Quantum dot-based immunofluorescent imaging and quantitative detection of DNER and prognostic value in prostate cancer

Abstract

DNER, Delta/Notch-like epidermal growth factor (EGF)-related receptor, is a neuron-specific transmembrane protein carrying extracellular EGF-like repeats. The prognostic value of DNER in prostate cancer has not been evaluated. Here we showed that the up-regulation of DNER protein was observed in prostate cancer detected by immunohistochemistry (IHC) and quantum dot-based immunofluorescent imaging and quantitative analytical system (QD-IIQAS). However, a higher accuracy of measurements of DNER expression in prostate cancer was found by QD-IIQAS than by IHC (AUC $=$ 0.817 and 0.617, respectively). DNER was significantly higher in patients undergoing bone metastasis (P $=$ 0.045, RR $=$ 3.624). In addition, DNER overexpression was associated with poor overall survival (OS) (P $=$ 0.028, adjusted HR $=$ 8.564) and recurrence-free survival (RFS) (P $=$ 0.042, adjusted HR $=$ 3.474) in patients suffering prostate cancer. Thus, QD-IIQAS is an easy and accurate method for assessing DNER and the DNER expression was an independent prognostic factor in prostate cancer.

1. Introduction

Prostate cancer is a significant health problem and it is the second leading cause of cancer-related death in men, which results in 913,000 new cases and over 261,000 deaths worldwide each year [1]. During the past decade, cancer stem cells (CSCs), defined the small populations of self-renewal and multipotential differentiation, have been increasing concept in many malignancies [2]. It indicates that one or more highly conserved signal transduction pathways involved in development and tissue homeostasis, including the Notch, Sonic hedgehog, and Wnt pathways are found to be persistently activated in CSCs [2]. In addition, the ability of CSCs contributes to the growth, metastasis, recurrence and drug resistance of cancer [3]. Therefore, targeting these pathways to control stem-cell replication, survival and differentiation will be future development of new treatment strategies.

Notch signaling plays an indispensable role on regulating the process of cell-fate decisions during prostate cancer progression [4, 5]. Constitutive Notch proteins are large transmembrane proteins and can be activated by the direct interaction of Notch ligands expressed on apposed cells such as DSL (Delta, Serrate and Lag) family members. There are share repeated EGF-like motifs in their extracellular regions between Notch and DSL family members for physiological interactions. Delta/Notch-like epidermal growth factor (EGF)-related receptor (DNER, also named HE60 and BET) is a transmembrane protein carrying extracellular EGF-like repeats and specifically expressed in somatodendritic regions of the cerebellum [6, 7]. However, DNER lacks the DSL binding domain which is perceived as the essential of Notch ligands [8, 9]. Although there is some structural difference, DNER is identified as a Notch ligand mediating cell-cell interaction [8]. In fact, it has been reported that DNER also pathological functions as both oncogenic and anti-oncogenic factors [10, 11].

Quantum dots (QDs) are semiconductor nanocrystals associated with unique photophysical properties including size-tunable symmetric emission bands, superior light absorbance, high fluorescent intensity, and strong photostability [12]. Their optical properties have led to increased use of QD-based nanotechnology in a wide variety of biomedical applications, such as cancer diagnosis, monitoring, treatment, and molecular pathology [13]. QD-based nanotechnology has the potential for wider application, particularly in the field of in vitro cancer molecule imaging and quantitative detection [14]. Therefore, we apply quantum dot-based immunofluorescent imaging and quantitative analytical system (QD-IIQAS) to analyze and quantify DNER protein expression in prostate cancer.

In order to better understand the potential involvement of DNER in prostate cancer progression, the main objective of this research was to demonstrate that DNER was overexpressed in prostate cancer tissues, as well as to determine and analyze prognostic value of DNER by QD-IIQAS in prostate cancer.

2. Materials and methods

2.1. Patients and specimens

Paraffin-embedded specimens from 152 patients with prostate cancer were collected from January 2009 to September 2014 from the Renmin Hospital of Wuhan University, China. All patients with prostate cancer who had not undergone chemotherapy or radiotherapy before receiving rapid autopsy program were eligible. Pathological parameters were collected, including tumor size, location, and number; lymph node status, distant metastasis status and histological grade were determined by hematoxylin-eosin staining. Clinic information including age, Gleason scores and PSA concentration were obtained from the medical records of all eligible patients. All patients were followed up in accordance to a regular follow-up schedule. For all specimens, informed consent was obtained as require, and the study protocol was approved by the ethics committee of Renmin Hospital of Wuhan University.

2.2. Immunohistochemistry

The expression of DNER (anti-DNER polyclonal antibody; 1:100 dilutions; Biorbyt (orb 156622)) was determined through immunohistochemistry (IHC).Paraffin-embedded tissues were cut at 10 mm thickness. And the major steps in this process included in order: deparaffinizing, antigen retrieval (pH $=$ 9.0, at 120${}^{\circ }$C temperatures and high pressures for 90 s), blocking, incubation with primary antibody (phosphate-buffered saline for control group), washing, blocking, incubation with biotinylated secondary antibody, washing, blocking, DAB, washing, mounting, and observation. Images of DNER expression on IHC were captured and evaluated using an Olympus BX51 fluorescence microscope equipped with an Olympus DP72 camera (Olympus Corporation, Tokyo, Japan). As for scoring immunostaining patterns, four-grade semi-quantitative scoring was used to describe the number of tumor cells with cytoplasmic staining: (a) $-$, none; (b) $+$, 10% of the cells stained; (c) $++$, $⩾$ 10% and 50% of the cells stained or slightly more than 50% of cells stained homogeneously; and (d) $+++$, cells with $>$ 50% strong staining. Besides, (a) and (b) grades were identified as negative expression while (c) and (d) grades were examined as positive expression [15].

2.3. QD-based immunofluorescent imaging

QD-based immunofluorescent imaging was similar to conventional IHC. The QD-conjugated streptavidin (QD-SA) probe (1:200; QDs-605-goat F(ab)2 anti-mouse immunoglobulin G conjugate; Wuhan Jiayuan Quantum Dots Co. Ltd.) was used as the secondary antibody in the QD-based immunofluorescent imaging. Briefly, the sequence of the procedure was as follows: deparaffinizing, antigen retrieval, blocking (2% bovine serum albumin, 37${}^{\circ }$C for 30 minutes), incubation with primary antibody (dilution 1:100, 37${}^{\circ }$C for 2 hours), washing, blocking, incubation with biotinylated secondary antibody (dilution 1:300, 37${}^{\circ }$C for 30 minutes), washing, blocking, application of QD-SA 605 probes (dilution 1:200, 37${}^{\circ }$C for 30 minutes, emitting red light), washing, mounting, and observation (Olympus BX51 fluorescence microscope; Olympus Corporation) with a blue light (wavelength of 450–490 nm) excitation. This procedure has previously been described in more detail in our previous study [14].

For the QD-based immunofluorescent imaging, the slides were examined under an Olympus BX51 fluorescence microscope equipped with an Olympus DP72 camera (Olympus Corporation) and a Nuance multispectral imaging system (Cambridge Research and Instrumentation Inc., Woburn, MA, USA). QD-605 was excited using blue light (wavelength of 450–490 nm). The QD images were captured by the Olympus DP72 camera. Spectral images for each slide containing complete spectral information at 10 nm wavelength intervals from 420 nm to 720 nm were captured by the Nuance system. All slides were captured under the same conditions at medium magnification (200 $\times$), rendering it more accurate and representative of the tumor marker tissue.

2.4. Quantitative analysis by QD-IIQAS

QD fluorescence signaling formation for all slides was analyzed using the Nuance system analysis software package (Nuance Version 2.8; Cambridge Research and Instrumentation Inc., Woburn, MA, USA). DNER fluorescence signals and distribution areas in the tumor were calculated numerically based on spectral unmixing using the following protocol: 1) selection of targets with different spectra: DNER and tissue autofluorescence were selected as red (605 nm) and green signals (automatically set by the software); 2) image unmixing and elimination of background noise: the target images with different spectra were then automatically unmixed by the software based on specific spectrum into two separate images, DNER image with red signal and green background, which was deleted at the last step of quantification; and 3) target spectrum quantification: DNER spectral signals were automatically quantified by Nuance system.

On each slide, five view fields were selected under the same conditions at medium magnification (200 $\times$). The average DNER area of the five view fields was then calculated and derived as the final DNER area. The final acquired average fluorescence areas of DNER were defined as the average of two tissue slides (each specimen was divided into two tissue slides).

3. Statistical analysis

All statistical analyses and all charts of survival probabilities were performed with SPSS 19.0 (IBM Corporation, Armonk, NY, USA). The differences in the expression of DNER between prostate cancer and benign disease tissue and the relationships between DNER and the baseline clinical characteristics of patients with prostate cancer were evaluated by Chi-square test. Receiver operating characteristic (ROC) curve analysis was used to evaluate the optimal point with the highest sum value of sensitivity and specificity was defined as the cutoff. Predictive factors for distant metastasis were determined by multivariable logistic regression analysis, in which factors that were statistically significant in the univariate analysis were entered into the multivariable logistic regression analysis. The Kaplan-Meier method was used to calculate the patient survival probability and the log-rank test was used to assess the heterogeneity in the survival data for each prognostic factor. Multivariate Cox proportional hazard regressions were used to obtain hazard ratios (HRs) and their respective 95% confidence intervals to show the strength of the estimated relative risks. A P-value of 0.05 was considered statistically significant.

4. Results

4.1. Expression levels of DNER are different between benign and cancer tissues

To determine whether expression levels of DNER exist differences among benign and prostate cancer tissues, its expression was further analyzed by IHC in benign and prostate cancer tissues. Positive DNER expression was indicated by brown staining in the plasma membrane and/or cytoplasm of breast cancer tissues (Fig. 1). Among the 152 prostate cancer tissues, positive staining for DNER was observed in the samples from 107 patients, whereas only 1 patients exhibited positive DNER staining in 17 prostate benign diseases. These results indicated that the expression of DNER was significantly higher in prostate cancer tissues than in tissues obtained from patients with prostate benign disease (P 0.001, Tables S1–S2).

Figure 1.

Expression levels of DNER protein were detected between normal and cancer tissues. Expression analysis of DNER was measured by IHC between prostate benign and cancer tissues. A hot spot was s identified using a low-power field (left, 10 $\times$) and sequentially enlarged (right, 40 $\times$).

4.2. DNER determination, spectral unmixing, and quantitative analysis

In order to analyze the patterns of DNER expression in prostate cancer tissues, the QD-IIQAS was adopted and the results showed that the red DNER signal was weak in some prostate cancer tissues, by contrast, the signal was strong and primarily distributed on cellular nucleus in other samples (Fig. 2A). While high DNER expression was observed in tissues with bone metastasis comparatively low DNER expression was observed in tissues without bone metastasis, that suggested that DNER overexpression was likely to promote bone metastasis in patients with prostate cancer (P $=$ 0.045, RR $=$ 3.624, 95% CI 1.026, 12.796) (Fig. 2C). The quantitative detection of DNER was calculated by computer-aided quantitative analytical system, and the overall mean DNER area was 56.47.

Figure 2.

The protein expression of DNER was measured by QD-based immunofluorescent imaging. DNER (red immunofluorescent signal by QD-605) in the cell nucleus was visible at high resolution against the clear discernible background. (A) The expression of DNER was higher in prostate cancer tissues with bone metastasis. (B) DNER overexpression was positively related to bone metastasis analyzed by the multivariable logistic regression analysis.

4.3. Comparison of DNER expression by IHC and QD-IIQAS

In the feasibility study, the detection of DNER was measured by IHC and QD-IIQAS. On imaging, the QD-IIQAS exhibited a clear fluorescence signal, which was strong, discernible, and easy for observers to quantify. Mean DNER expression using IHC was assessed by two observers, which was macroscopic, subjective, and semi-quantitative. The DNER area using QD-IIQAS was objectively calculated without the disturbance of background using computer-aided spectral unmixing and automated quantitative detection software. ROC analysis of the DNER expression according to Recurrence-free Survival (RFS) indicated that it could predict RFS. Optimal sensitivity and specificity of the ROC curve based on RFS status corresponded with area under the curve (AUC) of 0.817 and 0.617 for the DNER-QD and DNER-IHC respectively, suggesting that the DNER-QD was associated with a better prognostic value for RFS compared to DNER-IHC (Fig. 3, Table S3).

Figure 3.

ROC analysis of DNER of prostate cancer cases according to recurrence-free survival (RFS) status was applied to compared IHC and QD-IIQAS.

4.4. Relationship between DNER and clinical pathological parameters

Studies of the relationship between DNER expression diagnosed by QD-IIQAS and the clinical and pathological features of prostate cancer patients have shown that the expression of DNER in patients with prostate cancer was associated with the distant metastasis (P $=$ 0.027), especially bone metastasis (P $=$ 0.043) and revealed that the rate of bone metastasis increases with an increase in the DNER positive expression. No relationships were found between DNER and other clinical or pathological features, including age, grade, tumor size, lymph node metastasis, Gleason scores and PSA concentration (Table 1).

Table 1.

DNER and clinicopathologic characteristics of prostate cancer patients

Characteristic	DNER negative	DNER positive	P
	N $=$ 45	N $=$ 107
Follow-up time (months)	57.73 $\pm$ 1.27	53.27 $\pm$ 1.87	0.022
Age, years			0.164
$⩽$ 55	4	19
$>$ 55	41	88
Grade			0.765
Well	2	5
Moderately	16	44
Poorly	26	53
Undifferentiated	1	5
Tumor size			0.839
T1	20	40
T2	21	55
T3	3	8
T4	1	4
Lymph node metastasis			0.466
N0	43	101
N1	2	3
N2	0	3
Distant metastasis			0.027
M0	42	85
M1	3	22
Bone metastasis			0.043
No	42	86
Yes	3	21
Gleason scores			0.554
$⩽$ 6	32	81
$⩾$ 7	13	26
PSA (ng/ml)			0.05
$⩽$ 50	25	41
$>$ 50	20	66

PSA: Prostate-specific antigen.

4.5. DNER expression and the prognosis of patients with prostate cancer

In this study, the overall survival (OS) was 81.6% (28/152), with 76.6% (25/107) in positive DNER-QD group and 93.3% (3/42) in negative DNER-QD group. The median OS of prostate cancer patients with positive DNER-QD staining was 53.27 $\pm$ 1.87 months compared with 57.73 $\pm$ 1.27 months in patients with negative DNER-QD (Table 1). The Kaplan-Meier survival analysis showed that the RFS of patients with negative DNER-QD staining was significantly longer than that of patients with positive DNER-QD (P $=$ 0.02; Fig. 4A). Furthermore, a Cox multivariate regression analysis also showed that DNER overexpression (P $=$ 0.028, adjusted HR $=$ 8.564, 95% CI: 1.282, 58.123) and Gleason scores (P $=$ 0.03, adjusted HR $=$ 4.879, 95% CI: 1.171, 20.328) were independent prognostic factors for OS (Fig. 4A).

Figure 4.

Kaplan-Meier curves and multivariable analysis of overall survival (OS) and recurrence-free survival (RFS) were evaluated in different expressions of DNER. A. OS was based on DNER level, and both DNER level and Gleason scores were associated with poor survival in patients suffering from prostate cancer (P 0.05, adjust HR $=$ 8.564 and 4.879, respectively). B. RFS is based on DNER expression, and only DNER expression was associated with poor survival in patients suffering from prostate cancer (P 0.05, adjust HR $=$ 3.474).

Simultaneously, the median RFS of prostate cancer patients with positive DNER-QD staining was 47.35 $\pm$ 2.06 months compared with 55.19 $\pm$ 2.12 months in patients with negative DNER-QD, suggesting that patients with positive DNER-QD was likely to have a poor RFS through Kaplan-Meier survival analysis (P $=$ 0.022; Fig. 4B). Multivariable Cox regression model analyses revealed that the up-regulation of DNER-QD was associated with poor RFS in patients undergoing prostate cancer (P $=$ 0.042, adjusted HR $=$ 3.474, 95% CI: 1.048, 11.514; Fig. 4B).

5. Discussion

DNER, a neuron-specific EGF-like repeat-containing

transmembrane protein, acts as a functional ligand of Notch during cell morphogenesis of Bergmann glial cells and is known to mediate functional communication via cell-cell interactions in mouse cerebellum [8, 16]; however, the role of DNER in prostate cancer has not been studied previously. In this study, the prognostic value of DNER on prostate cancer progression was evaluated.

That DNER was highly expressed in various cancer tissues as our previous study [17]. Based on the results, we examined whether DNER was associated with prognosis of patients with prostate cancer. From our results, we observed that DNER expression was up-regulated in prostate cancer tissues. In terms of structural sequence, DNER was closely related to those of receptor Notch and its ligand Delta. Furthermore, Notch ligand Delta-like 4 (DLL4) acted as a positive driver for tumor growth in prostate cancer [18]. Besides, DLL4 expression was correlated with VEGFR-2 expression [19], and DLL4 expressed in tumor cells functions as a negative regulator of tumor angiogenesis by activating Notch1 in endothelial cells [18, 20]. These results suggested that DNER might function by Notch signaling in prostate cancer progression.

There were the contrary results about the role of DNER on tumor proliferation, tumorigenicity, and metastasis. Our results confirmed that the up-regulated expression of DNER resulted in poor survival in patients with prostate cancer, and DNER was observed to play a key effect on progression and metastasis in prostate cancer as our previous study [17]. However, it presented evidence that DNER could inhibit glioblastoma growth in vitro and engraftment as tumor xenografts [11]. As for the reasons, it was firstly speculated that DNER as a specific gene inducing glioblastoma differentiation was increased by histone deacetylase (HDAC) inhibitors, which may be a protective regulation that cells adjusted to the toxicity of HDAC inhibitors. In addition, the capacity of Notch to operate as a tumor suppressor in particular tissues is in part a consequence of its cell-intrinsic role in promoting cell cycle exit and differentiation [21]. Therefore, the functional mechanisms of DNER were distinguished between glioblastoma and other cancers. For glioblastoma, the differentiating effects of DNER in brain cancer stem-like cells was regarded as a noncanonical Notch ligand [11], while DNER in adipogenesis of human adipose tissue-derived mesenchymal stem cells (hAMSC) was regulated independently from Notch signaling [22]. In particular, further elucidation of the physiological function of DNER in diverse cancers is an important area of investigation.

There are a range of different approaches for detecting DNER, including 1) DNER gene detection such as qPCR, 2) DNER mRNA detection such as RT-PCR, and 3) DNER protein detection such as IHC, that is currently the most popular detection method in clinical practice to detect the DNER protein. However, it had been reported that the results derived from IHC had several drawbacks, being both subjective and semi-quantitative. In this study, we investigated the expression of DNER protein in prostate cancer tissues using QD-IIQAS. Compared to traditional IHC, we observed that QD-IIQAS exhibited good consistency with conventional IHC, with better image quality and accuracy, which was inferred from the greater area under the ROC curve. This is consistent with previous reports studying other biomarkers using the QD-based nanotechnology [23, 24]. Fundamentally, the image gained by QD-IIQAS can be spectrally unmixed without the disturbance of background and quantitatively analyzed by computer software to eliminate the bias that may be caused by manual counting in conventional IHC. Given these advantages over conventional methods, QD-IIQAS has the potential for broad clinical application in the future. As the function and features of DNER become better characterized and the standardization of QD-IIQAS across laboratories occurs, this new detection method is likely to be further refined and has great potential for application in clinical practice.

As Notch ligand, the function of DNER might be associated with tumor stemness through typical Notch signal pathways. Our previous results revealed that PC-3 cells ceased to stemness through DNER knockdown [17]. Meanwhile, inhibition of DNER using siRNA caused down-regulation of CD44 which displayed stem cell characteristics and reduced the expression of HES1 and GLI1, which were crucial mediator genes in Notch and Sonic hedgehog signaling respectively (In press). Based on a number of basic studies, the CD44 was a widely used CSCs marker in human cancers [25, 26, 27], while HES1 and GLI1 were similarly involved in the self-renewal and tumourigenicity of stem-like cancer cells in various cancers [28, 29, 30, 31]. Moreover, this interaction could account for the potential mechanisms among CD44, Hedgehog and Notch signaling. As previous studies presented, HES1 and GLI1 could inhibit expression of CD44 respectively [32, 33], while HES1 also mediated expression of GLI1 transcription [34]. Moreover, ligands specifically bonded to Notch1 expressing cells to induce intracellular cleavage of Notch1 at the S3 site and activate downstream target genes by means of different transcription factors. However, in contrast to classic Notch ligands, DNER lacks the so-called DSL Notch binding motif and instead binds to Notch1 by the first and second EGF-like repeats in its extracellular domain. The lower transactivation activity of DNER compared to Delta might reflect lower Notch receptor affinity to DNER. Alternatively, DNER might preferentially bind to other Notch family members such as Notch2 or Notch3 also expressing in prostate cancer [35, 36]. By the contrary, the research suggested that DNER was not a Notch ligand and its true function remained unknown [37]. Therefore, the effects of DNER on cancer progression should be further explored in the future.

This study has several limitations. First, this was a retrospective study using a nonrandomized patient cohort. Second, some of the sample slides were too old for QD-IIQAS to be performed accurately and were thus excluded from statistical error analysis. Third, the QD-IIQAS technology has not been popular, especially in basic hospitals, so the popularity of QD-IIQAS still needs more time to realization. Nevertheless, QD-based nanotechnology provides a new insight into reliable biomarker detection.

6. Conclusion

This study successfully conducted in situ immunoflu-

orescent imaging and quantitative detection of DNER, observing DNER up-regulated in prostate cancer tissues compared to benign lesions, and its overexpression strongly correlating with bone metastasis. In addition, the DNER-QD was associated with a better prognostic value for RFS compared to DNER-IHC and DNER was an independent prognosticator for both OS and RFS in patients with prostate cancer. Further studies will be needed to understand the specific mechanisms of DNER in the formation and maintenance of the stem cells of prostate cancer.

Supplementary data

Table S1.

DNER and clinicopathologic characteristics of prostate benign patients

Characteristic	No. of cases $n=$ 17
Age, years
$⩽$ 55	4
$>$ 55	13
PSA (ng/ml)
$⩽$ 50	15
$>$ 50	2
DNER expression
Negative	16
Positive	1

PSA: Prostate-specific antigen.

Table S2.

The relationship between DNER and all prostate patients

Characteristic	DNER negative	DNER positive	P
Malignant	45	107	0.001
Benign	16	1

Table S3.

DNER and clinicopathologic characteristics of prostate cancer patients

	Sensitivity	Specificity	AUC
DNER-QD	100%	37.9%	0.817
DNER-IHC	89.3%	33.9%	0.61

Conflict of interest

The authors declare no conflict of interest.