Abstract
After androgen ablation therapy (AAT), advanced prostate cancer (Pca) eventually progresses to castration-resistant Pca (CRPC); however, the biomarkers that are used to predict its prognosis are limited. In this study, serum samples from four patients with advanced Pca were collected at the time of the initial diagnosis and 3 months after AAT. Proteomic changes were analyzed with two-dimensional differential in-gel electrophoresis and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry. Altogether, nine proteins were differentially expressed in the samples collected at diagnosis and in the samples collected after AAT. Among them, the expression of transthyretin (TTR) was 1.58-fold lower and clusterin (CLU) was 1.51-fold higher in the sera of post-AAT patients compared with those in the sera from pre-AAT patients. The significant changes in serum TTR and CLU in post-AAT patients were further confirmed by a large-scale ELISA. Immunohistochemistical staining revealed that the expression levels of TTR and CLU were significantly higher in Pca tissue than in normal and benign prostate hyperplasia tissue. The expression levels of TTR and CLU in Pca tissue were found to be associated with the grade and stage of Pca. Overall, this study indicated that TTR and CLU might be used to monitor the efficacy of AAT therapy and serve as biomarkers for the prognosis of Pca.
Introduction
Prostate cancer (Pca) continues to be one of the most frequently diagnosed malignancies and a leading cause of cancer-related death among men in the United States [1]. Patients with organ-confined Pca can be cured with a radical prostatectomy or radiation therapy, whereas for patients with advanced tumors, androgen ablation therapy (AAT) still remains the most commonly used palliative treatment and the only effective method for the treatment of the cancer [2,3]. Although the tumor size decreases and the symptoms are alleviated soon after AAT, unfortunately, the majority of Pca patients will eventually progress to androgen-resistant Pca (ARPC), which leads to mortality [4]. Several mechanisms have been proposed to be involved in the progression of Pca. It has been suggested that the signaling pathways mediated by the androgen receptor [5–7] may play a role in this process. In addition, genomic instability has been shown to cause and promote the emergence of ARPC cells [8,9]. However, the exact mechanism of androgen resistance is still unknown.
It is well known that Pca is an epithelial-derived form of malignancy [10]. Some proteins, such as PSA, can leak out from the tissue into the blood and serve as diagnostic and prognostic markers [11–16]. With the development of proteomic technologies, serum proteomic profiling has emerged as a new screening method for Pca markers [17,18]. In this study, we analyzed the proteomic changes in the sera of patients with advanced Pca before AAT and 3 months after AAT using two-dimensional differential in-gel electrophoresis (DIGE) and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS). Several interesting proteins identified in the proteomic analysis were further studied by ELISA and immunohistochemistry (IHC) in samples from Pca patients.
Materials and Methods
Serum Sample Preparation
At the early stage of this study, four Pca patients were enrolled (ages, 65–77 years; prostate-specific antigen [PSA] levels, 22.3–55.6 ng/ml). The patients were diagnosed with advanced Pca by prostate biopsy, magnetic resonance imaging, and a nuclear bone scan. Serum samples were collected at the initial diagnosis of the disease and 3 months after AAT (orchectomy).
Blood samples were collected in glass tubes without additives, allowed to clot or sediment at room temperature for 1 hour, and centrifuged at -4°C, 1500g for 15 minute. Aliquots of the serum were collected and immediately stored at -80°C until further use. The identities of the patients from which the samples were taken were unknown to the investigators participating in the study, and the samples contained no features that would make it possible to identify the subjects. The serum samples were processed using the ProteoPrep Blue Albumin Depletion Kit (Sigma, St Louis, MO), which selectively removes albumin and immunoglobulin G from the serum sample, according to the manufacturer's instructions. The 2D Cleanup Kit (GE Healthcare, Chalfont St Giles, United Kingdom) was used to purify the protein extract samples before the determination of the sample concentrations with the 2D Quant Kit (GE Healthcare).
CyDye Labeling
Previous studies have used a pooled internal standard during DIGE to control for the variation between gels [19]. The protein extracts were labeled using fluorescent cyanine dyes (GE Healthcare) developed for two-dimensional DIGE technology following the manufacturer's protocols. The internal standard was prepared by combining equal portions of each of the eight test samples. The samples from the pre- and post-AAT groups were analyzed on four DIGE gels. The proteins (50 µg) from the pre- or post-AAT samples were labeled with Cy3 or Cy5, respectively, and 50 µg of the internal standard mixture was labeled with Cy2. The samples were incubated on ice for 30 minutes in the dark. The reactions were then quenched with the addition of 1 µl of 10 mM lysine for 10 minutes on ice in the dark. The quenched Cy3- and Cy5-labeled samples and the Cy2-labeled internal standard were pooled before analysis with two-dimensional DIGE. Meanwhile, two preparative gels, each containing 500 µg of the unlabeled mixture of internal standard proteins, were analyzed.
Two-dimensional DIGE
Two-dimensional DIGE was performed as previously described [19]. Briefly, immobilized dry strip (pH 4–7, NL 24 cm; GE Healthcare) was rehydrated for 12 hours in 450 µl of the rehydration buffer (7 M urea, 2 M thiourea, 4% wt/vol CHAPS, and 1% vol/vol IPG buffer, pH 4–7) using an Ettan IPGphor apparatus (GE Healthcare). After isoelectric focusing (IEF), the proteins were reduced and alkylated by successive 15-minute treatments with equilibration buffer containing 2% wt/vol DTT followed by 2.5% wt/vol iodoacetamide. The proteins were resolved in the second dimension on a 12.5% SDS-PAGE gel (24 cm x 20 cm) using an Ettan DALTsix instrument (GE Healthcare). The resolved proteins were then scanned with the Typhoon 9400 imager (Amersham, Biosciences, Uppsala, Sweden). The two preparative gels were stained with the Deep Purple Total Protein Stain (RPN6306; GE Healthcare) and scanned with a Typhoon 9400 imager.
DIGE Analysis
After the images were scanned, the GE Healthcare DeCyder software v6.0 was used for the differential gel analysis. The two-dimensional image of the gel from each post-AAT sample was compared to that of the pre-AAT sample after normalization to the internal standard sample. Relative protein quantitation across all the pre-AAT and post-AAT samples was performed with the software. The protein spots with significant differences in abundance (>1.5-fold) [19] were selected from the stained gels for further analysis.
Automated Protein Spot Handling
The protein spots chosen for the MALDI-TOF MS analysis were analyzed with an Ettan Spot Handling Workstation (GE Healthcare) as previously described [19]. In the automated procedure, the selected protein spots in the deep purple-stained gels were cut out, washed with 50 mM NH4HCO3 and 50% vol/vol methanol, and digested with 20 ng/µl trypsin (sequencing-grade; Promega, Madison, WI) in 20 mM NH4HCO3 for 2 hours at 37°C. The tryptic peptides were extracted with 50% vol/vol acetonitrile (ACN) and 0.5% vol/vol trifluoroacetic acid (TFA). The peptide samples were then dissolved in 5 mg/ml CHCA matrix in 50% vol/vol ACN and 0.1% vol/vol TFA. Finally, the samples were spotted on the MS sample plate.
MALDI-TOF MS and Database Searching
MALDI peptide mass fingerprinting (PMF) was performed on an Ettan MALDI-TOF mass spectrometer (GE Healthcare) operating in reflection mode. An internal calibration was performed using the trypsin autodigestion peaks at m/z 842.509 and 2211.104. Each spectrum corresponded to the sum of 200 acquisitions, each of eight laser pulses, for which the signal-to-noise ratio exceeded a set threshold value. Protein identification by PMF was performed using the MASCOT search engine (http://www.matrixscience.com/cgi/search_form.pl?FORMVER=2&SEARCH=PMF). The following parameters were used in the searches: trypsin digest, one missed cleavage allowed; species, Homo sapiens; mass value, monoisotropic; peptide tolerance, 50 ppm; and databases,NCBI and SWISS-PROT. The proteins that had significantly high MASCOT scores (P < .05) for the PMF and corresponded to at least four peptide hits were considered to be credibly identified.
ELISA
The serum levels of transthyretin (TTR) and clusterin (CLU) were determined by an ELISA. Serum samples were obtained from 20 pre-AAT patients, 20 post-AAT patients, and 20 ARPC patients. Commercially available ELISA kits were used to assess the levels of CLU (Biovendor, Modrice, Czech Republic) and TTR (Immundiagnostik, Bensheim, Germany) in the serum. The serum TTR and CLU levels were determined by a sandwich enzyme immunoassay. The median serum CLU and TTR levels in five healthy patients were 70.90 ng/ml (range from 63.72 to 96.30 ng/ml) and 14.51 ng/ml (range from 9.11 to 16.09 ng/ml), respectively.
Immunohistochemistry
IHC studies were performed using the avidin-biotin complex method as described previously [20]. In brief, sections were deparaffinized and rehydrated. Endogenous peroxidase activity was blocked with 3% hydrogen peroxide for 20 minutes. For antigen retrieval, the slides were heated in a microwave oven for 10 minutes in 10 mM citrate buffer, pH 6.0. Nonspecific binding was blocked with 10% normal rabbit serum for 10 minutes at room temperature. Incubation with the TTR and CLU monoclonal primary antibodies (Abcam, Cambridge, United Kingdom) was performed at 37°C for 60 minutes at a dilution of 1:100. Nonimmune mouse sera were included as negative controls. After a brief wash, the slides were incubated with avidin-biotin immunoperoxidase and developed with diaminobenzidine tetrahydrochloride at room temperature. The cytoplasmic and nuclear expression levels of TTR and CLU were evaluated. Immunostaining of TTR and CLU in the cytoplasm was evaluated with a semiquantitative scoring system. Briefly, both the staining intensity and the percentage of positively stained tumor cells were recorded. A staining index was obtained by times the intensity of cytoplasmic staining (weak = 1, moderate = 2, strong = 3) with the proportion of immunopositive tumor cells (<10% = 1, 10% and 50% = 2, >50% = 3). Thus, the values of the staining index ranged from 1 to 9. Immunostaining of TTR and CLU in the nucleus was scored according to the percentage of cells with stained nuclei. Lost samples, unrepresentative samples, and samples with too few tumor cells (<100 cells) were excluded from the data analysis. All the IHC results were evaluated by 2 independent pathologists who were unaware of the clinical data. When the observers disagreed about results on the same slide, the data were reviewed again until a consensus was reached.
The normal prostate tissues were obtained from patients who had received radical cystectomies to treat bladder cancer. The benign prostate hyperplasia (BPH) samples were obtained from patients who had received transurethral resections of the prostate to treat BPH. The prostate cancer (Pca) specimens were from prostate biopsies or patients who had received radical prostatectomies for Pca. All these specimens were collected from the Department of Pathology in our hospital.
All the patients were clearly informed about the purpose of our investigation and signed informed consent forms to be involved in the study. The study was approved by the Sun Yat-sen University review board and local committee.
Statistical Analysis
The SPSS software version 13.0 (SPSS, Inc, Chicago, IL) was used for the statistical analysis. The data were analyzed using a variety of methods. Student's t test was used to analyze differences in the proteomics results. A one-way analysis of variance was used to determine differences in the ELISA results. Fisher exact test was used to determine the differences in the IHC results. P < .05 was considered statistically significant.
Results
Fourteen Differentially Expressed Proteins Were Found by Two-dimensional DIGE Analysis and Identified with MALDI-TOF MS Analysis
To analyze the difference of serum proteomes between pre- and post-AAT patients, the serum proteins were collected and separated as described in the methods section. A representative two-dimensional DIGE gel is shown in Figure 1. A comparative analysis using the DeCyder software showed that seven protein spots were increased more than 1.5-fold in abundance and eight protein spots were decreased more than 1.5-fold in abundance in post-AAT sera compared with pre-AAT sera (P < .05; Table 1).
Figure 1.
The spots marked with numbers indicate proteins whose abundance was significantly different between pre- and post-AAT samples, with 95% confidence levels.
Table 1.
Comparison of CLU Expression in the Sera of Pre-AAT and Post-AAT Advanced Pca Patients.
| ID | Protein Name | Accession No. | % Coverage | MS Score | P | Peptides Matched | Mass (kDa) | pI | t Test | Average Ratio |
| Upregulated | ||||||||||
| 280 | Macroglobulin α2 | gi|224053 | 10 | 92 | .00016 | 10 | 162.1 | 5.95 | 0.034 | 2.14 |
| 419 | Macroglobulin α2 | gi|224053 | 22 | 70 | .028 | 5 | 162.07 | 5.95 | 0.009 | 1.93 |
| 424 | Macroglobulin α2 | gi|224053 | 7 | 88 | .00038 | 8 | 162.07 | 5.95 | 0.0075 | 1.84 |
| 425 | Macroglobulin α2 | gi|224053 | 8 | 92 | .00015 | 9 | 162.07 | 5.95 | 0.018 | 2.07 |
| 1091 | HP protein | gi|47124562 | 31 | 104 | 1e-05 | 10 | 31.6 | 8.48 | 0.025 | 2.12 |
| 1092 | HP protein | gi|47124562 | 32 | 117 | 5.2e-07 | 8 | 31.65 | 8.48 | 0.015 | 1.87 |
| 1126 | CLU precursor | gi|26665859 | 38 | 74 | .011 | 5 | 16.27 | 5.60 | 0.0047 | 1.51 |
| Downregulated | ||||||||||
| 24 | Fibronectin precursor | gi|109658664 | 7 | 102 | 1.6e-05 | 12 | 243.1 | 5.6 | 0.039 | .2.27 |
| 27 | Fibronectin 1, isoform | gi|119590936 | 8 | 75 | .0086 | 8 | 111.89 | 5.35 | 0.027 | -2.45 |
| 766 | Human Fcari bound To Iga1-Fc | gi|31615935 | 44 | 119 | 3.3e-07 | 8 | 23.64 | 7.12 | 0.004 | -1.61 |
| 887 | α2-HS glycoprotein | gi|2521981 | 18 | 68 | .039 | 5 | 36.27 | 5.20 | 0.026 | -1.65 |
| 1165 | Transthyretin precursor | gi|55669575 | 63 | 183 | 1.3e-13 | 8 | 12.8 | 5.33 | 0.014 | -1.58 |
| 1174 | Apolipoprotein E precursor | gi|4557325 | 27 | 99 | 3.2e-05 | 8 | 36.2 | 5.65 | 0.05 | .3.26 |
| 1225 | Igκ chain C region | IGKC_HUMAN | 50 | 79 | .0035 | 4 | 11.8 | 5.58 | 0.0003 | -1.77 |
| 1249 | Anti-Entamoeba histolytica immunoglobulin κ light chain | gi|5360675 | 32 | 78 | .0044 | 4 | 23.4 | 7.81 | 0.012 | -1.52 |
Based on the two-dimensional DIGE analysis, the 15 differentially expressed protein spots were selected for MS analysis. All the selected protein spots were picked from the preparative gel and robotically handled for the MALDI-TOF MS analysis. The PMF analysis and database searching were described in the Materials and Methods section. In total, 15 protein spots matched the PMF identification criteria and were unambiguously identified. The PMF analysis results are summarized in Table 1. After we excluded several spots identified as the same proteins, our proteomics analysis found nine proteins that changed expression levels in sera of Pca patients after AAT.
TTR Was Downregulated and CLU Was Upregulated in the Sera of Pca Patients after AAT
In total, we identified three proteins that increased and six proteins that decreased in abundance in the sera of Pca patients receiving AAT. Among these proteins, TTR and CLU were chosen for further analysis because they were previously reported to be associated with cancers such as lung and ovarian cancers [21,22]. Although transferrin was also reported to be associated with the development of some cancers, many factors can influence its expression in the serum. Therefore, it was not chosen for further analysis in this study.
TTR was found to be downregulated 1.58-fold in the sera of post-AAT patients when compared with the sera of pre-AAT patients (Figure 2). Meanwhile, we found that the CLU precursor, corresponding to a pI of 5.89 and a molecular mass (Mr) of 53 kDa, was upregulated 1.51-fold in the four posttreatment serum samples compared with the pretreatment serum samples (Figure 3).
Figure 2.
The expression level of TTR was downregulated in Pca patients receiving AAT compared with that in Pca patients before AAT. (A) A map of the differences in the spots and a three-dimensional view of pre- and post-AAT samples. (B, C) A MALDI-TOF-MS reflection spectrum of tryptic peptides from TTR; (D) matched peptides are shown in red. The underlined sequences were matched to the peptides observed by MS. (E) Using these data, the MASCOT program searched the proteins in the Swiss-Prot database.
Figure 3.
The expression level of CLU was upregulated in Pca patients receiving AAT compared with that of Pca patients before AAT. (A) The differences in the spot map and the three-dimensional view between pre- and post-AAT samples. (B, C) The MALDI-TOF-MS reflection spectrum of the tryptic peptides from CLU; (D) matched peptides are shown in red. The underlined sequences were matched to the peptides observed by MS. (E) Using these data, the MASCOT program searched the proteins in the Swiss-Prot database.
To validate the differential expression of TTR and CLU, the sera from the pre-AAT Pca, post-AAT Pca, and ARPC patients were analyzed by an ELISA (Table 2). The results showed that the levels of CLU were significantly higher in the posttreatment patients than in the pretreatment patients. Interestingly, the levels of CLU in the patients receiving AAT were even higher than the CLU levels in the ARPC patients (P < .05). The levels of CLU in the ARPC patients were higher than those in the pretreatment patients, but this difference was not statistically significant. The TTR levels in the sera of the post-AAT and ARPC patients were lower than the TTR levels in the pre-ATT patients (P < .05); however, the difference in the levels of TTR between the posttreatment and ARPC patients did not reach statistical significance.
Table 2.
Serum Levels of CLU and TTR Determined by an ELISA Assay.
| No. | Mean ± SD (ng/ml) | P | |
| CLU | |||
| Pre-AAT | 20 | 80.79 ± 19.82 | |
| Post-AAT | 20 | 105.51 ± 28.43 | .002* |
| ARPC | 20 | 93.72 ± 25.55 | .092† |
| TTR | |||
| Pre-AAT | 20 | 19.82 ± 1.44 | |
| Post-AAT | 20 | 17.88 ± 1.35 | <.001* |
| ARPC | 20 | 16.55 ± 2.06 | <.001† |
P represents the P value when the *post-AAT or †ARPC group was compared with the pre-AAT group. P < .05 was considered significant.
The Expressions Levels of TTR and CLU in Prostate Cancer Tissue Were High and Correlated with the Gleason Score and Stage of Pca
IHC was performed to assess whether the expression levels of TTR and CLU were different for normal prostate, BPH, and Pca tissues. Altogether, 10 normal prostate, 10 BPH, and 50 Pca tissue slides were used for the IHC analysis. The results revealed that the expression levels of TTR and CLU in Pca tissue were significantly higher than those in normal prostate and BPH tissues (Tables 3 and 4 and Figures W1–W4). The high expression level of TTR in the Pca tissues was in contrast to the lower expression level of TTR in the sera of Pca patients (Table 3).
Table 3.
Expression Level of Transthyretin in Different Tissues and Its Relationship with Pca Malignancy.
| Cases (No.) | - Cases (No.) | + Cases (No.) | ++ Cases (No.) | +++ Cases (No.) | χ2, P | |
| Specimen | ||||||
| NP | 10 | 9 | 1 | 0 | 0 | |
| BPH | 10 | 7 | 3 | 0 | 0 | |
| Pca | 52 | 1 | 9 | 27 | 15 | |
| χ2 = 52.087, P < .001 | ||||||
| Gleason | ||||||
| 2–4 | 10 | 1 | 4 | 5 | 0 | |
| 5–7 | 25 | 0 | 4 | 15 | 6 | |
| 8–10 | 17 | 0 | 1 | 7 | 9 | |
| χ2 = 13.89, P = .046 | ||||||
| Stage | ||||||
| A | 6 | 1 | 3 | 2 | 0 | |
| B | 24 | 0 | 5 | 17 | 2 | |
| C | 14 | 0 | 1 | 7 | 6 | |
| D | 8 | 0 | 0 | 1 | 7 | |
| χ2 = 27.15, P < .001 |
Gleason scores: 2–4 to 5–7, χ2 = 6.232, P = .145; 2–4 to 8–10, χ2 = 11.217, P = .014; 5–7 to 8–10: χ2 = 3.928, P = .269.
Stage: A to B, χ2 = 5.966, P = .166; A to C, χ2 = 8.026, P = .049; A to D, χ2 = 11.076, P = .007; B to C, χ2 = 6.078, P = .102; B to D, χ2 = 15.864, P = .001; C to D, χ2 = 4.032, P = .236.
Table 4.
Expression Level of CLU in Different Tissues and Its Relationship with Pca Malignancy.
| Cases (No.) | - Cases (No.) | + Cases (No.) | ++ Cases (No.) | +++ Cases (No.) | χ2, P | |
| NP | 10 | 9 | 1 | 0 | 0 | |
| BPH | 10 | 6 | 4 | 0 | 0 | |
| Pca | 52 | 2 | 6 | 27 | 17 | |
| χ2 = 50.144, P < .001 | ||||||
| Gleason | ||||||
| 2–4 | 10 | 2 | 3 | 5 | 0 | |
| 5–7 | 25 | 0 | 3 | 17 | 5 | |
| 8–10 | 17 | 0 | 0 | 5 | 12 | |
| χ2 = 23.278, P = .001 | ||||||
| Stage | ||||||
| A | 6 | 2 | 2 | 2 | 0 | |
| B | 24 | 0 | 4 | 16 | 4 | |
| C | 14 | 0 | 0 | 8 | 6 | |
| D | 8 | 0 | 0 | 1 | 7 | |
| χ2 = 25.282, P = .001 |
Gleason scores: 2–4 to 5–7, χ2 = 7.360, P = .082; 2–4 to 8–10, χ2 = 16.438, P = .001; 5–7 to 8–10, χ2 = 10.613, P = .010.
Stage: A to B, χ2 = 7.779, P = .067; A to C, χ2 = 10.247, P = .016; A to D, χ2 = 10.788, P = .009; B to C, χ2 = 4.306, P = .215; B to D: χ2 = 11.627, P = .006; C to D: χ2 = 4.053, P = .150.
In addition, the expression levels of TTR and CLU in the Pca tissues were found to correlate with the grade and stage of Pca. When the grade and stage were high, the expression levels of TTR and CLU were also high (Tables 3 and 4). These results indicated that TTR and CLU may serve as biomarkers for the prognosis of Pca.
Discussion
It is important to predict the prognosis and the efficacy of AAT for late-stage Pca. In this study, 14 proteins were identified in the serum of post-AAT patients, of which 3 were significantly increased and 6 were decreased compared with those in pre-AAT Pca samples. A MALDI-TOF MS analysis revealed some of the unique proteins that may be related to AAT. Among these differentially expressed proteins, TTR was found to be downregulated in Pca patients receiving AAT. To confirm that the downregulation of serum TTR in post-AAT patients was not a rare occurrence, an ELISA was performed to determine the differential expression levels of TTR in pretreatment, posttreatment and ARPC patients on a larger scale. The ELISA results showed that the serum levels of TTR were significantly lower in the post-AAT and ARPC patients than in the pre-AAT Pca patients, which confirmed the results from the proteomic analysis. TTR is a tryptophan-rich protein [23]. Bauer et al. [24] found that tryptophan metabolites, which may play a role in pregnancy and can be used to introduce immunotolerance, may suppress the activation of T cells. In our study, we found that TTR was downregulated in the sera of Pca patient after AAT. Therefore, the downregulation of TTR may activate the immune system, thus leading to the death of Pca cells. Although the serum levels of TTR in the ARPC patients were slightly lower than those in the post-AAT patients, the difference was not statistically significant. However, the expression level of TTR in the Pca tissue was significantly higher than in the normal and BPH tissues, which was in contrast to the lower expression level of TTR in the serum. Based on the theory of immunotolerance, which can be induced by TTR, the high expression level of TTR in the tissue can cause Pca cells to escape from immunological surveillance. Conversely, the lower expression level of TTR in the serum may stimulate immunological activity and the subsequent death of cancer cells in Pca patients; however, the high expression level of TTR in Pca tissue may help Pca cells escape from immunological surveillance. However, there are currently no other studies on TTR expression in cancer tissue to confirm this hypothesis.
Previously, Kozak et al. [25] improved the detection rate of early stage ovarian cancer by combining TTR with CA125. Moore et al. [21] and Zhang et al. [22] described the identity of the discriminatory peak of TTR (prealbumin) in ovarian cancer and regarded it as an early diagnostic marker in ovarian cancer. Liu et al. [26] reported reduced TTR expression levels in the sera of lung cancer patients. Therefore, TTR could be combined with PSA testing to predict the progression of Pca after AAT to compensate for the lack of specificity in PSA testing.
TTR is a transport protein for retinol-binding protein and thyroxin and is rapidly turned over [27]. A previous study reported that the intensity of the disease has no significant influence on serum TTR levels, indicating that a change in the serum TTR level is a general phenomenon that may associated with cancer-induced cachexia, which is already present at early stages [28]. The downregulation of serum TTR levels may be related to the nutritional status of the patient. Therefore, the detailed function of TTR in the progression of Pca warrants further study.
CLU is a secretory, sulfated glycoprotein that has antiapoptotic properties [29–32] and has been recognized as an important prognostic factor in Pca [33,34]. A higher CLU expression level was observed after tumorigenesis induction and promotion in experimental prostate carcinomas when compared with normal tissue in vivo. Sintich et al. [35] reported that the mechanism by which CLU protects target cells from cytotoxicity is at least partially mediated by an extracellular mechanism. CLU was originally thought to be repressed by androgens. However, it was later discovered that the increased CLU levels are most likely due to castration-induced apoptosis of the prostatic epithelium rather than the direct action of androgen. In addition, Cochrane et al. [36] found that the CLU levels increased when tumors progressed to androgen independence.
In this study, the serum level of CLU was 1.51-fold higher in Pca patients after AAT than that in pre-AAT Pca patients, which is consistent with previous reports [29,36,37]. It was thought that AAT activated the high expression level of CLU. However, the level of CLU at immediately following AAT may not be high enough to prevent the AAT-induced apoptosis of Pca cells. As the amount of CLU increases, its antiapoptotic effects will dominate the apoptotic effects of AAT. This phenomenon may explain how Pca can be effectively managed by AAT treatment during the early phase and how most Pca patients who receive AAT will eventually progress to ARPC.
The IHC study revealed that the expression levels of TTR and CLU were both higher in Pca tissue than in normal prostate and BPH tissue. It also showed that the TTR and CLU expression levels in Pca tissue were associated with the grade and stage of Pca. Higher expression levels of TTR and CLU were found in tissues that scored higher on the Gleason scale and were at later stages of Pca. As we mentioned above, the higher expression levels of CLU in Pca tissues could provide Pca cells with protection from apoptosis, and higher TTR levels in Pca tissues may help Pca cells escape from immunological surveillance. Therefore, the expression levels of TTR and CLU in Pca cancer tissue may serve as predictive markers for Pca prognosis. To our knowledge, this is the first report on TTR expression in Pca tissue and its relationship with the grade and stage of Pca.
The insufficiency of our study is clear. Because no Pca patients who received AAT were willing to allow biopsies when they were still in the androgen-sensitive phase, we could not study the tissue expression levels of TTR and CLU in these post-AAT Pca patients. Therefore, it was impossible to measure the trends in the TTR and CLU levels as the patient progressed from androgen-sensitive to androgen-resistant Pca.
In conclusion, we found a relationship between AAT and the serum levels of TTR and CLU, and we found that their expression levels in tissue correlate with Pca malignancy. Future studies will focus on better understanding the biologic roles of these proteins and their expression levels in ARPC after AAT. We also identified a total of nine proteins in this study that were differentially expressed before and after AAT. Therefore, the validation of the differential expression of other serum proteins identified in our study is also warranted.
Supplementary Material
Footnotes
The study was supported by National Natural Science Foundation, China (grant no. 30872584), The Fund for Doctoral Program of Higher Education, China (grant no. 20050558065), Guangdong Science and Technology Foundation (grant no. 2006B36001013), the key project of Guangdong Provincial Natural Science Foundation (8251008901000018), Guangdong Provincial Natural Science Foundation (10151008901000106), and the Fundamental Research Funds for the central universities (09ykpy42).
This article refers to supplementary materials, which are designated by Figures W1 to W4 and are available online at www.transonc.com.
References
- 1.Jemal A, Siegel R, Ward E, Murray T, Xu J, Thun MJ. Cancer statistics, 2007. CA Cancer J Clin. 2007;57:43–66. doi: 10.3322/canjclin.57.1.43. [DOI] [PubMed] [Google Scholar]
- 2.Denmeade SR, Lin XS, Isaacs JT. Role of programmed (apoptotic) cell death during the progression and therapy for prostate cancer. Prostate. 1996;28:251–265. doi: 10.1002/(SICI)1097-0045(199604)28:4<251::AID-PROS6>3.0.CO;2-G. [DOI] [PubMed] [Google Scholar]
- 3.Reid P, Kantoff P, Oh W. Antiandrogens in prostate cancer. Invest New Drugs. 1999;17:271–284. doi: 10.1023/a:1006344807086. [DOI] [PubMed] [Google Scholar]
- 4.Johansson B, Pourian MR, Chuan YC, Byman I, Bergh A, Pang ST, Norstedt G, Bergman T, Pousette A. Proteomic comparison of prostate cancer cell lines LNCaP-FGC and LNCaP-r reveals heatshock protein 60 as a marker for prostate malignancy. Prostate. 2006;66:1235–1244. doi: 10.1002/pros.20453. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Chen CD, Welsbie DS, Tran C, Baek SH, Chen R, Vessella R, Rosenfeld MG, Sawyers CL. Molecular determinants of resistance to antiandrogen therapy. Nat Med. 2004;10:33–39. doi: 10.1038/nm972. [DOI] [PubMed] [Google Scholar]
- 6.Parczyk K, Schneider MR. The future of antihormone therapy: innovations based on an established principle. J Cancer Res Clin Oncol. 1996;122:383–396. doi: 10.1007/BF01212877. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Rowland JG, Robson JL, Simon WJ, Leung HY, Slabas AR. Evaluation of an in vitro model of androgen ablation and identification of the androgen responsive proteome in LNCaP cells. Proteomics. 2007;7:47–63. doi: 10.1002/pmic.200600697. [DOI] [PubMed] [Google Scholar]
- 8.Cher ML, Bova GS, Moore DH, Small EJ, Carroll PR, Pin SS, Epstein JI, Isaacs WB, Jensen RH. Genetic alterations in untreated metastases and androgen-independent prostate cancer detected by comparative genomic hybridization and allelotyping. Cancer Res. 1996;56:3091–3102. [PubMed] [Google Scholar]
- 9.Nupponen NN, Visakorpi T. Molecular cytogenetics of prostate cancer. Microsc Res Tech. 2000;51:456–463. doi: 10.1002/1097-0029(20001201)51:5<456::AID-JEMT8>3.0.CO;2-H. [DOI] [PubMed] [Google Scholar]
- 10.Ronquist KG, Carlsson L, Ronquist G, Nilsson S, Larsson A. Prostasome-derived proteins capable of eliciting an immune response in prostate cancer patients. Int J Cancer. 2006;119:847–853. doi: 10.1002/ijc.21895. [DOI] [PubMed] [Google Scholar]
- 11.Adam BL, Qu Y, Davis JW, Ward MD, Clements MA, Cazares LH, Semmes OJ, Schellhammer PF, Yasui Y, Feng Z, et al. Serum protein fingerprinting coupled with a pattern-matching algorithm distinguishes prostate cancer from benign prostate hyperplasia and healthy men. Cancer Res. 2002;62:3609–3614. [PubMed] [Google Scholar]
- 12.Carter HB, Pearson JD, Waclawiw Z, Metter EJ, Chan DW, Guess HA, Walsh PC. Prostate-specific antigen variability in men without prostate cancer: effect of sampling interval on prostate-specific antigen velocity. Urology. 1995;45:591–596. doi: 10.1016/S0090-4295(99)80049-1. [DOI] [PubMed] [Google Scholar]
- 13.Saito S. Prostate-specific antigen cut-off point of 2.5 ng/ml and increasing the number of prostate biopsies results in the detection of curable prostate cancer even in Japanese population. Int J Urol. 2007;14:709–712. doi: 10.1111/j.1442-2042.2007.01800.x. [DOI] [PubMed] [Google Scholar]
- 14.Scher HI, Kelly WM, Zhang ZF, Ouyang P, Sun M, Schwartz M, Ding C, Wang W, Horak ID, Kremer AB. Post-therapy serum prostatespecific antigen level and survival in patients with androgen-independent prostate cancer. J Natl Cancer Inst. 1999;91:244–251. doi: 10.1093/jnci/91.3.244. [DOI] [PubMed] [Google Scholar]
- 15.Thompson IM, Pauler DK, Goodman PJ, Tangen CM, Lucia MS, Parnes HL, Minasian LM, Ford LG, Lippman SM, Crawford ED, et al. Prevalence of prostate cancer among men with a prostate-specific antigen level < or =4.0 ng per milliliter. N Engl J Med. 2004;350:2239–2246. doi: 10.1056/NEJMoa031918. [DOI] [PubMed] [Google Scholar]
- 16.Howe HL, Wingo PA, Thun MJ, Ries LA, Rosenberg HM, Feigal EG, Edwards BK. Annual report to the nation on the status of cancer (1973 through 1998), featuring cancers with recent increasing trends. J Natl Cancer Inst. 2001;93:824–842. doi: 10.1093/jnci/93.11.824. [DOI] [PubMed] [Google Scholar]
- 17.Semmes OJ, Malik G, Ward M. Application of mass spectrometry to the discovery of biomarkers for detection of prostate cancer. J Cell Biochem. 2006;98:496–503. doi: 10.1002/jcb.20855. [DOI] [PubMed] [Google Scholar]
- 18.Petricoin EF, III, Ornstein DK, Paweletz CP, Ardekani A, Hackett PS, Hitt BA, Velassco A, Trucco C, Wiegand L, Wood K, et al. Serum proteomic patterns for detection of prostate cancer. J Natl Cancer Inst. 2002;94:1576–1578. doi: 10.1093/jnci/94.20.1576. [DOI] [PubMed] [Google Scholar]
- 19.Liu W, Zhou XW, Liu S, Hu K, Wang C, He Q, Li M. Calpaintruncated CRMP-3 and -4 contribute to potassium deprivation-induced apoptosis of cerebellar granule neurons. Proteomics. 2009;9:3712–3728. doi: 10.1002/pmic.200800979. [DOI] [PubMed] [Google Scholar]
- 20.Bi J, Lau SH, Hu L, Rao HL, Liu HB, Zhan WH, Chen G, Wen JM, Wang Q, Li B, et al. Downregulation of ZIP kinase is associated with tumor invasion, metastasis and poor prognosis in gastric cancer. Int J Cancer. 2009;124:1587–1593. doi: 10.1002/ijc.24164. [DOI] [PubMed] [Google Scholar]
- 21.Moore LE, Fung ET, McGuire M, Rabkin CC, Molinaro A, Wang Z, Zhang F, Wang J, Yip C, Meng XY, et al. Evaluation of apolipoprotein A1 and posttranslationally modified forms of transthyretin as biomarkers for ovarian cancer detection in an independent study population. Cancer Epidemiol Biomarkers Prev. 2006;15:1641–1646. doi: 10.1158/1055-9965.EPI-05-0980. [DOI] [PubMed] [Google Scholar]
- 22.Zhang Z, Bast RC, Jr, Yu Y, Li J, Sokoll LJ, Rai AJ, Rosenzweig JM, Cameron B, Wang YY, Meng XY, et al. Three biomarkers identified from serum proteomic analysis for the detection of early stage ovarian cancer. Cancer Res. 2004;64:5882–5890. doi: 10.1158/0008-5472.CAN-04-0746. [DOI] [PubMed] [Google Scholar]
- 23.Ando Y. [Immunological and serological laboratory tests: transthyretin] Rinsho Byori. 2005;53:554–557. [PubMed] [Google Scholar]
- 24.Bauer TM, Jiga LP, Chuang JJ, Randazzo M, Opelz G, Terness P. Studying the immunosuppressive role of indoleamine 2,3-dioxygenase: tryptophan metabolites suppress rat allogeneic T-cell responses in vitro and in vivo. Transpl Int. 2005;18:95–100. doi: 10.1111/j.1432-2277.2004.00031.x. [DOI] [PubMed] [Google Scholar]
- 25.Kozak KR, Su F, Whitelegge JP, Faull K, Reddy S, Farias-Eisner R. Characterization of serum biomarkers for detection of early stage ovarian cancer. Proteomics. 2005;5:4589–4596. doi: 10.1002/pmic.200500093. [DOI] [PubMed] [Google Scholar]
- 26.Liu L, Liu J, Dai S, Wang X, Wu S, Wang J, Huang L, Xiao X, He D. Reduced transthyretin expression in sera of lung cancer. Cancer Sci. 2007;98:1617–1624. doi: 10.1111/j.1349-7006.2007.00576.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Ando Y. [Transthyretin—its function and pathogenesis] Rinsho Byori. 2006;54:497–502. [PubMed] [Google Scholar]
- 28.Gericke B, Raila J, Sehouli J, Haebel S, Konsgen D, Mustea A, Schweigert FJ. Microheterogeneity of transthyretin in serum and ascitic fluid of ovarian cancer patients. BMC Cancer. 2005;5:133. doi: 10.1186/1471-2407-5-133. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Scaltriti M, Bettuzzi S, Sharrard RM, Caporali A, Caccamo AE, Maitland NJ. Clusterin overexpression in both malignant and nonmalignant prostate epithelial cells induces cell cycle arrest and apoptosis. Br J Cancer. 2004;91:1842–1850. doi: 10.1038/sj.bjc.6602193. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 30.Zhang H, Kim JK, Edwards CA, Xu Z, Taichman R, Wang CY. Clusterin inhibits apoptosis by interacting with activated Bax. Nat Cell Biol. 2005;7:909–915. doi: 10.1038/ncb1291. [DOI] [PubMed] [Google Scholar]
- 31.Sensibar JA, Sutkowski DM, Raffo A, Buttyan R, Griswold MD, Sylvester SR, Kozlowski JM, Lee C. Prevention of cell death induced by tumor necrosis factor alpha in LNCaP cells by overexpression of sulfated glycoprotein-2 (clusterin) Cancer Res. 1995;55:2431–2437. [PubMed] [Google Scholar]
- 32.Humphreys DT, Carver JA, Easterbrook-Smith SB, Wilson MR. Clusterin has chaperone-like activity similar to that of small heat shock proteins. J Biol Chem. 1999;274:6875–6881. doi: 10.1074/jbc.274.11.6875. [DOI] [PubMed] [Google Scholar]
- 33.Steinberg J, Oyasu R, Lang S, Sintich S, Rademaker A, Lee C, Kozlowski JM, Sensibar JA. Intracellular levels of SGP-2 (clusterin) correlate with tumor grade in prostate cancer. Clin Cancer Res. 1997;3:1707–1711. [PubMed] [Google Scholar]
- 34.Miyake H, Nelson C, Rennie PS, Gleave ME. Acquisition of chemoresistant phenotype by overexpression of the antiapoptotic gene testosterone-repressed prostate message-2 in prostate cancer xenograft models. Cancer Res. 2000;60:2547–2554. [PubMed] [Google Scholar]
- 35.Sintich SM, Steinberg J, Kozlowski JM, Lee C, Pruden S, Sayeed S, Sensibar JA. Cytotoxic sensitivity to tumor necrosis factor-alpha in PC3 and LNCaP prostatic cancer cells is regulated by extracellular levels of SGP-2 (clusterin) Prostate. 1999;39:87–93. doi: 10.1002/(sici)1097-0045(19990501)39:2<87::aid-pros2>3.0.co;2-z. [DOI] [PubMed] [Google Scholar]
- 36.Cochrane DR, Wang Z, Muramaki M, Gleave ME, Nelson CC. Differential regulation of clusterin and its isoforms by androgens in prostate cells. J Biol Chem. 2007;282:2278–2287. doi: 10.1074/jbc.M608162200. [DOI] [PubMed] [Google Scholar]
- 37.Miyake H, Yamanaka K, Muramaki M, Kurahashi T, Gleave M, Hara I. Enhanced expression of the secreted form of clusterin following neoadjuvant hormonal therapy as a prognostic predictor in patients undergoing radical prostatectomy for prostate cancer. Oncol Rep. 2005;14:1371–1375. [PubMed] [Google Scholar]
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