4, 10, 24, and 48 h with 10 μM RA (Fluka Chemical Corp., Ronkonkoma, NY). Control cells received the solubilization vehicle, DMSO. All manipulations involving RA were conducted under subdued lighting or yellow lights in order to minimize photo-oxidation. Melan-a cells (non-malignant mouse melanocytes) were a generous gift from Dr. Dorothy Bennett (St. George's Hospital, London, England). They were grown in RPMI 1640 medium with 5% fetal bovine serum supplemented with 200 nM phorbol 12-myristate 13-acetate plus antibiotics.
RNA extraction and DNA microarray analysis
Control and RA-treated B16 cells were harvested at 4, 10, 24, and 48 h of treatment. Melan-a and B16 cells were seeded at appropriate cell numbers so that at harvest (72 h after seeding) they would be at approximately the same degree of confluence on the tissue culture dishes. RNA was extracted using TRI Reagent (Sigma Chemical Co., St. Louis, MO) according to the manufacturer's suggested protocol. RNA quality was assessed by electrophoretic analysis on an Agilent Model 2100 Bioanalyzer (Santa Clara, CA). All RNA samples used for expression profiling had RNA Integrity Numbers greater than 9.
MWG Biotech (Ebersberg, Germany) Mouse 30K arrays were used to identify genes in B16 melanoma whose expression was altered by treatment with 10 μM RA for the various time periods using a balanced block design. All treatments were repeated 6 times to provide biological replicates for the microarray analysis. Labeled cDNAs were prepared from total RNA using the Invitrogen SuperScript II direct cDNA labeling system in the presence of either Cyanine-3 (Cy3) dCTP or Cyanine-5 (Cy5) dCTP (Perkin Elmer, Waltham, MA). For each time point, three of the arrays were hybridized with Cy3 labeled cDNA from the untreated cells and Cy5 labeled cDNA from treated cells. The remaining three arrays were hybridized with the labeling reversed to eliminate the effects of differential dye incorporation. The amounts of dye incorporated into cDNA were measured on a NanoDrop spectrophotometer (ThermoFisher Scientific, Pittsburgh, PA). Equimolar amounts of Cy3 and Cy5 labeled cDNAs were combined and added to MWG hybridization solution. This solution was heated for 3 min. at 95°C, cooled on ice for 3 min., and added to the microarray slide. Hybridizations were carried out for 16 h at 42°C on a GeneTAC hybridization station (Genomic Solutions, Ann Arbor, MI). Slides were washed on the automated hybridization station according to a Genomic Solutions protocol. Slides were scanned on a PerkinElmer ScanArray Express Microarray Scanner.
For the melan-a vs. B16 melanoma expression profiles, we employed the same balanced block design with a dye swap using six biological replicates. Equimolar amounts of the Cy3 and Cy5 labeled cDNAs were loaded onto Agilent Whole Mouse Genome Arrays, and hybridized for 17 h at 60°C using a MAUI hybridization system (BioMicroSystems, Salt Lake City, UT). The microarray slides were then washed using Agilent Gene Expression Wash Buffer and scanned using a Perkin Elmer ScanArray Express scanner.
Statistical Analysis of Microarray Data
Feature intensities for the RA time course were extracted from the scanned image using PerkinElmer ScanArray software with the default lowess normalization settings; for melan-a vs. B16 comparisons, intensities were extracted using ImaGene software (BioDiscovery, El Segundo, CA). All extracted data were exported to Microsoft Excel (Microsoft Corporation, Redmond, WA) as tab delimited files. For each feature, a low intensity filter was applied in which data were only included if the total background-subtracted intensity of the two channels was greater than 400. The log base 2 of the expression ratios of RA-treated samples to untreated samples or the log base 2 of the expression ratios of melan-a samples to B16 samples were computed, and assembled into a single tab-delimited file for each comparison. Each file was imported to the Multiple Experiment Viewer (MeV) v4.0  to perform statistical analysis.
Calculated log ratios were compared for significant deviation from zero using one-class Significance Analysis of Microarrays (SAM) . This analysis was performed independently at each time point for the RA treatment experiments and for the melan-a vs. B16 experiment. In each case, only probes for which at least three of the six replicates passed the low intensity filter were included in the analysis. SAM was performed with the maximum number of unique permutations available, and delta values were chosen to give a median False Discovery Rate of 10%. All other parameters were set to the MeV defaults. Features found to be statistically significant by this analysis were subsequently filtered for a minimum fold change greater than 1.5. In both array platforms, a number of genes are represented by multiple probes. Additionally, a number of identical probes are represented as multiple features on the array. For genes which were determined to be significantly differentially expressed and which were represented by multiple features on the array, we report the feature with the largest fold change. In the melan-a vs. B16 comparison, three genes represented by multiple features were reported to be significantly differentially expressed with some features showing overexpression in melan-a and some showing underexpression in melan-a. These genes, which were not reported as significantly differentially expressed at any time point in the RA treatment comparison, were eliminated from the results as being internally inconsistent.
We compiled a set of all genes differentially regulated by RA at one or more time points and intersected this set with those genes differentially expressed in the melan-a vs. B16 cell experiment. We restricted this intersection to those genes which were upregulated by RA and overexpressed in melan-a cells OR which were downregulated by RA and underexpressed in melan-a cells. Hierarchical clustering was performed on the resulting gene set using MeV. Both gene clusters and sample clusters were computed using the Euclidean metric and average linkage. Microarray data may be accessed at the NCBI Gene Expression Omnibus (GEO) database (accession # GSE11588).
Independent verification of gene expression changes
We selected seven genes from the 203 gene set to validate the microarray results. Total RNA was extracted from untreated and 48 h RA-treated B16 cells and melan-a cells as described above. qRT-PCR assays were used to measure expression of the selected genes. For qRT-PCR, total RNA was reverse transcribed into cDNA with the Advantage RT-for-PCR system (Clontech, Mountain View, CA) in the presence of random primers. The real time amplifications were carried out using a TaqMan Universal PCR Mastermix (ABI, Foster City, CA) and TaqMan probes specific for BRCA2, CDC2A, TINAGL, PLAT, TOP2A, DHRS3 and FGF1. All amplifications were performed on an ABI Model 7000 Sequence Detection System with the following cycles: 95°C for 10 min, and 40 cycles of 95°C for 15 s and 60°C for 1 min. Each assay was performed in triplicate and resulting qRT-PCR data was analyzed used a modification of the ΔΔCt method that accounts for variations in primer efficiency . 18S rRNA was used as the reference gene for all normalizations. p values were computed for the RA-treated and melan-a ΔCt values compared by T test to the untreated B16 ΔCt values and corrected by Bonferroni correction for multiple hypothesis testing.
In order to identify molecular interactions among the protein products of 203 genes, we entered the expression data from these genes into Pathway Studio software [Rockville, MD] and set the analysis for the identification of direct interactions within the gene set. We used Fisher's Exact Test as implemented by Pathway Studio to determine the p-value associated with the biological processes, and subsequently adjusted for multiple hypothesis testing using the Holm-Bonferroni method  with an overall type-1 error rate of 0.05. In order to determine statistical significance of the "hubs" in the graphical output, we directly implemented Fisher's Exact Test with the same parameters using custom-written Java code. The Holm-Bonferroni adjusted p-values reported for the hubs indicate the significance level for the number of connections to other genes in our set, accounting for the number of known connections in the Pathway Studio database.
The authors wish to thank Margaret McFarland for her help in preparing some of the illustrations. We also thank Dr. Dorothy Bennett, St. George's Hospital, University of London for providing the melan-a cells. This work was supported in part by NIH NCRR grants 5P20RR020180 awarded to RMN and 5P20RR016477 which supports the WV-INBRE program and the Marshall University Genomics Core Facility. Points of view in this document are those of the author(s) and do not necessarily represent the official position or views of the NIH.
- Wartman D, Weinstock M: Are we overemphasizing sun avoidance in protection from melanoma?. Cancer Epidemiol Biomarkers Prev. 2008, 17: 469-70. 10.1158/1055-9965.EPI-07-0301.PubMedView ArticleGoogle Scholar
- Wolbach SB, Howe PR: Tissue changes following deprivation of fat soluble vitamin A. J Exp Med. 1925, 62: 753-777. 10.1084/jem.42.6.753.View ArticleGoogle Scholar
- Petkovich M, Brand NJ, Krust A, Chambon P: A human retinoic acid receptor which belongs to the family of nuclear receptors. Nature. 1987, 330: 440-450. 10.1038/330444a0.View ArticleGoogle Scholar
- Giguere V, Ong ES, Segui P, Evans RM: Identification of a receptor for the morphogen retinoic acid. Nature. 1987, 330: 624-629. 10.1038/330624a0.PubMedView ArticleGoogle Scholar
- Mangelsdorf DJ, Ong ES, Dyck JA, Evans RM: Nuclear receptor that identifies a novel retinoic acid response pathway. Nature. 1990, 345: 224-229. 10.1038/345224a0.PubMedView ArticleGoogle Scholar
- Levin AA, Sturzenbecker LJ, Kazmer S: 9-cis retinoic acid stereo-isomer binds and activates the nuclear receptor RXR-alpha. Nature. 1992, 355: 359-361. 10.1038/355359a0.PubMedView ArticleGoogle Scholar
- Hoopes CW, Taketo M, Ozato K, Lin Q, Howard TA, Linney E, Seldin MF: Mapping of the Rxr loci encoding nuclear retinoic × receptors RXR alpha, RXR beta and RXR gamma. Genomics. 1992, 14: 611-617. 10.1016/S0888-7543(05)80159-4.PubMedView ArticleGoogle Scholar
- Keller H, Dreyer C, Medin J, Mahfoudi A, Ozato K, Wahli W: Fatty acids and retinoids control lipid metabolism through activation of peroxisome proliferator-activated receptor-retinoid × receptor heterodimers. Proc Natl Acad Sci USA. 1993, 90: 2160-2164. 10.1073/pnas.90.6.2160.PubMedPubMed CentralView ArticleGoogle Scholar
- Kliewer SA, Umesono K, Mangelsdorf DJ, Evans RM: Retinoid × receptor interacts with nuclear receptors in retinoic acid, thyroid hormone and vitamin D3 signaling. Nature. 1992, 355: 446-449. 10.1038/355446a0.PubMedView ArticleGoogle Scholar
- Leid M, Kastner P, Lyons R: Purification, cloning and RXR identity of the HeLa cell factor with which RAR or TR heterodimerizes to bind target sequences efficiently. Cell. 1992, 68: 377-395. 10.1016/0092-8674(92)90478-U.PubMedView ArticleGoogle Scholar
- Marks MS, Hallenbeck PI, Nagata T, Segars JH, Apella E, Nikodem VM, Ozato K: H-2 RIIBP (RXR-beta) heterodimerization provides a mechanism for combinatorial diversity in the regulation of retinoic acid and thyroid hormone responsive genes. EMBO J. 1992, 11: 1419-1435.PubMedPubMed CentralGoogle Scholar
- Umesono K, Murakami KK, Thompson CC, Evans RM: Direct repeats as selective response elements for the thyroid hormone, retinoic acid, and vitamin D3 receptors. Cell. 1991, 65: 1255-1266. 10.1016/0092-8674(91)90020-Y.PubMedView ArticleGoogle Scholar
- Mader S, Leroy P, Chen J-Y, Chambon P: Multiple parameters control the selectivity of nuclear receptors for their response elements: selectivity and promiscuity in the response element recognition by retinoic acid receptors and retinoid × receptors. J Biol Chem. 1993, 268: 591-600.PubMedGoogle Scholar
- Shibata H, Spencer TE, Onate SA, Jenster G, Tsai SY, Tsai MJ, O'Malley BW: Role of co-activators and co-repressors in the mechanism of steroid/thyroid receptor action. Recent Prog Horm Res. 1997, 52: 141-164.PubMedGoogle Scholar
- Sporn MB, Roberts AB: Role of retinoids in differentiation and carcinogenesis. Cancer Res. 1983, 43: 3034-3040.PubMedGoogle Scholar
- Niles RM: Vitamin A (retinoids) regulation of mouse melanoma growth and differentiation. J Nutr. 2003, 133: 282S-286S.PubMedGoogle Scholar
- Melnikova VO, Bolshakov SV, Walker C, Ananthaswamy HN: Genomic alterations in spontaneous and carcinogen-induced murine melanoma cell lines. Oncogene. 2004, 23: 2347-2356. 10.1038/sj.onc.1207405.PubMedView ArticleGoogle Scholar
- Gray-Schopfer VC, Cheong SC, Chong H, Chow J, Moss T, Abdel-Malik ZA, Marais R, Wynford-Thomas D, Bennett DC: Cellular senescence in naevi and immortalization in melanoma: a role for p16?. Br J Cancer. 2006, 95: 496-505. 10.1038/sj.bjc.6603283.PubMedPubMed CentralView ArticleGoogle Scholar
- Kuman R, Angelini S, Hemnenki K: Activating BRAF and N-ras mutations in sporadic primary melanomas: an inverse association with allelic loss on chromosome 9. Oncogene. 2003, 22: 9217-9224. 10.1038/sj.onc.1206909.View ArticleGoogle Scholar
- Bennett DC, Cooper PJ, Hart IR: A line of non-tumorigenic mouse melanocytes, syngeneic with the B16 melanoma and requiring a tumour promoter for growth. Int J Cancer. 1987, 39: 414-418. 10.1002/ijc.2910390324.PubMedView ArticleGoogle Scholar
- Gruber JR, Desai S, Blusztajn JK, Niles RM: Retinoic acid specifically increases nuclear PKCα and stimulates AP-1 transcriptional activity in B16 mouse melanoma cells. Exp Cell Res. 1995, 221: 377-384. 10.1006/excr.1995.1388.PubMedView ArticleGoogle Scholar
- Tusher V, Tibshirani R, Chu G: Significance analysis of microarrays applied to transcriptional responses to ionizing radiation. Proc Natl Acad Sci USA. 2001, 98: 5116-5121. 10.1073/pnas.091062498.PubMedPubMed CentralView ArticleGoogle Scholar
- deThe H, Marchio A, Tiollais P, Dejean A: Differential expression and ligand regulation of the retinoic acid receptor α and β genes. EMBO J. 1989, 8: 429-433.Google Scholar
- Cerignoli F, Guo X, Cardinali B, Rinaldi C, Casaletto J, Frati L, Screpanti I, Gudas LJ, Gulino A, Thiele CJ, Giannini G: RetSDR1, a short-chain retinol dehydrogenase/reductase, is retinoic acid-inducible and frequently deleted in human neuroblastoma cell lines. Cancer Res. 2002, 62: 1196-1204.PubMedGoogle Scholar
- Niles RM, Combs R: The relationship between susceptibility to retinoic acid treatment and protein kinase C α expression in murine melanoma cell lines. Exp Cell Res. 1996, 223: 21-28. 10.1006/excr.1996.0054.View ArticleGoogle Scholar
- Yuen T, Wurmbach E, Pfeffer RL, Ebersole BJ, Sealfon SC: Accuracy and calibration of commercial oligonucleotide and custom cDNA microarrays. Nucl Acids Res. 2002, 30: : e48-10.1093/nar/30.10.e48. doi: 10.1093/nar/30.10.e48View ArticleGoogle Scholar
- Freemantel SJ, Kerley JS, Olsen SL, Gross RH, Spinella MJ: Developmentally-related candidate retinoic acid target genes regulated early during neuronal differentiation of human embryonal carcinoma. Oncogene. 2002, 21: 2880-2889. 10.1038/sj.onc.1205408.View ArticleGoogle Scholar
- Harris TM, Childs G: Global gene expression patterns during differentiation of F9 embryonal carcinoma cells into parietal endoderm. Funct Integr Genomics. 2002, 2: 105-119. 10.1007/s10142-002-0062-6.PubMedView ArticleGoogle Scholar
- Cheepala SB, Syed Z, Trutschi M, Cvek U, Clifford JL: Retinoids and skin: microarrays shed new light on chemopreventive action of all-trans retinoic acid. Mol Carcinog. 2007, 46: 634-639. 10.1002/mc.20346.PubMedView ArticleGoogle Scholar
- Hoek K, Rimm DL: Expression profiling reveals novel pathways in the transformation of melanocytes to melanomas. Cancer Res. 2004, 64: 5270-5282. 10.1158/0008-5472.CAN-04-0731.PubMedView ArticleGoogle Scholar
- Ryu B, Kim DS, Deluca AM, Alani RM: Comprehensive expression profiling of tumor cell lines identifies molecular signatures of melanoma progression. PLos ONE. 2007, 3: 609-611.Google Scholar
- Seykora JT, Jih D, Elenitaas R, Horng WH, Elder DE: Gene expression profiling of melanocytic lesions. Am J Dermatopathol. 2003, 25: 6-11. 10.1097/00000372-200302000-00002.PubMedView ArticleGoogle Scholar
- Jaeger J, Koczan D, Thiesen HJ, Ibrahim SM, Gross G, Spang R, Kunz M: Gene expression signatures for tumor progression, tumor subtype. Clin Cancer Res. 2007, 13: 806-815. 10.1158/1078-0432.CCR-06-1820.PubMedView ArticleGoogle Scholar
- Haeseleer F, Huang J, Lebioda L, Saari JC, Palczewski K: Molecular characterization of a novel short-chain dehydrogenase/reductase that reduces all-trans retinal. J Biol Chem. 1998, 283: 21790-21799. 10.1074/jbc.273.34.21790.View ArticleGoogle Scholar
- Wang C, Li N, Liu X, Zheng Y, Cao X: A novel endogenous human CaMKII inhibitory protein suppresses tumor growth by inducing cell cycle arrest via p27 stabilization. J Biol Chem. 2008, 283: 11565-11574. 10.1074/jbc.M800436200.PubMedPubMed CentralView ArticleGoogle Scholar
- Zhang H, Rosdahl I: Expression profiles of p53, p21, bax, and bcl2 proteins in all-trans-retinoic acid treated primary and metastatic melanoma cells. Int J Oncol. 2004, 25: 303-308.PubMedGoogle Scholar
- Vertuani S, Dubrovska E, Levitsky V, Jager MJ, Kiessling R, Levitskaya J: Retinoic acid elicits cytostatic, cytotoxic, and immunomodulatory effects on uveal melanoma cells. Cancer Immunol Immunother. 2007, 56: 193-204. 10.1007/s00262-006-0185-z.PubMedView ArticleGoogle Scholar
- Tanaka T, Suh KS, Lo AM, DeLuca LM: p21 WAF-1/CIP-1 is a common mechanism through retinoic acid receptor (RAR)/retinoid × receptor (RXR) heterodimer and RXR/RXR homodimer. J Biol Chem. 2007, 282: 29987-29997. 10.1074/jbc.M701700200.PubMedView ArticleGoogle Scholar
- Claus R, Lubbert M: Epigenetic targets in hematopoietic malignancies. Oncogene. 2003, 22: 6489-6496. 10.1038/sj.onc.1206814.PubMedView ArticleGoogle Scholar
- Gompel M, Soulie C, Ceballos-Picot I, Meijer L: Expression and activity of cyclin-dependent kinases and glycogen synthase kinase-3 during NT2 neuronal differentiation. Neurosignals. 2004, 13: 134-143. 10.1159/000076567.PubMedView ArticleGoogle Scholar
- Finklestein R, Weinberg RA: Differential regulation of N-myc and c-myc expression in F9 teratocarcinoma cells. Oncogene Res. 1988, 3: 287-292.PubMedGoogle Scholar
- Prasad KN, Chors RJ, Sharma OK: Decreased expressions of c-myc and H-ras oncogenes in vitamin E succinate induced morphologically differentiated murine B-16 melanoma cells in culture. Biochem Cell Biol. 1990, 68: 1250-1255.PubMedView ArticleGoogle Scholar
- Gao Q, Zhou J, Huang X, Chen G, Ye F, Lu Y, Li K, Zhuang L, Huang M, Xu G, Wang S, Ma D: Selective targeting of checkpoint kinase 1 in tumor cells with a novel potent oncolytic adenovirus. Mol Ther. 2006, 13: 928-937. 10.1016/j.ymthe.2005.12.009.PubMedView ArticleGoogle Scholar
- Saeed AI, Sharov V, White J, Li J, Liang W, Bhagabati N, Braisted J, Klapa M, Currier T, Thiagarajan M, Sturn A, Snuffin M, Rezantsev A, Popov D, Ryltsov A, Kostukovich E, Borisovsky I, Liu Z, Vinsavich A, Trush V, Quackenbush J: TM4: a free, open-source system for microarray data management and analysis. Biotechniques. 2003, 34: 374-8.PubMedGoogle Scholar
- Pfaffl M: A new mathematical model for relative quantification in real-time RT-PCR. Nucleic Acids Res. 2001, 29: e45-10.1093/nar/29.9.e45.PubMedPubMed CentralView ArticleGoogle Scholar
- Holm S: A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics. 1979, 6: 65-70.Google Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.