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Keywords:

  • prognostic makers;
  • hyaluronic acid;
  • HA synthase;
  • HYAL-1;
  • HA receptors;
  • hyaluronidase;
  • diagnosis;
  • recurrence

Abstract

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

BACKGROUND:

Cancer biomarkers are the backbone for the implementation of individualized approaches to bladder cancer (BCa). Hyaluronic acid (HA) and all 7 members of the HA family, that is, HA synthases (HA1, HA2, HA3), HYAL-1 hyaluronidase, and HA receptors (CD44s, CD44v, and RHAMM), function in tumor growth and progression. However, the diagnostic and prognostic potential of these 7 HA family members has not been compared simultaneously in any cancer. We evaluated the diagnostic and prognostic potential of HA family members in BCa.

METHODS:

Using quantitative PCR and immunohistochemistry, expression of HA family members was evaluated in prospectively collected bladder tissues (n = 72); mean and median follow-up were 29.6 ± 5.3 and 24 months, respectively. Transcript levels were also measured in exfoliated urothelial cells from urine specimens (n = 148).

RESULTS:

Among the HA family members, transcript levels of the HA synthases, HYAL-1, CD44v, and RHAMM were 4- to 16-fold higher in BCa tissues than in normal tissues (P < .0001); however, CD44s levels were lower. In univariate and multivariate analyses, tumor stage (P = .003), lymph node invasion (P = .033), HYAL-1 (P = .019), and HAS1 (P = .027) transcript levels, and HYAL-1 staining (P = .021) were independently associated with metastasis. Tumor stage (P = .019) and HYAL-1 (P = .046) transcript levels were also associated with disease-specific mortality. Although HA synthase and HYAL-1 transcript levels were elevated in exfoliated urothelial cells from BCa patients, the combined HAS2–HYAL-1 expression detected BCa with an overall sensitivity of 85.4% and a specificity of 79.5% and predicted BCa recurrence within 6 months (P = .004; RR = 6.7).

CONCLUSIONS:

HYAL-1 and HAS1 expression predicted BCa metastasis, and HYAL-1 expression also predicted disease-specific survival. Furthermore, the combined HAS2–HYAL-1 biomarker detected BCa and significantly predicted its recurrence. Cancer 2011. © 2010 American Cancer Society.

Clinical and pathological parameters such as tumor grade, stage, and lymph node invasion provide important prognostic information but have limited ability to predict development of metastases or survival among bladder cancer (BCa) patients.1 Hyaluronic acid (HA), HA receptors, and HA-degrading enzyme, or hyaluronoglucosaminidase (HAase), have been implicated in tumor growth, angiogenesis, and metastasis.2-4 HA is a nonsulfated glycosaminoglycan involved in many physiological functions.2 Some of the molecules in the HA signaling pathway function in tumor growth and progression and are useful biomarkers for cancer diagnosis and prognosis.2-6 Elevated HA in urine is an accurate diagnostic marker (HA test) for detecting BCa, regardless of tumor grade and stage.7-12 HA is synthesized by HA synthases (HAS1, HAS2, and HAS3).13 Two studies reported that HAS1 expression is elevated in BCa and promotes tumor growth, infiltration, and angiogenesis.14, 15 Similarly, HAS2 and HAS3 also promote tumor growth and metastasis.16, 17

CD44 is a well-characterized HA receptor.2 CD44 mRNA is frequently alternatively spliced, and these mRNA splice variants generate different CD44 isoforms. In BCa, contradictory findings have been reported regarding the correlation of CD44 expression with tumor grade and disease-free survival.18-21 Miyake et al reported that the ratio of the CD44 variant isoform (v8-10) and CD44s (CD44 standard isoform) correlates with disease-free survival.20 RHAMM is another well-characterized HA receptor involved in cell migration and motility and compensates for certain CD44 functions.22 Only 1 study has been reported regarding RHAMM expression in BCa, and it showed that RHAMM levels, examined by immunohistochemistry, are increased in BCa tissues.18

HYAL-1-type HAase is expressed by tumor cells and promotes tumor growth, infiltration, and angiogenesis.3, 23 HYAL-1 expression in prostate cancer specimens is an independent predictor of biochemical recurrence following surgery.24, 25 HAase level is elevated in the urine of high-grade BCa patients (HAase test10) and, together with HA (HA-HAase test), detects BCa with high accuracy.10, 11 Eissa et al showed that HYAL-1 mRNA expression measured by semiquantitative PCR is a marker for BCa.26 Recently, we showed that HYAL-1 expression is elevated in bladder tumor tissues and is an independent predictor of muscle invasion.27

In this study we examined the expression of 7 HA family members in bladder tissues and urine specimens by quantifying mRNA and protein levels to compare their diagnostic and prognostic accuracy alone and as a biomarker profile. We chose all the molecules from the same biological pathway, rather than biomarkers in different functional pathways relevant to the malignant phenotype, because we hypothesized that as a result of their functional synergy, the combination of some of the HA-family molecules might have better diagnostic and prognostic potential than the individual molecules.

MATERIALS AND METHODS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

Tissue Specimens and Patients: Tissue Specimens

All specimens were obtained based on the availability of the specimen for research purposes and under a protocol approved by the University of Miami's institutional review board. Normal bladder (NBL) tissues (n = 28) were obtained either from organ donors or from patients who had undergone cystectomy. One portion of each BCa (n = 44) and NBL tissue was paraffin-embedded, and the other was flash-frozen. Total RNA was isolated from frozen bladder tissue (∼30 mg) using an RNeasy Mini kit (Qiagen, Valencia, CA). The characteristics of all bladder specimens are presented in Table 1.

Table 1. Specimen and Patient Characteristics
  1. Characteristics of bladder tissue and urine specimens are shown. *Normal, normal individuals were healthy volunteers who did not have chronic and/or acute illness and were not taking any prescription medication at the time specimen collection. Normal individuals in group 1 were included in all figures and calculations. Group 2 individuals were age-matched individuals with respected to BCa patients. Biomarkers were assayed among group 2 individuals to determine whether the biomarker levels varied with respect to age. In the BGU category, 12 patients had microscopic or gross hematuria and underwent cystoscopy to rule out BCa. All HXBCa patients underwent cystoscopy.

Tissue specimens, n=72Urine specimens, n=137
NBL=28 (organ donors [NBL-O]=18; BCa patients undergoing cystectomy [NBL-B]=10)
BCa=44; transurethral resection (TURBT)=11; cystectomy=33Normal*n=29
Group 1, n=18
Age: 38.3±13.9 y; median, 30.5 y
Sex: female, 6; male, 12
Group 2, n=11
Age: 63.2±10.1 y; median, 61 y
Sex: female, 6; male, 5
SexFemale, n=9; Male, n=35BCan=48
LG, n=14
Age: 68.4±8.5 y; median, 70.5 y
Sex: female, 8; male, 6
HG, n=34
Ta= 14;
T1=13
T2=13
T3=7
T4=1
Age: 68.3±10.1 y; median, 68.5 y
Sex: female, 10; male, 23
SmokerPositive=25BGUn=34
Negative=2Urinary tract infection=6
Unknown=17Benign prostatic hyperplasia=9
Urethral strictures=4
Chronic prostatitis=4
Urolithiasis=3
Renal cyst=2
Dysuria=1
Benign adrenal mass=2
History of cervical cancer=2
Hydrocele=1
Age: 57±12.3 y; median, 59 y
Sex: female, 10; male, 24
GradeLG=7 (all, stage Ta)HXBCan=31
HG=37Age: 64.8±7.5 y; median, 63 y
Sex: female, 8; male, 23
StageTa=8; T1=3;Prostate cancern=6
T2=12; T3=16;Age: 66.6±5.7 y; median, 65 y
T4=5;
Concomitant CIS=4
LNPositive=11
Negative=26
Unknown=7
Metastasis (all BCa patients)Negative=19
Positive=16
Unknown=9
Metastasis in patients with stage ≥T2 tumorNegative=12
Positive=16
Unknown=5
Age (y)Metastasis positive: 63.7±11; median, 60
Metastasis negative: 65±12.3; median, 68
Neoadjuvant chemotherapyPositive=9
Negative=28
Unknown=7
Neoadjuvant chemotherapyPositive=9
Negative=28
Unknown=7
Adjuvant chemotherapyPositive=11
Negative=34
Unknown=7
RadiationPositive=6
Negative=29
Unknown=9
DeathNegative=23
Positive=19;
BCa specific=16Unknown=2
Mean follow-up (mo) for patients with stage ≥T2 tumor29.6±5.3; median, 24 (11-43)
28.7±6.1; median, 20 (11-27) 

Urine Specimens

Urine specimens (n = 148) were prospectively collected from healthy individuals and from patients with BCa, benign genitourinary (BGU) conditions, or a history of BCa (HXBCa; Table 1). Clinical follow-up was collected on patients with HXBCa. All urine specimens were brought to the laboratory within 2 hours of collection and processed for total RNA isolation using a ZR urine isolation kit (Zymo Research Corp., Orange, CA). Briefly, urine specimens (100-150 mL) were passed through a syringe filter provided in the kit, and the exfoliated cells captured on the filter were lysed in an RNA lysis buffer. RNA was then purified as per the manufacturer's instructions. No fixation or isolation of exfoliated urothelial cells is necessary when using this kit.

Quantitative RT-PCR (Q-PCR)

Total RNA isolated from tissues or exfoliated cells was subjected to Q-PCR using an iQ real-time PCR system (BioRad, Hercules, CA) and primers and probes specific for each transcript (Table 2).14, 28 Each cDNA sample was simultaneously subjected to β-actin Q-PCR. Normalized transcript levels for each gene were calculated as 1/2Δct × 100; ΔCt = Ct (transcript) − Ct (β-actin). The variance of the PCR assay was examined by performing Q-PCR on RNA isolated from 15 specimens in quadruplicate for each marker and then computing the intraclass correlation. The intraclass coefficient for all markers varied between 0.955 and 0.994 with P < .001. This indicated that the replicate values within a sample were highly correlated or that the variance was low.

Table 2. Sequences of Primers and Probes Used in Q-PCR Assays
GeneForward PrimerReverse PrimerProbe
  1. For CD44v, the primer and probe sequences were designed in exon 12, which is alternatively spliced in CD44s but is present in all variant isoforms. Therefore, CD44v primers and probes will amplify any and all alternatively spliced variants.

HAS15-GGTGGGGACGTGCGGATC-35-ATGCAGGATACACAGTGGAAGTAG-3FAM 5-CCCGCTCCACATTGAAGGCTACCCAG-3BHQ
HAS25-TGAACAAAACAGTTGCCCTTT-35-TTCCCATCTATGACCATGACAA-3FAM 5-ATCGCTGCCTATCAAGAAGATCCAGAC-3 BHQ1
HAS35-CTCTACTCCCTCCTCTATATGTC-35-AACTGCCACCCAGATGGA-3FAM 5-AATGAGGCCAATGAAGTTCACCACAAT-3 BHQ1
CD44s5-CTGTACACCCCATCCCAGAC-35-TGTGTCTTGGTCTCTGGTAGC-3FAM 5-TGGATCACCGACAGCACAGAC AGAAT-3BHQ
CD44v5-CAGGTGGAAGAAGAGACC CAA-35-GCTGAGGTCACTGGGATGAA-3FAN 5-ACCCACACACGAAGGAAAGCAGGACC-3BHQ
RHAMM5-CAGCTGGAAGATGAAGAAGGA-35-GCATGTAGTTGTAGCTGAAAAGG-3FAM 5-TGAAGAAATTAACAAGTG GCGTCT-3 BHQ
HYAL-15-AGCCAGGGTAGCATC GACA-35-AAGCCCTCCTCCTCCTTAACC-3FAM 5-CAGGCACAGATGGCTGTG GAGTT-3 BHQ1

Immunohistochemistry

Five-micron sections of paraffin-fixed bladder tissue were placed on positively charged slides. The slides were sequentially deparaffinized, rehydrated, and subjected to antigen retrieval by heating the slides at 95°C for 25 minutes in Target-Retrieval Solution (DakoCytomation, Carpentaria, CA). The slides were incubated at 4°C for 16 hours, with the following primary reagents: 1) biotinylated HA binding protein (1 μg/mL; for HA staining), 2) anti-HYAL-1 IgG (1 μg/mL), 3) anti-HAS1 IgG (1.3 μg/mL), 4) anti-HAS2 IgG (0.5 μg/mL), and 5) anti-HAS3 IgG (1.5 μg/mL). The slides were developed using a Dako LSAB kit and 3,3′-diaminobenzidine staining. The same batch of antibodies and commercial reagents were used in all experiments.

The specificity and validation of the HA-binding protein and of rabbit polyclonal antibodies for HAS1, HAS2, and HYAL-1 have been reported previously.14, 15, 23-25, 27, 28 The affinity-purified anti-HAS3 antibody was custom-synthesized against a c-terminal sequence in HAS3 protein (ARRCGKKPEQYSLA) by Genscript Corporation (Piscataway, NJ). The specificity of anti-HAS3 antibody was established by down-regulating HAS3 expression in BCa cell lines by HAS3siRNA transfection, followed by immunoblotting (data not shown). As outlined in the reviews by Bordeaux et al and Bonner et al,29, 30 for validation purposes, specimen slides were incubated with anti-HAS1, -HAS2, -HAS3, and -HYAL-1 antibodies in the presence of peptides (against which the respective antibodies were generated); for the biotinylated HA-binding protein, the slides were incubated with biotinylated HA-binding protein and HA (1 mg/mL). As a control for the biotin-streptavidin conjugated-link solution in the LSAB kit Universal (Dako, North America Inc., Carpinteria, Calif), IHC was performed by eliminating the primary antibody.

Stained slides were graded by 2 individuals in a blinded fashion. To account for the heterogeneity in staining, each specimen was graded for staining intensity (0 to 3+) and then multiplied by the area in the specimen staining with that intensity (eg, 25% × 0 = 0%; 50% × 1+ = 50%, and 25% × 2+ = 50%). The intensity scores in all areas were added to obtain the staining score for the entire specimen (eg, 0 + 50 + 50 = 100). Therefore, each specimen received a staining score between 0 and 300.27 The intensity scores of the 2 readers then were averaged to obtain the final score. The slides were also evaluated using IP Image Analysis software, and the results were comparable to the readers' scores. There was significant correlation between staining scores of the 2 readers (Spearman r = 0.852; 95% CI, 0.773-0.967; P ≤ .001) and between the average scores of the 2 readers and the IP image analysis scores (Spearman r = 0.863; 95% CI, 0.804-0.915; P = .003).

Statistical Analyses

Differences in biomarker levels among bladder tissues (eg, NBL versus low grade and NBL versus high grade) were compared using the Mann-Whitney U test because the data showed a non-normal distribution. Similar analysis was conducted when comparing the biomarker levels in various categories of urine specimens (eg, BGU versus tumor). All the P values reported in this study are 2-tailed. A logistic regression single-parameter model (ie, univariate analysis) was used to determine: 1) the association of clinical parameters and biomarker levels (ie, transcript levels or staining scores) with metastasis and disease-specific survival, and 2) the association of urinary biomarker levels with BCa. A Cox proportional hazards model (ie, multivariate analysis) was used to determine which of the pre- and postoperative parameters and/or tissue biomarkers predicted metastasis and disease-specific survival.

The levels of the combined biomarkers (eg, HAS2–HYAL-1) for each study subject were calculated as follows: intercept + (α × [HAS2]1) +(β × [HYAL-1]1); with α and β, HAS2 and HYAL-1 coefficients, respectively, and [HAS2]1 and [HYAL-1)1, HAS2 and HYAL-1 levels in subject 1, respectively. The intercept and coefficients for each marker were computed by simultaneously analyzing the 2 variables (ie, HAS2 and HYAL-1) in the logistic regression model (ie, bivariate analysis).

Receiver operating characteristic (ROC) curves were generated to determine the association of tissue biomarker levels with metastasis or disease-specific survival and of the various urine biomarkers (both single and combined) with BCa. Cutoff values were selected from the ROC curve data by a statistical program (JMP6 software; SAS, Cary, NC) for calculating the sensitivity and specificity of each biomarker. A biomarker level that yielded the highest efficacy (ie, sensitivity − [1 − specificity]) was selected by the program as the cutoff limit. Cross-validation using bootstrap modeling (specific sampling rate, 0.5; resampling, 104) was performed to obtain the mean ± SD and 95% CI for the sensitivity, specificity, and accuracy of each biomarker. Statistical analyses were carried out using JMP software program (version 6.0; SAS Institute, Cary, NC).

RESULTS

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

HA Synthase, HYAL-1, CD44v, and RHAMM Expression Increases But CD44s Decreases in BCa Tissue

Transcript expression

We measured the levels of HA synthases, HYAL-1, and HA receptor transcripts in 72 bladder tissues by Q-PCR. Figure 1 shows that when compared with NBL tissues, HAS1, HAS2, HAS3, and HYAL-1 levels were 4- to 16-fold higher in both low-grade (P < .0001 for all markers) and high-grade (P < .0001) BCa tissues. However, the differences in the transcript levels between low- and high-grade tumor tissues were not statistically significant. Among HA receptors, although in BCa tissue CD44s mRNA decreased 3- to 6-fold (P < .0001), CD44v levels were approximately 5-fold higher (P < .0001; Fig. 1). The CDD4v/CD44s ratio was 14.6-fold (111.4 ± 69.2; P < .0001) and 37.3-fold (283.4 ± 60.8; P < .0001) higher in low- and high-grade BCa tissues, respectively, when compared with NBL tissues (7.6 ± 15.6). RHAMM expression was also significantly elevated in low-grade (P =.007) and high-grade (P < .0001) tissues (Fig. 1). In this study NBL tissues were obtained from organ donors (NBL-O) and from BCa patients (NBL-B). As shown in Figure 1, the levels of each HA-family member were very similar among NBL-O and NBL-B tissues (P > .05 for each marker). The sensitivity and specificity of HA-family molecules for distinguishing NBL and BCa tissues were: HAS1, 72% and 75%; HAS2, 81.4% and 100%; HAS3, 72.1% and 89.3%; HYAL-1, 81.4% and 82.1%; CD44s, 73.2% and 81.1%; and CD44v, 78.1% and 85.7%, respectively.

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Figure 1. Scatter diagrams of HA synthase and HYAL-1 mRNA levels in bladder tissues are shown. mRNA levels of each of the 7 HA-family molecules in bladder specimen are shown. Mean ± SD scores for each biomarker are indicated (NBL, normal bladder; NBL-O, NBL tissue obtained from organ donors; NBL-B, NBL tissue obtained from BCa patients at the time of cystectomy; LG, low-grade BCa; HG, high-grade BCa).

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Protein expression

Because hyaluronic acid synthase (HAS)—HAS1, HAS2, and HAS3—and HYAL-1 transcript levels were significantly elevated in BCa tissues, we performed IHC in the same set of tissues to determine whether the observed differences in the transcript levels among normal and tumor specimens were accompanied by similar changes in protein expression. Figure 2A shows that both the tumor-associated stroma and tumor cells expressed HA, HAS1, HAS2, and HAS3; HYAL-1 expression was observed only in tumor cells. As shown in Figure 2B, for HA and HAS2 staining, the difference between low-grade tumors and NBL and between high-grade tumors and NBL were statistically significant (P < .0001; Fig. 2B). For HAS1, HAS3, and HYAL-1 staining, only the differences between high grade and NBL were statistically significant (P < .0001). For HYAL-1, the differences between low- and high-grade BCa tissues were also statistically significant (P = .002). The overall sensitivity and specificity of HA-family proteins in distinguishing NBL and tumor tissues were: HA, 95% and 95.2%; HAS1, 77.5% and 85.7%; HAS2, 77.5% and 90.5%; HAS3, 67.4% and 100%; and HYAL-1, 80% and 86.4%, respectively. No consistent staining pattern was observed among NBL and BCa tissues for RHAMM and CD44 (data not shown).

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Figure 2. Analyses of HA synthase and HYAL-1 expression in bladder tissues are shown. (A) HA, HAS1, HAS2, HAS3, and HYAL-1 were localized in normal bladder and in low-grade (LG) and high-grade (HG) BCa tissues by IHC. Representative specimens from each category are shown. (B) Scatter diagrams of staining scores of HA, HAS1, HAS2, HAS3, and HYAL-1 in bladder specimens are shown. Five NBL specimens could not be stained because of poor fixation, resulting in the loss of tissue from the slides during staining.

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Association of HA-family members with metastasis

In this study, the majority of the patients had high-grade (n = 37) and muscle-invasive (n = 33) BCa. Among patients with high-grade BCa who later experienced metastasis, the mean and median transcript levels of HAS1 (5.1 ± 5.5 and 2.4) and HYAL-1 (18 ± 12.5 and 13.2) were significantly higher compared with those who did not develop metastasis (HAS1, 1.4 ± 1.1 and 1.1; HYAL-1, 5.5 ± 5.5 and 4.0). Similarly, the mean and median transcript levels of HAS1 (5.7 ± 5.1 and 2.1) and HYAL-1 (16.3 ± 9.2 and 10.7) were higher among patients who died of the disease than among those who did not (HAS1, 2.0 ± 1.7 and 1.6; HYAL-1, 8.3 ± 6.8 and 5.9). Among all markers, only the mean and median staining inferences for only HYAL-1 were higher among patients who had metastasis (271.8 ± 62.3 and 300) or died from BCa (242 ± 102 and 300), when compared with those who did not experience metastasis (HYAL-1: 126.8 ± 58 and 120) or die of BCa (162 ± 86 and 160).

Univariate analysis showed that stage, lymph node status, HYAL-1 and HAS1 transcript levels, and HYAL-1 staining inferences were significantly associated with metastasis and disease-specific mortality (Table 3A). In the multivariate model, stage, lymph node, HYAL-1 and HAS1 mRNA levels, and HYAL-1 staining were independently associated with metastasis (Table 3B). For disease-specific mortality, only stage and HYAL-1 mRNA levels were significant predictors (Table 3B).

Table 3. Determination of Association Between Metastasis/Disease-Specific Mortality and Clinical Parameters and HA Family Members
A. Univariate Analysis
 MetastasisDisease-Specific Mortality
ParameterChi SquarePOdds Ratio; 95% CIChi SquarePOdds Ratio; 95% CI
  1. The pre- and postoperative parameters included age, sex, tumor grade, stage, lymph node status, and concomitant presence of CIS. A. Univariate Analysis: Logistic regression analysis was used to determine the association of pre- and postoperative parameters and biomarker levels with outcome (ND, not determined for parameters that did not reach significance; *significant parameter). B. Multivariate Analysis: Cox proportional hazards analysis was performed by including all of the pre- and postoperative parameters and either the transcript levels or the staining scores of the HA-family molecules.

Stage8.64.003*5.4; 2.2-22.48.6.00346.45; 2.4-29.4
Grade0.01.94ND0.01.92ND
Lymph node4.53.033*3.71; 1.3-16.45.82.0163.9; 1.5-13.9
Sex0.1.77ND0.001.0ND
Age0.13.73ND1.23.27ND
CIS0.74.39ND0.15.7ND
HYAL-17.1.008*1.76; 1.27-3.05.71.0171.63; 1.2-2.7
HAS15.13.024*1.83; 1.21-3.53.8.0491.31; 1.1-2.0
HAS21.78.18ND0.02.9ND
HAS32.86.09ND3.5.06ND
RHAMM0.46.5ND0.97ND
CD44s2.43.1ND0.77.38ND
CD44v0.7.4ND0.11.74ND
CD44v/CD44s0.81.37ND0.13.72ND
HYAL-1 staining*10.9.009*1.03; 1.02-1.064.13.041.36; 1.02-2.4
B. Multivariate Analysis
ParameterChi SquarePRisk Ratio; 95% CIChi SquarePRisk Ratio; 95% CI
Stage5.66.0174*5.7; 1.4-59.45.47.0193*2.92; 1.2-8.3
Lymph node7.26.007*10.2; 1.9-71.41.9.17ND
HYAL-15.35.019*1.76; 1.1-2.853.98.046*1.15; 1.05-1.28
HAS14.97.027*1.37; 1.2-1.981.88.17ND
HYAL-1 staining5.33.021*1.65; 1.05-2.930.3.58ND

Although, the number of specimens was limited, HYAL-1 mRNA (cutoff limit, 9.5) and HAS1 mRNA (cutoff, 1.83) levels had a sensitivity of 80% and 87.5% and a specificity of 100% and 73.7%, respectively, to associate with metastasis. For HYAL-1 staining (cutoff, 270), the sensitivity and specificity were 93.7% and 100%, respectively. However to predict disease-specific mortality, the sensitivity and specificity were modest for HYAL-1 mRNA and HYAL-1 staining: a sensitivity of 70.2% and 80&percnt and a specificity of 80% and 65.7%, respectively.

HA Synthase and HYAL-1 Expression Is Increased in Exfoliated Urothelial Cells From BCa Patients

We used the Q-PCR assay to measure the transcript levels of HA-family members in exfoliated urothelial cells in urine specimens. HAS1, HAS2, HAS3, and HYAL-1 levels were elevated in urine specimens from BCa patients compared with those in the control categories (Fig. 3). CD44s expression was low in exfoliated cells, and it decreased in BCa patients by 3- to 7-fold. CD44v and RHAMM transcripts levels did not change significantly among various categories. Univariate analyses showed that HAS1, HAS2, HAS3, and HYAL-1 mRNA levels were significantly associated with the presence of BCa (Table 4). For HAS1, HAS2, and HAS3, only the differences in mRNA levels among high-grade BCa patients and each control subgroup were statistically significant (P < .001; data not shown). HYAL-1 mRNA levels were significantly elevated in the exfoliated cells of both patients with high-grade BCa (P < .0001) and patients with low-grade BCa (P = .0011). Among HA receptors, only CD44s mRNA levels were significantly different among BCa patients and the control category (Table 4).

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Figure 3. Scatter diagrams of HA synthase and HYAL-1 mRNA levels in exfoliated cells are shown. The distribution of mRNA levels of each of the 7 HA-family members among the study cohort is shown. Mean ± SD scores for each biomarker are indicated.

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Table 4. Determination of Association Between Presence of BCa and Levels of HA Synthases, HYAL-1, and HA Receptor Transcripts in Exfoliated Urothelium
BiomarkerChi SquarePOdds Ratio95% CI
  1. Logistic regression single parameter analysis was used to determine the association between the presence of BCa and a biomarker.

HAS120.2<.00011.471.26-1.75
HAS225.4<.00011.571.33-1.89
HAS315.4<.00011.651.3-2.14
HYAL-120.2<.00011.341.21-1.59
HAS1 + HYAL-121.09<.00012.851.95-4.76
HAS2 + HYAL-123.5<.00013.442.23-6.13
HAS3 + HYAL-122.3<.00011.941.5-2.63
HAS1 + HAS225.9<.00012.852.0-4.5
HAS1 + HAS320.9<.00012.441.7-3.76
HAS2 + HAS325.5<.0011.771.44-2.26
RHAMM0.96.340.990.98-1.00
CD44s8.1.00554.75.3-1140
CD44v0.44.511.00.97-1.01
CD44v/CD44s0.65.420.990.98-1.01

In regard to normal individuals, the data shown in Figure 3 and Table 4 pertain to 18 volunteers categorized as group 1 (as shown in Table 1). The mean and median ages of these individuals were significantly lower than those in the other control categories of individuals (BGU and HxBCa) and in BCa patients. The mean and median ages of those in group 2 (Table 1) were not significantly different from those of the patients with BGU conditions, HxBCa, or BCa. Means and medians of all 7 markers in group 2, normal individuals, were: HAS1, 1.3 ± 1.1 and 1.1; HAS2, 3.5 ± 1.9 and 2.8; HAS3, 0.9 ± 0.43 and 0.93; HYAL-1, 2.3 ± 1.3 and 2.3; CD44s, 0.22 ± 0.18 and 0.12; CD44v, 3.8 ± 1.9 and 3.7; RHAMM, 0.13 ± 0.24 and 0.15, respectively. The differences in biomarker levels between group 1 and the age-matched group 2 were not statistically significant (P > .05), showing that the significant alterations observed among the BCa and control categories of individuals were not related to age.

Efficacy of HA-Family Molecules to Detect BCa

Based on the cutoff values generated by ROC curves, all 3 HA synthases and the HYAL-1 markers had the same sensitivity (72.9%); however, the specificity was higher for HAS2 and HYAL-1 (81%-83%; Table 5). The levels of CD44s, CD44v or RHAMM had either low sensitivity or specificity to detect BCa. HA synthase markers had low sensitivity (≤50%) to detect low-grade BCa but high sensitivity (>80%) to detect high-grade BCa; in contrast, HYAL-1 had similar sensitivity to detect both low- and high-grade BCa (71.4% and 73.5%, respectively; Table 6).

Table 5. Determination of Sensitivity, Specificity, and Accuracy of HA-Family Molecules for Detecting BCa
BiomarkerAUCCutoffSensitivity (Mean; 95% CI)SpecificityAccuracy
  1. Sensitivity, specificity, and accuracy of the biomarkers were determined using the cutoff values determined from the ROC curves (AUC, area under the curve). For all markers except CD44s, transcript levels greater than or equal to the cutoff value indicated a positive inference. For CD44s, levels ≤0.026 indicated a positive inference (either true positive, that is, BCa, or false positive). Six patients with prostate cancer were not included in the non-BCa category for specificity calculations. Mean and 95% CI for sensitivity, specificity, and accuracy of each biomarker were computed by bootstrap modeling.

HAS10.804.072.9% (35/48) (77.85; 74.5-81.2)71.1% (59/83) (68.5; 65.2-72)71.8% (94/131) (73.3; 72.4-74.1)
HAS20.866.672.9% (35/48) (67.0; 64.5-69.5)83.1% (69/83) (86.4; 84-88.8)79.4% (104/131) (76.7; 75.8-77.6)
HAS30.772.472.9% (35/48) (80.1; 77.6-82.5)74.7% (62/83) (66.6; 64-69.1)74.1% (97/131) (73.3; 72.4-74.1)
HYAL-10.836.672.9% (35/48) (68.5; 65.7-71.4)81.9% (68/83) (85.8; 83.2-88.3)78.6% (103/131) (77.2; 76.2-78.2)
HAS1–HYAL-10.855.077.1% (37/48) (75; 72.9-77.1)78.3% (65/83) (84.1; 82.3-85.9)77.9% (102/131) (79.6; 78.9-80.3)
HAS2–HYAL-10.895.285.4% (41/48) (83.8; 82.1-85.5)79.5% (66/83) (83.1; 81.5-84.7)81.7% (107/131) (83.4; 82.8-84.1)
HAS3–HYAL-10.845.770.8% (34/48) (76.9; 74.7-79.3)79.5% (66/83) (80.6; 78.4-82.8)76.3% (100/131) (78.8; 78.1-79.5)
HAS1-HAS20.876.175% (36/48) (77.1; 74.8-79.5)80.7% (67/83) (84.4; 82.3-86.6)78.6% (103/131) (80.8; 80.2-81.4)
HAS2-HAS30.857.187.5% (42/48) (85.2; 83.5-86.9)69.9% (58/83) (74.1; 72.4-75.8)76.3% (100/131) (79.7; 79.0-80.4)
HAS1-HAS30.814.175% (36/48) (85.2; 83.3-87.2)69.9% (58/83) (67.9; 65.7-70.2)71.8% (94/131) (76.6; 75.9-77.2)
RHAMM0.520.1739.6% (19/48) (45.1; 41-49.3)78.3% (65/83) (75.3; 71.4-79.3)64.1% (84/131) (60.3; 59.3-61.2)
CD44S0.740.02662.5% (30/48) (66.3; 63.4-69.2)73.5% (61/83) (80.8; 77.6-80.04)69.5% (91/131) (73.6; 72.5-74.6)
CD44v0.454.481.3% (39/48) (78.5; 73.8-82.3)26.5% (22/83) (40.9; 36.3-45.8)46.7% (61/130) (59.7; 58.9-60.5)
CD44v/CD44s0.722364.5% (31/48) (74.3; 70.3-78.3)72.3% (60/83) (58.5; 52.4-64.7)69.5% (91/131) (66.4; 64.6-68.2)
Table 6. Analysis of Sensitivity by Tumor Grade and of Specificity by Non-BCa Conditions
MarkerSensitivitySpecificity
LGHGNormalHXBCaBGU
  1. For each marker, the cutoff limit generated by the ROC curve was used to determine the sensitivity and specificity of each marker for tumor grade and non-BCa conditions (normal, HXBCa, and BGU), respectively.

HAS150% (7/14)82.4% (28/34)94.4% (17/18)64.5% (20/31)64.7% (22/34)
HAS242.9% (6/14)85.3% (29/34)100% (18/18)80.6% (25/31)76.5% (26/34)
HAS357.1% (8/14)79.4% (27/34)100% (18/18)67.7% (21/31)67.7% (23/34)
HYAL-171.4% (10/14)73.5% (25/34)100% (18/18)80.6% (25/31)73.5% (25/34)
HAS1–HYAL-11.4% (10/14)79.4% (27/34)100% (18/18)77.4% (24/31)67.7% (23/34)
HAS2–HYAL-178.6% (11/14)88.2% (30/34)100% (18/18)77.4% (24/31)70.6% (24/34)
HAS3–HYAL-164.3% (9/14)73.5% (25/34)100% (18/18)74.2% (24/31)70.6% (24/34)
HAS1-HAS242.9% (6/14)82.4% (28/34)94.4% (17/18)80.6% (25/31)73.5% (25/34)
HAS2-HAS378.6% (11/14)91.2% (31/34)100% (17/18)71% (22/31)55.9% (19/34)
HAS1-HAS357.1% (8/14)82.4% (28/34)94.4% (17/18)70.1% (20/31)64.7% (21/34)

The specificity of all 7 biomarkers among normal individuals was high (Table 6). However, only HYAL-1 and HAS2 had reasonable specificity for the BGU (76.5% and 73.5%) and HXBCa (80.6%) categories, respectively. Among the 31 patients with HXBCa, 5 recurred within 6 months. As shown in Table 7, HYAL-1 and HAS2 mRNA levels were significantly associated with recurrence within 6 months.

Table 7. Mantael-Haenszel Chi-Square Analysis to Evaluate Predictive Potential of HA-Family Markers for BCa Recurrence
ParameterChi-SquareP ValueRisk Ratio95% CI
  1. Chi-square analyses were performed to determine the predictive value of each marker.

HAS12.9.088NDND
HAS219.5<.000120.83-147
HAS34.0.0454.20.92-19.2
HYAL-110.7.0018.32.0-35.4
HAS1–HYAL-18.3.0046.91.6-30
HAS2–HYAL-18.3.0046.71.7-30
HAS3–HYAL-18.3.0046.71.7-30
HAS1–HAS210.7.0018.31.9-35.3
HAS1–HAS31.6.21NDND
HAS2–HAS35.1.0244.91.1-22.1

Combination of HA Synthase and HYAL-1 Biomarkers to Detect BCa

As shown in Table 4, all 6 HA synthase and/or HYAL-1 combinations were significantly associated with BCa. The HAS2–HYAL-1 combination had the highest efficacy for detecting BCa (Table 5), with high sensitivity for low- and high-grade tumors and reasonable specificity for symptomatic controls. The HAS2–HYAL-1 combination was also significantly associated with recurrence within 6 months (Table 7). These results show that the combination of HAS2 and HYAL-1 significantly increases the efficacy of detecting BCa and of predicting BCa recurrence before its clinical detection.

In this study, biomarker expression was also evaluated in exfoliated urothelial cells in urine specimens from 6 prostate cancer patients. HAS3 and the HAS2 + HYAL-1 combination detected 5 patients (83.3%), whereas the other markers individually or in combination detected 50%-66% of the patients. This suggests that if prostate tumor cells are shed in urine, they can be detected by HA-family markers.

DISCUSSION

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

HA-family molecules have been shown to promote tumor growth, metastasis and angiogenesis, but some of them, such as HA synthases and HA receptors have overlapping functions. It is relatively unknown whether these proteins with overlapping functions are simultaneously expressed in normal and tumor tissues and what if any, correlation exists between their expression and the invasive potential of a tumor. In BCa, urinary HA levels are elevated,9-12 however, it is unknown whether 1 or all 3 HA synthases contribute to these elevated levels. Some qualitative PCR studies have reported increased HYAL-1 expression in urine sediments of BCa patients.26, 31 However, HYAL-1 transcript levels have not been measured in tissues and exfoliated urothelial cells, to establish whether the increased urinary HAase levels in high-grade BCa patients are due to increased HYAL-1 levels. For the HA receptors, different CD44 isoforms appear to have different clinical correlations. For example, the loss of CD44 and CD44v6 has been shown to associate with tumor stage and with poor outcome.19, 32, 33 However, CD44v8-10 expression may potentiate tumor progression,34 and increased CD44 mRNA expression in exfoliated cells has been reported as a marker for BCa.20, 21, 35 No study has reported RHAMM expression at the mRNA levels in BCa. Therefore, this is the first study to simultaneously evaluate the expression of all members of the HA family of molecules at both the transcript and protein levels in any tissue.

The second objective of the study was to compare the diagnostic and prognostic potentials of all 7 members of the HA family in prospectively collected specimens. The transcript levels of HA-family molecules measured either in tissue or exfoliated cells had similar sensitivity and specificity to distinguish between normal and tumor specimens. The overlap observed among normal and BCa tissues with respect to the expression of various markers could be because of the genetic variability found in bladder tumors,36 because in this study we did not analyze multiple tumor foci from the same patient or use laser dissection to analyze different portions of the same tissue. In the case of exfoliated cells, such sorting of cells and their analysis, even by a sensitive technique such as Q-PCR, would not be practical because of the detection limit and issues regarding RNA quality.

IHC was performed mainly to examine the pattern of expression of the HA-family molecules. Nevertheless, the sensitivity and specificity of the staining of HA-family molecules in bladder tissue specimens were higher. This could be attributable to the finding that IHC is semiquantitative and requires higher expression for detection than is required for detection by Q-PCR. The semiquantitative nature of IHC most likely contributed to increasing the differences in NBL and tumor tissues in staining scores, which, in turn, resulted in higher specificity (or less overlap between NBL and BCa tissues). The use of quantitative fluorescence imaging technique could improve quantification of the expression of HA-family molecules at the protein level.37-39 Nevertheless, unlike urine markers, which have utility in the diagnostic arena, tissue markers have the most utility in providing prognostic information.

Our study demonstrates that although HAS1 and HYAL-1 expression in BCa tissues may correlate with metastasis (and disease-specific survival—HYAL-1), combined HAS2–HYAL-1 mRNA levels in exfoliated urothelial cells display high sensitivity in detecting BCa and may predict BCa recurrence. Although these conclusions are similar to those reported before regarding increased HA and HAase levels,9-12 the present study revealed the molecular basis for this increase, that is, increased HA synthase and HYAL-1 transcript levels. That HAS2 levels were significantly associated with BCa diagnosis but HAS1 levels were associated with tumor metastasis suggests there are functional differences among HA synthases regarding tumor behavior. It remains to be determined why all 3 HA synthases are elevated in BCa, despite each of the 3 genes being on a different chromosome in the human genome (HAS1, 19q13.3-q13.4; HAS2, 8q24.12; HAS3, 16q22.1).40

Although in this study the number of patients with follow-up was small, it was sufficient to demonstrate that increased HYAL-1 expression was an independent prognostic indicator for BCa metastasis and survival and HAS1 expression was associated with metastasis. However, one reason some study individuals did not develop metastasis might that insufficient follow-up time for those patients. Only a study with a large cohort of patients and sufficient follow-up time will definitively address whether transcript levels of HA family members can identify patients who subsequently develop metastasis.

Regarding the diagnostic potential of the 7 HA family members, the combined inference of the HAS2 and HYAL-1 mRNA levels increases the sensitivity for BCa detection. This is consistent with our previous observation regarding the HA-HAase test, which has higher accuracy to detect BCa than does the individual HA and HAase tests, which measure urinary HA and HAase levels.11 The increased transcript levels of HAS2 and HYAL-1 in tumor cells confirm the molecular basis for increased urinary HA and HAase levels.

The major limitation of any Q-PCR assay is RNA stability. The ZR urine isolation kit allows onsite capturing of urothelial cells from urine and resuspending them in a lysis buffer. The lysis buffer stabilizes the RNA, which can be then shipped to a reference laboratory for Q-PCR assays. One limitation of our study is that it was a single-institution study, and the number of patients with high-grade BCa with variable clinical follow-up; the latter possibly could have skewed conclusions regarding the prognostic capability of the HA family of markers. The second limitation may be that patients in the BCa, BGU, and HxBCa categories were not age-matched. However, this was not a case-control study, and specimens were obtained from consecutive patients to mimic the clinical scenario. That the biomarker levels did not differ among normal individuals (both group 1 and group 2), BGU patients, and patients with HxBCa demonstrates that neither sex nor age influences biomarker levels; however, the presence of BCa does. Another perceived limitation of this study could be that the transcript levels were measured in mixed-cell populations, where the ratio of cell populations (tumor versus normal) may change. This is a common limitation for any biomarker for any type of tumor, unless, the test itself distinguishes between normal and tumor cells (morphology or immunofluorescence-based tests). Although, theoretically, it is possible to first isolate the tumor cells using a cell-based technique and then performing biomarker assays such those described here, such tests would not be feasible given their technical complexity, the associated cost, and the inherent variability of such an approach. Our study shows that normalization of biomarker levels to actin provides a reliable method to distinguish between control and BCa categories of individuals with relatively high sensitivity and specificity.

Taken together, this study showed that HYAL-1 and HAS1 expression are likely independent predictors of BCa metastasis (and possibly disease-specific survival), and combined HAS2–HYAL-1 mRNA expression in exfoliated urothelial cells is a biomarker for detecting BCa and plausibly for monitoring recurrence. These findings need to be confirmed in an independent set of samples using the cut points established in this study for each biomarker.

Acknowledgements

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

We gratefully acknowledge the statistical advice provided by Dr. Robert C. Duncan, Department of Epidemiology and Public Health, University of Miami, Miller School of Medicine, and by Ms. Sue Walsh, JMP Technical Support, SAS Institute. We thank Ms. Anaid Benitez and Ms. Estrelle Crespo for their help.

CONFLICT OF INTEREST DISCLOSURES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES

Supported by National Institutes of Health grants 2R01CA072821-11, R01 CA 123063-03, and R01 CA 123063-04 (to Vinata B. Lokeshwar); Florida Department of Health—James and Esther King Biomedical Research Program (10KT-01), CURED Department of Urology (to to Kristell Acosta); the Woman's Cancer Association of the University of Miami (to Mark Soloway); and the International Academy of Life Sciences, Biomedical Science Exchange Program fellowship (to Mario Kramer).

REFERENCES

  1. Top of page
  2. Abstract
  3. MATERIALS AND METHODS
  4. RESULTS
  5. DISCUSSION
  6. Acknowledgements
  7. CONFLICT OF INTEREST DISCLOSURES
  8. REFERENCES