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. Author manuscript; available in PMC: 2013 May 1.
Published in final edited form as: Cancer. 2011 Sep 1;118(9):2394–2402. doi: 10.1002/cncr.26520

MOLECULAR CHARACTERIZATION OF KIDNEY CANCER: ASSOCIATION OF HYALURONIC ACID FAMILY WITH HISTOLOGICAL SUBTYPES AND METASTASIS

Andrew Chi 1,*, Samir P Shirodkar 2,*, Diogo O Escudero 3, Obi O Ekwenna 4, Travis J Yates 5, Rajinikanth Ayyathurai 6, Michael Garcia-Roig 7, Jeffrey C Gahan 8, Murugesan Manoharan 9, Vincent G Bird 10,♠, Vinata B Lokeshwar 11,♠
PMCID: PMC3232339  NIHMSID: NIHMS317894  PMID: 21887686

Abstract

Background

Molecular profiling of renal cell carcinomas (RCC) may improve the distinction between oncocytoma and malignant RCC subtypes and aid in early detection of metastasis. Hyaluronic acid (HA) family includes HA-synthases (HAS1, HAS2, HAS3), hyaluronidases (HYAL-1, HYAL-2, HYAL-3, HYAL-4, PH20, HYAL-P1), and HA receptors (CD44s, CD44v and RHAMM). HA family members promote tumor growth and metastasis. We evaluated the expression of HA family members in kidney specimens.

Methods

Using quantitative PCR, mRNA levels of twelve HA family members were measured in tumor specimens obtained from 86 consecutive patients undergoing nephrectomy; 80 of them also provided normal specimens. Mean and median follow-up: 15.2 ± 8.8 and 13.8 months. RCC specimens included: clear cell RCC (ccRCC): 65; papillary: 10; chromophobe: 5; oncocytoma: 6; metastasis (+): 17.

Results

Median HAS1, CD44s and RHAMM transcript levels were 3–25 elevated in ccRCC, papillary and chromophobe tumors when compared to normal tissues. HYAL-4, CD44s and RHAMM levels were 4–12-fold elevated in ccRCC and papillary tumors when compared to oncocytomas; only HYAL-4 levels distinguished between chromophobe and oncocytoma (P=0.009). CD44s and RHAMM levels were significantly higher in tumors < 4-cm (510±611; 19.6±20.8, respectively) when compared to oncocytoma (46.4±20; 3.8±2.5; P≤0.006). In univariate and multivariate analyses, CD44s (P<0.0001), RHAMM (P<0.0001), stage, tumor size, and/or renal vein involvement significantly associated with metastasis. The combined CD44s+RHAMM marker had 82% sensitivity and 86% specificity to predict metastasis.

Conclusion

CD44s and RHAMM levels distinguish between oncocytoma and RCC subtypes regardless of tumor size and are potential predictors of RCC metastasis.

Keywords: Prognostic markers, HA-synthase, Hyaluronidase, HA-receptors, Renal cell carcinoma, metastasis, oncocytoma

INTRODUCTION

In 2010 an estimated 58, 240 new cases of kidney cancer were diagnosed in the US and 13,040 deaths were attributed to this disease (1). Ninety-two percent of all kidney tumors are renal cell carcinomas (RCC), which arise in the lining of the proximal convoluted tubule. Clear cell carcinoma (CC) accounts for ~ 70% of all RCC cases; papillary (types I and II), chromophobe and oncocytoma cases are less frequent. Cross-sectional imaging techniques allow better detection of renal masses that are smaller in size, but 1/4th of these tumors have benign pathology (2). Percutaneous biopsy can differentiate between oncocytoma and renal cell carcinoma; however, it relies on the presence of histopathological architecture to yield a definitive diagnosis (3–6). Furthermore, neither tumor size nor tumor growth rate can distinguish an oncocytoma from other renal tumors (7). Nearly 1/3rd of patients with RCC have metastatic disease at their first office visit. Despite surgery and subsequent targeted therapies, the median survival of patients with metastatic RCC is < 2 years (8).

Hyaluronic acid (HA) family of molecules promotes tumor growth, metastasis and angiogenesis. HA is a non-sulfated glycosaminoglycan made up of repeating disaccharide units D-glucuronic acid and N-acetyl-D-glucosamine. It is a component of the tissue matrix and fluids where it maintains the osmotic balance and the hydration status. HA levels are elevated in a variety of tumors and are either a diagnostic or a prognostic marker depending on the tissue origin of the cancer (10,11). HA is synthesized by HA synthases (HAS1, HAS2 and HAS3). Overexpression of any of the three HA synthases promotes tumor growth, angiogenesis and metastasis (11–14). Targeting HA-synthesis with a HA synthesis inhibitor, 4-methylumbelliferone, inhibits tumor growth, invasion and angiogenesis in prostate cancer models (15). HAS1 expression correlates with metastasis in bladder and lymphoid cancers (16–18). As a combined marker, HAS2 mRNA levels together with HYAL-1 mRNA levels are an accurate diagnostic marker for bladder cancer (18).

HYAL-1 is a tumor-associated hyaluronidase (HAase) that degrades HA into fragments, some of which are angiogenic. The human genome contains five HAase genes (HYAL-1, HYAL-2, HYAL-3, HYAL-4, PH20) and a pseudo gene, HYAL-P1 (19). HYAL-1 expression is elevated and is an independent prognostic indicator of metastasis for bladder and prostate carcinomas (18, 19–22). Targeting of HAase with sulfated-HA, an inhibitor of HAase activity, inhibits tumor growth, invasion and angiogenesis in prostate cancer models (23). However, HYAL-1 expression is decreased in endometrial and ovarian carcinomas (24,25).

Cellular effects of HA and the angiogenic HA fragments are mediated by HA receptors, CD44 and RHAMM (26,27). CD44 mRNA is frequently alternatively spliced, resulting in several alternatively spliced variant isoforms. We recently reported that in bladder cancer, while the expression of CD44 standard isoform (CD44s) decreased, the expression of CD44 variant isoform(s) (CD44v) and RHAMM was elevated; however, the expression of none of the HA receptors correlated with disease progression (18). In RCC, the only HA family member that has been evaluated is CD44. Several studies have explored the expression of CD44s by immunohistochemistry in RCC tissues. While some studies report that CD44s expression correlates with metastasis and/or disease specific survival, other studies report either no such correlation or that CD44s positive carcinomas significantly associate with lower pathological stage and that papillary tumors are negative for CD44 expression (27–32).

In this study we investigated the expression of all twelve HA-family members in kidney tissues to compare their potential to distinguish oncocytomas from other RCC types. We also compared their prognostic accuracy for predicting metastasis either alone or as a biomarker profile. We hypothesized that since these molecules function in the same biological pathway, they may demonstrate improved prognostic potential when used in combination.

MATERIALS AND METHODS

Tissue specimens

Normal kidney (n=80) and tumor (n=86) specimens were collected from 86 consecutive patients undergoing radical or partial nephrectomy for renal mass between July 2007 and November 2009. All specimens were obtained based on their availability for research purpose and under a protocol approved by University of Miami’s Institutional Review Board; a written consent was obtained from study individuals. Total RNA was isolated from tissues (~ 30 mg) using the miRNeasy Mini Kit (Qiagen, Valencia CA). Patient and tissue characteristics are presented in Table 1. At the time of performing quantitative RT-PCR (Q-PCR), the specimens were not linked to any clinical and pathological information (i.e., grade, stage, node status, etc), except that the specimen was either a tumor or a normal specimen. Patients were followed for metastatic progression to correlate whether or not Q-PCR data predicted metastatic progression. After the completion of the laboratory studies, Q-PCR data was linked to clinical, pathological information and later to follow-up information, as it became available.

Table 1.

Description of tissues and clinical characteristics of 86 patients with RCC

Tissue specimens: Normal: 80; Tumor 86
Age: 63.4 ±14; Median: 64 Range 19 – 88
Gender Male: 60
Female: 26
Tumor Type Clear Cell: 65
Papillary: 10A
Chromophobe: 5
Oncocytoma: 6
Tumor grade Stage 0 (Oncocytoma): 6
Grade 1: 5
Grade 2: 32
Grade 3: 25
Grade 4: 18
Tumor stage(n) Stage 0 (Oncocytoma): 6
T1 (a+b): 40
T2: 13
T3: 21
T4: 6
Metastasis at surgery (M) (+) 6
(−) 80
Lymph node (N) (+) 3
(−) 83
Renal vein involvement Positive: 15
Negative: 68
No data: 3
Lymphovascular invasion Positive: 9
Negative: 45
No data: 32
Tumor size (excluding oncocytoma) < 4 cm: 17
> 4 cm: 68
Unknown: 1
Follow-up 15.2 ± 8.8; Median: 13.8; 0.33 – 36.3
Metastasis (total number) Negative: 69; Age: 64.3±13.4; 66
Positive: 17; Age: 59.5±16.1; 61
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A

: papillary sub-types: Type I: 5 patients; papillary type I with focal clear cell: 1; undefined: 2; papillary oncocytoma: 1; Mixed: 1.

Q-PCR

Total RNA isolated from tissues was subjected to Q-PCR using the iQ real time PCR system (BioRad, Hercules, CA) and primers specific for each transcript (Table 2; ref, 14,28). The expression of four housekeeping genes, 18S, β-actin, cyclophilin A (or PPIA-v4), and TATA- binding protein (TBP) was also analyzed simultaneously in each tissue by Q-PCR. As shown in Figure 1, normalized transcript levels for each gene were calculated as (1/2Δct × 100); ΔCt = Ct (transcript) − Ct (TBP). The variance of the PCR assay has been examined previously (18); the intra-class coefficient for this assay varies between 0.955 and 0.994 with P < 0.001 (18).

Table 2. Sequences of the primers and probes used in Q-PCR assays.

For CD44v, the primer sequences were designed in exon 12, which is alternatively spliced in CD44s but is present in all variant isoforms. Therefore, CD44v primers will amplify any and all alternatively spliced variants.

Marker Forward Primer Reverse Primer
CD44s 5′CTGTACACCCCATCCCA GAC3′ 5′TGTGTCTTGGTCTCTGGTAGC3′
CD44v 5′CAGGTGGAAGAAGAGACC CAA3′ 5GCTGAGGTCACTGGGATG AA3′
RHAMM 5′CAGCTGGAAGATGAAGAAGGA3′ 5′GCATGTAGTTGTAGCTGAAAAGG3′
HYAL-1 5′AGCCAGGGTAGCATCGACA3′ 5′AAGCCCTCCTCCTCCTTAACC3′
HYAL-2 5′CCTGGACGAGACACTTGCTT3′ 5′AGACTGGGAGTGCATGGTTG3′
HYAL-3 5′CTATGTCCGCCTCACACACC3′ 5′CTCACACCAATGGACTGCACAA3′
HYAL-4 5′CATCTGGAAAAAGCTGACCAA3′ 5′AACATCTTTTGAGTTCCAGTTCC3′
PH20 5′AAACTGTTGCTCTGGGTGCT3′ 5′GCAAAGCACTTGGCTACACA3′
HYAL-P1 5′TTGGCTTTAGAAATGAGACCAAA3′ 5′TTTTCCCACAGCCACAATTT3′
HAS1 5′GGTGGGGACGTGCGGATC3′ 5′CAGGATACACAGTGGAAGTAG3′
HAS2 5′CCATAAAGTTGAACAAAACAGTTG3′ 5′CACAGATTGCAAACATTTCCTTA3′
HAS3 5′CTCTACTCCCTCCTCTATATGTC3′ 5′AACTGCCACCCAGATGGA3′
18S 5′GCTTAATTTGACTCAACACGGGA3′ 5′AGCTATCAATCTGTCAATCCTGTC3′
TBP 5′TGCACAGGAGCCAAGAGTGAA3′ 5′CACATCACAGCTCCCCACCA3′
PPIA-v4 5′TCATCTGCAACTGCCAAGACTG3′ 5′CATGCCTTCTTTCACTTTGCC3′
β-Actin 5-′CAACTGGGACGACATGGA3′ 5′GTTGGCCTTGGGGTTCAG3′
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Figure 1. Expression of 18S RNA, β-actin, TBP and PPIA-v4 in normal and tumor kidney tissues.

Figure 1

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The mean±Sd Ct values for each gene are shown; Median levels were as follows: 18S: Normal, 17.8; tumor: 15.4; β-actin: Normal, 19.6; tumor: 17.5; PPAI-v4: Normal: 18.6; tumor: 17.6; TBP: Normal: 26.3; tumor: 25.2. The break in the TBP tumor column is because of the segmented Y-axis.

Statistical analyses

Differences in biomarker levels among kidney tissues (e.g., normal versus ccRCC, ccRCC versus oncocytoma) were compared using the Mann-Whitney U-test, because the data showed a non-normal distribution. The P-values reported in this study are two-tailed. Logistic regression single-parameter model (i.e., univariate analysis) was used to determine the association of clinical parameters, and transcript levels with metastasis. Cox-proportional hazards model (i.e., multivariate analysis) was used to determine which of the clinical and pathologic parameters (i.e., age, gender, tumor grade, stage, tumor size, renal vein involvement, lymphovascular invasion) parameters and/or tissue biomarkers to predict metastasis.

The levels of the combined biomarkers (e.g., CD44s+RHAMM) for each study subject were calculated as follows: [intercept +(α × (CD44s)1) + (β × (RHAMM)1)]; α and β: CD44s and RHAMM coefficients, respectively and (CD44s)1 and (RHAMM)1: CD44s and RHAMM levels in subject # 1, respectively. The intercept and coefficients for each marker were computed by simultaneously analyzing the two variables (i.e., CD44s and RHAMM) in the logistic regression model (i.e., bivariate analysis).

Receiver operating characteristic (ROC) curves were generated to determine the association between tissue biomarker levels and metastasis. Cut-off values were selected from the ROC curve data by a statistical program (JMP®7 software program) for calculating sensitivity and specificity of each biomarker. The biomarker level that yielded the highest efficacy (i.e., sensitivity – (1-specificity)) was selected by the program as the cut-off limit. Cross-validation using boot-strap modeling (specific sampling rate = 0.5; re-sampling = 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 the JMP®7 software program (SAS Institute, Cary, NC).

RESULTS

Differential expression of HA-family molecules in normal and RCC tissues

We measured the levels of HA-synthases (HAS1, HAS2, HAS3), HAases (HYAL-1, HYAL-2, HYAL-3, HYAL-4, PH20, HYAL-P1) and HA receptors (CD44s, CD44v, RHAMM) in 86 tumor specimens. In 80 patients, matched normal kidney specimens were also analyzed for the expression of HA-family members. In order to choose an appropriate housekeeping gene for normalization of the transcript levels, we simultaneously measured the transcript levels of 18S RNA, β-actin, PPIAv-4 and TBP. As shown in Figure 1, the average cycle threshold (Ct) values for 18S, β-actin and PPIA-v4 were higher in normal kidney tissues when compared to tumor tissues, even when the same amount of total RNA was used as the starting material for the reverse transcription reaction and the same amount cDNA was used in each Q-PCR reaction. The differences between normal and tumor kidney specimens with respect to the expression of 18S RNA, βactin and PPIA-v4 were statistically significant. This indicated that the expression of 18S RNA,β-actin and PPIA-v4 was significantly lower in normal kidney when compared to tumor specimens, and therefore, these three house keeping genes could not be used for normalization. The Ct values for TBP were not significantly different among normal kidney and RCC specimens (Figure 1), and therefore, the transcript levels of HA family members were normalized to TBP.

Among the HA family members, HAS3, HYAL-2, HYAL-3 PH20, HYAL-P1 and CD44v transcript levels were not significantly different among normal and tumor tissues (data not shown). Figure 2 and Table 3 show that the expression of HAS1, CD44s and RHAMM was significantly elevated in ccRCC tissues, when compared to normal kidney tissues; the median levels of HAS1 (1.07), CD44s (283) and RHAMM (17.7) were 3–25-fold elevated in ccRCC tissues. However, HYAL-1 expression was significantly lower in tumor tissues (median level: 264) when compared to normal tissues (median level: 650). HAS1, CD44s and RHAMM levels were also 2–25-fold higher in papillary tumors (median levels: 36, 588, 14, respectively) when compared to normal kidney specimens (median levels: 0.044, 100, 1.9; Table 3). Similarly, RHAMM and HAS1 expression was 3 and 250-fold higher in chromophobe RCC (median levels: 5.8 and 11.7, respectively) when compared to normal kidney (Table 3).

Figure 2. Levels of HA-family members in kidney tissues.

Figure 2

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mRNA levels (mean±SD) of HA-family members, which showed significant differences between normal and tumor tissues and/or between each RCC subtype and oncocytoma are shown. Chromo: chromophobe; Oncocyt: oncocytoma. The break in the HYAL-1 and HAS1 chromophobe columns is because of the segmented Y-axis.

Table 3. Comparison of the expression of HA-family markers among normal and RCC tissues.

The significance of the differences in the expression of each HA family member among normal and different RCC subtypes was evaluated by the Mann-Whitney test.

Comparison HYAL-1 HYAL-4 HAS1 HAS2 CD44s RHAMM
Norm. vs. Clear Cell P<0.0001* 0.2829 P<0.0001* 0.4141 <0.0001* P<0.0001*
Norm. vs. Papillary 0.6134 0.3141 P<0.0001* 0.2229 0.0092* 0.0005*
Norm. vs. Chromophobe 0.285 0.138 0.0010* 0.5225 0.701 0.0310*
Norm. vs. Oncocytoma 0.14 0.022 0.1569 0.0865 0.018* 0.2633
CC vs. Oncocytoma 0.3067 0.0470* 0.3277 0.1033 0.0005* 0.0033*
Pap vs. Oncocytoma 0.3132 0.0075* 0.3277 0.0813 0.0075* 0.0559
Chromo vs. Oncocytoma 0.178 0.009* 0.1775 0.1255 0.125 0.1775
Low-grade vs. high-grade 0.236 0.127 0.251 0.921 0.041* 0.0042*
Low-stage vs. high-stage 0.083 0.148 0.295 0.063 0.108 0.0833
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*

Statistically significant.

Differential expression of HA-family members in RCC subtypes

HYAL-4, CD44s and RHAMM levels were significantly higher in ccRCC and papillary tumors when compared to oncocytomas (Table 3). However, only HYAL-4 levels were significantly higher in chromophobe RCC when compared to oncocytoma (median levels: 214 versus 5.8; Table 3). Stratification of RCC subtypes other than oncocytoma, with respect to tumor size, yielded 17 specimens from tumors that were < 4-cm (2.9±0.55; median: 3.0). As shown in Figure 3, CD44s and RHAMM transcript levels were significantly higher in RCC subtypes with tumor size < 4-cm when compared to oncocytoma (mean tumor size: 4.2±0.98; median: 4.6).

Figure 3. Comparison of the expression of CD44s and RHAMM levels between small RCC tumors (< 4 cm) and oncocytoma.

Figure 3

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In a subgroup analysis with respect to tumor size, among all HA family markers, CD44s and RHAMM levels were found to be significantly higher in tumors < 4-cm when compared to oncocytoma. mRNA levels (mean±SD) of CD44s and RHAMM are shown for 17 tumors that were < 4-cm and 6 oncocytomas.

Association of HA-family members with metastasis

In the cohort of 86 patients, 17 patients were positive for metastasis during a median follow-up of 14 months (Table 1). Among these 17 patients, 6 were positive for metastasis at surgery. The TMN status of all patients is presented in Table 1. The mean and the median levels of were significantly higher in tumor specimens from patients who developed metastatic RCC, when compared to those who did not develop metastasis (Figure 4). Univariate analysis showed that among the clinical parameters, tumor grade, stage, tumor size significantly associated with metastasis (Table 4). Among HA family markers, only HAS2, CD44s and RHAMM levels significantly associated metastasis.

Figure 4. Comparison of the expression of HA-family markers with respect to metastasis.

Figure 4

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mRNA (mean±SD) levels of those HA-family members which showed significant differences between normal and tumor tissues and/or between each RCC subtype and oncocytoma were reanalyzed with respect to metastasis.

Table 4. Determination of the association between metastasis and clinical parameters and HA-family members.

The pre- and post-operative parameters included age, gender, tumor grade, stage, tumor size, renal vein invasion, lymphovascular invasion. Logistic regression analysis was used to determine the association of pre- and post-operative parameters and biomarker levels with metastasis.

Marker Chi-Square P value Odds Ratio 95% CI
Age 1.58 0.208 ND ND
Gender 0.45 0.504 ND ND
Grade 13.5 0.0002* 4.65 2.2 – 11.5
Stage 6.87 0.009* 2.1 1.2 – 3.3
Tumor size 8.57 0.0034* 1.3 1.09 – 1.5
Renal vein invasion 0.39 0.532 ND ND
Lymphovascular invasion 1.08 0.299 ND ND
HYAL-1 1.63 0.202 ND ND
HYAL-4 3.78 0.052 ND ND
HAS1 0.49 0.484 ND ND
HAS2 4.42 0.036* 1.01 1.00 – 1.02
CD44s 12.19 0.0005* 1.02 1.01–1.05
RHAMM 13.94 0.0002* 1.06 1.03 – 1.1
CD44s+RHAMM 14.4 0.0002* 2.13 1.5 – 3.4
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In the multivariate analysis, along with CD44s and RHAMM, stage, renal vein invasion and tumor size (in CD44s only) were independently associated with metastasis (Table 5). Although, during the duration of this study only 17 patients became positive for metastasis, both CD44s and RHAMM levels showed ~ 80% sensitivity for predicting metastasis. The specificity and accuracy of RHAMM for predicting metastasis were significantly higher (~ 90%) than CD44s (72–74%). HAS2 had poor specificity to predict metastasis (Table 6).

Table 5. Multivariate analyses of pre- and post-operative parameters and HA-family members to predict metastasis.

Cox proportional hazards analysis was performed by including all of the pre- and post-operative parameters and transcript levels of each HA-family member. When only clinical parameters were included in the model, no parameter reached significance. Inclusion of all HA-family makers in the model simultaneously, along with clinical parameters, resulted in an unstable model. When HYAL-1, HYAL-4, HAS1 and HAS2 were included in the model, individually, along with the clinical parameters, no single parameter reached statistical significance. Significant parameters are shown.

Parameter Chi-square P-value Hazard ratio 95% CI
RHAMM in the model
Stage 7.48 0.0062 2.58 0.76 – 4.73
Renal vein 5.5 0.0189 4.5 0.77 – 8.9
RHAMM 19.9 < 0.0001 33.33 16.67 – 64.1
CD44 in the model
Stage 11.1 0.0009 5.3 2.0 – 11.8
Tumor size 6.2 0.012 1.6 1.5 – 8.3
Renal vein 14.7 0.0001 14.7 6.1 – 28.7
CD44 28.5 < 0.0001 250 142 – 500
CD44s + RHAMM in the model
Stage 5.9 0.015 14 1.7 – 200
RHAMM 24.84 < 0.0001 2.0 1.4 – 3.9
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Table 6. Determination of sensitivity, specificity and accuracy of HA-family molecules for predicting metastasis.

Sensitivity, specificity and accuracy of the biomarkers were determined using the cut-off values determined from the ROC curves. AUC: area under the curve. Mean ± SD and 95% CI for sensitivity, specificity and accuracy values were obtained by bootstrap modeling.

Marker Cut-off AUC Sensitivity Specificity Accuracy
CD44s 428 0.791 82.4% (14/17)
81.9±12.4%; 78.8 – 83.6		 72.5% (50/69)
77±9.7%; 75.1 – 78.8		 74.4% (64/86)
79.1±5.3%; 78.1 – 80.1
RHAMM; 0.897 76.4 (13/17)
85.1±9.9%; 83.1 – 86.9		 92.8% (64/69)
86.5±10.3%; 84.4 – 88.5		 89.5% (77/86)
85.8±4.6%; 84.9 – 86.7
HAS2 10.8 0.659 80% (12/15)
76.2±22.6%; 71.8 – 80.6		 46.3% (31/67)
60.4±22.1%; 56.1 – 64.7		 64.2% (43/86)
68.3±5.8%; 67.2 – 69.4
CD44s+RHAMM 5.5 0.914 82.4% (14/17)
89.7±9.6%; 87.8 – 91.6		 86.9% (60/69)
83.4±8.1%; 81.8 – 85		 86% (74/86)
86.6±4.6%; 85.7 – 87.5
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Since both CD44s and RHAMM were independent prognostic indicators for predicting RCC metastasis, we determined whether their combination might improve sensitivity for predicting metastasis. As shown in Table 6, the combined marker had slightly better sensitivity but lower specificity (82.4% and 76.4%, respectively) than RHAMM alone (76.4% and 92.8%). As expected, the combined marker also significantly predicted metastasis in both univariate and multivariate analyses. These results show that CD44s, and in particular RHAMM transcript levels in RCC tissues significantly associate with and are potential predictors of metastasis.

DISCUSSION

Using the HA family of molecules that function in tumor growth and metastasis, our study attempted to investigate two important clinical issues in the management of RCC. First, do any of the twelve HA family molecules significantly distinguish between oncocytoma and other RCC types? Second, do HA family members associate with metastatic RCC, and if so, can they serve as prognostic markers?

With the exception of CD44s, the expression of other HA family molecules has not been evaluated in RCC. Furthermore, it is unknown whether certain HA family molecules such as HYAL-1, HAS1 and HAS2 that are overexpressed in one of type tumor and associated with metastasis (18, 20, 21), also have the same expression pattern and prognostic potential in other types of cancer, especially from the same functional tract (e.g., urinary system). In this regard the expression of HA family of molecules revealed several features unique to RCC. First, different HA family members even with similar function (e.g., HA-synthesis or HA degradation) were differentially expressed in RCC tissues. For example, among the six HAase genes, only HYAL-1 and HYAL-4 were differentially expressed in normal and tumor tissues. However, contrary to observations in bladder, breast and prostate carcinomas (18, 21, 33), HYAL-1 expression was lower in ccRCC when compared to normal kidney tissues. We recently showed that the combined HAS2 and HYAL-1 expression (HAS2-HYAL1 marker) had high accuracy in detecting bladder cancer; however, HAS2 was not even differentially expressed in RCC and normal kidney tissues.

Among the HA family of molecules, RHAMM expression was consistently elevated in all RCC subtypes when compared to normal kidney and oncocytoma. Interestingly, in the case of CD44s, which is the other HA receptor, the expression was significantly lower in oncocytoma when compared to normal kidney tissues. When distinguishing various RCC subtypes from oncocytoma, none of the markers except HYAL-4 distinguished chromophobe tumors from oncocytoma. HYAL-4 is a virtually unknown member of the HA-family with putative chondroitinase activity (34).

Since ≥ 30% of RCC patients have metastasis at the time of diagnosis or develop it after surgery, even with a relatively short median follow up of 14 months, it was feasible to evaluate a correlation between metastasis and the expression of various markers. Since HYAL-1 has been shown to be associated with metastasis in bladder and prostate carcinomas, we expected to find a similar correlation in RCC. However, in neither univariate nor multivariate analysis, HYAL-1 expression was significantly associated with metastasis. Furthermore, HYAL-1 levels were lower in ccRCC tissues than in normal kidney. Nyokopp et al reported lower HYAL-1 expression in endometrioid and ovarian carcinomas (24, 25). Therefore, similar to HA expression, the pattern of HYAL-1 expression may also be dependent on the tissue origin of a tumor.

In this study, both HA receptors were found to be independent prognostic indicators for metastasis. RHAMM is a well studied HA receptor; however, its expression has not been evaluated with respect to cancer progression. In this study RHAMM mRNA expression was a strong predictor of metastasis with high specificity (~ 93%) and reasonable sensitivity (~ 76%). The combination of CD44s and RHAMM expression was also an independent predictor of metastasis with high sensitivity and specificity. Therefore, it may be possible to design a molecular nomogram consisting of CD44s and RHAMM expression to confirm the existence of metastasis at diagnosis or predict its occurrence in the future.

We also attempted immunohistochemistry (IHC) for the detection of CD44 and RHAMM proteins in the same RCC specimens. However, IHC for RHAMM could not be performed because one of the two commercial antibodies (rabbit polyclonal, Santa Cruz Laboratory) showed high background and multiple bands on immunoblots. The second antibody (mouse monoclonal; Novocastra) did not stain paraffin fixed tissues. All commercially available CD44 antibodies detect both CD44s and CD44 variants; the variants contain the entire sequence of CD44s and one or more additional exons. At the present time no CD44 antibody detects the junction region which connects exon 5 to exon 15 in CD44s after alternative splicing; this junction region is unique to CD44s. CD44v levels were not different among normal kidney and RCC specimens and did not correlate with metastasis (Unpublished results), and therefore, IHC for CD44 would result in spurious inferences. This may also be the reason why evaluation of CD44 expression in RCC by IHC has yielded mixed results in different studies. In addition, immunohistochemistry inferences depend upon the source of the antibody, epitope recognition, antibody specificity and concentration and incubation conditions, and may further add to ambiguity of results.

In this study we obtained 80 matched normal and tumor specimens. No marker had significantly different expression when comparing normal kidney specimens from patients with oncocytoma to those from patients with other RCC subtypes. Furthermore, the expression of the markers was not significantly different in normal specimens from patients who developed metastasis when compared to those who did not. This suggests that there are no “field changes” with respect to the expression of HA family of molecules in the uninvolved kidney when a tumor is present.

The limitations of this study are a relatively short follow up and examination of specimens from a single institution. The technical limitations relate to RNA quality preservation and Q-PCR. Nevertheless the potential clinical value of this research would be that HA family markers can differentiate oncocytoma from chromophobe RCC, and may predict those at risk for metastatic progression.

Acknowledgments

Grant support: Women’s Cancer Association of University of Miami pilot award (VGB and VBL); R01 CA 72821-12 (VBL). Florida Department of Health – James and Esther King Biomedical Research Program (10KT-01; VBL – University of Miami site)

We thank Drs. John Shields and Mohamed Aziz, University of Miami Miller School of Medicine for their help in specimen collection.

Abbreviations used

cc

clear cell

HA

hyaluronic acid

HAase

hyaluronidase

HAS

Hyaluronic acid synthase

RCC

renal cell carcinoma

TBP

TATA binding protein

Contributor Information

Andrew Chi, Email: litlchi@gmail.com, Department of Urology, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6596; Fax: (305) 243-6893.

Samir P. Shirodkar, Email: samirps@yahoo.com, Department of Urology, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6596; Fax: (305) 243-6893.

Diogo O Escudero, Email: descudero@med.miami.edu, Department of Cell Biology and Anatomy, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6321; Fax: (305) 243-6893

Obi O. Ekwenna, Email: oekwenna@med.miami.edu, Department of Urology, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6596; Fax: (305) 243-6893

Travis J. Yates, Email: tyates@med.miami.edu, Sylvester Comprehensive Cancer Center, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6596; Fax: (305) 243-6893

Rajinikanth Ayyathurai, Email: rayyathurai@med.miami.edu, Departments of Urology, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6321; Fax: (305) 243-6893

Michael Garcia-Roig, Email: mgarciaroig@med.miami.edu, Department of Urology, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6596; Fax: (305) 243-6893.

Jeffrey C. Gahan, Email: jgahan@med.miami.edu, Department of Urology, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6596; Fax: (305) 243-6893

Murugesan Manoharan, Email: mmanoharan@.med.miami.edu, Departments of Urology, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6596; Fax: (305) 243-6893

Vincent G. Bird, Email: Vincent.Bird@urology.ufl.edu, (present address): Department of Urology, University of Florida College of Medicine, P.O. Box 100247, Gainesville, Florida, 32610. Phone: (352) 273-6815.

Vinata B. Lokeshwar, Email: vlokeshw@med.miami.edu, Departments of Urology, Cell Biology and Anatomy, Sylvester Comprehensive Cancer Center, University of Miami School of Medicine, P.O. Box 016960, Miami, Florida. Phone: (305) 243-6596; Fax: (305) 243-6893.

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