Gene expression profiles in breast tumors regarding the presence or absence of estrogen and progesterone receptors
Article first published online: 20 MAY 2004
Copyright © 2004 Wiley-Liss, Inc.
International Journal of Cancer
Volume 111, Issue 6, pages 892–899, 10 October 2004
How to Cite
Nagai, M. A., da Rós, N., Neto, M. M., de Faria Junior, S. R., Brentani, M. M., Hirata, R. and Neves, E. J. (2004), Gene expression profiles in breast tumors regarding the presence or absence of estrogen and progesterone receptors. Int. J. Cancer, 111: 892–899. doi: 10.1002/ijc.20329
- Issue published online: 3 AUG 2004
- Article first published online: 20 MAY 2004
- Manuscript Accepted: 26 FEB 2004
- Manuscript Revised: 20 JAN 2004
- Manuscript Received: 6 OCT 2003
- Fundaćao Amparo à Pesquisado Estadode São Paulo (FAPESP). Grant Number: 99/07009-4
- breast cancer;
- gene expression;
- estrogen receptor;
- progesterone receptor
Estrogen acts via its receptor (ER) to stimulate cell growth and differentiation in the mammary gland. ER and progesterone receptor (PR), which is regulated by estrogen via ER, have been used as prognostic markers in clinical management of breast cancer patients. Patients with ER− breast tumors have a poorer prognosis than patients with ER+ tumors. The aim of the present study was the identification of tumor-associated genes differentially expressed in breast tumors regarding the presence or absence of ER and PR hybridized with cDNA microarrays containing 4,500 tumor-derived expressed sequence tags generated using the ORESTES technique. Samples of human primary breast carcinomas from 38 patients were analyzed. The experiments were performed in triplicates and data from each element were acquired by phosphoimage scanning. Data acquisition was performed using the ArrayVision software. After normalization statistical analysis was applied. In a preliminary analysis, 98 differentially expressed transcripts were identified, 46 were found to be more expressed in ER+/PR+ and 52 were found to be more expressed in ER−/PR− breast tumors. The biochemical functions of the genes in the reported expression profile are diverse and include metabolic enzymes, protein kinases, helicases, transcription factors, cell cycle regulators and apoptotic factors. ER−/PR− breast tumors displayed increased levels of transcripts of genes associated with neurodegeneration and genes associated with proliferation were found in ER+/PR+ tumors. © 2004 Wiley-Liss, Inc.
Estrogens exert important roles in the development, differentiation and maintenance of several target tissues and are also associated with the development and growth of hormone-dependent tumors such as breast cancer.1
Most estrogen actions are thought to be mediated through its nuclear estrogen receptors, ERα and ERβ, which are members of the nuclear receptors superfamily that are ligand-induced transcription factors.2 The mechanism by which estrogen receptor (ER) mediates the transactivation of gene expression is complex.3 Besides the classical ligand-dependent mechanism of ER action in which the hormone-receptor complex regulates gene transcription through its interaction with ERE consensus DNA sequences, the ERs can also regulate gene transcription interacting with other promoter elements such as AP1,4 SP15 and CREs.6 Thus, estrogen receptors ERα and ERβ can transduce different hormonal signals depending on the ligand and the nature of the hormone-responsive element (HRE).2 The transactivation elicited by those receptors complexed with E2 may result in opposite signal transduction leading to opposite biologic response in the presence of AP1 and/or CRE sites.7, 8 In addition, several E2-responsive genes are regulated by DNA-independent or -dependent interactions of the ERα and SP1 proteins.9 Alternatively, the signal transduction by growth factors and their tyrosine kinase receptors, such as EGFR, IGFR, erbB-2 and other molecules like cAMP and dopamine, may lead to a ligand-independent ER activation, resulting from the phosphorylation of serine and tyrosine amino acid residues in the AF1 and AF2 domains in the estrogen receptor molecule.10 ER phosphorylation promotes receptor dimerization, association with coregulatory proteins and transcription transactivation.11
During the malignant transformation, the epithelial mammary cells can maintain the basal levels of ER or express high levels or loss of this receptor expression. The majority of breast cancers express ER, yet ER− cancers comprise about 30% of all breast cancers.12 The origin of ER− human breast cancer is unknown but it is possible that ER− cancers may arise from a mammary epithelial cell that was ER− during its normal phenotype or from an ER+ cell in which ER was transcriptionally downregulated.13
Patients with ER− tumors show shorter disease-free interval and overall survival than patients with ER+ tumors.14, 15, 16 In addition, lymph node metastasis, considered to be the most important prognostic factor in breast cancer, does not alter these findings.17, 18 However, the ER expression is gradually lost during tumor progression, leading to a more undifferentiated state.19 The majority of ER+ tumors are diploid, well differentiated, more common in postmenopausal women and less aggressive than ER− breast tumors.15, 20 However, the plethora of genes that might be associated with each tumor phenotype remains to be fully explored.
cDNA microarray technique allows the analysis of hundreds or thousands of genes simultaneously in a tissue or cell. This technique has been used to analyze differential gene expression in breast cancer cell lineages and in primary breast tumors to identify genes regulated by estrogen,21 tumor classifications and disease prognosis22, 23, 24, 25, 26, 27 and therapeutic response.28, 29
In the present study using cDNA microarrays containing 4,500 tumor-derived expressed sequence tags (ESTs), we have attempted to identify tumor-associated genes that can lead to a more precise classification of hormone-dependent and -independent primary breast tumors.
MATERIAL AND METHODS
Samples of human primary breast carcinomas and adjacent normal tissue from 58 patients were obtained from A.C. Camargo Hospital (São Paulo, Brazil). Tumor samples were macrodissected to remove residual normal tissue and stored in liquid nitrogen. Only tumor samples with more than 60% of tumor cells were included in the present study. The largest diameter of the tumors was recorded. The number of lymph node metastases was determined by microscopic examination of an average of 24 lymph nodes per patient. Tumor metastases at lymph nodes were detected in 24 patients. All cases were submitted to a histopathologic review of tumor slides in order to confirm diagnosis. All tumors were classified according to the 1982 WHO Histological Typing of Breast Tumors. The clinical stage of the patients was determined according to the 1978 UICC TNM (tumor, nodes, metastases) staging system. Tumors studied were infiltrating ductal carcinomas. The median age of the patients at the time of diagnosis was 47.5 years (range, 25–75 years). Estrogen and progesterone receptor (PR) binding assays were performed by the classical dextran-coated charcoal method (DCC) as previously described.30
Tissue specimens were pulverized under liquid nitrogen and total RNA was isolated according to the guanidine isothiocyanate method.31 The quality of the RNA samples was determined by 1% agarose gel electrophoresis and ethidium bromide staining. All RNA samples were treated with DNaseI for 30 min at 37°C to eliminate genomic DNA contamination.
cDNA microarrays, hybridization and data acquisition
A set of 4,500 tumor-derived EST clones generated by ORESTES32 was used to make cDNA microarray blots. Briefly, ORESTES clones were amplified by PCR using primers from sequences flanking the cloning site. The PCR products were purified using QIAquick 96 PCR purification kit (Qiagen, Chatsworth, CA), analyzed by 1.5% agarose gels and spotted onto nylon membranes robotically (GenomeSystem, St. Louis, MO). The 33P-labeled cDNA probes were synthesized from 30 μg of total RNA treated with DNAseI using Superscript II reverse transcriptase (Life Technologies, Bethesda, MD) by oligo-dT-primed polymerization. The membranes were prehybridized for 40 min in 50% (V/V) phosphate solution (1 N Na+, 0.5 N PO4-3), 7% SDS, 1% BSA and 1 mM EDTA at 65°C. Hybridization was carried out for 16 hr using 50% (V/V) phosphate solution (1 N Na+, 0.5 N PO4-3), 7% SDS, 1% BSA and 1 mM EDTA at 65°C. The filters were washed twice for 30 min at 65°C in 50% phosphate solution, 1% SDS and 1 mM EDTA. The filters were then exposed to a phosphor-imaging screen for 6 hr to 2 days and scanned on the Storm Imager 860 (Molecular Dynamics, Sunnyvale, CA). The experiments were performed in triplicate. Samples were hybridized simultaneously against 2 nylon membranes on the same hybridization chamber. For tests purposes, a third hybridization has been done as a follow-up control but not considered for the analysis. Data acquisition was performed using the ArrayVision software (Molecular Dynamics), and normalization and statistic analysis were applied.
Data analysis was performed using R (http://cran.r-project.org), an open-source-interpreted computer language for statistical computation and graphics, and tools from the Bioconductor project (http://www.bioconductor.org), adapted to our needs. After image acquisition and quantification, saturated spots and spots with signal lower than or equal to background were identified and excluded from the analysis. Next, background-subtracted spot intensities were normalized by a global mean normalization procedure33, 34 and the final result was the average of duplicates. Next, we searched our data for differentially expressed genes in the ER+/PR+ and ER−/PR− comparisons. We used a nonparametric test (nonpaired Wilcoxon or Mann-Whitney test) to determine the p-value for each individual gene without multiple-test correction. Next, we used Fisher's linear discrimination analysis to search for pairs and trios of genes such that data points representing signal intensities for all 3 genes for each sample would be well separated by a plane in the corresponding space. More precisely, for a given set of genes, this linear classification method searches for linear combinations of their expressions with large ratios between groups and within groups sum of squares.35 Along this direction where the ratio is largest, the 2 groups of data points, corresponding to ER+/PR+ and ER−/PR− samples, are well separated by a line or a plane, depending whether we are considering pairs or trios of genes. This maximal ratio of sum of square, or its square root, which is denoted here by SVD, measures how well separated the 2 groups are.
Quantitative real-time PCR (qPCR)
qPCR was performed by using the GenAmp 5700 sequence detector (PE Applied Biosystems, Foster City, CA). cDNA was generated using the Prostar First Strand RT-PCR kit (Stratagene, La Jolla, CA). Each cDNA sample was analyzed in duplicate. PCR reactions were carried out in a total volume of 50 μl according to the manufacturer's instructions for SYBR Green PCR Core reagent (PE Applied Biosystems). The PCR primers used were as follows: Seladin-1 (DHCR24), forward primer 5′-GGTGCAGGACATGCTGGTGCC-3′, reverse primer 5′-CTCTGCCTCATTTCCTTTGGG-3′; amyloid β precursor (APP), forward primer 5′-ACCGCCGCCGCCTGGCCCTGG-3′, reverse primer 5′-GGACCGGATCTGAGCGGCTTTC-3′; heterologous nuclear ribonucleoprotein K (HNRPK), forward primer 5′-GTTGCCATCACCCACTGCAAC-3′, reverse primer 5′-CCTGCTAGACTCTGATGAA-3′; J domain containing protein 1 (JDP1), forward primer 5′-GGCGAAGGAGCCAGATGTCG-3′, reverse primer 5′-GTCTTGTCAGATTCTTCCAG-3′; glyceraldehyde-3-phosphate dehydrogenase (GAPDH), forward primer 5′-GAGCACCAGGTGGTCTCCT-3′, reverse primer 5′-TACCAGGAAATGAGCTTGACAAAG-3′. The relative expression was calculated by 2−ΔΔCT, where CT = fluorescence threshold value; ΔCT = CT of the target gene − CT of the reference gene (GADPH); ΔΔCT = ΔCT of the tumor sample − ΔCT of the reference sample. The average value of all 4 normal tissue samples served as reference sample.
In this study aiming to identify tumor-associated genes related to breast cancer, we compared the pattern of gene expression between primary breast tumors with different estrogen and progesterone receptors status using cDNA microarrays. The status of the progesterone receptor was taken in consideration as an indication of the ER functionality. Total RNA isolated from 38 primary breast tumors (19 ER−/PR− and 19 ER+/PR+) was hybridized with triplicates of a cDNA array containing 4,500 tumor-derived ESTs comprising 114 membranes. Data obtained from these membranes were normalized according to the methods described above. Two sets of 38 membranes (i.e., 76 membranes) were used in the analysis and a set of 38 membranes has been kept as a follow-up control. Data obtained from these 114 membranes indicate strong reproducibility. Indeed, the correlation coefficient between the 3 sets of 38 membranes (e.g., A, B and C) are 0.95 (A and B), 0.92 (A and C), 0.92 (B and C); as a comparison value, 0.77 is the correlation of ER+ and ER− for all 114 membranes. The Wilcoxon test was applied to each cDNA spotted in the membranes to identify differentially expressed genes between the ER−/PR− and ER+/PR+ breast tumors. A graphic representation of this analysis is shown in Figure 1. Each point in this volcano plot corresponds to a given transcript spotted into the membranes. The x-axis represents the difference between ER−/PR− and ER+/PR+ tumor sample median log performed gene expressions and the y-axis corresponds to the p-values observed. The outlier's dots correspond to upregulated or downregulated genes between ER−/PR− and ER+/PR+ breast tumors. The p-values obtained by the Wilcoxon test were used to rank the differentially expressed genes. Using this classification, we were able to identify a set of 98 cDNAs that could be good candidates for discriminating breast tumors regarding the steroid hormone receptor status. Forty-six of these transcripts were classified as more expressed in the ER+/PR+ breast tumors and 52 were classified as more expressed in the ER−/PR− breast tumors. The genes in this expression profile are related to several biochemical and regulatory pathways. The differentially expressed genes are listed in Tables I and II. Increased expression levels of the Seladin-1 (DHCR24) gene transcripts were observed in ER−/PR− breast tumor relative to the ER+/PR+ breast tumors. The expression of Seladin-1 gene was also analyzed, stratifying the breast cancer cases by other conventional prognostic factors, such as lymph node status and clinical stage. As shown in Figure 2, in addition to the negative correlation observed between Seladin-1 expression and steroid hormone receptor positivity, we found a positive correlation between the expression levels of Seladin-1 and advanced-stage tumors (stages III and IV).
|Accession #||Accession #||Description|
|HS.348733||AF27304.8||CTCL tumor antigen se20-9 mRNA|
|Hs.260720||NM_021800||JDP1: J domain containing protein 1; DnaJ/HSP40 proteins|
|HS.249184||NM_007109||Transcription factor 19 (SC1) (TCF19)|
|Hs.129548||NM_002140||Heterogeneous nuclear ribonucleoprotein K (HNRPK)|
|HS.22350||AK27248||Hypothetical protein LOC56757 (FLJ23595)|
|Hs.171889||NM_020244||Choline phosphotransferase 1 (CHPT1)|
|HS.351575||NM_153029||Hypothetical protein FLJ31821|
|Hs.24817||NM_017684||Hypothetical protein FLJ20136|
|HS.146668||NM_020755||TDE1L: likely ortholog of mouse tumor differentially expressed 1|
|HS.344000||AK026100||Hypothetical protein FLJ22447|
|Hs.19054||NM_018530||Hypothetical protein PRO2521|
|Hs.23744||NM_024594||PANK3: pantothenate kinase 3|
|Hs.78921||NM_003488||AKAP1: A kinase (PRKA) anchor protein 1|
|Hs.380359||NM_005673||Solute carrier family 25 (SLC25A16)|
|Hs.12692||NM_017691||Hypothetical protein FLJ20156|
|Hs.183800||NM_002883||Ran GTPase activating protein 1 (RANGAP1; KIAA1835)|
|Hs.296847||NM_003119||SPG7: spastic paraplegia 7, paraplegin|
|HS.13011||NM_019096||GTP binding protein 2; putative GTP binding protein 2 (GTPBP2)|
|Hs.119257||NM_005231||EMS1: ems1 sequence|
|Hs.170160||NM_004761||RAB2L: RAB2, member RAS oncogene family-like, RGL2, KE1.5, HKE1.5|
|Hs.131905||NM_054020||CATSPER2: cation channel, sperm-associated 2|
|MGC14597||BE061374||EST similar to C20orf114: chromosome 20 open reading frame 114|
|Hs.321579||NM_021095||SLC5A6: solute carrier family 5 (sodium-dependent vitamin transporter), member 6|
|Hs.88556||NM_004964||HDAC1: histone deacetylase 1;HD1, RPD3, RPD3L1|
|HS_325530||NM_015219||EXO70: likely ortholog of mouse exocyst component protein 70 kDa homolog|
|HS.25682||NM_032478||MRPL38: mitochondrial ribosomal protein L38|
|Hs.234680||NM_013451||Fer-1-like 3, myoferlin (FER1L3)|
|Hs.107381||NM_018359||Hypothetical protein FLJ11200|
|HS.155313||NM_022105||DATF1: death-associated transcription factor 1|
|HS.31141||NT_022459||ROBO2: roundabout, axon guidance receptor, homolog 2 (Drosophila)|
|HS.109390||BF343288||EST Homo sapiens cDNA, 5′ end|
|Hs.6582||NM_015313||ARHGEF12: Rho guanine nucleotide exchange factor (GEF) 12|
|Hs.458148||NM_006013||RPL10: ribosomal protein L10|
|Hs.64096||NM_014772||Hypothetical protein KIAA0427|
|Hs.349106||AF113616||Mucin 3A, intestinal (MUC3)|
|HS.7149||AL834396||Formin homology 2 domain containing 2; (FHOD2) KIAA1902|
|Hs.75074||NM_004759||MAPKAPK2: mitogen-activated protein kinase-activated protein kinase 2|
|Hs.284162||NM_016304||C15orf15: chromosome 15 open reading frame 15|
|Hs.158203||NM_002313||ABLIM1: actin-binding LIM protein 1|
|HS.152925||AB033094||Hypothetical protein KIAA1268|
|Hs.7392||NM_024045||DDX50: DEAD (Asp-Glu-Ala-Asp) box polypeptide 50|
|Hs.194110||NM_02522||Hypothetical protein PRO2730|
|HS.3187||NM_002504||Nuclear transcription factor, X-box binding 1 (NFX1)|
|Accession #||Accession #||Description|
|HS.319378||Homo sapiens transcribed sequence with moderate similarity to protein pir:I60307 (E. coli)|
|HS.155546||NM_015044||GGA2: golgi-associated, gamma adaptin ear containing, ARF-binding protein 2|
|Hs.367593||NM_052888||LOC114659: KIAA0563-related gene|
|Hs.198273||NM_005004||NADH dehydrogenase (ubiquinone) 1 beta (NDUFB8)|
|Hs.14732||NM_002395||Malic enzyme 1, NADP(+)-dependent, cytosolic (ME1)|
|Hs.177486||NM_000484||Amyloid beta (A4) precursor protein (APP)|
|Hs.3100||NM_005548||Lysyl-tRNA synthetase (KARS)|
|Hs.198427||NM_000189||Hexokinase 2 (HK2)|
|Hs.79356||NM_006762||Lysosomal-associated multispanning membrane protein-5 (LAPTM5)|
|Hs.135941||NM_014911||Adaptor-associated kinase 1 (AAK1) (KIAA1048)|
|Hs.23449||NM_018842||LOC55971:insulin receptor tyrosine kinase substrate|
|Hs.154672||NM_006636||Methylene tetrahydrofolate dehydrogenase (NAD+-dependent) (MTHFD2)|
|Hs.74497||NM_004559||Nuclease-sensitive element binding protein 1 (NSEP1)|
|AF086762||NM_013279||C11orf9: chromosome 11 open reading frame 9 (KIAA0954)|
|Hs.278573||NM_000611||CD59: CD59 antigen p18–20|
|Hs.6879||NM_020188||DC13 protein (DC13)|
|Hs.79706||NM_000445||Plectin 1 (PLEC1)|
|Hs.79089||NM_006378||Semaphorin 4D (SEMA4D)|
|Hs.282410||NM_006888||Calmodulin 1 (phosphorylase kinase, delta) (CALM1)|
|Hs.3059||NM_016451||Coatomer protein complex, subunit beta (COPB)|
|HS.7736||NM_016504||Mitochondrial ribosomal protein L27 (MRPL27)|
|Hs.77290||NM_006755||Transaldolase 1 (TALDO1)|
|Hs279903||NM_005614||Ras homolog enriched in brain 2 (RHEB2)|
|HS.173108||NM_181784||SPRED2: sprouty-related, EVH1 domain containing 2|
|Hs.172207||NM_007363||NONO: non-POU domain containing, octamer-binding|
|Hs.153884||NM_005783||ATP-binding protein associated with cell differentiation (APACD)|
|Hs.158688||NM_015904||Translation initiation factor (IF2; KIAA0741)|
|Hs.81182||NM_006895||HNMT: histamine N-methyltransferase|
|Hs.75616||NM_014762||24-dehydrocholesterol reductase (DHCR24; SELADIN1)|
|HS.149377||NT_008705||Hypothetical protein DKFZp761L0424; KIAA1217|
|Hs.3260||NM_000021||Presenilin 1 (Alzheimer's disease 3) (PSEN1)|
|Hs.43857||NM_018837||SULF2: similar to glucosamine-6-sulfatases|
|Hs.75721||NM_005022||Profilin 1 (PFN1)|
|Hs.279784||NM_013388||Prolactin regulatory element binding (PREB)|
|Hs.4114||NM_005032||PLS3: plastin 3 (T isoform)|
|HS.288550||AK026809||Hypothetical protein FLJ23156|
|Hs.2877||NM_001793||CDH3: cadherin 3, type 1, P-cadherin (placental)|
|Hs.278562||NM_001307||Claudin 7 (CLDN7; claudin 9)|
|Hs.178710||NM_004859||CLTC: clathrin, heavy polypeptide (Hc)|
|Hs.16621||BE072346||Hypothetical protein DKFZP434I116 (KIAA1429)|
|Hs.3887||NM_002807||Proteasome (prosome, macropain) 26S subunit, non-ATPase, 1 (PSMD1)|
|HS.299269||Ye17d07.sl Homo sapiens cDNA, 3′ end (T93574)|
|Hs.70877||NM_015421||Hypothetical protein DKFZP564K2062|
|Hs.130740||NM_017758||Hypothetical protein FLJ20308|
|Hs.159263||NM_001849||Collagen, type VI, alpha 2 (COL6A2)|
|Hs.159479||NM_000512||GALNS: galactosamine (N-acetyl)-6-sulfate sulfatase|
|Hs.268555||NM_012255||XRN2: 5′–3′ exoribonuclease 2|
|Hs.21321||NM_145808||Granule cell differentiation protein (FLJ31098)|
|Hs.343564||NM_003193||TBCE: tubulin-specific chaperone|
|Hs.433222||NM_006432||NPC2: Niemann-Pick disease, type C2|
After selecting the 98 differentially expressed cDNA clones, we used a supervised analysis (described above) and made an exhaustive search in order to find pairs and trios of genes whose pattern of expression in individual arrays allows a good linear separation between breast tumors with different steroid hormone receptor status. We found some pairs of genes showing this potential. A linear correlation between the expression of the JDP1 and HNRPK gene transcripts was able to make a good separation between the ER+/PR+ and ER−/PR− breast tumors (Fig. 3).
The transcripts of the Seladin-1, APP, HNRPK and JDP1 genes were further validated in a series of 20 primary breast tumors (ER+/PR+, 10 cases; ER−/PR−, 10 cases) and 4 normal breast tissue samples by qPCR (Fig. 4). Overall, the expression patterns of these genes observed by qPCR analysis were consistent with the results generated by the microarray method.
To investigate the potential regulation of the HNRPK and JDP1 by estrogen, using the BLAT tool we searched for 5′ flanking sequences that might act as promoter region for these genes. No potential promoter region could be found for the JDP1 gene. A CpG-rich sequence of 1,815 bp located 5′ upstream of the HNRPK gene was identified as a potential promoter region for this gene (Fig. 5). A search for potential transcription factor-binding sites was done using publicly available tools.36 This promoter region contains motif sequences for several transcription factors, including AP1, SP1 and CREB. In addition, this region also contain several widely spaced EREs and one perfect palindromic ERE.
A large proportion of the differentially expressed transcripts identified here are new genes, represented by anonymous ESTs (without RefSeq) or hypothetical proteins. Several known genes associated with different biologic pathways already known to be associated with the tumorigenic process were also identified as differentially expressed in the breast tumors stratified by steroid hormone receptors status. Among the known genes are those related to the transduction of mitogenic signal, cellular adhesion and motility, metabolic enzymes, protein kinases, several transcription factors and apoptotic factors. Due to the diversity and complexity of the expression profile observed, we will focus our discussion on a limited number of genes.
The gene expression profile displayed by ER−/PR− breast tumors included increased transcript levels of genes that act as apoptotic and antiapoptotic factors in cells of the nervous central system and are associated with neurodegeneration, such as Seladin-1, APP and PSEN1. Seladin-1 is a new gene and its physiologic role in breast epithelium is unknown. The Seladin-1 gene (also named KIAA0018 and DHCR24, 24-dehydrocholesterol reductase) contains 9 exons spanning approximately 46 Kb of genomic DNA on chromosome 1p33-p31.1.37, 38, 39 Previous data from our group identified Seladin-1 gene transcripts in ER−/PR− primary breast tumors using differential display reverse transcriptase (DDRT)-PCR.40 In the present study, we have also observed that advanced-stage breast tumors (stages III and IV) showed higher levels of Seladin-1 gene expression compared to early-stage breast tumors (stages I and II), possibly reflecting its association with a more aggressive nature. Seladin-1 encodes for a protein with homology with a family of FAD-dependent oxireductases, which play a role in cholesterol biosynthesis.39 In neuroglioma cells, Seladin-1 acts as a antiapoptotic factor inhibiting caspase 3 in cells overexposed to toxicity mediated by amyloid β accumulation or by oxidative stress.38 The overexpression of Seladin-1 may function as a protective factor against the toxicity elicited by the amyloid β accumulation and its downregulation might be associated to an increased susceptibility to Alzheimer's disease (AD) development.38 The APP (amyloid β precursor) and PSEN1 are also associated with AD. The biologic function of the Presenilin-1 has not been fully established yet,41 but this protein appears to play an important role in APP processing and mutations in the PSEN1 gene have been associated with 18–50% of the familial AD cases.42
The role played by estrogen in the transcription regulation of Seladin-1 and Presenilin-1 is not known yet, but the regulation of the APP gene has been attributed to the estrogen action. Epidemiologic studies have shown an association between the reduction of estrogen levels and the development of AD, which represents one of the most common neurodegenerative disease in postmenopausal women.43 Estrogen replacement therapy has been associated with a reduction in the relative risk for AD development following menopause.43 In addition, experimental studies have shown that estrogen decreases the levels of APP expression, alters APP processing, decreases amyloid β production and the apoptosis induced by amyloid β in neuronal cells.44, 45 Our data suggest that estrogen receptors might be associated with the repression of the transcriptional regulation of Seladin-1, APP and PSEN1; however, experimental studies are needed to address that. Investigating the effects of the BRCA1 expression in the transcriptome of EcR-293 cells in culture, Welsch et al.46 identified APP as being induced by the BRCA1 expression. High levels of BRCA1 expression inhibit ERα transcriptional activity.47 These data suggest that the upregulation of APP transcripts by BRCA1 might be a result of the inhibition of ER transactivation activity.
Using a supervised analysis, we found pairs of genes whose pattern of expression would be useful as markers to better identify the hormone-responsive phenotype. Among those we identified a pair of genes composed by JDP1 (J domain containing protein 1; DnaJ/Hsp40) and HNRPK (heterologous nuclear ribonucleoprotein K; hnRNP K) that appeared to be particularly interesting. The JDP1 and HNRPK transcripts were found to be more expressed in ER+/PR+ breast tumors.
The JDP1 gene encodes a DnaJ/Hsp40 protein, which is a member of the small-molecular-weight heat shock protein family. DnaJ/Hsp40 proteins are expressed in several tissues and acts as a cochaperone regulating protein folding, transport, translation initiation and gene expression.48 Hsp40 proteins coregulate the activity of Hsp70 proteins in an ATP-dependent manner, preventing the accumulation of improperly folded proteins and facilitating their degradation via ubiquitin.48, 49 Schneider et al.50 provide evidence that the eukaryotic DnaJ homologue can interact with the major cytoplasmatic molecular chaperone Hsp90, which interacts with several proteins, including steroid hormone receptors, such as ER, preventing receptor activity in the absence of the hormone.51 In yeast, Ydj1/Hsp40 mutants displayed high basal levels of ER activity in the absence of estrogen, suggesting that Ydj1/Hsp40 is important for the receptor regulation by the Hsp90 folding pathway.52 These data suggest that the cochaperone activity of the DnaJ/Hsp40 in the Hsp90 pathway might also be important for the integrity and functionality of ER in the mammary normal gland and breast tumor tissue.
The HNRPK gene encodes for the hnRNP K protein initially described as a member of the heterologous ribonucleoproteins family that bind nascent pre-mRNA and participate in the mRNA processing and transportation to the cytoplasmic compartment.53 hnRNP K was also characterized as a transcription factor that can bind DNA double strand at pyrimidine-rich motifs (CCCTCCCCA; CT element), interact with TATA-binding proteins (TBPs) and regulate gene transcription of several genes, such as c-myc54 and SRC.55 As the expression of JDP1 and HNRPK transcripts showed a positive linear correlation associated with the positivity for estrogen and progesterone receptors, we speculate about the possible estrogen responsiveness of those genes. We identified a putative promoter region 5′ upstream of the HNRPK gene that contains several transcription factor motifs, including many SP1 sites and half ERE consensus sequences and a perfect palindromic ERE, suggesting that ER might play an important role in the transcriptional regulation of this gene. HNRPK transcript upregulation via ER could occur by a hormone-dependent or -independent mechanism. On the other hand, the transactivation of HNRPK might be a result of the cellular cross-talk between ER and growth factor signaling pathways.10
Increased levels of HNRPK transcripts were observed in MCF-7 breast cancer cells in the presence of EGF or HRG (heregulin-β4).56 Mandal et al.56 also demonstrate that the expression of hnRNP K significantly increases c-myc expression and the proliferation rate of MCF-7 cells. In addition, they observed increased expression levels of hnRNP K in grade III primary breast tumors, suggesting that the expression of HNRPK might be associated with enhanced growth rate of breast cancer cells.
In summary, we have identified different sets of genes whose transcripts are differentially expressed in breast tumors regarding the estrogen and progesterone receptor status. Our data could provide new information in estrogen signaling and tumor growth. Additional studies are required to determine the functional and clinical significance of these genes in breast cancer development and its potential to be used as tumor biomarkers.
The authors thank Dr. Luis Fernando Reis (Ludwig Institute for Cancer Research São Paulo Branch) for kindly providing the cDNA arrays.
- 20Why are estrogen receptor-negative breast cancers more agressive than the estrogen receptor-positive breast cancers? Inv Metastasis 1994; 14: 329–36., , , , .
- 35The elements of statistical learning. New York: Springer, 2001., , .