To gain insight into the role of the CCN genes in human breast carcinomas, we quantified connective tissue growth factor (CTGF), WISP-1, CYR61, and human NOV (NOVH) mRNA expression levels in samples from 44 primary breast tumors and seven normal breasts using quantitative real-time PCR assay. Overexpression of CTGF, WISP-1, CYR61, and NOVH was found in 55 (24 of 44), 46 (20 of 44), 39 (17 of 44), and 11% (5 of 44) primary breast tumors, respectively. Statistical univariate analysis was performed to explore the links between expression of the CCN genes and clinical and pathological parameters. Interestingly, significant associations were found between CTGF expression versus stage, tumor size, lymph node status, and age at diagnosis; WISP-1 mRNA levels versus stage, tumor size, lymph node, and HER-2/neu overexpression; and CYR61 expression with stage, tumor size, lymph node, age, and estrogen receptor expression. In contrast to CTGF, WISP-1, and CYR61, no significant correlation was found between NOVH expression and any of the clinical and pathological factors. Furthermore, multivariate classification tree model analysis showed that stage and lymph node involvement were important for predicting CTGF expression in breast cancers; the stage, age, and HER-2/neu status were key factors for WISP-1 expression; and the stage, age, and estrogen receptor were valuable predictors for CYR61 expression. In summary, these results suggest that CTGF, WISP-1, and CYR61 may play a role in the progression of breast cancer and might serve as a valuable tool for monitoring tumor status of breast cancer patients.

The recent discovery that CYR61 is highly expressed in breast cancers and associated with more advanced disease has brought to light an emerging family of conserved and modular proteins (1, 2, 3). This protein family now consists of six distinct members, including CTGF3(4, 5), NOVH (nephroblastoma overexpressed gene; Refs. 6 and 7), CYR61 (cysteine-rich protein; Ref. 8), WISP-1 (wnt-1 inducible gene; Ref. 6), WISP-2, also termed rCop-1, and WISP-3 (9). CTGF, CYR61, and NOV were among the first of this group resulting in members of this protein family called the “CCN family” (10). The primary translational product of CCN family members contain 343–381 residues and generate proteins of Mr 35,000–40,000 with homologies ranging from 60 to 90%. These proteins contain 38 conserved cysteine residues that are organized into four distinct structural modules. All members of the CCN gene family possess a secretory signal peptide at the NH2 terminus, indicating that they are secreted proteins. The biological properties of CCN proteins include stimulation of cell proliferation, migration, adhesion, and extracellular matrix formation. They also regulate more complex biological processes, such as angiogenesis and tumorigenesis (reviewed in Refs. 11, 12, 13).

Several lines of evidence support a role for CCN molecules in tumorigenesis. Consistent with its profibrotic properties, CTGF is overexpressed in pancreatic cancers (14) and melanomas (15). WISP-1 is strongly expressed in the fibrovascular stroma of breast tumors developing in Wnt-1 transgenic mice (6). Moreover, forced overexpression of WISP-1 in normal rat kidney fibroblasts (NRK-49F) was sufficient to induce their transformation (16). Recently, several studies have suggested that CYR61 was overexpressed in breast cancers and may be involved in estrogen-mediated tumor development (1, 2, 3).

To quantify expression of CCN genes in breast cancer, we performed real-time quantitative RT-PCR based on TaqMan methodology to measure mRNA levels of four genes: CTGF, WISP-1, CYR61, and NOVH. Measurements were made from the same total RNA preparation for each of 44 primary breast tumors and seven paired normal breast tissue samples. Furthermore, we determined whether overexpression of one of the CCN genes was correlated with clinical and pathological parameters of breast cancer by univariate analysis and classification tree model.

### Patients and Samples

We analyzed tissue from excised primary breast tumors of 44 women treated at Saitama Cancer Center, Saitama, Japan, from 1992 to 2000. The samples were examined histologically for the presence of tumor cells.

### RNA Extraction and cDNA Synthesis

Total RNA was extracted from breast specimens by using TRIzol reagent (Life Technologies, Inc.) according to the standard protocol. The quality of the RNA samples was determined by electrophoresis through agarose gels and staining with ethidium bromide, and the 18S and 28S RNA bands were visualized under UV light. Total RNA (μg) was processed directly to cDNA by reverse transcription with Superscript II (Life Technologies, Inc.), according to the manufacturer’s protocol in a total volume of 50 μl.

### Real-Time RT-PCR

#### Theoretical Basis.

Reactions are characterized at the point during cycling when amplification of the PCR product is first detected, rather than the amount of PCR product accumulated, after a fixed number of cycles. The parameter Ct is defined as the fractional cycle number at which the fluorescence generated by cleavage of the probe passes a fixed threshold above baseline. The CCN target message in unknown samples is quantified by measuring Ct and by using a standard curve to determine the quantity of starting target message. We also quantified transcripts of β-actin as the endogenous RNA control, and each sample was normalized on the basis of its β-actin content. For each experimental sample, the amount of the targets and endogenous reference is determined from the standard curve. The target amount is divided by the endogenous reference amount to obtain a normalized target value. The relative gene target expression level was also normalized to a mean value (value = 1) from seven normal breast tissue samples (calibrator). Final results, expressed as N-fold difference in CCN gene expression relative to the β-actin and the calibrator, termed ΔCCN, were determined as follows:

${\Delta}CCN{=}\frac{CCN\ \mathrm{sample}}{{\beta}-\mathrm{actin\ sample}}\ \left/\right.\ \frac{CCN\ \mathrm{calibrator}}{{\beta}-\mathrm{actin\ calibrator}}$

Another housekeeping gene, 18S, was used as a second endogenous reference gene to determine the consistency of normalization.

#### Primers and Probes.

Primers and probes for the CCN and β-actin genes (Table 1) were designed using software PRIMER3.4 We conducted BLASTN searches against dbEST and nr (the nonredundant set of GenBank, EMBL, and DDBJ database sequences) to confirm the total gene specificity of the nucleotide sequences chosen for the primers and probes and the absence of DNA polymorphisms. To avoid amplification of contaminating genomic DNA, one of the two primers or the probe was placed at the junction between two exons or in a different exon. Primers were purchased from Life Technologies, Inc., and probes were from Perkin-Elmer Applied Biosystems.

#### Standard Curve Construction.

The standard curve was constructed with 10-fold serially diluted total RNA extracted from the MDA-MB-231 cells. Fig. 1 shows the real-time RT-PCR standard curve for the CTGF gene. A strong linear relationship between the Ct and the log of the starting copy number was always demonstrated. The efficiency of the reaction (E), calculated by the formula: E = 101/m − 1, where “m” is the slope of the standard curve, ranged from 90 to 100% in the different assays. The standard curves for WISP-1, CYR61, and NOVH were also constructed (data not shown).

#### PCR Amplification.

Amplification reactions contained 5 μl of cDNA, 12.5 μl of the Universal Taqman 2 × PCR mastermix (Applied Biosystems), and 2.5 μl of each of the specific primers and the probe. Primer and TaqMan probe concentrations in the final volume of 25 μl were 500 and 100 nm, respectively. All reactions were performed in triplicate in an iCycler iQ system (Bio-Rad, Hercules, CA), and the thermal cycling conditions were as follows: 2 min at 50°C, 10 min at 95°C, followed by 45 cycles of 95°C for 15 s and 60°C for 1 min.

### Statistical Analysis

χ2 test, t test, and Wilcoxon’s rank-sum test were used to study the association of each gene with single clinical factors (age, stage, tumor size, lymph node status, hormone receptors, and HER-2/neu status). A logistic regression model was developed to associate the probability of being a positive CCN marker with various clinical features. Stage and tumor size were dichotomized as stages I/II and III/IV and tumor size ≤50 mm (1a/2a) and >50 mm (3a/4b). Backward procedure was used for predictor selection. Classification tree analysis was also carried out to explore the association of gene status with clinical factors. κ statistics was used to assess the relationship between all pairs of the four genes. The κ value, its SE, and 95% confidence interval were reported.

To determine the pattern of expression of CCN genes in primary breast cancers, RNAs were isolated from quick-frozen breast samples obtained at initial surgery from 44 individuals. The relative levels of expression of CCN genes were quantified in 44 tumors and seven matched normal breast tissues by performing real-time PCR. The expression levels were determined as a ratio between either CYR61, CTGF, WISP-1, or NOVH and the reference gene β-actin to correct for variation in the amounts of RNA. The relative target gene expression was also normalized to a mean value (value = 1) for the seven normal breast tissue samples (calibrator). Each of the normalized target values was divided by the calibrator normalized target value to generate the final relative expression levels. Because the expression values of the seven normal breast tissues were between 0.4 and 2.4, we set values of ≥3 as the cutoff point for overexpression of CCN genes at the RNA level in the breast cancers.

To determine whether β-actin is suitable for the calibrator of normalization, a second housekeeping gene, 18S, was also used as a reference calibrator for CYR61, CTGF, WISP-1, and NOVH in four normal breast and five breast tumor samples. The levels of expression of the CCN genes were comparable with those when β-actin was used as the reference gene (data not shown).

### Expression of CTGF in Primary Breast Cancers.

Among the 44 breast tumor RNA samples tested, 24 of 44 (55%) showed CTGF gene overexpression (Fig. 2,A). Univariate analysis (Table 2 A) showed either a significant or borderline significant association between breast cancer stage, tumor size, lymph node status, and HER-2/neu status in the primary tumor, as well as age at onset of disease compared with whether the primary tumor overexpressed CTGF. Analysis of stage showed that only 7 of 27 (26%) of the patients with stage I and II breast cancer overexpressed CTGF; in contrast, all 17 (100%) of those with stage III or IV overexpressed CTGF (P = 0.001). Similarly, only 8 of 28 (29%) individuals whose primary breast cancer was ≤50 mm overexpressed CTGF; in comparison, all 16 of those whose primary tumor was ≥50 mm overexpressed CTGF (P = 0.001). Furthermore, of the 17 patients who were lymph node negative, only 5 (29%) overexpressed CTGF in their primary breast cancer. In contrast, 19 individuals overexpressed CTGF in their primary tumors among the 27 individuals who were lymph node positive (70%, P = 0.008).

The mean and median ages of patients who had a low level of CTGF mRNA were 52.5 (SD ± 9.5) and 51, respectively, compared with mean and median ages of 60.8 (SD ± 12.5) and 63.5 for those who overexpressed CTGF (P = 0.019). A borderline significant association (P = 0.054) occurred between levels of expression of HER-2/neu and CTGF. No significant statistical difference was noted between either ER (P = 0.45) or progesterone (P = 0.88) status and level of expression of CTGF in the primary breast tumors.

### Expression of WISP-1 in Primary Breast Cancers.

Overexpression of WISP-1 was found in 20 of 44 (46%) breast cancer samples (Fig. 2,B). The correlations of WISP-1 mRNA levels with clinical and pathological parameters were similar to those of CTGF except for age at diagnosis (Table 2 B). A strongly significant association existed between stage, tumor size, lymph node status, and HER-2/neu status versus WISP-1 expression. For stage, only 7 of 27 (26%) cases with stage I or II overexpressed WISP-1 compared with 13 of 17 (76%) samples from patients with stage III or IV (P = 0.001) breast cancers. Concerning tumor size, only 8 of 28 (29%) primary tumors, which were ≤50 mm, overexpressed WISP-1, contrasted to 12 of 16 (75%) primary tumors, which were ≥50 mm and overexpressed WISP-1 (P = 0.003). Similarly, WISP-1 was overexpressed in 4 of 17 (24%) women having no lymph node involvement compared with 16 of 27 (59%) women having positive lymph nodes at the time of removal of the primary tumor (P = 0.02). A significant association was noted between those who were HER-2/neu positive and overexpressed WISP-1 (9 of 13, 69%) versus those who were HER-2/neu positive with low levels of WISP-1 (11 of 31, 35%; P = 0.040). Statistical analysis showed no significant difference between age (P = 0.39), ER (P = 0.09), and progesterone (P = 0.45) versus WISP-1 expression in the primary breast cancers of 44 individuals.

### Expression of Cyr61 in Primary Breast Cancers.

Seventeen specimens overexpressed CYR61 among 44 samples tested (39%; Fig. 2,C). This is very similar to what we noted earlier by Northern analysis (1). Correlations between CYR61 expression and the clinical and pathological features of these patients is similar to those that we reported previously (Table 2 C; Ref. 1). A significant correlation was found between elevated levels of CYR61 and advanced stage and size of the primary tumor and lymph node involvement at the time of removal of the primary tumor. Also, overexpression of CYR61 is strongly correlated with ER positivity of the primary tumor; in contrast, no correlation with ER expression was noted for either CTGF or WISP-1.

### Expression of NOVH in Primary Breast Cancers.

In contrast to the other members of CCN gene family, only five (11%) primary breast tumors overexpressed NOVH among the 44 patients (Fig. 2,C). Statistical analysis showed no difference between the clinical and pathological parameters versus level of expression of NOVH in the primary breast cancers (Table 2 D).

### Classification Tree Multivariate Models of CTGF, WISP-1, and CYR61.

Classification tree analysis was also carried out to explore the association of CTGF, WISP-1, and CYR61 and clinical features of the disease (Fig. 3). Stage and lymph node status were the important predictors for CTGF expression (Fig. 3,A). This model showed that 100% of stage III or IV breast cancers had high levels of CTGF. For WISP-1, the age, HER-2/neu expression, and stage were the important predictors (Fig. 3,B). Stage I/II breast cancers with HER-2/neu positivity have an 80% chance of being WISP-1 positive, a four times higher frequency than those whose breast cancers were HER-2/neu negative. Furthermore, if the patient has an advanced breast tumor (stage III/IV) and is over age 59, the primary tumor will most likely (91%) overexpress WISP-1. Age, ER status, and stage are important predictors for CYR61 expression (Fig. 3 C). The classification tree posits that ER is only important in women under age 60, whereas stage is only important in women over age 60. CYR61 expression was low in all these ER breast cancers.

### Correlations among CTGF, WISP-1, CYR61, and NOVH Expression in Primary Breast Cancers.

Table 3 shows κ statistical analysis, a measure of agreement/correlation, where a value of κ ≥ 0.5 denotes significant association and a value of κ ≤ 0.5 implies no significant correlation. κ statistical analysis showed that significant concordance occurred among CTGF, WISP-1, and CYR61 expression; however, the association of NOVH with the other three genes was not significant. Classification tree analysis was performed to study the links among CTGF, WISP-1, and CYR61 with all seven clinical and pathological parameters. This analysis showed that ER, HER-2/neu, and stage were important predictors for having prominent expression of two or three CCN proteins (Fig. 3 D).

Several recent studies demonstrated that CYR61 is overexpressed in breast cancers and may be involved in estrogen-mediated breast tumor development (1, 2, 3). Our earlier studies showed that CYR61 was expressed at high levels in the invasive breast cancer cell lines MDA-MB-231, T47D, and MDA-MB-157 and expressed at very low levels in the less tumorigenic MCF-7 and BT-20 breast cancer cells, and it was undetectable in the normal breast cell line MCF-12A (1). Moreover, expression of Cyr61 mRNA levels increased 3–5-fold in MCF-7 cells after their exposure to estrogen. This induction was blocked by tamoxifen and ICI182,780, inhibitors of the ER. Furthermore, our studies also showed that stable expression of Cyr61 cDNA under the regulation of a constitutive cytomegalovirus promoter in MCF-7 cells enhanced their anchorage-independent cell growth in soft agar and significantly stimulated their ability to form large tumor in nude mice. Similar observations were also reported by two other groups. Sampath et al.(2) showed that Cyr61 was overexpressed in 70% of infiltrating ductal carcinomas of the breast, and the level of Cyr61 protein was higher in those breast tumors that were ER positive than those which were ER negative. Overexpression of Cyr61 was also noted by Tsai et al.(3) in 30% of breast cancers by immunohistochemistry, and they showed that Cyr61 is necessary for heregulin-mediated chemomigration. These findings concerning Cyr61 and breast cancer stimulated us to examine the relationship of breast cancer and expression of the other CCN gene family members, because all of the CCN genes have highly homologous DNA sequences and conserved protein domains.

In this study, we performed real-time PCR to quantify the mRNA levels of CTGF, WISP-1, CYR61, and NOVH. RNA quantitation using real-time PCR was made even more precise and reproducible by being based on Ct values established in the early exponential phase of the PCR reaction rather than the end point measurement of the amount of accumulated PCR product. Real-time PCR has high intra-assay and interassay reproducibility and gives statistical confidence. Overexpression of CYR61 was identified in 17 of the 44 breast cancer patients (39%). This frequency is similar to what we found previously by Northern blot analysis (1) and what others reported by immunohistochemistry (3). Previous studies suggested that CYR61 is involved in angiogenesis through its interaction with integrin ανβ3(17, 18, 19). Integrin signals are involved in a variety of cellular activities, including cell migration, proliferation, and survival, as well as diverse biological processes, including angiogenesis and tumor progression (reviewed in Refs. 20, 21, 22). Understanding the interaction of CYR61 with integrins may provide insight into how it might be involved in tumor development.

CTGF was identified as a mitogen found in the conditioned medium of human umbilical vein endothelial cells (23). It encodes a protein of 349 aa with 43% sequence identity to CYR61, and all 38 cysteines in CTGF and CYR61 are completely conserved. CTGF is transcriptionally activated with rapid kinetics in fibroblasts by serum growth factors (24) and transforming growth factor-β (25, 26). Moreover, CTGF has been implicated in cellular proliferation, migration, and tube formation of vascular endothelial cells in culture and angiogenesis in vivo(27, 28). Consistent with these properties, CTGF is often overexpressed in melanomas, sarcomas, chondrosarcomas, and pancreatic cancer cells (14, 15, 29, 30). Recently, overexpression of CTGF has also been found in acute lymphoblastic leukemia and pediatric myofibroblastic tumors (31, 32). Expression of CTGF in breast cancer was noted by Northern blot analysis in 7 of 11 (64%) human invasive mammary ductal carcinomas and xenografts (9). This study examined only a small number of primary samples, no normal breast samples served as control, and the correlation between CTGF expression and clinical and pathological parameters was not analyzed. In the present study, expression of CTGF was quantified by real-time PCR, and a high level of CTGF mRNA was noted in 24 of 44 (55%) breast cancer samples. Univariate analysis showed significant association between age at diagnosis, breast cancer stage, tumor size, lymph node status, and HER-2/neu status compared with whether the primary tumor overexpressed CTGF. Considering that CTGF is closely related to CYR61 and each signal through integrins (18, 19), both CYR61 and CTGF may trigger downstream events via integrin signaling.

WISP-1 was identified as a gene up-regulated in Wnt-1 transformed C57 MG mouse mammary epithelial cells (6). WISP-1 encodes a protein with a secretory signal peptide and has complete conservation of all 38 cysteine residues with those of CYR61 and CTGF. To our knowledge, expression of WISP-1 has not been studied in either normal or cancerous human breast cells. We found it was highly expressed in 20 of 44 (46%) primary breast cancers. Interestingly, statistical analysis showed a strongly significant association between stage, tumor size, lymph node status, and HER-2/neu expression versus mRNA level of WISP-1. The role of WISP-1 in breast cancer is unclear. Overexpression of WISP-1 induced morphological transformation, increased cellular saturation density, promoted growth in normal rat kidney fibroblasts, and induced tumor formation in nude mice (16).

NOV was identified as an aberrantly expressed gene in chicken nephroblastomas induced by myeloblastosis-associated virus (33). NOVH encodes a protein of 357 aa with a secretory signal peptide and complete conservation of the 38 cysteines found in CYR61, CTGF, and WISP-1. Whereas CTGF, WISP-1, and CYR61 were reported to act as positive regulators of cell growth, NOV provided the first example of a CCN protein with negative regulatory properties and the first example of aberrant expression being associated with tumor development (33). Whereas expression of the full-length NOV has a growth inhibitory effect in chicken embryo fibroblasts, expression of an NH2-terminally truncated form of NOV can transform these cells (33). However, because this truncated form lacks the secretory signal, its transforming activity is not likely mediated through interaction with a cell surface receptor. In our study, overexpression of NOVH was found in only 5 of 44 (11%) breast tumors, and no significant correlation was linked between expression of this gene and the clinical and pathological features of the breast cancers. These findings suggesting NOVH are not involved in either the development or progression of breast cancer.

In summary, our data indicate that overexpression of CTGF, WISP-1, and CYR61 may be involved in the process of breast cancer development and points to an association between expression of these proteins and clinical and pathological features of breast cancer. This comprehensive elucidation of CCN gene expression in breast tumors is an important first step to explore the mechanism and function of these genes in the development of breast cancer. Clinically, our studies showed that prominent expression of the genes coding for CTGF, WISP-1, and CYR61 is associated with an advanced stage of breast cancer at diagnosis. Future studies will attempt to determine whether measurement of these proteins at diagnosis can provide prognostic data and suggest those tumors that might be responsive to therapy. Hopefully, understanding the aberrant signaling pathways that are activated by high levels of expression of these CCN proteins may offer useful therapeutic targets.

Fig. 1.

Standard curve of CTGF mRNA by real-time PCR. A, amplification plots for reactions with five points of the CTGF standard curve (10-fold serially diluted human breast cancer cell MDA-MB-231 cDNAs). Cycle number is plotted versus change in normalized reporter signal (ΔRn). ΔRn represents the normalized reporter signal (Rn) − the baseline signal established in the first 18 PCR cycles. Ct represents the fractional cycle number at which a significant increase in Rn above a baseline signal (horizontal thick line) can first be detected. B, standard curve generated after determination of Ct values plotted against starting quantity of target DNA. The data from the Ct valves were plotted to give a log-linear regression standard curve. Black dots, data for standard curve samples; □, data for breast cancer patients performed in triplicate.

Fig. 1.

Standard curve of CTGF mRNA by real-time PCR. A, amplification plots for reactions with five points of the CTGF standard curve (10-fold serially diluted human breast cancer cell MDA-MB-231 cDNAs). Cycle number is plotted versus change in normalized reporter signal (ΔRn). ΔRn represents the normalized reporter signal (Rn) − the baseline signal established in the first 18 PCR cycles. Ct represents the fractional cycle number at which a significant increase in Rn above a baseline signal (horizontal thick line) can first be detected. B, standard curve generated after determination of Ct values plotted against starting quantity of target DNA. The data from the Ct valves were plotted to give a log-linear regression standard curve. Black dots, data for standard curve samples; □, data for breast cancer patients performed in triplicate.

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Fig. 2.

CCN gene expression in breast tissues. Relative expression levels of CTGF (A), WISP-1 (B), CYR61 (C), and NOVH (D) are shown in seven normal breast tissues and 44 primary breast cancer samples. Expression levels are displayed as a ratio between the target genes and a reference gene (β-actin) to correct for variation in the amounts of RNA. The relative expression level has been normalized in such a manner that the mean ratio of the seven normal breast samples equals a value of 1.

Fig. 2.

CCN gene expression in breast tissues. Relative expression levels of CTGF (A), WISP-1 (B), CYR61 (C), and NOVH (D) are shown in seven normal breast tissues and 44 primary breast cancer samples. Expression levels are displayed as a ratio between the target genes and a reference gene (β-actin) to correct for variation in the amounts of RNA. The relative expression level has been normalized in such a manner that the mean ratio of the seven normal breast samples equals a value of 1.

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Fig. 3.

Classification tree analysis of CTGF, WISP-1, and CYR61. The numbers in the boxes or circles are: the number of positive marker/total number of patients, % (1 patient without ER and PR values was excluded from the analysis). In A, tree model of CTGF shows that stage and lymph node status were the important predictors for CTGF expression. In B, tree model of WISP-1 demonstrates that age, HER-2/neu, and stage were the important predictors for WISP-1 status. C, tree model of CYR61. This model shows that age, ER, and stage were important predictors for CYR61 status. D, tree model of correlation among CTGF, WISP-1, and CYR61. This model showed that ER, HER-2/neu, and stage were important predictors for having two or more genes being highly expressed.

Fig. 3.

Classification tree analysis of CTGF, WISP-1, and CYR61. The numbers in the boxes or circles are: the number of positive marker/total number of patients, % (1 patient without ER and PR values was excluded from the analysis). In A, tree model of CTGF shows that stage and lymph node status were the important predictors for CTGF expression. In B, tree model of WISP-1 demonstrates that age, HER-2/neu, and stage were the important predictors for WISP-1 status. C, tree model of CYR61. This model shows that age, ER, and stage were important predictors for CYR61 status. D, tree model of correlation among CTGF, WISP-1, and CYR61. This model showed that ER, HER-2/neu, and stage were important predictors for having two or more genes being highly expressed.

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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.

1

Supported in part by NIH, Marcia Schwartz Trust, Thornworth Fund, and the C. and H. Koeffler Research Fund. H. P. K. is a member of the Jonsson Comprehensive Cancer Center and holds the endowed Mark Goodson Chair of Oncology Research at Cedars-Sinai Medical Center/UCLA School of Medicine.

3

The abbreviations used are: CTGF, connective tissue growth factor; RT-PCR, reverse transcription-PCR; ER, estrogen receptor; NOVH, human NOV; PR, progesterone receptor.

4

Table 1

Oligonucleotide primers and probe sequences used for RT-PCR

GeneOligonucleotideSequencesPCR product size (bp)
5′-primer 5′-ACTTCATGGTCCCAGTGCTC-3′
CYR61 3′-primer 5′-AAATCCGGGTTTCTTTCACA-3′ 100
Probe 5′-TTACCAATGACAACCCTGAGTGCCG-3′
5′-primer 5′-GCAGGCTAGAGAAGCAGAGC-3′
CTGF 3′-primer 5′-ATGTCTTCATGCTGGTGCAG-3′ 105
Probe 5′-TGCGAAGCTGACCTGGAAGAGAACA-3′
5′-primer 5′-AGGTATGGCAGAGGTGCAAG-3′
WISP-1 3′-primer 5′-GTGTGTGTAGGCAGGGAGTG-3′ 106
Probe 5′-TAACTCACTGCCTAGGAGGCTGGCC-3′
5′-primer 5′-AGCATGCAGAGTGTGCAGAG-3′
NOV 3′-primer 5′-GGTGTGCCACTTACCTGTCC-3′ 101
Probe 5′-TTGCCTGACCTTCCTGCTTCTCCAT-3′
5′-primer 5′-GATCATTGCTCCTCCTGAGC-3′
β-actin 3′-primer 5′-ACTCCTGCTTGCTGATCCAC-3′ 156
Probe 5′-CTCGCTGTCCACCTTCCAGCAGAT-3′
GeneOligonucleotideSequencesPCR product size (bp)
5′-primer 5′-ACTTCATGGTCCCAGTGCTC-3′
CYR61 3′-primer 5′-AAATCCGGGTTTCTTTCACA-3′ 100
Probe 5′-TTACCAATGACAACCCTGAGTGCCG-3′
5′-primer 5′-GCAGGCTAGAGAAGCAGAGC-3′
CTGF 3′-primer 5′-ATGTCTTCATGCTGGTGCAG-3′ 105
Probe 5′-TGCGAAGCTGACCTGGAAGAGAACA-3′
5′-primer 5′-AGGTATGGCAGAGGTGCAAG-3′
WISP-1 3′-primer 5′-GTGTGTGTAGGCAGGGAGTG-3′ 106
Probe 5′-TAACTCACTGCCTAGGAGGCTGGCC-3′
5′-primer 5′-AGCATGCAGAGTGTGCAGAG-3′
NOV 3′-primer 5′-GGTGTGCCACTTACCTGTCC-3′ 101
Probe 5′-TTGCCTGACCTTCCTGCTTCTCCAT-3′
5′-primer 5′-GATCATTGCTCCTCCTGAGC-3′
β-actin 3′-primer 5′-ACTCCTGCTTGCTGATCCAC-3′ 156
Probe 5′-CTCGCTGTCCACCTTCCAGCAGAT-3′
Table 2

Relationship between levels of expression of CTGF, WISP-1, CYR61, and NOV in the primary breast cancers with the clinical and pathological features of individuals

A.
Clinical characteristicsCTGF negative n = 20CTGF positive n = 24P
Age
Mean ± SD 52.5 ± 9.5 60.8 ± 12.5 aP = 0.019
Median 51 63.5
Stage
I–II 20 bP = 0.001
III–IV 17
Tumor size
≤50 mm 20 bP = 0.001
>50 mm 16
Lymph node status
Node negative 12 bP = 0.008
Node positive 19
HER-2/neu
Negative 17 14 bP = 0.054
Positive 10
ER
− (≥10 fm/mg) 11 10 bP = 0.451
+ (<10 fm/mg) 13
PR
− (≥10 fm/mg) 10 12 bP = 0.887
+ (<10 fm/mg) 10 11
A.
Clinical characteristicsCTGF negative n = 20CTGF positive n = 24P
Age
Mean ± SD 52.5 ± 9.5 60.8 ± 12.5 aP = 0.019
Median 51 63.5
Stage
I–II 20 bP = 0.001
III–IV 17
Tumor size
≤50 mm 20 bP = 0.001
>50 mm 16
Lymph node status
Node negative 12 bP = 0.008
Node positive 19
HER-2/neu
Negative 17 14 bP = 0.054
Positive 10
ER
− (≥10 fm/mg) 11 10 bP = 0.451
+ (<10 fm/mg) 13
PR
− (≥10 fm/mg) 10 12 bP = 0.887
+ (<10 fm/mg) 10 11
B.
Clinical characteristicsWISP-1 negative n = 24WISP-1 positive n = 20P
Age
Mean ± SD 55.6 ± 11.4 58.8 ± 12.5 aP = 0.391
Median 53 63.5
Stage
I–II 20 bP = 0.001
III–IV 13
Tumor size
≤50 mm 20 bP = 0.003
>50 mm 12
Lymph node status
Node negative 13 bP = 0.020
Node positive 11 16
HER-2/neu
Negative 20 11 bP = 0.040
Positive
ER
− (≥10 fm/mg) 14 bP = 0.091
+ (<10 fm/mg) 13
PR
− (≥10 fm/mg) 13 bP = 0.451
+ (<10 fm/mg) 10 11
B.
Clinical characteristicsWISP-1 negative n = 24WISP-1 positive n = 20P
Age
Mean ± SD 55.6 ± 11.4 58.8 ± 12.5 aP = 0.391
Median 53 63.5
Stage
I–II 20 bP = 0.001
III–IV 13
Tumor size
≤50 mm 20 bP = 0.003
>50 mm 12
Lymph node status
Node negative 13 bP = 0.020
Node positive 11 16
HER-2/neu
Negative 20 11 bP = 0.040
Positive
ER
− (≥10 fm/mg) 14 bP = 0.091
+ (<10 fm/mg) 13
PR
− (≥10 fm/mg) 13 bP = 0.451
+ (<10 fm/mg) 10 11
C.
Clinical characteristicsCYR61 negative n = 27CYR61 positive n = 17P
Age
Mean ± SD 53.1 ± 10.4 63.4 ± 11.6 aP = 0.004
Median 52 66
Stage
I–II 22 bP = 0.001
III–IV 12
Tumor size
≤50 mm 22 bP = 0.002
>50 mm 11
Lymph node status
Node negative 14 bP = 0.023
Node positive 13 14
HER-2/neu
Negative 22 bP = 0.043
Positive
ER
− (≥10 fm/mg) 17 bP = 0.007
+ (<10 fm/mg) 13
PR
− (≥10 fm/mg) 15 bP = 0.289
+ (<10 fm/mg) 11 10
D.
Clinical characteristics NOV negative n = 39 NOV positive n = 5 P
Age
Mean ± SD 56.2 ± 11.8 64.0 ± 11.1 cP = 0.16
Median 53 65
Stage
I–II 26 bP = 0.065
III–IV 13
Tumor size
≤50 mm 27 bP = 0.051
>50 mm 12
Lymph node status
Node negative 15 bP = 1.000
Node positive 24
HER-2/neu
Negative 28 bP = 0.623
Positive 11
ER
− (≥10 fm/mg) 20 bP = 0.345
+ (<10 fm/mg) 18
PR
− (≥10 fm/mg) 19 bP = 0.674
+ (<10 fm/mg) 19
C.
Clinical characteristicsCYR61 negative n = 27CYR61 positive n = 17P
Age
Mean ± SD 53.1 ± 10.4 63.4 ± 11.6 aP = 0.004
Median 52 66
Stage
I–II 22 bP = 0.001
III–IV 12
Tumor size
≤50 mm 22 bP = 0.002
>50 mm 11
Lymph node status
Node negative 14 bP = 0.023
Node positive 13 14
HER-2/neu
Negative 22 bP = 0.043
Positive
ER
− (≥10 fm/mg) 17 bP = 0.007
+ (<10 fm/mg) 13
PR
− (≥10 fm/mg) 15 bP = 0.289
+ (<10 fm/mg) 11 10
D.
Clinical characteristics NOV negative n = 39 NOV positive n = 5 P
Age
Mean ± SD 56.2 ± 11.8 64.0 ± 11.1 cP = 0.16
Median 53 65
Stage
I–II 26 bP = 0.065
III–IV 13
Tumor size
≤50 mm 27 bP = 0.051
>50 mm 12
Lymph node status
Node negative 15 bP = 1.000
Node positive 24
HER-2/neu
Negative 28 bP = 0.623
Positive 11
ER
− (≥10 fm/mg) 20 bP = 0.345
+ (<10 fm/mg) 18
PR
− (≥10 fm/mg) 19 bP = 0.674
+ (<10 fm/mg) 19
a

t test.

b

χ2 test.

c

Wilcoxon’s rank-sum test

Table 3

Correlations of expression among CYR61, CTGF, WISP-1, and NOVH

A.
CYR61CTGFWISP-1NOVH
++++
CYR61 a 27  18 22 26
+b  17 15 15 13
CTGF −   20  17 19
24 17 20
WISP-1 −     24  22
20 17
NOVH −       39

A.
CYR61CTGFWISP-1NOVH
++++
CYR61 a 27  18 22 26
+b  17 15 15 13
CTGF −   20  17 19
24 17 20
WISP-1 −     24  22
20 17
NOVH −       39

B.
Comparison κcSE95%Confidence interval
CYR61 and CTGF 0.510 0.122 0.271, 0.749
CYR61 and WISP-1 0.675 0.111 0.457, 0.893
CYR61 and NOVH 0.228 0.123 −0.013, 0.469
CTGF and WISP-1 0.549 0.123 0.307, 0.791
CTGF and NOVH 0.108 0.085 −0.059, 0.275
WISP-1 and NOVH 0.071 0.104 −0.133, 0.276
B.
Comparison κcSE95%Confidence interval
CYR61 and CTGF 0.510 0.122 0.271, 0.749
CYR61 and WISP-1 0.675 0.111 0.457, 0.893
CYR61 and NOVH 0.228 0.123 −0.013, 0.469
CTGF and WISP-1 0.549 0.123 0.307, 0.791
CTGF and NOVH 0.108 0.085 −0.059, 0.275
WISP-1 and NOVH 0.071 0.104 −0.133, 0.276
a

Gene negative.

b

Gene positive.

c

κ statistics showed the significant associations that occurred among CYR61, CTGF, and WISP-1. The associations of NOVH with the other three genes were not significant.

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