Systematic Comparative Protein Expression Profiling of Clear Cell Renal Cell Carcinoma

Abstract

Proteome-based technologies represent powerful tools for the analysis of protein expression profiles, including the identification of potential cancer candidate biomarkers. Thus, here we provide a comprehensive protein expression map for clear cell renal cell carcinoma established by systematic comparative two-dimensional gel electrophoresis-based protein expression profiling of 16 paired tissue systems comprising clear cell renal cell carcinoma lesions and corresponding tumor-adjacent renal epithelium using overlapping narrow pH gradients. This approach led to the mapping of 348 distinct spots corresponding to 248 different protein identities. By implementing restriction criteria concerning their detection frequency and overall regulation mode, 28 up- and 56 down-regulated single target spots were considered as potential candidate biomarkers. Based on their gene ontology information, these differentially expressed proteins were classified into distinct functional groups and according to their cellular distribution. Moreover, three representative members of this group, namely calbindin, gelsolin, and heart fatty acid-binding protein, were selected, and their expression pattern was analyzed by immunohistochemistry using tissue microarrays. Thus, this pilot study provides a significant update of the current renal cell carcinoma map and defines a number of differentially expressed proteins, but both their potential as candidate biomarkers and clinical relevance has to be further explored in tissues and for body fluids like serum and urine.

Human renal cell carcinoma (RCC)¹ is a heterogeneous disease with increasing incidence over the past 20 years. It is classified into various subtypes according to morphological features of the tumors. The most common subtype, clear cell (cc) RCC, accounts for ∼75% of all kidney cancers, whereas the papillary, chromophilic RCC (10–15%), the chromophobic RCC (5%), tumors of unclassified subtypes (<2%), collecting duct RCC (<1%), and the benign tumors of the oncocytic subtype (<1%) are less frequently represented (1). So far, surgery is the first line curative treatment option for organ-defined disease, but more than 30% of RCC patients have already developed an advanced disease or metastases at the time of presentation leading to a 5-year survival rate of less than 20% (2). Despite its radio- and chemotherapy resistance, 10–20% of RCC patients respond to immunotherapies (3, 4) and to targeted molecules, including inhibitors of growth factors, growth factor receptors, and signal transduction molecules (5–7).

During the last decade, classical molecular genetics, cytogenetics, and expression profiling have increased the knowledge about the biology and pathophysiology of the various histological subtypes of RCC (8–12). Results of cDNA profiling support the existence of an individual fingerprint of each RCC subtype regarding its biological features, clinical behavior, and unique sensitivity to therapy. The identification of a number of excellent diagnostic tools allows the differentiation between the respective RCC subtypes (8). These include for example the c-kit gene, which is up-regulated in chromophobic RCC (13), as a marker or genes of the carbonic anhydrase II for chromophobic RCC, GST for ccRCC, and α-methylacyl racemase for chromophilic RCC (14).

However, sequential changes in cellular mechanisms defining the transformation process from a normal to a malignant phenotype not only occur at the transcriptional level but also involve posttranscriptional alterations, particularly protein modifications like phosphorylation and glycosylation, which are not detected by microarray analysis. Therefore, the characterization of the differential protein expression pattern in malignant versus normal cells/tissues is at least complementary to the cDNA microarrays to identify putative biomarkers involved in cancer-related processes.

Among the proteome technologies, two-dimensional electrophoresis (2DE) followed by mass spectrometric analysis is the most established method, resulting in the identification of differentially expressed proteins. Indeed, proteome studies have identified important protein sets that are involved in the molecular mechanisms underlying the malignant behavior of tumor cells. This might consequently lead to the definition of biomarkers and novel therapeutic targets. Using distinct proteome-based strategies, a number of differentially expressed proteins have recently been identified in RCC lesions and cell culture models that might significantly contribute to the understanding of the development of this disease (15–21). These candidate biomarkers belong to different protein families, including annexins, metabolic enzymes, proteins of signal transduction pathways, antigen processing and presentation components, growth factors, differentiation markers, tumor suppressor genes, cytoskeletal components, and stress proteins as well as proteins involved in chemotherapy resistance.

Despite these studies, the information existing for RCC is limited to a small number of differentially expressed proteins, which often lack proper verification by RT-PCR and/or immunohistochemistry on a large series of RCC tissue specimens. In addition, the overall RCC proteomes still remain to be elucidated. Thus, validated biomarkers used for diagnosis, prognosis, and treatment monitoring of this disease are still urgently needed. Therefore, in this pilot study, two virtual ccRCC maps highlighting 348 differentially expressed protein spots on either acidic (pH 4–7) and/or on basic (pH 6–11) 2DE consensus gels were established. Upon annotation of the identified spots, the map represents 248 distinct protein identities. Moreover, immunohistochemical stainings using a tissue microarray (TMA) comprising ccRCC but also other malignant and benign RCC subtypes as well as corresponding tumor-adjacent tissue sections were performed for three representative differentially expressed proteins selected based on their novelty. The data, although not sufficiently conclusive for any of the tested candidate targets, nevertheless demonstrate that each of the analyzed targets provides at least some hints and thus that this approach is indeed suited to uncover candidate biomarkers, which upon further verification and validation might indeed define novel diagnostic, prognostic, and/or therapeutic markers for this malignancy.

EXPERIMENTAL PROCEDURES

Patients and Tissue Samples

Tissue samples from 16 primary ccRCC lesions and autologous tumor-adjacent renal tissue were obtained from RCC patients who had undergone radical nephrectomy at the University Hospital of the Johannes Gutenberg University in Mainz, Germany. Informed consent was obtained from each of the patients. None of the patients had received chemotherapy or radiation in advance. Histopathological classification of each tumor was performed according to previously proposed criteria (22, 23), which are in line with the recent classification by the World Health Organization. The criteria include stage of disease and tumor invasion according to the TNM system as well as tumor grade as outlined in Table I and Ref. 24. The tumor samples used for analyses were either snap frozen (proteomics) or paraffin-embedded (IHC) immediately after resection and have been previously described in detail (25–27).

Table I. Characteristics of RCC lesions analyzed.

The characteristics of the 16 RCC biopsy systems (RCC lesion/corresponding tumor-adjacent renal tissue) including TNM status, tumor grading, metastasis formation, the patients' follow-up in months, and the survival of patients are summarized. In addition the number of technical replicates for each tissue type and pH gradient is given. All samples were obtained from the Department of Urology, University Hospital, Johannes Gutenberg University, Mainz, Germany. NN, tumor-adjacent renal epithelium; TU, tumor.

Biopsy system no.	TNM status	Grading	Metastasis	Follow-up	Survival	Replicates
						NN		TU
						pH 4–7	pH 6–11	pH 4–7	pH 6–11
				months
2884	pT2, N0, Mx	G3	Yes	10	Yes	3	3	5	4
2891	pT1, Nx, Mx	G2–3	No	25	Yes	4	4	4	3
2894	pT3, Nx, Mx	G1–2	No	24	Yes	4		4
2907	pT2, Nx, Mx	G1	No	17	Yes	4	3	3	3
2908	pT1, Nx, Mx	G2	No	0	Noa	4	3	4	3
2911	pT2, Nx, Mx	G2	Yes	8	Yes	4	3	5	4
2913	pT2, Nx, Mx	G2	No	17	Yes	3	4	3	3
2914	pT1, Nx, Mx	G2	No	17	Yes	5	3	5	3
2920	pT1, Nx, Mx	G2	No	16	Yes	4	3	4	3
2922	pT3a, N0, Mx	G2	No	16	Yes	3	4	4	4
2923	pT3a, N1, Mx	G3	Not known	31	Yes	4	4	4	3
2926	pT1, Nx, Mx	G1	Not known	30	Yes	3	5	4	3
2932	pT2, N0, Mx	G2–3	No	29	Yes	5	5	3	5
2938	pT2, N0, Mx	G2	No	27	Yes	4		4
2940	pT1, Nx, Mx	G2	No	27	Yes	5	5	5	4
2944	pT1b, Nx, Mx	G2	Yes	24	Yes	5	5	4	4

^a Death not related to cancer.

2D Gel Electrophoresis of Tissue Specimen

The set of tissue specimens was continuously collected over a time period of 3 years but directly subjected to 2DE upon receipt. For sample preparation, ∼100 mg of frozen tissue material directly cryoconserved in liquid nitrogen upon organ resection were transferred into a sterile 6-well plate (Falcon, BD Biosciences), thawed, thoroughly sliced with a disposable scalpel (PMF AG, Cologne, Germany), and resuspended in 500 μl of PBS (Cambrex Bio Science, Viviers, Belgium). The cell suspension was centrifuged for 2 min at 10,000 × g and washed twice with PBS. In case that the separation in the first dimension was performed in the pH 4–7 range, the resulting cell pellet was directly resuspended in 200 μl of lysis buffer (7 m urea (AppliChem Biochemica, Darmstadt, Germany), 2 m thiourea (Sigma-Aldrich), 0.2 m dimethylbenzylammonium propane sulfonate (NDSB-256, ICN Biomedicals, Eschwege, Germany), 1% DTT (AppliChem Biochemica), 4% w/v CHAPS (AppliChem Biochemica), 0.5% v/v Pharmalyte (Amersham Biosciences), and a trace of the dye bromophenol blue (Serva, Heidelberg, Germany) and further processed using the PlusOne sample grinding kit (Amersham Biosciences) according to the manufacturer's guidelines. Finally, the tissue lysate was subjected to sonication (2 × 5 cycles, 0.5 s at 100% power; Bandelin UW 2070 sonicator, MS 73 needle, Bandelin, Berlin, Germany) in the presence of the grinding resin. Isoelectric focusing, second dimension SDS-PAGE separation, and gel staining procedures were carried out as described previously (28). Prior to the protein separation in the pH 6–11 range, the lysates were additionally TCA/acetone-precipitated according to the method described by Görg et al. (29).

Image Analysis

Quantitative image analysis was carried out with the Proteomweaver software package (Versions 3.1 or 4.0; Bio-Rad GmbH) according to the manufacturer's instructions. The mapping data reflect the mean of at least three to five technical replicates. The digitized spot pattern of each gel was first matched within the given group (ccRCC or tumor-adjacent kidney) and subsequently between the two experimental groups. The matching procedures of each step were visually controlled and reedited if necessary prior to the normalization process. The Proteomweaver software relies on a prematch and pair match-based normalization as recently described in more detail (30). Protein spots were considered as differentially expressed if a 2-fold altered expression level was observed. In addition, the spots of interest were statistically analyzed with the Student's t test for each matched sample set and only those selected for mass spectrometric identification with a p value <0.05. In addition, spots displaying a protein expression pattern restricted to either the tumor or the corresponding tumor-adjacent renal epithelium were also selected for mass spectrometric identification. Furthermore, to gain information about the quality of the differentially expressed proteins as potential biomarkers and to exclude the individual genetic heterogeneity, the overall regulation frequency across the complete set of samples was compared.

Microanalytical Characterization

Protein identification was performed as described previously (31). Relevant spots were excised from the gel, in-gel digested, and then subjected to mass spectrometry. For spot excision and tryptic in-gel hydrolysis of 2D gel spots, 2D gels were colloidal Coomassie Blue-stained overnight (50% methanol, 10% acetic acid, and 0.05% colloidal Coomassie Blue R-250) and destained for 8 h (5% methanol and 7% acetic acid). The gel fragments were briefly washed with 50 μl of 50 mm NH₄HCO₃ and then washed three times with 50 μl of 30% ACN and 50 mm NH₄HCO₃ before they were incubated twice for 20 min in 100 μl of ACN and subsequently air-dried. The resulting pellets were resuspended in 1–3 μl of 5 mm Tris buffer, pH 8.0 containing 0.03 μg/μl bovine trypsin (Roche Diagnostics), and then 5–15 μl of 5 mm Tris buffer, pH 8.0 (depending on the size of the gel piece) were added and incubated overnight at 37 °C.

MALDI target preparation was performed using the following procedure: 0.45 μl of the supernatant was loaded on the target and allowed to dry at room temperature before 0.45 μl of matrix solution (5 mg/ml α-cyano-4-hydroxycinnamic acid in 50% ACN and 0.1% TFA) was added and also dried at room temperature. If necessary the tryptic peptides were desalted with ZipTip C₁₈ reverse-phase tips according to the manufacturer's instructions (Millipore, Schwalbach, Germany) and directly eluted on the MALDI target plate. MS and MS/MS spectra were acquired with 2500 laser shots (neodymium-doped yttrium aluminium garnet laser, 355 nm, N₂ collision in the MS/MS mode) using a 4700 MALDI-TOF-TOF mass spectrometer (Applied Biosystems, Foster City, CA). Peak lists were generated using a minimum S/N of 5 within the window of 200 m/z and cluster area S/N of 12 for PMF and an S/N of 5 within the window of 200 m/z and cluster area S/N of 8 for MS/MS.

The PMF data set was analyzed using the Mascot search engine with the following parameters: fixed modification, carbamidomethyl; variable modifications, oxidation (Met), pyro-Glu (Glu), cleavage enzyme, trypsin (*KR), maximum missed cleavages, 1; and mass tolerance (monoisotopic), 50.0 ppm. MS/MS spectra were searched as above using a peptide mass tolerance of 200 ppm and a fragment mass tolerance of 0.5 Da.

The confidence in terms of the target identification is based on the overall sequence coverage of matching peptide fragments following digestion with trypsin and/or the probability-based Mowse score with the web-based Mascot search engine searching the Swiss-Prot Knowledgebase Release (Versions 41.18–53.1) (Matrix Science Ltd., London, UK) and the probability score (web-based Profound Peptide Mapping program, Version 4.10.5; The Rockefeller University, New York, NY). Proteins were assigned when the Mascot/Profound score exceeded 56/1.6 according to the defined significance threshold for false-positive events at p < 0.05. Targets that could not be assigned to distinct identities based on the analytical data were eliminated from the target list to minimize the chance of false-positive protein identification. Specific protein isoforms were assigned if a specific peptide for those forms had been detected. Detailed analytical MS/MS data analyses were only performed when the initial PMF data were not conclusive.

Data Mining

Mass spectrometric results were analyzed by a self-made web-based tool, which was programmed in php combined with a mySQL database. To combine the information from the spots obtained by Proteomweaver analysis and the protein spots identified by mass spectrometry, the raw data were reorganized into the database. To eliminate redundancy, the heterogeneous data sets of the identified proteins were linked to the most recent Swiss-Prot identities (UniProt Knowledgebase Release 14.9), which served as the major source for additional information such as the chromosomal localization, and gene ontology, which provides information about gene function and cellular localization. Based on this information, the protein families and compartment localizations were assigned to each protein. In terms of the cellular function, the proteins were distributed into the categories cell adhesion, cell motility, expression control, metabolism, multiple, other (not further specified), proliferation, signal transduction, structural molecules, stress proteins, transport proteins, and unknown function. The categories for the cellular localization include the cytoplasm, cytoskeleton, endoplasmic reticulum, extracellular region, membrane, mitochondrion, multiple compartments, nucleus, other (not further specified), and unknown. Furthermore, a consensus gel for each pH gradient with all spots identified was generated for a comprehensive spot overview. To understand probable interactions of the various regulated proteins, these targets were further assigned if possible to known pathways.

Immunohistochemistry

Paraffin blocks of tumor and tumor-adjacent renal tissues were used for representative immunohistochemical stainings. The samples were selected and cut into 5-μm sections using a cryostat. For the preparation of TMAs, at least three tissue cylinders each (4 mm in diameter) were punched from selected regions of the respective donor blocks and placed on 25 × 35-mm paraffin blocks. The panel of formalin-fixed and paraffin-embedded RCC lesions represented on the TMAs comprised primary RCCs, including up to 40 ccRCC lesions, 31 chromophilic RCC lesions, 16 chromophobic RCC lesions, and nine renal adenomas of oncocytic subtype as well as corresponding tumor-adjacent renal tissue. For antigen retrieval, consecutive sections were incubated for 2–8 min in citrate buffer in a microwave oven followed by a washing procedure with Tris-buffered saline and an additional incubation with normal swine serum (dilution 1:5; Dako, Hamburg, Germany) for 10 min. All sections were stained with hematoxylin and eosin to identify tumor tissue. Immunohistochemistry was performed as described previously (27) by applying the mAbs directed against calbindin (1:100; ab9481, Abcam plc, Cambridge, UK) and gelsolin (1:50; 610412, BD Transduction Laboratories) or a polyclonal Ab directed against heart fatty acid-binding protein (H-FABP; 1:50; 18-003-42444, GenWay Biotech Inc., San Diego, CA) for 1 h at room temperature. Immunoreactivity was detected using the commercially available streptavidin-biotin (labeled streptavidin-biotin technique)-peroxidase kit and 3-amino-9-ethylcarbazol (Dako). Negative controls were performed by omitting the primary antibodies. The extent of immunostaining was scored according to the following criteria: negative, <5% positive cells; weak positive, 5–25% positive tumor cells; intermediate positive, 26–50% positive tumor cells; and strong positive, >50% positive tumor cells.

RESULTS

Systematic Comparison of Protein Expression Profiles of ccRCC Lesions and Corresponding Tumor-adjacent Kidney Epithelium

To determine the differential protein expression pattern of RCC, total protein extracts obtained from 16 tissue systems consisting of primary RCC lesions of the ccRCC subtype and corresponding tumor-adjacent kidney epithelium were subjected to classical 2DE using overlapping pH gradients of pH 4–7 and pH 6–11. To strengthen the quality and reproducibility of the expression profiling, the systematic comparisons relied on three to five technical replica gels per sample. Because of the limited amount of sample, the systems 2894 and 2938 had to be excluded from the analysis in the alkaline pH range (Table I). The quality and reproducibility of the individual profiling is summarized in supplemental Fig. 1A, which provides information regarding the average spot count and its coefficient of variation within each of the completed gel series. On average, between 798 ± 80 and 484 ± 49 protein spots per gel were detected in the acidic and basic pH gradients, respectively.

In total, 348 spots displaying either a restricted expression pattern to tumor or tumor-adjacent tissue or a regulation factor >2 were analyzed and mapped. These spots represent 248 distinct protein identities as verified by either conclusive PMF and/or MS/MS analysis from which 231 spots represent single target spots (containing one protein identity), 84 spots represent double target spots (containing two distinct proteins), and 33 spots represent multiple target spots (containing more than two proteins). Representative virtual 2DE maps of combined consensus protein expression profiles of ccRCC lesions/tumor-adjacent kidney epithelium are shown in Fig. 1. The mapped spots are marked by numbers. High resolution views of the representative gels used for the design of the consensus maps are provided in supplemental Fig. 1B.

Fig. 1.

Consensus master maps of differentially expressed proteins in ccRCC lesions compared with tumor-adjacent renal epithelium. Representative 2DE gels displaying the consensus protein expression profiles characteristic for ccRCC using pH 4–7 (A) and pH 6–11 (B) gradients are shown. The numbers assigned to the mapped protein spots correspond to the proteins listed in Table II and supplemental Table 1.

In terms of the distribution pattern, 71% (n = 176) of the mapped proteins were identified in the pH 4–7 range, and 24% (n = 59) were identified in the pH 6–11 range. The remaining 5% (n = 13) of the identified proteins were found in both pH gradients (supplemental Fig. 2A). 151 of the proteins presented in this report are shared with the 461 proteins that have already been described using RCC-specific profilings. However, with 97 additional entries, the number of proteins identified is now increased to 558 (supplemental Fig. 2B) (17–21).

By applying restriction criteria such as a minimal detection frequency set at 5, requiring detection in at least five independent tissue pairs, and merely focusing on the group of single target spots, the number of candidate spots was reduced to 132 members. Nine of these 132 spots represent blood contaminants such as serum albumin (five spots), serotransferrin (two spots), Ig α-1 chain C region (one spot), and haptoglobin (one spot), which were further excluded from the data. Some key features for the remaining subset of 123 differentially expressed spots are summarized in Table II, which lists the virtual map ID next to the given Swiss-Prot ID, protein name, function, cellular localization, detection frequency, theoretical and experimental pI values, molecular weight, and the regulation mode. The respective data sets for less frequently detected single, double, and multiple target spots are provided in supplemental Table 1.

Table II. Differentially expressed proteins identified in RCC biopsies compared with tumor-adjacent renal tissues.

Spot, spot ID on consensus master 2D gel; Swiss-Prot ID, official Swiss-Prot accession number; Protein, official protein name; Theor. pI, theoretical isoelectric point; Theor. mass, theoretical mass in kDa; Exp. pI, experimental isoelectric point; Exp. mass, experimental mass in kDa; Up, up-regulated spots (regulation ≥2; t test ≤0.05 or restricted to tumor); Dn, down-regulated spots (regulation ≤0.5; t test ≤0.05 or restricted to tumor-adjacent renal tissue); NR/ND, spots not regulated or not detected. The bold protein spots represent potential markers as defined by their overall regulation ratios (up- versus down-regulated ≥2×) and detection frequency (≥5) across the total number of analyzed sample sets.

Spot	Swiss-Prot ID	Protein	Cellular function	Cellular compartment	Theor. pI	Theor. mass	Exp. pI	Exp. mass	Dn	Up	NR/ND
Targets identified on pH 4–7 2DE gels
2	P12109	Collagen α-1(VI) chain	Cell adhesion	Cytoskeleton	5.26	108.5	5.14	115.3	3	6	7
4	P38646	Stress-70 protein	Stress protein	Multiple compartment	5.87	73.68	5.37	73.16	5	1	10
8	P05787	Keratin, type II cytoskeletal 8	Structural molecule	Cytoskeleton	5.52	53.57	5.35	58.98	3	4	9
11	P08670	Vimentin	Cell motility	Cytoskeleton	5.06	53.52	4.7	54.92	1	6	9
12	P52566	Rho GDP dissociation inhibitor 2	Multiple	Cytoplasm	5.1	22.86	5.06	30.11	1	6	9
13	P07858	Cathepsin B	Other	Mitochondrion	5.88	37.82	5.19	28.45	6	2	8
32	P09525	Annexin A4	Signal transduction	Cytoplasm	5.85	35.75	5.85	38.76	4	3	9
33	P30084	Enoyl-CoA hydratase	Metabolism	Mitochondrion	8.34	31.39	5.68	92.19	11	2	3
34	P02753	Plasma retinol-binding protein	Transport protein	Extracellular region	5.76	23.01	5.3	24.93	5	1	10
35	P22352	Glutathione peroxidase 3	Stress protein	Extracellular region	8.2	25.51	4.81	21.08	11	2	3
36	P22352	Glutathione peroxidase 3	Stress protein	Extracellular region	8.2	25.51	4.87	20.96	7	1	8
39	P07148	Fatty acid-binding protein, liver (L-FABP)	Multiple	Cytoplasm	6.6	14.21	6.34	7.17	11	1	4
43	P16083	Ribosyldihydronicotinamide dehydrogenase (quinone)	Transport protein	Cytoplasm	5.88	25.82	6.06	28.73	6	2	8
50	O00217	NADH dehydrogenase (ubiquinone) iron-sulfur protein 8	Stress protein	Mitochondrion	6	23.71	5.09	26.59	7	2	7
53	O75891	10-Formyltetrahydrofolate dehydrogenase	Metabolism	Cytoplasm	5.63	98.83	5.96	90.05	0	5	11
56	P11021	78-kDa glucose-regulated protein	Stress protein	ER	5.07	72.33	5.02	81.47	1	4	11
59	P10809	60-kDa heat shock protein	Stress protein	Mitochondrion	5.7	61.05	5.32	68.04	6	1	9
69	O75947	ATP synthase subunit d, mitochondrial	Transport protein	Mitochondrion	5.22	18.36	5.2	24.68	8	2	6
78	P19404	NADH dehydrogenase (ubiquinone) flavoprotein 2	Transport protein	Mitochondrion	8.22	27.39	6.07	27.78	10	1	5
85	P02753	Plasma retinol-binding protein	Transport protein	Extracellular region	5.76	23.01	5	21.66	10	1	5
87	P05937	Calbindin	ion binding	Cytoplasm	4.7	29.89	4.47	29.83	6	0	10
90	P52565	Rho GDP dissociation inhibitor 1	Cell motility	Cytoskeleton	5.03	23.08	4.77	32.13	3	2	11
99	P09104	γ-Enolase	Metabolism	Cytoplasm	4.91	47.14	6.38	42.3	0	5	11
100	Q13162	Peroxiredoxin-4	Other	Cytoplasm	5.86	30.54	5.76	32.13	5	4	7
102	O15540	Fatty acid-binding protein, brain	Multiple	Cytoplasm	5.41	14.76	5.24	8.99	2	4	10
103	P51687	Sulfite oxidase	Metabolism	Mitochondrion	5.35	53.88	5.41	71.39	4	3	9
104	P10809	60-kDa heat shock protein	Stress protein	Mitochondrion	5.7	61.05	5.23	68.04	8	3	5
106	P11142	Heat shock cognate 71-kDa protein	Stress protein	Multiple compartment	5.37	70.9	5	49.45	2	4	10
111	P25705	ATP synthase subunit α	Transport protein	Mitochondrion	9.16	59.75	4.72	9.34	8	1	7
112	P20674	Cytochrome c oxidase subunit 5A	Transport protein	Mitochondrion	6.3	16.77	4.72	9.61	9	3	4
113	P10599	Thioredoxin	Signal transduction	Cytoplasm	4.82	11.61	4.77	8.11	8	1	7
115	P22352	Glutathione peroxidase 3	Stress protein	Extracellular region	8.2	25.51	5.83	26.72	7	2	7
116	P50053	Ketohexokinase	Metabolism	Cytoplasm	5.64	32.73	6.16	36.28	8	2	6
120	P05413	Fatty acid-binding protein, heart	Proliferation	Cytoplasm	6.34	14.73	6.34	9.43	7	3	6
121	P14625	Endoplasmin	Stress protein	ER	4.76	92.47	4.74	65.83	1	5	10
122	Q16891	Mitochondrial inner membrane protein	Proliferation	Mitochondrion	6.08	83.68	6.18	87.01	1	6	9
123	Q92597	Protein NDRG1	Proliferation	Multiple compartment	5.49	42.84	5.74	55.41	2	5	9
131	P19971	Thymidine phosphorylase	Multiple	Extracellular region	5.36	49.96	5.24	52.33	0	6	10
138	P18669	Phosphoglycerate mutase 1	Metabolism	Cytoplasm	6.75	28.67	6.78	38.42	2	4	10
147	P31937	3-Hydroxyisobutyrate dehydrogenase	Metabolism	Mitochondrion	8.38	35.33	5.77	36.77	6	1	9
148	Q9NVS9	Pyridoxine-5′-phosphate oxidase	Metabolism	Unknown	6.61	29.99	6.34	31.83	5	1	10
154	P09960	Leukotriene A-4 hydrolase	Stress protein	Cytoplasm	5.8	69.15	6.17	72.45	4	3	9
163	P07237	Protein-disulfide isomerase	Multiple	Multiple compartment	4.76	57.12	4.73	69.68	1	5	10
169	P42126	3,2-trans-Enoyl-CoA isomerase	Metabolism	Mitochondrion	8.8	32.82	6.38	32.57	7	1	8
172	P04792	Heat shock protein β-1	Stress protein	Multiple compartment	5.98	22.78	6.25	31.98	3	4	9
181	P10809	60-kDa heat shock protein	Stress protein	Mitochondrion	5.7	61.05	6.01	56.15	5	2	9
182	P30084	Enoyl-CoA hydratase	Metabolism	Mitochondrion	8.34	31.39	5.9	28.05	11	1	4
183	P09525	Annexin A4	Signal transduction	Cytoplasm	5.85	35.75	6.84	14.14	5	1	10
184	P25705	ATP synthase subunit α	Transport protein	Mitochondrion	9.16	59.75	4.88	8.99	9	1	6
186	P11021	78-kDa glucose-regulated protein	Stress protein	ER	5.07	72.33	5.02	81.47	1	5	10
189	P14550	Alcohol dehydrogenase (NADP+)	Metabolism	Cytoplasm	6.35	36.44	5.84	35.8	5	2	9
199	Q9Y2T3	Guanine deaminase	Metabolism	Other	5.44	51	5.59	56.91	5	1	10
205	Q9Y2T3	Guanine deaminase	Metabolism	Other	5.44	51	5.5	56.65	3	2	11
213	P14625	Endoplasmin	Stress protein	ER	4.76	92.47	46.9	68.69	3	4	9
214	P21281	Vacuolar ATP synthase subunit B, brain isoform	Transport protein	Cytoplasm	5.57	56.5	5.54	65.83	2	3	11
216	Q9P2R7	Succinyl-CoA ligase (ADP-forming) β chain	Metabolism	Mitochondrion	7.05	50.32	6	52.79	8	1	7
223	P08670	Vimentin	Cell motility	Cytoskeleton	5.06	53.52	5.09	67.71	2	4	10
224	P08670	Vimentin	Cell motility	Cytoskeleton	5.06	53.52	4.94	63.72	5	2	9
233	P02679	Fibrinogen γ chain	Proliferation	Extracellular region	5.37	51.51	5.69	61.15	3	5	8
239	P07148	Fatty acid-binding protein, liver (L-FABP)	Multiple	Cytoplasm	6.6	14.21	6.34	7.17	4	3	9
241	P06396	Gelsolin	Structural molecule	Multiple compartment	5.9	85.7	5.96	90.05	0	5	11
242	Q92597	Protein NDRG1	Proliferation	Multiple compartment	5.49	42.84	5.71	55.66	2	4	10
244	P52565	Rho GDP dissociation inhibitor 1	Cell motility	Cytoskeleton	5.03	23.08	4.93	30.96	4	2	10
246	Q99497	Protein DJ-1	Signal transduction	Nucleus	6.33	19.89	6.49	26.08	5	2	9
247	P02766	Transthyretin	Transport protein	Extracellular region	5.52	15.89	5.6	12.48	5	2	9
249	P20674	Cytochrome c oxidase subunit 5A	Transport protein	Mitochondrion	6.3	16.77	5.96	10.88	5	0	11
252	P26038	Moesin	Cell motility	Cytoskeleton	6.09	67.69	6.69	81.47	3	3	10
253	P17987	T-complex protein 1 subunit α	Metabolism	Cytoplasm	5.8	60.34	6.12	69.35	3	4	9
256	Q14697	Neutral α-glucosidase AB	Metabolism	ER	5.74	106.9	6.02	95.61	2	4	10
257	Q14697	Neutral α-glucosidase AB	Metabolism	ER	5.74	106.9	6.19	95.03	2	3	11
260	P26038	Moesin	Cell motility	Cytoskeleton	6.09	67.69	6.57	81.47	2	3	11
266	P67936	Tropomyosin α-4 chain	Cell motility	Cytoskeleton	4.67	28.39	4.53	47.55	3	3	10
267	P07951	Tropomyosin β chain	Structural molecule	Cytoskeleton	4.66	32.85	4.6	46.94	3	3	10
270	O75891	10-Formyltetrahydrofolate dehydrogenase	Metabolism	Cytoplasm	5.63	98.83	5.9	94.44	0	5	11
271	Q9BSE5	Agmatinase	Metabolism	Mitochondrion	8.02	37.76	6.16	36.28	3	2	11
272	P23526	Adenosylhomocysteinase	Metabolism	Cytoplasm	5.92	47.58	6.39	55.17	4	3	9
274	P50440	Glycine amidinotransferase	Metabolism	Mitochondrion	8.26	48.46	6.57	57.16	4	1	11
275	P16219	Short-chain-specific acyl-CoA dehydrogenase	Metabolism	Mitochondrion	8.13	44.3	6.66	37.09	6	2	8
278	P14625	Endoplasmin	Stress protein	ER	4.76	92.47	4.62	96.01	4	6	6
293	P08670	Vimentin	Cell motility	Cytoskeleton	5.06	53.52	4.62	51.2	3	6	7
299	P08670	Vimentin	Cell motility	Cytoskeleton	5.06	53.52	5.14	63.43	0	8	8
300	P02679	Fibrinogen γ chain	Proliferation	Extracellular region	5.37	51.51	5.41	61.15	0	5	11
301	P02679	Fibrinogen γ chain	Proliferation	Extracellular region	5.37	51.51	5.5	59	3	4	9
309	P30837	Aldehyde dehydrogenase X	Metabolism	Mitochondrion	6.41	57.22	6.27	69.02	2	4	10
310	P31040	Succinate dehydrogenase (ubiquinone) flavoprotein subunit	Transport protein	Mitochondrion	7.06	72.69	6.63	75.01	3	4	9
311	P05165	Propionyl-CoA carboxylase α chain	Metabolism	Mitochondrion	6.63	77.35	6.75	74.63	4	6	6
312	P13645	Keratin, type I cytoskeletal 10	Structural molecule	Cytoskeleton	5.13	59.52	6.49	52.56	5	1	10
313	Q14894	μ-Crystallin homolog	Other	Cytoplasm	5.06	33.78	4.87	43.98	4	1	11
323	P21980	Protein-glutamine γ-glutamyltransferase 2	Cell adhesion	Extracellular region	5.11	77.33	5.05	85.56	2	6	8
326	P48637	Glutathione synthetase	Metabolism	Unknown	5.67	52.38	5.84	60.05	3	2	11
328	P61981	14-3-3 protein γ	Signal transduction	Cytoplasm	4.8	28.17	4.51	31.83	4	2	10
329	P63104	14-3-3 protein ζ/δ	Signal transduction	Multiple compartment	4.73	27.75	4.42	32.13	4	4	8
331	P09467	Fructose-1,6-bisphosphatase 1	Metabolism	Unknown	6.61	36.68	6.92	48.59	5	1	10
332	P50440	Glycine amidinotransferase	Metabolism	Mitochondrion	8.26	48.46	6.88	56.91	4	2	10
333	Q14764	Major vault protein	Other	Multiple compartment	5.34	99.2	5.41	107	3	4	9
334	Q14764	Major vault protein	Other	Multiple compartment	5.34	99.2	5.33	106.2	2	5	9
335	Q14764	Major vault protein	Other	Multiple compartment	5.34	99.2	5.33	106.2	2	3	11
Targets identified on pH 6–11 2DE gels
341	P09467	Fructose-1,6-bisphosphatase 1	Metabolism	Unknown	6.61	36.68	6.4	43.48	7	0	7
343	P05062	Fructose-bisphosphate aldolase B	Metabolism	Cytoplasm	8.06	39.34	7.52	44.63	7	1	6
347	Q99798	Aconitate hydratase	Metabolism	Mitochondrion	7.36	85.43	7.13	78.53	4	3	7
360	P25705	ATP synthase subunit α	Transport protein	Mitochondrion	9.16	59.75	7.16	58.87	7	2	5
367	P00918	Carbonic anhydrase 2	Metabolism	Cytoplasm	6.86	29.11	6.96	32.45	3	3	8
369	P63261	Actin, cytoplasmic 2	Cell motility	Cytoskeleton	5.31	41.79	7.16	58.87	6	2	6
373	P25705	ATP synthase subunit α	Transport protein	Mitochondrion	9.16	59.75	8.01	63.3	2	3	9
388	P06733	α-Enolase	Expression control	Multiple compartment	6.99	47.04	6.7	63.85	5	3	6
392	P50991	T-complex protein 1 subunit δ	Proliferation	Cytoplasm	8.13	57.79	8.33	40.85	3	2	9
393	Q99807	Ubiquinone biosynthesis protein COQ7 homolog	Metabolism	Mitochondrion	8.77	24.31	6.45	30.6	5	3	6
395	P30038	Δ1-Pyrroline-5-carboxylate dehydrogenase	Metabolism	Mitochondrion	8.25	61.72	6.72	63.85	5	1	8
396	P04040	Catalase	Stress protein	Mitochondrion	6.95	59.62	7.51	50.37	6	0	8
402	P11310	Medium-chain-specific acyl-CoA dehydrogenase	Metabolism	Mitochondrion	8.61	46.59	6.64	48.85	4	2	8
404	P09651	Heterogeneous nuclear ribonucleoprotein A1	Expression control	Multiple compartment	9.26	38.71	8.44	28.83	7	0	7
411	P25705	ATP synthase subunit α	Transport protein	Mitochondrion	9.16	59.75	9.12	45.13	6	2	6
414	P30038	Δ1-Pyrroline-5-carboxylate dehydrogenase	Metabolism	Mitochondrion	8.25	61.72	6.5	65.29	7	0	7
424	Q16822	Phosphoenolpyruvate carboxykinase (GTP)	Metabolism	Mitochondrion	7.56	70.64	6.61	69.63	5	1	8
442	Q16836	Hydroxyacyl-coenzyme A dehydrogenase	Metabolism	Mitochondrion	8.88	34.28	8.28	37.28	3	2	9
495	P04075	Fructose-bisphosphate aldolase A	Metabolism	Unknown	8.39	39.29	7.91	47.21	3	4	7
497	P00558	Phosphoglycerate kinase 1	Metabolism	Cytoplasm	8.3	44.48	8.07	46.68	3	4	7
541	P07355	Annexin A2	Proliferation	Membrane	7.56	38.47	6.82	42.22	2	3	9
552	Q02252	Methylmalonate-semialdehyde dehydrogenase (acylating)	Metabolism	Mitochondrion	8.72	57.84	7.51	57.9	3	3	8
562	P05062	Fructose-bisphosphate aldolase B	Metabolism	Cytoplasm	8.06	39.34	7.22	45.13	6	0	8
563	P27144	Adenylate kinase isoenzyme 4	Metabolism	Mitochondrion	8.47	25.27	8.22	31.28	4	1	9
575	P24752	Acetyl-CoA acetyltransferase	Metabolism	Mitochondrion	8.98	45.2	8.12	39.38	0	10	4
576	P24752	Acetyl-CoA acetyltransferase	Metabolism	Mitochondrion	8.98	45.2	7.8	39.67	0	11	3

By additionally focusing on spots with a predominant regulation tendency (meaning that the overall distribution ratio of the regulated spots had to reach a ratio of 2:1), the list of potential candidate spots was further trimmed to 84 members, representing 28 up- and 56 down-regulated spots. The remaining 39 proteins within the group of single target spots showed a heterogeneous expression pattern.

However, it is noteworthy that although not considered as putative biomarker candidates all single target spots including those identified in <5 biopsy systems as well as the double and multiple target spots (supplemental Table 1) are represented in the updated 2DE maps (Fig. 1). Moreover, it is noteworthy that 105 proteins are represented by more than a single spot ID on the established 2DE maps (Fig. 1), suggesting the frequent occurrence of posttranslational modifications (PTMs) in renal tissues. Because the majority of these proteins were detected with co-migrating proteins, they are largely represented in the groups of double and/or multiple target spots (Table II and supplemental Table 1). The discrepancy between the total number of mapped protein spots and the number of identified proteins is due to the clustering of the spots into single, double, and multiple targets and the occurrence of PTM or splice variants, all of which is in line with previously described 2DE analyses (16, 32).

Detailed analytical data for each of the mapped spots such as the sequence coverage, score, and number of matched and unmatched peptides for proteins identified by PMF analyses as well as the peptide mass and MS/MS score for identifications based on MS/MS analyses are summarized in supplemental Table 2. A representative MS spectrum leading to the identification of annexin 4 by PMF analysis is shown in supplemental Fig. 3.

Classification of Differentially Regulated Proteins

Based on existing gene ontology information, the group of differentially expressed proteins (Table II) was clustered according to their cellular function and localization (Fig. 2, A and B). They are involved in different biological processes that facilitate tumor-relevant adjustments concerning growth, invasion, apoptosis, and inflammation. A significant number of the differentially expressed proteins are associated with alterations in the cellular metabolism (40%), stress (11%), transport (10%), cell proliferation (7%), signal transduction (6%), multiple functions (6%), or cell motility (5%). Less frequently represented are proteins with known but not further categorized ontology information (4.5%); proteins associated with structural integrity (4.5%), gene/protein expression control (2.3%), and cellular adhesion (2.3%); and proteins with ion binding characteristics (1.1%). Thus, the categories exhibiting a frequency of <5% are summarized under “other” (Fig. 2A).

Fig. 2.

Classification of differentially expressed proteins according to their cellular function and their cellular localization. The pie charts display the classification of the subset of differentially expressed proteins listed in Table II into 12 functional categories (A) and into 10 distinct cellular compartments (B). The pool of proteins comprises 84 distinct protein identities defining the 123 spots, which were identified in at least five independent biopsy systems. The distribution frequencies in regard to the specified categories within the given chart pie are indicated in percentage of the total number of entries. For each pie chart a cutoff value was set to 5%, meaning that all of the cellular functions/compartments represented below this threshold were summarized under other.

Concerning their cellular distribution, these proteins are localized in the mitochondrion (33%), cytoplasm (27%), multiple compartments (12%), cytoskeleton (10%), and the extracellular region (7%) or have unknown localization. The category other combines subcellular compartments with a cutoff frequency <5% such as proteins with as yet unknown localization (4.5%), the ER (3.4%), membrane (1.1%), other (1.1%), or nucleus (1.1%) (Fig. 2B and Table II). A similar distribution pattern was detected by clustering all mapped proteins (Table II and supplemental Table 1). Although the majority of proteins detected in both pH gradients are metabolic enzymes, proteins associated with stress, transport, cell motility, signal transduction processes, multiple functions, and structural integrity are more frequently represented in gels separated in the pH 4–7 range than in the corresponding pH 6–11 gradients, whereas proteins involved in gene/protein expression control and cell proliferation were more often detected in gels representing the basic pH gradient. The most frequent compartment localization in the acidic pH range is the mitochondrion followed by the cytoplasm and the cytoskeleton, although the number of mitochondrial proteins is even more pronounced in the alkaline pH range. In addition, a strong decrease of cytoskeletal proteins and proteins with as yet unspecified compartment localization was found in pH 6–11 gels. Moreover, the genes coding for the differentially expressed proteins were randomly distributed over a wide range of chromosomes (data not shown). Thus, the functional and local distributions of the proteins identified are likely to represent specific characteristics of the ccRCC proteome in comparison with tumor-adjacent kidney epithelium.

Verification of Selected Targets in RCC Lesions of Distinct Subtypes Using Immunohistochemistry

An important step in the cancer biomarker identification is the verification of the putative diagnostic or prognostic biomarkers on a large series of tumors and/or body fluids like serum and urine. Here we selected three candidate biomarkers for immunohistochemical stainings based on commercially available antibodies and the novelty of the specific antigens in the context of ccRCC. These include (i) calbindin, representing a member of the single target spot cluster, which was selected because of its restricted regulation pattern, (ii) gelsolin, one of the top scoring single target spots in terms of its prevalence, and (iii) H-FABP, a representative member of the FABP protein family, which has been shown in a previous report to be heterogeneously expressed in RCC (28). Moreover, H-FABP also seems to exhibit posttranslational modifications because it was detected in two single target spots on the consensus maps.

Calbindin Immunostaining

Immunohistochemical staining of tumor-adjacent renal tissues with a calbindin-specific antibody demonstrated a strong cytoplasmic staining for calbindin in mesangial cells of the glomeruli and the epithelium of the proximal and distal tubule system as well as in endothelial cells. However, calbindin is not ubiquitously expressed in tumor-adjacent renal tissue because only some, but not all, cells of the proximal (solid arrow) as well as the distal tubule system (open arrow) in non-tumorous kidney tissues exhibited a positive staining pattern (Fig. 3, A and B). In contrast, lesions of ccRCC and chromophobic RCC lacked calbindin staining (Fig. 3, C and D).

Fig. 3.

Immunohistochemical analysis for calbindin expression in tumor-adjacent renal epithelium and distinct kidney tumor subtypes. Randomly selected sections of the TMA representing tumor-adjacent renal epithelium and various RCC subtypes were used to document the immunohistochemical staining with the anti-human calbindin-specific mAb. A shows the immunohistochemical analysis of calbindin in tumor-adjacent kidney tissue at 200× magnification; B refers to the staining pattern at a magnification of 400×. Calbindin is expressed in some but not all cells of the proximal (solid arrows) as well as the distal tubule system (open arrows) in non-tumorous kidney tissues. Yet both stainings show sporadic strong positive stainings of the epithelium of both the proximal and the distal tubule systems. In contrast, the ccRCCs (C; magnification, 400×) and RCC of the chromophobic type (D; magnification, 400×) lack calbindin staining.

The TMA stainings using the calbindin-specific mAb further strengthen the use of calbindin as a putative marker to differentiate among normal tissue, benign renal tissue, and malignant biopsies because a heterogeneous calbindin staining pattern was found in different RCC subtypes when compared with tumor-adjacent kidney epithelium (Fig. 4). Calbindin staining was detectable in less than 5% of the analyzed sections and was thus categorized as negative staining in ccRCC (Fig. 4A). For chromophilic RCC lesions (Fig. 4B), a strong positive or intermediate positive cytoplasmic staining for calbindin was found in only 3.2%, respectively, whereas a weak positive reaction was seen in 19.4% and a total lack of calbindin staining was seen in 74.2% of the analyzed lesions. A similar staining pattern was detected in the chromophobic RCC subtype showing weak cytoplasmic staining in 6.2% of biopsies analyzed and a lack of staining with the anti-calbindin mAb in 93.8% of the tumor lesions (Fig. 4C). In contrast, the benign oncocytoma displayed a strong positive staining for calbindin in 33.3% and weak and negative stainings in 22.3 and 44.4%, respectively, of the samples analyzed (Fig. 4D). Thus, a predominant lack of calbindin expression might be associated with the malignant phenotype of the disease.

Fig. 4.

Immunohistochemical analysis for calbindin expression using TMA technology. Immunohistochemical staining of the TMA representing the four major RCC subtypes along with corresponding tumor-adjacent renal epithelium was performed using a human anti-calbindin-specific mAb (TMA panel: 40 ccRCC lesions, 31 chromophilic RCC lesions, 16 chromophobic lesions, and nine oncocytic lesions, each flanked by corresponding tumor-adjacent renal tissue sections). The classification of the staining pattern indicated in each panel is based on the criteria defined under “Experimental Procedures.” Black segments indicate strong positive staining, dark gray segments indicate intermediate staining intensity, light gray segments indicate weak staining intensity, and white segments indicate negative staining. The data are expressed as relative percentage of total samples stained. The staining pattern for calbindin is shown on ccRCC (A), chromophilic RCC (B), chromophobic RCC (C), and renal cell adenomas of oncocytic type (D).

Gelsolin Immunostaining

Staining of the tumor-adjacent kidney tissue sections with a gelsolin-specific antibody showed that gelsolin is expressed in the epithelium of the distal tubule system and collecting ducts, whereas the epithelium of the proximal tubule system and the glomeruli revealed no gelsolin expression (Fig. 5A). This is in accordance with the staining pattern of RCC lesions demonstrating that 30% of ccRCCs (Fig. 5B) and about 42% of papillary RCCs both derived from the proximal tubules lack gelsolin expression (data not shown), whereas 75% of chromophobic RCC (Fig. 5C) and 55% of oncocytoma (Fig. 5D) originating from the distal tubule system/collecting ducts displayed a strong and intermediate staining for gelsolin, respectively.

Fig. 5.

Immunohistochemical analysis for gelsolin expression in tumor-adjacent renal epithelium and distinct kidney tumor subtypes. Randomly selected sections of the TMA representing tumor-adjacent renal epithelium and RCC subtypes were used to document the immunohistochemical staining with the anti-human gelsolin-specific mAb. In tumor-adjacent kidney tissues (A), expression of gelsolin is mainly observed in the epithelium of the distal tubule system and collecting ducts, whereas the epithelium of the proximal tubule system as well as the glomeruli (despite not being represented on this section) stained negative. More than 30% of the ccRCCs lack gelsolin staining (B). In contrast, tumors originating from the distal tubule system/collecting ducts like chromophobic RCC (C) and from benign renal adenoma of the oncocytic type (D) displayed strong or intermediate positive staining for gelsolin in more than 75% (C) and 55% (D) of all cases analyzed. Magnification in A–D, 400×.

The IHC data obtained from TMA analyses using the gelsolin-specific mAb revealed that ccRCC and chromophilic RCC displayed a strong positive cytoplasmic gelsolin staining with a frequency of 25 and 22.6%, respectively, and an intermediate staining for gelsolin in 15% of the ccRCC and 3.2% of the chromophilic RCC subtype. 27.5% of ccRCC and 32.3% of chromophilic RCC showed a weak staining, and 32.5% of the ccRCC and 41.9% of the chromophilic RCC subtype showed a lack of gelsolin expression (Fig. 6, A and B). 18.8% of chromophobic RCC lesions tested negative for gelsolin staining, 6.3% exhibited a weak staining, 18.8% exhibited an intermediate staining, and the remaining 56.3% exhibited a strong positive cytoplasmic staining (Fig. 6C). In contrast, renal cell adenomas of oncocytic type (Fig. 6D) displayed a strong positive staining for gelsolin in 11.1%, an intermediate staining in 44.4%, and a weak or negative staining in 22.2% of the cases analyzed. Thus, the results of the proteomics approach and IHC revealed a distinct regulation of gelsolin in the ccRCC lesions.

Fig. 6.

Immunohistochemical analysis for gelsolin expression using TMA technology. The immunohistochemical staining pattern for gelsolin using the RCC-specific TMA consisting of ccRCC (A), chromophilic RCC (B), chromophobic RCC (C), and renal cell adenomas of the oncocytic type (D) are shown. The composition of the TMA panel, classification of the staining pattern, and color code are the same as in Fig. 4.

H-FABP Immunostaining

Using 2DE-based technology a distinct protein expression pattern for FABP family members in RCC has been described recently, but because of the lack of an IHC-suitable H-FABP-specific mAb this family member had not been yet been verified by IHC (28). As shown in Fig. 7, representative IHC stainings using a recently available H-FABP-specific polyclonal antibody confirmed the heterogeneous expression pattern for H-FABP in the various RCC subtypes and tumor-adjacent kidney epithelium, which might allow distinguishing among normal, benign, and malignant tissues. In tumor-adjacent renal kidney tissue, the cytoplasm of cells representing epithelium of the proximal and distal tubule system and mesangial cells were strongly stained with the H-FABP-specific Ab (Fig. 7A). Although ccRCC showed a rather heterogeneous staining pattern, for example showing a strong positive cytoplasmic staining (Fig. 7B), chromophilic RCCs are defined by a weak to even negative staining pattern (Fig. 7C). In contrast, all oncocytomas analyzed showed a strong cytoplasmic staining pattern similar to that of the tumor-adjacent epithelium (Fig. 7D).

Fig. 7.

Immunhistochemical analysis for H-FABP expression in tumor-adjacent renal epithelium and distinct kidney tumor subtypes. Randomly selected sections of the TMA representing tumor-adjacent renal epithelium and RCC subtypes were used for immunohistochemical staining with the anti-human H-FABP-specific polyclonal Ab. In tumor-adjacent kidney tissue, the epithelium of the proximal and distal tubule systems as well as mesangial cells show strong cytoplasmic staining for H-FABP (A). RCCs of the clear cell subtype (B) demonstrate strong positive cytoplasmic staining in only 23% of all tumors tested, whereas more than 60% of chromophilic RCCs (C) were negative for H-FABP. In contrast, all benign oncocytomas (D) exhibit a strong positive cytoplasmic staining for H-FABP. Magnification in A–D, 200×.

Staining of the TMA with the anti-H-FABP Ab confirmed an almost evenly distributed staining pattern of ccRCC with 23% of the analyzed tissue sections exhibiting either a strong, intermediate, or weak cytoplasmic H-FABP staining, whereas the remaining 31% of ccRCC lesions fully lacked H-FABP expression (Fig. 8A). The staining pattern of chromophilic RCC is defined by 13% strong, 10% intermediate, 13% weak, and 64% negative cytoplasmic stainings (Fig. 8B). In contrast, the pattern characteristics for chromophobic RCC exhibited a strong positive cytoplasmic staining in 68%, an intermediate staining in 13%, and a negative staining in 19% of RCC lesions analyzed (Fig. 8C). Moreover, all adenomas of oncocytic type demonstrated a strong positive cytoplasmic staining for H-FABP (Fig. 8D). Thus, the distinct staining pattern for H-FABP might be suited to distinguish between various RCC subtypes. Concerning the staining pattern for ccRCC, the heterogeneity of the staining is in line with the distinct regulation pattern observed in the proteomics profiling of different ccRCC lesions.

Fig. 8.

Immunohistochemical analysis for H-FABP expression using TMA technology. Immunohistochemical staining of the TMA representing the four major RCC subtypes along with corresponding tumor-adjacent renal epithelium is shown. The composition of the TMA panel in this analysis was slightly modified by representing just 26 ccRCC lesions. Besides that, the classification of the staining pattern and the color code are the same as those in Fig. 4. The frequency of the different staining patterns for H-FABP on sections representing ccRCC (A), chromophilic RCC (B), chromophobic RCC (C), and adenomas of oncocytic type (D) are shown.

DISCUSSION

Proteomics profiling is now widely accepted as a tool for gaining insights into the dynamics of protein expression profiles reflecting complex cellular processes, including malignant transformation. To date, different semiquantitative and quantitative proteome-based technologies have been implemented for the profiling of clinical samples like diseased or cancerous tissues, serum, and urine. These technologies allowed the identification of candidate biomarkers, which are particularly important for the development of early detection, prognosis, and diagnosis of diseases and therapeutic treatment modalities, including renal cell carcinoma (17, 19, 20, 33–37).

So far, the information available for RCC is restricted to a relatively small number of differentially expressed proteins defined in few selected RCC cell lines/lesions when compared with tumor-adjacent epithelium, emphasizing the need to identify novel putative biomarkers in a broader range of RCC specimens. Although Perroud et al. (21) recently described 105 proteins with a statistically significant grade-dependent change in regard to their protein expression levels in ccRCC, caused by their experimental approach, none of these candidate biomarkers could be assigned to a RCC map. Moreover, the most prominent RCC protein expression map hosted on the ExPASy Proteomics Server mainly relies on the data presented by Sarto et al. (38) more than 10 years ago, although a number of groups have contributed substantially more RCC-specific candidate markers as recently reviewed (17–19).

Thus, in this report, a systematic 2DE-based proteomics profiling of biopsy samples representing ccRCC in comparison with corresponding tumor-adjacent renal epithelium was performed. To the best of our knowledge, we provide here for the first time comprehensive ccRCC-specific protein expression maps using acidic and basic pH gradients in the first dimension to increase the spot resolution. However, despite the analysis of up to 16 pairs of tissue specimens, the presented consensus maps must still be regarded as preliminary. In line with the known disadvantages of 2DE-based proteome analysis, the identification of low abundant, hydrophobic, very basic, high and low molecular weight, and small proteins is still limited (17, 19, 34, 36, 39, 40). The majority of the differentially expressed proteins identified in the ccRCC/tumor-adjacent kidney biopsy systems analyzed in this study mainly reflect high abundant molecules, which have also been defined at least in part in other organ-specific protein expression maps. On the other hand, one could also speculate that the identification of high abundant proteins, which are altered in ccRCC when compared with tumor-adjacent epithelium, still might represent those with an impact on RCC-specific phenotypic changes.

Moreover, it is noteworthy that our consensus maps delineate 55 of the 105 proteins altered in a grade-dependent manner in ccRCC (21). 37 of the candidate biomarkers defined recently by Perroud et al. (21) are even represented as single target spots on our consensus maps, and the vast majority thereof showed a regulation pattern comparable with that obtained by the shotgun proteomics approach.

Overall, 348 protein spots, corresponding to 248 protein identities, were mapped. The plotting of the theoretical versus the experimental masses for each identified protein in a scatter diagram underscores the quality of the profiling (supplemental Fig. 4). Larger deviations of the experimental masses toward a higher molecular weight might be due to PTMs, whereas lower masses might reflect fragmentation events, but the characterization of such modifications was not the focus of this study.

Interestingly, the protein expression profiling using two overlapping pH gradients resulted not only in the mapping and verification of previously described members of the RCC proteome (17–21) but also led to the identification of 97 additional novel protein entries. Thus, a gain of 17% in the number of differentially expressed proteins in RCC was obtained, thereby now covering 558 protein identities (supplemental Fig. 2).

Furthermore, a database containing the complete 2DE-based profiling information of the 16 ccRCC lesions and corresponding tumor-adjacent kidney epithelium was generated, subsequently allowing not only the mapping of a large number of proteins and the establishment of the respective consensus maps for the RCC proteome but also the selection of a core subset of differentially expressed proteins prioritized and refined according to the detection frequency (>5) and their predominant regulation tendency (>2:1 ratio) (Table II). The group of differentially expressed proteins largely comprises enzymes involved in the energy metabolism and detoxification processes, cellular stress, transport, cell proliferation, and motility, which are in line with previous reports, but nevertheless some novel potential targets are highlighted.

Profiling of RCC versus tumor-adjacent renal epithelium using various “ome”-based profiling strategies such as classical 2DE-based proteomics, PROTEOMEX, and transcriptomics showed that each technology has its strengths and limitations (17, 41). In contrast to the expected significant overlap between the targets defined by gel-based approaches like proteomics and PROTEOMEX, the overlap of targets shared between transcriptomics and proteomics profiling is strikingly limited to about 10%, strongly arguing for the complementary implementation of transcriptomic and proteomic profilings. Moreover, it is evident that target validation is even more limited in cDNA microarray studies when compared with proteomics/PROTEOMEX profiling. Thus, the majority of the currently validated candidate markers are almost exclusively linked to proteome-based approaches. Some recent reviews highlight the contribution and impact of ome-based strategies for the discovery of candidate biomarkers in RCC and provide lists of partially validated markers (16–19, 41). Because the number of validated targets for RCC is still very limited, more verification/validation studies are urgently required to determine the diagnostic and prognostic value of candidate biomarkers and to design novel therapeutic approaches in this disease.

Consequently, three representative candidate markers identified in the context of the proteomics profiling of the 16 ccRCC biopsy systems analyzed in this report, namely calbindin, gelsolin, and H-FABP, were selected, and their protein expression pattern was independently analyzed by immunohistochemical staining performed on a TMA consisting of a large number of biopsies representing not only ccRCC but also chromophilic and chromophobic RCC subtypes and benign oncocytoma as well as corresponding tumor-adjacent epithelium.

Using this strategy, potentially interesting and novel differences in the protein expression profiles between tumor-adjacent kidney tissues and various RCC subtypes were found (Figs. 4–8). The TMA staining essentially confirmed the results obtained via the proteomics profiling with the exception of calbindin. Although only two of six regulated calbindin spots were only expressed in the tumor-adjacent renal tissues, the remaining four spots initially categorized as differentially spots displayed at least the same regulation tendency, a strong calbindin down-regulation. Nevertheless, based on the immunohistochemical staining pattern, calbindin might indeed be used for proper distinction of ccRCC from the other RCC subtypes because of its total lack of expression in ccRCC. Furthermore, this loss appears to be associated with the malignant phenotype of RCC because only 44% of the benign oncocytomas exhibited negative calbindin staining, whereas a strong cytoplasmic staining for calbindin was described in the tumor-adjacent epithelium. The functional role of calbindin is the partial stabilization of intracellular calcium levels, which can confer protection against different proapoptotic stimuli (42, 43). It has been further found to protect the embryonal kidney epithelium cell line HEK293 from parathyroid hormone-induced apoptosis (44). Although calcium signals can determine the fate of cancer cells (45), a role of calbindin expression in the development or progression of RCC has not been reported. However, calbindin is up-regulated in renal distal tubular cells following treatment with reverse transcriptase inhibitors, which further led to an enhanced immunogenicity of these cells associated with an improved induction of CD8⁺ effector memory cells (46). The lack of calbindin expression in ccRCC could be explained by its origin from the proximal tubule system because calbindin is mainly expressed in distal tubules. In discrepancy with the current literature, the IHC staining of the TMA clearly showed neither a ubiquitous nor a unique expression in the proximal or distal tubule system of the kidney (Fig. 3, A and B). Furthermore, the TMA staining is supported by the findings of Martignoni et al. (47) addressing the expression pattern of calcium-binding proteins in chromophobic RCC. The differential expression pattern of calbindin in benign versus malignant kidney tumors and its putative association with malignancy suggests that calbindin might serve as a candidate biomarker unlikely alone but at least in combination with other proteins to distinguish between the different renal tumor subtypes and their malignant potential.

Although gelsolin is known to be involved in cell motility, cancer progression, metastasis formation, signaling pathways, and apoptosis (48), a conclusive expression pattern of this protein has not been identified in solid tumors during carcinogenesis. For example, a decreased gelsolin expression was found in bladder, prostate, and breast cancers, thereby suggesting a tumor- and metastasis-suppressing role in these malignancies. In accordance with our study, decreased gelsolin expression in RCC of grade 2 tumors associated with a poor patient survival has been described previously (49). In contrast, an up-regulation of gelsolin was associated with poor prognosis of patients with non-small cell lung cancer (50). These data argue for a tissue-specific activity of gelsolin, but the molecular mechanisms by which gelsolin exerts its biological function in cancer development have not yet been defined. Because of its heterogeneous expression pattern in ccRCC versus tumor-adjacent kidney epithelium, the implementation of gelsolin as a candidate biomarker for RCC is unlikely.

H-FABP is a member of the FABP family, which represents homologous intracellular proteins with a low molecular weight. FABPs are expressed in a variety of tissues, playing role in the fatty acid metabolism (51), and are therefore involved in a number of biological processes such as cell differentiation, cell growth, and apoptosis. Although H-FABP has been primarily identified to be expressed in heart and striated muscle, low H-FABP expression levels were also detected in kidney, lung, cerebral cortex, and placenta tissues. Intriguingly, H-FABP overexpression, which was associated with disease progression, tumor aggressiveness, and poor patient survival, has been described in a subset of human gastric carcinomas (52). These data correlate with the role of other FABP members, which might serve as prognostic markers to predict the outcome of patients, including RCC patients. Recently, the importance of L-FABP and brain FABP expression in RCC has been described (28). The proteomics profiling-based results as well as the verification data obtained from the immunohistochemical staining of the TMA demonstrated a heterogeneous but unique H-FABP expression pattern in the different RCC subtypes, suggesting the use of H-FABP for the classification of different RCC subtypes. In contrast to previously described IHC staining patterns on renal tissues (53), the H-FABP expression was not restricted to the distal tubule system but also was detectable in some cells of the proximal tubules as well as endothelial cells. However, the clinical significance of H-FABP in RCC has still to be proven.

In conclusion, the established ccRCC maps using pH 4–7 and pH 6–11 gradients provide a largely extended and improved resource for the tracking and identification of ccRCC-specific proteins. This pilot study resulted in a list of frequently dysregulated proteins (Table II), although the verification of these putative candidate proteins still has to be performed in clinical settings on a large series of RCC biopsies. Nevertheless, it seems reasonable that at least some members of these candidate lists might represent the basis for the design and development of diagnostic tools such as the design of multiparameter protein arrays, which might be suited not only for the discrimination of the various RCC subtypes from tumor-adjacent tissue but also, in combination with clinical data, for the monitoring of treatment regimens as well as for an improved prediction of risk and progression of RCC. In addition, the knowledge gained by these differentially expressed proteins will also contribute to a better understanding of the pathophysiology of RCC and likely provide starting points for functional experiments.