Genetically engineered mouse mammary cancer models have been used over the years as systems to study human breast cancer. However, much controversy exists on the utility of such models as valid equivalents to the human cancer condition. To perform an interspecies gene expression comparative study in breast cancer we used a mouse model that most closely resembles human breast carcinogenesis. This system relies on the transplant of p53 null mouse mammary epithelial cells into the cleared mammary fat pads of syngeneic hosts. Serial analysis of gene expression (SAGE) was used to obtain gene expression profiles of normal and tumor samples from this mouse mammary cancer model (>300,000 mouse mammary-specific tags). The resulting mouse data were compared with 25 of our human breast cancer SAGE libraries (>2.5 million human breast-specific tags). We observed significant similarities in the deregulation of specific genes and gene families when comparing mouse with human breast cancer SAGE data. A total of 72 transcripts were identified as commonly deregulated in both species. We observed a systematic and significant down-regulation in all of the tumors from both species of various cytokines, including CXCL1 (GRO1), LIF, interleukin 6, and CCL2. All of the mouse and most human mammary tumors also displayed decreased expression of genes known to inhibit cell proliferation, including NFKBIA (IKBα), GADD45B, and CDKN1A (p21); transcription-related genes such as CEBP, JUN, JUNB, and ELF1; and apoptosis-related transcripts such as IER3 and GADD34/PPP1R15A. Examples of overexpressed transcripts in tumors from both species include proliferation-related genes such as CCND1, CKS1B, and STMN1 (oncoprotein 18); and genes related to other functions such as SEPW1, SDFR1, DNCI2, and SP110. Importantly, abnormal expression of several of these genes has not been associated previously with breast cancer. The consistency of these observations was validated in independent mouse and human mammary cancer sets.

This is the first interspecies comparison of mammary cancer gene expression profiles. The comparative analysis of mouse and human SAGE mammary cancer data validates this p53 null mouse tumor model as a useful system closely resembling human breast cancer development and progression. More importantly, these studies are allowing us to identify relevant biomarkers of potential use in human studies while leading to a better understanding of specific mechanisms of human breast carcinogenesis.

The utility of genetically engineered mouse models of mammary cancer as relevant tools for the study of human breast cancer is a topic of constant debate in the breast cancer research community. Some of the major criticisms refer to the fact that most transgenic models based on the overexpression of dominantly acting oncogenes (e.g., Wnt, HRas, and Erbb2) generate tumors that are estrogen receptor-negative and that regress once the oncogenic stimuli are suppressed or withdrawn (1, 2). Phenotypically, these tumors differ considerably from their human counterparts histopathologically and in their progression from early to more advanced lesions (3). Furthermore, tumors produced by these models are mostly diploid and rarely metastasize. These observations differ considerably from the human condition in which most breast cancers are aneuploid, intraductal in origin, estrogen receptor-positive, and regional, and distant metastases is the major complicating factor during tumor progression even from very early stages. Recently, however, a few models have been developed that closely mimic various characteristics of human breast carcinogenesis. The p53 null model of mammary carcinogenesis (4, 5) reproduces many of the critical features of human breast cancer. In this model, epithelial cells from p53 null mice are transplanted into cleared mammary fat pads of syngeneic hosts. More than 60% of these transplants develop invasive mammary adenocarcinomas, and upon hormonal stimulation 100% of the grafts are tumorigenic. Most tumors are intraductal in origin, and premalignant lesions are detectable, such as atypical hyperplasias and in situ lesions. Most of these early lesions are aneuploid, express estrogen and progesterone receptors, and ∼20% of the invasive adenocarcinomas that finally develop are estrogen receptor-positive (4, 5).

In a previous gene expression study we focused on the early effects that lack of p53 function exerts in normal mouse mammary epithelium in vivo(6). In the present study, we chose this p53 null model to obtain a comprehensive gene expression profile of spontaneous mammary tumors by serial analysis of gene expression (SAGE) and more importantly to perform an interspecies comparison with human breast cancer SAGE data generated in our laboratory. The final goal is the identification of commonly deregulated transcripts and pathways in both species that could lead to better understanding of the mechanisms of breast carcinogenesis and to identification of relevant biomarkers.

Mouse Mammary Samples.

Mouse mammary tumors developed spontaneously from intramammary fat pad transplanted p53 null mammary epithelium (4). Three p53 null mammary tumors (bulk samples) were used for generating three independent SAGE libraries (MT1, MT2, and MT3). Thirteen additional p53 null mammary tumors were dissected and snap frozen for RNA isolation and validation studies. As normal control for SAGE and Northern analyses, enriched mammary epithelium (>90% epithelial cells) from p53 wild-type and p53 null transplants were used as described previously (5, 6). To decrease the chances potential artifacts due to sample heterogeneity, each normal sample (MN1 and MN2) represents a pool of mammary epithelial samples from five age-matched separate mice (6).

Human Breast Samples.

Snap-frozen samples were obtained from the M.D. Anderson breast cancer tumor bank for total RNA isolation. A total of 25 stage I to stage II breast carcinomas (bulk frozen samples) were used to generate the SAGE libraries. A set of 12 additional human breast tumors was used for real-time quantitative reverse transcription-PCR (RT-PCR) validation studies. Normal mammary epithelial organoids were isolated from four different reduction mammoplasty specimens and used as normal controls for validation studies.

Serial Analysis of Gene Expression Analysis.

The 25 human and 5 mouse SAGE libraries were generated following standard procedures as described previously (7, 8) and using commercially available reagents (I-SAGE Kit, Invitrogen, Carlsbad, CA). Sequencing was performed using an ABI 3700 DNA Analyzer (Applied Biosystems, Foster City, CA). Human SAGE libraries were generated at an approximate resolution of 100,000 SAGE tags per library, and mouse SAGE libraries reached ∼60,000 tags per library. The additional SAGE data of 4 normal human breast tissue samples used in the final comparisons were downloaded from the Cancer Genome Anatomy Project (CGAP)-SAGE Genie database6 (libraries generated at the laboratory of Dr. Kornelia Polyak, Dana-Farber Cancer Institute, Boston, MA). The libraries identified as Human Normal #1 and #2 were generated from human luminal mammary epithelial samples BerEp4 antibody-purified cells, whereas libraries #3 and #4 were generated from human breast epithelial organoids (9, 10).

Northern Blot Analyses.

Total RNA from mouse samples was isolated, gene probes generated, and hybridization performed as described previously using standard procedures (8).

Real-Time Quantitative RT-PCR Analyses.

Total RNA from human samples was isolated, and cDNA was synthesized following standard procedures. Primers and probes were either designed using Primer Express 1.5 software (Applied Biosystems) or directly obtained from Applied Biosystems (Assays-on-Demand, Gene Expression Products). All of the real time RT-PCR reactions were performed using the TaqMan PCR Core Reagents kit and the ABI Prism 7700 Sequence Detection system (Applied Biosystems). Experiments were performed in triplicate for each data point. Amplification of 18S rRNA was used as the control for normalization.

Bioinformatics and Statistical Analyses.

SAGE tag extraction from sequencing files was performed by using the SAGE2000 software version 4.0 (a kind gift of Dr. Kenneth Kinzler, John Hopkins University, Baltimore, MD). SAGE data management, tag to gene matching, as well as additional gene annotations and links to publicly available resources such as Gene Ontology (GO), UniGene, and LocusLink, were performed using a suite of web-based SAGE library tools developed by us.7 The large number of human libraries allowed us to use robust rank-based methods (Mann-Whitney test) to assess differential expression (4 normals versus 25 breast cancers). Due to the smaller number of libraries involved, this was not possible with the mouse libraries (2 normals versus 3 tumors). Here, a parametric model, overdispersed logistic regression, was used to assess differential expression between the groups. This model was chosen to capture the two types of variability that we have noticed with SAGE libraries: Poisson sampling variability within a library, and natural variation in the true proportion of a tag between different individuals in the same group. The logistic model captures the first, overdispersion the second, and using both groups provides a pooled estimate of the SD associated with the difference.8 This was implemented in R using the glm function with a quasibinomial link. All of the proportions (tag count/library size) were slightly modified (tag count +1)/(library size +1) to avoid convergence problems with the logistic fits. Ps for the logistic coefficients were computed using likelihood ratio tests. For both statistical methods used, significant differences were considered at P < 0.05.

To enable visualization and illustration of the commonly deregulated genes in mouse and human breast carcinomas, we used the TIGR MultiExperiment Viewer (MeV 2.2) software. This tool was used for normalization and average clustering of the SAGE based on the fold change of tag counts for each transcript comparing tumor to normal in the mouse and human libraries, respectively. To this end we calculated the ratio of tag counts for each transcript in each tumor over the average representation of the same individual tags in the corresponding normal libraries. The calculated ratios were subjected to a log2 transformation before the clustering analysis. Two-way (by gene and by tumor sample) hierarchical clustering was used to examine the relationships among the human breast libraries. Mouse tumor clustering was based on one-way hierarchical clustering (by tumor sample), maintaining the same order of sorted genes as in the human breast cancer analysis.

For automated functional annotation and classification of genes of interest based on GO terms, we used the EASE software (11) available at the Database for Annotation, Visualization and Integrated Discovery (DAVID) (12).9 The EASE software calculates over-representation of specific GO terms with respect to the total number of genes assayed and annotated. Statistical measures of specific enrichment of GO terms are determined by an EASE score. The EASE score is a conservative adjustment of the Fisher exact probability that weights significance in favor of themes supported by more genes and is calculated using the Gaussian hypergeometric probability distribution that describes sampling without replacement from a finite population (11). This allows one to identify biological themes within a specific list of EASE analyzed genes.

All of the raw SAGE data reported as Supplementary Tables in this article are publicly available.

Primary Mouse SAGE Analyses

We generated five mouse SAGE libraries from three p53 null mammary tumors (MT1, MT2, and MT3) and two samples from p53 wild-type and p53 null mammary transplants representing normal mouse mammary epithelium (MN1, MN2 respectively). This resulted in the sequencing of ∼300,000 tags (60,000 tags per sample), thus monitoring the behavior of a total of 21,850 transcript tags.

In a previous study we observed that the p53 wild-type and null normal mammary epithelial samples were very comparable having the expression of only very few genes (0.2%) significantly different between the referred two normal samples (6).

Using the overdispersed logistic regression test, we identified 499 differentially expressed genes (P < 0.05) when comparing SAGE data from the p53 null mammary tumors versus normal mammary epithelium (see Supplementary Table 1).

To validate some of the most significant observations, we performed Northern analyses of various relevant transcripts. As shown in Fig. 1 A and as predicted by SAGE, we confirmed the consistent increase in expression of several genes in most mouse mammary tumors, including cell proliferation related transcripts such as CDC28 protein kinase 1 (Cks1), stathmin 1 (Stmn1 or Op18, oncoprotein 18), and CDK2 (cyclin-dependent kinase 2)-associated protein 1 (Cdkap1). Other validated up-regulated genes include ring-box 1 (Rbx1), protein phosphatase 1, catalytic subunit, γ isoform (Ppp1cc), stromal cell derived factor receptor 1 (Sdfr1), and selenoprotein W 1 (Sepw1).

Examples of SAGE predicted transcripts with decrease expression in tumors are shown in Fig. 1 B. As can be observed, a significant loss of expression of several transcripts was confirmed in all of the p53 null mammary tumors, including transcription factors Fos (FBJ osteosarcoma oncogene), c-Jun, and Elf1 (E74-like factor 1); genes inhibiting cell proliferation such as nuclear factor of κ light chain gene enhancer in B-cells inhibitor, α [Nfkbia/inhibitor of nuclear factor-κB α (IκBα), nuclear factor κB (NFκB) inhibitor]; proapoptosis regulator immediate early response gene 3 (Ier3 or Iex-1, immediate early response gene X-1); various cytokines such as leukemia inhibitory factor (Lif) and chemokine (C-X-C motif) ligand 1 (Cxcl1 or Gro1 oncogene); chemokine (C-C motif) ligand 7 (Ccl7) and tumor necrosis factor, α-induced protein 2 (Tnfaip2); and RIKEN cDNA 2310004N11 gene (homologue of MGC4796, ser/thr like kinase).

Comparative Mouse-Human Mammary Cancer SAGE Studies

We performed a SAGE interspecies (mouse-human) comparative analysis of genes commonly deregulated in mammary cancer. To this end, every statistically significant, differentially expressed gene found in the mouse SAGE experiments (Supplementary Table 1) was investigated for its status of expression in a set of 25 human SAGE libraries, which, in turn, were compared with four normal human breast epithelial libraries.

These 25 stage I to II human breast cancer SAGE libraries were generated at a resolution of 100,000 transcript tags per library, representing >2.5 million tags. This allowed us to monitor the pattern of expression of ∼35,000 human transcript tags.

To compare the SAGE data from the mouse and human mammary cancers, we used National Center for Biotechnology Information LocusLink and HomologGene databases to identify the human homologue genes for the commonly deregulated mouse transcripts (Supplementary Table 1). Human serial analysis of gene expression tags to gene matches were determined by the CGAP SAGE Genie database. Only reliable (unambiguous) SAGE tags matching mouse/human genes were considered in our analyses.

Among the 499 differentially expressed mouse transcripts (Supplementary Table 1), 43 genes have no known human homologues (8.6%), and 12 transcripts have no reliable human tag matches. Thus, a total of 444 genes were reliably represented in the SAGE libraries from both species. The comparative behavior of these 444 transcripts was analyzed in our 25 human breast cancer SAGE libraries relative to the expression observed in 4 normal human breast epithelial SAGE libraries.

Interestingly, we determined that 72 genes were commonly deregulated in tumors from both species; in other words, 72 transcripts that behaved in the same fashion both in mouse mammary tumors and human breast cancers. Fig. 2 depicts the expression level fold changes of these 72 genes when compared with the corresponding normal mammary epithelial samples in both species. The raw tag values for each of these transcripts in both species and for each normal and tumor sample is available as Supplementary Table 2.

Nineteen of the 72 transcripts were overexpressed in mouse and human tumors when compared with their normal counterparts, whereas 53 genes were underexpressed in tumors from both species relative to normal mammary epithelium.

Table 1 shows the 72 genes classified by their main functional roles based on literature searches and information obtained from LocusLink. Nevertheless, these 72 genes were additionally analyzed for over-representation of functional categories by the EASE software available at DAVID on-line resource (see Materials and Methods; refs. 11, 12). The EASE software performs the automated annotation and classification based on GO terms of the genes of interest (genes shown in Table 1, in our case) to biological themes and calculates their representation relative to the whole human breast cancer SAGE data set (see Supplementary Table 3). This analysis largely agreed with the classification categories shown in Table 1. Among the statistically significant over-represented categories under “Molecular Function,” we found cytochrome oxidases, genes with cytokine, growth factor activity, and transcriptional regulator activity. Categories of genes found in the extracellular space are also highly represented (EASE score 0.003). Among the gene categories falling under Biological Processes, genes related to cell proliferation, protein kinase cascades, and cell death received highly statistically significant EASE scores (0.002, 0.002, and 0.009, respectively; see Supplementary Table 3).

To validate these SAGE comparative observations in additional human breast carcinomas, we performed real-time quantitative RT-PCR analyses of representative transcripts in an independent set of 12 invasive ductal breast carcinomas compared with normal human breast epithelial organoids representing four different donors. We analyzed expression levels for a total of 9 transcripts from the list of 72. As representative of up-regulated transcripts in breast carcinomas, we validated the overexpression of genes: CKS1B* (CDC28 protein kinase 1), STMN1 (Stathmin 1, oncoprotein 18), DNCI2* (Dynein, cytoplasmic, intermediate chain 2), SDFR1* (Stromal cell derived factor receptor 1), SEPW1* (Selenoprotein W1), and SP110* (SP110 nuclear body protein). The down-regulated transcripts validated were those representing genes IER3 (immediate early response 3), TNFAIP2* (tumor necrosis factor, α-induced protein 2), and MGC4796* (Ser/Thr-like kinase). All of the transcripts analyzed behaved as predicted by the SAGE observations (Fig. 3). It is worth mentioning that to the best of our knowledge, of the validated 9 transcripts, those marked with an asterisk (*) have not been associated previously with breast cancer.

Specific Transcripts Commonly Deregulated in Human and Mouse Mammary Cancers

Cytokine Down-Regulation in Mammary Carcinomas.

Among transcripts with decreased expression in mammary tumors from both species, we identified several cytokines. This is in agreement with similar observations in other, more limited human breast cancer SAGE studies (9, 10). Here we not only observed the significant down-regulation of such cytokines in each of the human breast carcinomas analyzed, but strikingly, the exact same phenomenon was observed in every mouse mammary tumor analyzed as compared with normal epithelium (Fig. 1,B and Fig. 2)

The cytokine transcripts more consistently down-regulated were those encoding for LIF (leukemia inhibitory factor), interleukin 6 (IL-6), small inducible cytokine A2 (CCL2), and CXCL1 (GRO1); the latter being several hundred-fold down-regulated. All of these transcripts are highly expressed in normal mammary epithelia from both mouse and humans. Intriguingly, the exact functional role for these abundantly secreted cytokines in normal mammary epithelium is unclear at this point.

LIF is a glycoprotein highly conserved between species and originally identified as an IL-6-like cytokine that induces macrophage differentiation (13). Numerous additional functional roles have been assigned to this molecule. A variety of transcriptional pathways have been reported to be activated by LIF binding to its receptor, thus affecting differentiation and proliferation in various cell types. A progressive increase in LIF expression in mouse mammary epithelium after weaning has been reported recently, suggesting that LIF plays an important role in apoptosis induction during the first stage of mammary gland involution, an event at least partially mediated by Stat3 activation, a known downstream signal transduction target of LIF (14, 15). Interestingly, it was also suggested that LIF exerts growth-inhibitory effects on normal and malignant breast epithelial cells (16). However, the opposite effect in breast cancer cell lines was also reported by other investigators (17).

In our SAGE studies, LIF expression was significantly decreased in both human and mouse mammary tumors. As seen in Fig. 1 B, these observations were validated by Northern analysis of 15 mouse mammary tumors that showed an almost complete lack of LIF expression comparing with normal mammary epithelial cells. On the basis of these observations and the aforementioned physiologic roles of LIF in apoptosis induction and potential growth-controlling functions in mammary epithelial cells, we speculate that the significant down-regulation observed could be associated to an escape of tumor cells from the constraints of such growth regulatory mechanisms.

IL-6 is also a multifunctional cytokine very similar to LIF, known to affect the proliferation of epithelial cells, and its effect depends on the cell type. The cytostatic effects of IL-6 on breast carcinoma cells, its normal production and secretion by normal mammary epithelial cells, and its loss of expression in breast carcinomas has been studied by various groups (18, 19). IL-6 in particular was shown to inhibit the growth of estrogen receptor-positive carcinoma cells, and its expression correlates with estrogen receptor status (20, 21). Nevertheless, the true role of this cytokine in breast carcinogenesis is still unclear and a matter of controversy.

CXCL1 is also known as GRO1 oncogene or melanoma growth stimulating activity, α (MGSA). GRO1 was originally identified as a mitogenic substance secreted by melanoma cells (22). CXC chemokines are multifunctional molecules that, other than their role in immunomodulation, have been associated with invasion, tumor cell migration, and neovascularization. However, other than the transformation-related role for CXCL1/GRO1 in melanoma (23), there is a paucity of clear examples or studies demonstrating a protumorigenic role for CXCL1/GRO1 in carcinoma development and none in breast cancer. Furthermore, in early studies, it was observed that GRO1 was highly expressed by normal cultured human mammary epithelial cells, whereas most carcinoma cell lines analyzed did not express this cytokine (24).

In our studies, we observed a significant decrease in CXCL1/GRO1 expression in breast carcinomas when compared with normal mammary epithelium in both species and in all of the carcinomas (Fig. 1,B and Fig. 2). Interestingly, we also observed that CXCL1/GRO1 expression increases considerably in normal mouse mammary epithelium during the first stage of mammary gland involution after weaning, shadowing the previously mentioned expression behavior of LIF (data not shown). On the basis of these and previously discussed observations, we can speculate that the role of CXCL1/GRO1 in breast epithelium appears to be related to a transformation-protective role, perhaps related to regulation of apoptosis and cell proliferation.

CCL2, or monocyte chemoattractant protein 1 (MCP-1), is also structurally related to the CXC subfamily of cytokines. The role of this cytokine in breast cancer development is also controversial. It has been shown recently that estrogen decreases the levels of this chemokine in mouse mammary tissues, an event apparently contributing to mammary tumor development (25). Similar to our observations in breast cancer and in contrast to other neoplasias, it was observed that MCP-1 is not expressed in the carcinomatous tissue of prostate (26). Furthermore, it was concluded recently that CCL2/MCP1 could be a relevant negative regulator of pancreatic cancer progression (27). These observations allow us to speculate that CCL2 could play a similar transformation protective role in mammary epithelium.

In summary, the down-regulation of specific cytokines in 100% of mouse and human tumors (LIF, IL6, CXCL1, and CCL2) appears to point to a very relevant and evolutionary conserved event related to mammary carcinogenesis.

Transcription Factors.

In this study, we also detected 13 transcription factors deregulated both in mouse and human mammary tumors (Table 1).

ELF1 (E74-like factor 1) was found highly expressed in normal mammary epithelium both from mouse and humans (Fig. 1,B and Supplementary Table 2). This observation is in agreement with a very recent report that analyzed expression of various Ets transcription factors in mammary tissues (28). ELF1 is an Ets-related transcription factor and is known to be a key component in the transcriptional program during hematopoietic stem cell development (29). We observed a very significant down-regulation in expression of this transcription factor in all three of the mouse tumor SAGE libraries, in most mouse tumors analyzed by Northern (Fig. 1,B), and in 13 of 25 human breast carcinoma serial analysis of gene expression libraries (Table 1, Supplementary Table 2, and Fig. 2). The decreased expression of ELF1 observed in breast cancer appears contrary to most Ets transcription factors of which the overexpression is usually associated with malignant processes.

It has been shown that impairment of AP-1 transcription complex activity leads to failure of involution in the mouse mammary gland implying that at least postlactational programmed cell death depends on functional AP-1 (30). It was also shown that breast cancer cells have lower AP-1 activity (c-Fos and c-Jun levels) than normal mammary epithelial cells in vitro(31). Although many reports have been written on AP-1 family members in breast cancer biology, therapeutic response, and prognosis, the picture on the true role of each of these factors is far from clear (32). Intriguingly, our SAGE study with p53 null mammary tumors revealed a significant decrease of Fos expression in all of the tumors. Whereas the normal mammary epithelial samples expressed on average 70 SAGE tags representing Fos, the three mammary tumors expressed 5, 2, and 3 tags (per 60,000 total tags, MT1, MT2, and MT3 respectively, see Supplementary Table 1). Furthermore, we also observed that other AP-1 members (e.g., Jun and Junb) were systematically down-regulated in all three of the p53 null mammary tumors. These observations were additionally validated in 15 additional mouse tumors by Northern analysis (Fig. 1,B). It is known that Fos and Junb transcription can be regulated by p53. Therefore, the systematic down-regulation of AP-1 family members may be due to p53 loss and could be a characteristic intrinsic to this mouse model. However, when comparing with human breast cancer SAGE data, we detected that Jun and Junb expression was also decreased in the majority of stage I and II breast tumors (18 of 25 and 17 of 25, respectively) when comparing with normal epithelium (Table 1 and Supplementary Table 2). On the other hand, we did not observe the same phenomenon affecting FOS in these human breast carcinomas. Therefore, we can conclude that at least some members that could form active AP-1 transcription factor complexes are down-regulated in breast carcinomas from both species and that such decrease in expression may be important in the carcinogenesis process.

NFκB Pathway-Associated Transcripts.

It has been postulated by various authors that activation of the NFκB pathway is a very important event in breast cancer development (reviewed in ref. 33). In support of such a hypothesis and previous observations, in our SAGE studies we observed that several genes that appear to play inhibitory NFκB roles were greatly down-regulated not only in mouse mammary tumors but also in human breast cancers. For instance, NFKBIA/IκBα, the major inhibitor of NFκB, was observed significantly down-regulated in 100% of mouse and human breast carcinomas (Fig. 1,B, Table 1, and Supplementary Tables 1 and 2). Secretory leukocyte protease inhibitor (SLP1) was shown to prevent NFκB activation by inhibiting the degradation of IκBα and IκBβ, and we observed decreased expression of this gene in all of the mouse tumors and in 21 of 25 human breast carcinomas (Table 1; ref. 34). Human breast cancer data also showed that all of the tumors displayed lower expression of the diphtheria toxin receptor/heparin-binding epidermal growth factor-like growth factor (DTR; Table 1). This protein was demonstrated to suppress NFκB activation by inhibiting IκB kinase activation and IκB degradation (35).

One of the roles of clusterin (CLU) is to inhibit NFκB signaling through stabilization of IκBs (36). We observed decreased expression of the transcript encoding clusterin in all of the mouse tumors and 44% of human breast carcinomas. We also found down-regulation in every tumor of the transcript for serine/threonine kinase MGC4796, a protein reported to inhibit tumor necrosis factor-induced NFκB activation and p53-induced transcription (37).

The significant decrease in expression of the aforementioned transcripts, all of which are related in different ways to preventing or decreasing NFκB activity, strongly supports the hypothesis that high NFκB activity is of critical relevance in human breast cancer (reviewed in ref. 33).

Cell Proliferation and Apoptosis.

Not surprisingly our SAGE comparative study detected the common overexpression of positive regulators of cell proliferation such as CCND1, whereas negative regulators of proliferation-like growth arrest and DNA-damage-inducible β (GADD45B) and cyclin-dependent kinase inhibitor 1A (CDKN1A/p21) were found underexpressed in all three of the mouse tumors and most of human breast tumors (Table 1 and Fig. 2). Two genes regulating apoptosis, IER3 and protein phosphatase 1, regulatory (inhibitor) subunit 15A (PPP1R15A/GADD34), also showed decreased expression in mouse and human tumors.

Interestingly, we detected the overexpression of CDC28 protein kinase regulatory subunit 1B (CKS1B) in human and mouse mammary tumors. These observations were validated both with mouse and human carcinoma samples (Fig. 1,A and Fig. 3). CKS1B functions as an important adaptor of SCFSkp2 ubiquitin ligase and facilitates SCFSkp2 targeting of the cell proliferation inhibitor p27 (Kip1) for ubiquitination and subsequent degradation (38). It was also suggested that CKS1B may be involved in p21 degradation in a similar fashion (39). Recently, overexpression of CKS1B has been observed in human non-small lung cancer and in gastric and colorectal carcinomas (40, 41, 42). Here we provide evidence for the first time that increased CKS1B expression is frequent in mammary carcinomas from both species, human and mouse.

The IER3 (immediate early response 3) or IEX-1 (immediate early response gene X-1) was suggested to play a role in regulation of cell growth and apoptosis. Its expression can be regulated by multiple transcription factors, including p53, NFκB/rel, c-Myc and Sp-1 (reviewed in ref. 43). We observed a significant down-regulation on the expression of IER3 in human and mouse mammary tumors (Fig. 1,B, Fig. 3, Table 1, and Supplementary Tables). This is consistent with previous studies reporting that IER3 inhibited proliferation of breast cancer cells (43). Our finding additionally suggests that IER3 may be an important and common player in mammary carcinogenesis.

Various other interesting genes involved in signaling transduction and with other functional roles were found commonly deregulated in mouse and human breast tumors (Table 1). Interestingly, as mentioned previously, we identified and validated several transcripts of which the deregulation was never reported previously to be associated to human breast cancer such as CKS1B, DNCI2, SDRF1, SEPW1, SP110, TNFAIP2, and Ser/Thr-like kinase MGC4796.

In previous work, Desai et al.(44), using cDNA microarrays, compared gene expression profiles of mammary tumors generated in various transgenic mammary cancer models. They observed that the transgene-generated tumors shared similarities in their expression profiles as compared with normal mammary gland. However, major differences were observed that depended on the distinct oncogene-specific patterns of gene expression of each model. Very few similarities of commonly deregulated genes across all of the models were observed that were comparable with similar changes observed in human breast cancers (44).

On the basis of the results here reported and those previous observations we concluded that the p53 null model more closely resembles human breast cancer than the commonly used oncogene-driven transgenic models of mammary cancer.

In summary, to the best of our knowledge, this is the first mouse-human SAGE comparative study of mammary cancer gene expression profiles, providing information on evolutionary conserved pathway abnormalities involved in this malignancy. We observed that deregulation of genes related to the control of cell proliferation, differentiation, apoptosis, and mammary gland development is frequent in mammary carcinomas from both species. In addition, this comparative analysis validates the p53 mouse tumor model as a useful system that quite closely resembles human breast cancer development and progression. The significant similarities here described at the gene expression level add to the earlier mentioned biological and pathological commonalities. More importantly, these studies allow us to identify relevant biomarkers of potential use in human studies while eventually leading to a better understanding of specific mechanisms of human breast carcinogenesis. Future studies are needed to focus on the functional characterization and mechanistic aspects of the most promising targets from the list of commonly deregulated genes across mammary tumors in this interspecies comparison.

Fig. 1.

Representative Northern blot analysis of commonly deregulated genes in p53 null mouse mammary tumors. A, transcripts with increased expression in p53 null mammary tumors as compared with normal mouse mammary epithelium. B, significantly down-regulated transcripts in p53 null mammary tumors relative to normal mouse mammary epithelium. *2310004N11Rik, homologue of human MGC4796 gene.

Fig. 1.

Representative Northern blot analysis of commonly deregulated genes in p53 null mouse mammary tumors. A, transcripts with increased expression in p53 null mammary tumors as compared with normal mouse mammary epithelium. B, significantly down-regulated transcripts in p53 null mammary tumors relative to normal mouse mammary epithelium. *2310004N11Rik, homologue of human MGC4796 gene.

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Fig. 2.

Heat map of commonly deregulated genes in mouse and human tumors displayed in fold change relative to normal tissue counterparts. Color scale at the bottom depicts the approximate fold change in expression for each transcript and library relative to normal mammary epithelium: negative fold change (transcripts with decreased expression in tumors) is represented in green, and positive fold change (transcripts with over-expression in tumors) is represented in red.

Fig. 2.

Heat map of commonly deregulated genes in mouse and human tumors displayed in fold change relative to normal tissue counterparts. Color scale at the bottom depicts the approximate fold change in expression for each transcript and library relative to normal mammary epithelium: negative fold change (transcripts with decreased expression in tumors) is represented in green, and positive fold change (transcripts with over-expression in tumors) is represented in red.

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Fig. 3.

Real-time PCR validation of 9 deregulated genes in 12 additional human breast carcinomas. 18S rRNA was used as control. Bar charts were plotted based on the mean values ±2 SEM of the ratio between target and control genes in normal human breast epithelium and invasive carcinomas. Values shown in the top left corner of each plot represent the equivalent fold change between the mean of tumors and normal tissue values. ∗, expression not detected in normal breast tissue.

Fig. 3.

Real-time PCR validation of 9 deregulated genes in 12 additional human breast carcinomas. 18S rRNA was used as control. Bar charts were plotted based on the mean values ±2 SEM of the ratio between target and control genes in normal human breast epithelium and invasive carcinomas. Values shown in the top left corner of each plot represent the equivalent fold change between the mean of tumors and normal tissue values. ∗, expression not detected in normal breast tissue.

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Grant support: NIH National Cancer Institute Grants U19 CA84978 (C. M. Aldaz), U01 CA84243 (D. Medina), and center grant ES-07784; L. Deng is supported by a fellowship from the W.M. Keck Foundation to the Gulf Coast Consortia.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

Note: Supplementary data for this article can be found at http://sciencepark.mdanderson.org/ggeg/sage_proj_8.htm.

Requests for reprints: C. Marcelo Aldaz, The University of Texas M.D. Anderson Cancer Center, Department of Carcinogenesis, Science Park-Research Division, Smithville, TX 78957. Phone: 512-237-2403; Fax: 512-237-2475; E-mail: maldaz@odin.mdacc.tmc.edu

6

Internet address: http://cgap.nci.nih.gov/SAGE.

7

Internet address: http://spi.mdacc.tmc.edu/bitools/about/sage_lib_tool.html.

8

Keith Baggerly, unpublished observations.

9

Internet address: http://david.niaid.nih.gov/david.

Table 1

Commonly deregulated genes in mouse and human mammary tumors

Human gene nameMouse tagMouse LocusIDHuman tagHuman LocusIDFrequency in human tumors*
Regulation of cell proliferation       
CCND1 (Cyclin D1) ⇑ GTCCAGGAAA 12443 AAAGTCTAGA 595 20/25 (80%) 
 CKS1B (CKS1) ⇑ TTACAAGCCT 54124 TTAAAAGCCT 1163 18/25 (72%) 
STMN1 (oncoprotein 18) ⇑ AGAAGGAGGT 16765 AAGCTGAGGT 3925 9/25 (36%) 
NFKBIA (IKBα) ⇓ TGCTTAAAAG 18035 TAACAGCCAG 4792 25/25 (100%) 
GADD45B (MYD118) ⇓ CAGAGGCTGG 17873 AACTCCCAGT 4616 25/25 (100%) 
CDKN1A (p21) ⇓ TATTGTGGCT 12575 TGTCCTGGTT 1026 21/25 (84%) 
Apoptosis related       
IER3 (IEX-1) ⇓ GATTGTCAGA 15937 ACCATCCTGC 8870 25/25 (100%) 
PPP1R15A (GADD34) ⇓ GGCACGCCTT 17872 ATCCGGACCC 23645 25/25 (100%) 
CLU (Clusterin) ⇓ TCTCCAGGCG 12759 CAACTAATTC 1191 11/25 (44%) 
Regulation of transcription related       
MSRB (Pilin-like transcription factor) ⇑ TGAATTGAGT 76467 GTACGTCTGG 22921 12/25 (48%) 
SP110 (nuclear body protein) ⇑ AATACTAGAC 109032 GCATCTTCAA 3431 10/25 (40%) 
KLF5 (Kruppel-like f 5) ⇓ AAGCGCCACC 12224 AAAAGCAGAA 688 22/25 (88%) 
CEBPB (C/EBP-β) ⇓ GCGGCCGGTT 12608 GCTGAACGCG 1051 22/25 (88%) 
JUN (c-Jun) ⇓ CTAACGCAGC 16476 CCTTTGTAAG 3725 18/25 (72%) 
JUNB ⇓ GCCCCCTTCC 16477 ACCCACGTCA 3726 17/25 (72%) 
CSDA (DBPA) ⇓ GGGATTGCCC 56449 ATTTAAAAAA 8531 16/25 (64%) 
STAT3 ⇓ GCATCCTGTT 20848 TGAGGAGCTG 6774 15/25 (60%) 
ELF1 (E74-like factor 1) ⇓ TAAAAGTTCT 13709 AAAAATTGGA 1997 13/25 (52%) 
SAP18 (Sin3 associated p. 18) ⇓ GCAGTTCACA 20220 AGACCATATT 10284 12/25 (48%) 
Signal transduction related       
SARA2 (SAR1B) ⇑ CAATAAAACA 66397 CAATAAAACA 51128 17/25 (68%) 
MGC4796 (Ser/Thr-like kinase) ⇓ TCAGCAATAA 74178 TCAGCAATAA 83931 25/25 (100%) 
DTR (HB-EGF) ⇓ TCTGAACTGT 15200 GTCCTTTCTG 1839 25/25 (100%) 
DSCR1 (Calcipressin 1) ⇓ CTTTGGGGAC 54720 CTTTGGAAAT 1827 25/25 (100%) 
ITPKC ⇓ GACTACGTGG 233011 ACAACACCCC 80271 25/25 (100%) 
MAP2K3 (MKK3) ⇓ GTTTGGAGCT 26397 GTTTGGAGCT 5606 24/25 (96%) 
TACSTD2 ⇓ AAGCGCCTCA 56753 GCCTACCCGA 4070 23/25 (92%) 
MT2A (Metallothionein 2) ⇓ TAACTGACAA 17750 GATCCCAACT 4502 21/25 (84%) 
DUSP8 (Dual S.phosphatase 8) ⇓ AGACGGATGT 18218 GTGGAGGGGC 1850 20/25 (80%) 
Cytokines       
CXCL1 (GRO1) ⇓ TGTGGGAGGC 14825 TTGAAACTTT 2919 25/25 (100%) 
IL-6 ⇓ AAGAACAACT 16193 GGCACCTCAG 3569 25/25 (100%) 
LIF ⇓ GTAGCGGCTT 16878 GCCTTGGGTG 3976 25/25 (100%) 
CCL2 (MCP-1) ⇓ AATACTAAAA 20296 GTACTAGTGT 6347 25/25 (100%) 
ECM, cell adhesion-communication, secreted proteins       
COL4A1 (Procollagen IV A1) ⇑ GCTTTCCTGT 12826 GACCGCAGGA 1282 11/25 (44%) 
SDFR1 ⇑ TACTTGTGTT 20320 TACTTGTGTG 27020 9/25 (36%) 
EMP1 (Epithelial membrane P1) ⇓ AGACGTAAAT 13730 TAATTTGCAT 2012 24/25 (96%) 
TNFAIP2 ⇓ ATTCGAGGCT 21928 ACTCAGCCCG 7127 24/25 (96%) 
FST (Follistatin) ⇓ TAAATGTGCA 14313 AAGGAAGCTG 10468 22/25 (88%) 
MFGE8 ⇓ TTCCATTCCG 17304 GGTTGGCAGG 4240 21/25 (84%) 
SLPI (Antileukoproteinase) ⇓ GCTCCCGGCT 20568 TGTGGGAAAT 6590 21/25 (84%) 
CYR61 (IGFBP10) ⇓ TTTACGGATG 16007 AGTGTCTGTG 3491 20/25 (80%) 
JUP (Plakoglobin) ⇓ GGTTTGGGGG 16480 GTGTGGGGGG 3728 9/25 (36%) 
Chaperones       
FKBP9 ⇑ CAAATGCTGT 27055 GAATAAATGT 360132 8/25 (32%) 
HSPA1A (HSP70-1) ⇓ TGTTCAGTTT 193740 CAGAGATGAA 3303 25/25 (100%) 
CRYAB (Crystallin α2) ⇓ TTCGTCCTGC 12955 GTTTCATCTC 1410 19/25 (76%) 
Cytoskeleton related       
DNCI2 (dynein, interm.chain 2) ⇑ TCGGACAAAA 13427 TGGTAGTTAC 1781 11/25 (44%) 
CAPZA1 (Capping prot. α1) ⇑ CCACACTGTC 12340 TCAAAAAAAG 829 8/25 (32%) 
TUBA6 (Tubulin α6) ⇓ TGCTGCCATT 22146 GCTGCCCTTG 84790 23/25 (92%) 
KRT14 (Keratin 14) ⇓ CTGCTCAGGC 16664 GATGTGCACG 3861 23/25 (92%) 
MYO5B (Myosin Vb) ⇓ ACGTAAAAAA 17919 TTTCCAGCAT 4645 21/25 (84%) 
LMNA (Lamin A/C) ⇓ CCAAAGTCTT 16905 CGCAAGCTGG 4000 20/25 (80%) 
KIAA0992 (Palladin) ⇓ GGCCTATCTC 72333 GACCTATCTC 23022 11/25 (44%) 
DNA repair related       
RPA3 (Replication protein A3) ⇑ AATGAGCTTC 68240 ATTGATCTTG 6119 9/25 (36%) 
TOP1 (Topoisomerase I) ⇓ AATTCCCTTT 21969 AATTCCCTTT 7150 18/25 (72%) 
RNA and protein biosynthesis and metabolism       
RPL13 (Ribosomal protein L13) ⇓ ATACTGAAGC 270106 CCCGTCCGGA 6137 22/25 (88%) 
HNRPA0 (HnRNA binding protein) ⇓ CCTGGAATTT 77134 CCTGAAATTT 10949 24/25 (96%) 
SBDS ⇓ CTACCAAAAC 66711 CTGCCATAAC 51119 16/25 (64%) 
SUI1 (Putative transl.initiation f.) ⇓ CAATAAACTG 20918 CAATAAACTG 10209 16/25 (64%) 
EEF1G (Elongation factor 1G) ⇓ TGGGCAAAGC 67160 TGGGCAAAGC 1937 11/25 (44%) 
EEF2 (Elongation factor 2) ⇓ TGCTCGCAAA 13629 AGCACCTCCA 1938 10/25 (40%) 
PABPC1 (PolyA binding prot.1) ⇓ CTCGAGTCTC 18458 AAAAGAAACT 26986 9/25 (36%) 
Metabolism, mitochondrion, miscellaneous       
SEPW1 (Selenoprotein W, 1) ⇑ TTTCCAGGTG 20364 TCTTCCCCAG 6415 20/25 (80%) 
COX7B (Cytochrome C ox.VIIB) ⇑ GAAACTAGGT 66142 GACATATGTA 1349 19/25 (76%) 
SURF1 (Surfeit 1) ⇑ TGAAGTAAGG 20930 TGCTACTGGT 6834 18/25 (72%) 
COX6C (Cytochrome Cox.Vic) ⇑ AATATGTGTG 12864 AATATGTGGG 1345 17/25 (68%) 
ADH5 (Alcohol dehydrogen. 5) ⇑ AACTAGGCAA 11532 AACCTGTTTT 128 16/25 (64%) 
TIPARP ⇓ TTACCATTGC 99929 AAATGGCCAA 25976 24/25 (96%) 
HMOX1 (Hemoxygenase) ⇓ GGCACTGGGC 15368 CGTGGGTGGG 3162 23/25 (92%) 
Function unknown       
HSPC148 ⇑ GAGAAATATA 66070 GAGAAATATA 51503 16/25 (64%) 
BM-009 ⇑ TTTAAGAATG 223601 TCTAAAGAAT 51571 8/25 (32%) 
FLJ23231 ⇓ TATGCTGTGT 230738 TAAATTTTAA 80149 25/25 (100%) 
KIAA0346 ⇓ CTGGGCGCGG 216850 GTGCTCAAAC 23135 21/25 (84%) 
HSHIN1 ⇓ TAGGGCATTG 73945 GTGTGATGCT 54726 10/25 (40%) 
Human gene nameMouse tagMouse LocusIDHuman tagHuman LocusIDFrequency in human tumors*
Regulation of cell proliferation       
CCND1 (Cyclin D1) ⇑ GTCCAGGAAA 12443 AAAGTCTAGA 595 20/25 (80%) 
 CKS1B (CKS1) ⇑ TTACAAGCCT 54124 TTAAAAGCCT 1163 18/25 (72%) 
STMN1 (oncoprotein 18) ⇑ AGAAGGAGGT 16765 AAGCTGAGGT 3925 9/25 (36%) 
NFKBIA (IKBα) ⇓ TGCTTAAAAG 18035 TAACAGCCAG 4792 25/25 (100%) 
GADD45B (MYD118) ⇓ CAGAGGCTGG 17873 AACTCCCAGT 4616 25/25 (100%) 
CDKN1A (p21) ⇓ TATTGTGGCT 12575 TGTCCTGGTT 1026 21/25 (84%) 
Apoptosis related       
IER3 (IEX-1) ⇓ GATTGTCAGA 15937 ACCATCCTGC 8870 25/25 (100%) 
PPP1R15A (GADD34) ⇓ GGCACGCCTT 17872 ATCCGGACCC 23645 25/25 (100%) 
CLU (Clusterin) ⇓ TCTCCAGGCG 12759 CAACTAATTC 1191 11/25 (44%) 
Regulation of transcription related       
MSRB (Pilin-like transcription factor) ⇑ TGAATTGAGT 76467 GTACGTCTGG 22921 12/25 (48%) 
SP110 (nuclear body protein) ⇑ AATACTAGAC 109032 GCATCTTCAA 3431 10/25 (40%) 
KLF5 (Kruppel-like f 5) ⇓ AAGCGCCACC 12224 AAAAGCAGAA 688 22/25 (88%) 
CEBPB (C/EBP-β) ⇓ GCGGCCGGTT 12608 GCTGAACGCG 1051 22/25 (88%) 
JUN (c-Jun) ⇓ CTAACGCAGC 16476 CCTTTGTAAG 3725 18/25 (72%) 
JUNB ⇓ GCCCCCTTCC 16477 ACCCACGTCA 3726 17/25 (72%) 
CSDA (DBPA) ⇓ GGGATTGCCC 56449 ATTTAAAAAA 8531 16/25 (64%) 
STAT3 ⇓ GCATCCTGTT 20848 TGAGGAGCTG 6774 15/25 (60%) 
ELF1 (E74-like factor 1) ⇓ TAAAAGTTCT 13709 AAAAATTGGA 1997 13/25 (52%) 
SAP18 (Sin3 associated p. 18) ⇓ GCAGTTCACA 20220 AGACCATATT 10284 12/25 (48%) 
Signal transduction related       
SARA2 (SAR1B) ⇑ CAATAAAACA 66397 CAATAAAACA 51128 17/25 (68%) 
MGC4796 (Ser/Thr-like kinase) ⇓ TCAGCAATAA 74178 TCAGCAATAA 83931 25/25 (100%) 
DTR (HB-EGF) ⇓ TCTGAACTGT 15200 GTCCTTTCTG 1839 25/25 (100%) 
DSCR1 (Calcipressin 1) ⇓ CTTTGGGGAC 54720 CTTTGGAAAT 1827 25/25 (100%) 
ITPKC ⇓ GACTACGTGG 233011 ACAACACCCC 80271 25/25 (100%) 
MAP2K3 (MKK3) ⇓ GTTTGGAGCT 26397 GTTTGGAGCT 5606 24/25 (96%) 
TACSTD2 ⇓ AAGCGCCTCA 56753 GCCTACCCGA 4070 23/25 (92%) 
MT2A (Metallothionein 2) ⇓ TAACTGACAA 17750 GATCCCAACT 4502 21/25 (84%) 
DUSP8 (Dual S.phosphatase 8) ⇓ AGACGGATGT 18218 GTGGAGGGGC 1850 20/25 (80%) 
Cytokines       
CXCL1 (GRO1) ⇓ TGTGGGAGGC 14825 TTGAAACTTT 2919 25/25 (100%) 
IL-6 ⇓ AAGAACAACT 16193 GGCACCTCAG 3569 25/25 (100%) 
LIF ⇓ GTAGCGGCTT 16878 GCCTTGGGTG 3976 25/25 (100%) 
CCL2 (MCP-1) ⇓ AATACTAAAA 20296 GTACTAGTGT 6347 25/25 (100%) 
ECM, cell adhesion-communication, secreted proteins       
COL4A1 (Procollagen IV A1) ⇑ GCTTTCCTGT 12826 GACCGCAGGA 1282 11/25 (44%) 
SDFR1 ⇑ TACTTGTGTT 20320 TACTTGTGTG 27020 9/25 (36%) 
EMP1 (Epithelial membrane P1) ⇓ AGACGTAAAT 13730 TAATTTGCAT 2012 24/25 (96%) 
TNFAIP2 ⇓ ATTCGAGGCT 21928 ACTCAGCCCG 7127 24/25 (96%) 
FST (Follistatin) ⇓ TAAATGTGCA 14313 AAGGAAGCTG 10468 22/25 (88%) 
MFGE8 ⇓ TTCCATTCCG 17304 GGTTGGCAGG 4240 21/25 (84%) 
SLPI (Antileukoproteinase) ⇓ GCTCCCGGCT 20568 TGTGGGAAAT 6590 21/25 (84%) 
CYR61 (IGFBP10) ⇓ TTTACGGATG 16007 AGTGTCTGTG 3491 20/25 (80%) 
JUP (Plakoglobin) ⇓ GGTTTGGGGG 16480 GTGTGGGGGG 3728 9/25 (36%) 
Chaperones       
FKBP9 ⇑ CAAATGCTGT 27055 GAATAAATGT 360132 8/25 (32%) 
HSPA1A (HSP70-1) ⇓ TGTTCAGTTT 193740 CAGAGATGAA 3303 25/25 (100%) 
CRYAB (Crystallin α2) ⇓ TTCGTCCTGC 12955 GTTTCATCTC 1410 19/25 (76%) 
Cytoskeleton related       
DNCI2 (dynein, interm.chain 2) ⇑ TCGGACAAAA 13427 TGGTAGTTAC 1781 11/25 (44%) 
CAPZA1 (Capping prot. α1) ⇑ CCACACTGTC 12340 TCAAAAAAAG 829 8/25 (32%) 
TUBA6 (Tubulin α6) ⇓ TGCTGCCATT 22146 GCTGCCCTTG 84790 23/25 (92%) 
KRT14 (Keratin 14) ⇓ CTGCTCAGGC 16664 GATGTGCACG 3861 23/25 (92%) 
MYO5B (Myosin Vb) ⇓ ACGTAAAAAA 17919 TTTCCAGCAT 4645 21/25 (84%) 
LMNA (Lamin A/C) ⇓ CCAAAGTCTT 16905 CGCAAGCTGG 4000 20/25 (80%) 
KIAA0992 (Palladin) ⇓ GGCCTATCTC 72333 GACCTATCTC 23022 11/25 (44%) 
DNA repair related       
RPA3 (Replication protein A3) ⇑ AATGAGCTTC 68240 ATTGATCTTG 6119 9/25 (36%) 
TOP1 (Topoisomerase I) ⇓ AATTCCCTTT 21969 AATTCCCTTT 7150 18/25 (72%) 
RNA and protein biosynthesis and metabolism       
RPL13 (Ribosomal protein L13) ⇓ ATACTGAAGC 270106 CCCGTCCGGA 6137 22/25 (88%) 
HNRPA0 (HnRNA binding protein) ⇓ CCTGGAATTT 77134 CCTGAAATTT 10949 24/25 (96%) 
SBDS ⇓ CTACCAAAAC 66711 CTGCCATAAC 51119 16/25 (64%) 
SUI1 (Putative transl.initiation f.) ⇓ CAATAAACTG 20918 CAATAAACTG 10209 16/25 (64%) 
EEF1G (Elongation factor 1G) ⇓ TGGGCAAAGC 67160 TGGGCAAAGC 1937 11/25 (44%) 
EEF2 (Elongation factor 2) ⇓ TGCTCGCAAA 13629 AGCACCTCCA 1938 10/25 (40%) 
PABPC1 (PolyA binding prot.1) ⇓ CTCGAGTCTC 18458 AAAAGAAACT 26986 9/25 (36%) 
Metabolism, mitochondrion, miscellaneous       
SEPW1 (Selenoprotein W, 1) ⇑ TTTCCAGGTG 20364 TCTTCCCCAG 6415 20/25 (80%) 
COX7B (Cytochrome C ox.VIIB) ⇑ GAAACTAGGT 66142 GACATATGTA 1349 19/25 (76%) 
SURF1 (Surfeit 1) ⇑ TGAAGTAAGG 20930 TGCTACTGGT 6834 18/25 (72%) 
COX6C (Cytochrome Cox.Vic) ⇑ AATATGTGTG 12864 AATATGTGGG 1345 17/25 (68%) 
ADH5 (Alcohol dehydrogen. 5) ⇑ AACTAGGCAA 11532 AACCTGTTTT 128 16/25 (64%) 
TIPARP ⇓ TTACCATTGC 99929 AAATGGCCAA 25976 24/25 (96%) 
HMOX1 (Hemoxygenase) ⇓ GGCACTGGGC 15368 CGTGGGTGGG 3162 23/25 (92%) 
Function unknown       
HSPC148 ⇑ GAGAAATATA 66070 GAGAAATATA 51503 16/25 (64%) 
BM-009 ⇑ TTTAAGAATG 223601 TCTAAAGAAT 51571 8/25 (32%) 
FLJ23231 ⇓ TATGCTGTGT 230738 TAAATTTTAA 80149 25/25 (100%) 
KIAA0346 ⇓ CTGGGCGCGG 216850 GTGCTCAAAC 23135 21/25 (84%) 
HSHIN1 ⇓ TAGGGCATTG 73945 GTGTGATGCT 54726 10/25 (40%) 

NOTE. 72 genes commonly deregulated in mouse and human breast carcinoma SAGE libraries (normal VS tumor P < 0.05). 19 genes were found over expressed (⇑) and 53 genes with decreased expression (⇓) in breast carcinomas compared to normal mammary epithelium.

*

Transcript tags changing >3-fold when compared with normal in at least 8 of 25 (32%) human breast cancer SAGE libraries.

Tag matches >1 gene.

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Supplementary data