The association between histone 3 lysine 27 trimethylation (H3K27me3) and prostate cancer: relationship with clinicopathological parameters

Abstract

Background: It is well established that genetic and epigenetic alterations are common events in prostate cancer, which may lead to aberrant expression of critical genes. The importance of epigenetic mechanisms in prostate cancer carcinogenesis is increasingly evident. In this study, the focus will be on histone modifications and the primary objectives are to map H3K27me3 marks and quantify RAR beta 2, ER alpha, SRC3, RGMA, PGR, and EZH2 gene expressions in prostate cancer tissues compared to normal tissues. In addition, a data analysis was made in connection with the clinicopathological parameters.

Methods: 71 normal specimens and 66 cancer prostate tissues were randomly selected in order to assess the proportion of the repressive H3K27me3 mark and gene expression. H3K27me3 level was evaluated by ChIP-qPCR and mRNA expression using RT-qPCR between prostate cancer and normal tissues. Subsequently, western-blotting was performed for protein detection. The analysis of variance (ANOVA) was performed, and Tukey's test was used to correct for multiple comparisons (p-value threshold of 0.05). The principal component analysis (PCA) and discriminant factorial analysis (DFA) were used to explore the association between H3K27me3 level and clinicopathological parameters.

Results: The study demonstrated that H3K27me3 level was significantly enriched at the RAR beta 2, ER alpha, PGR, and RGMA promoter regions in prostate cancer tissues compared to normal tissues. After stratification by clinicopathological parameters, the H3K27me3 level was positively correlated with Gleason score, PSA levels and clinical stages for RAR beta 2, ER alpha, PGR, and RGMA. High H3K27me3 mark was significantly associated with decreased RAR beta 2, ER alpha, PGR and RGMA gene expressions in prostate cancer sample compared to the normal one. Moreover, the results showed that mRNA level of EZH2, AR and SRC3 are upregulated in prostate cancer compared to normal prostate tissues and this correlates positively with Gleason score, PSA levels and clinical stages. Obviously, these observations were confirmed by protein level using western-blot.

Conclusions: This data clearly demonstrated that H3K27me3 level correlated with aggressive tumor features. Also this study revealed that reverse correlation of RAR beta 2, ER alpha, PGR, and RGMA expressions with EZH2, SRC3, and AR expressions in prostate cancer tissues suggests that these genes are the target of EZH2. Therefore, all therapeutic strategies leading to histone demethylation with epigenetic drugs such as histone methyltransferase inhibitor may be relevant treatments against prostate cancer.

Figure 1

Assessment of H3K27me3 marks in normal and tumoral tissues using ChIP-qPCR. ChIP analysis indicates the change of H3K27me3 marks at six gene loci in prostate cancer tissues. H3K27me3 level on EZH2 was found to be lower in prostate cancer tissues (n = 32) versus normal tissues (n = 33). Contrariwise, the H3K27me3 level of RAR beta 2, PGR, ER alpha and RGMA was significantly higher in tumoral tissues that in normal tissues. The H3K27me3 level on SRC3 in cancer tissue did not reach statistical significance compared in normal tissue. The data is expressed as % of input. Analysis of variance, followed by a Tukey multiple comparison test, was used for statistical analysis. The statistical significant between groups was indicated by letters “a”, “b” and “c”. (N = normal; GS = Gleason score).

Figure 2

Principal component analysis (PCA) carried out to explore the relationships between H3K27 levels and clinicopathological parameters. H3K27me3 level was evaluated by PCA analysis. H3K27me3 levels on ER alpha, RAR beta 2, RGMA and PGR genes correlated positively with PSA level (PSA), clinical stage (patho) and Gleason score (Gleason).

Figure 3

Factorial Discriminant Analysis (FAD) carried out to describe the variables that discriminate the three groups of patients. RAR beta 2, ER alpha, RGMA and PGR variables conflict to EZH2. Variable SRC3 being away from the circle, it wears a low information (left panel). The graphical representation of patients was used to identify homogeneous groups within the population from the point of view of variable studied (H3K27me3). Red plots represent normal patients (N), green plots represent patients with Gleason score ≤ 7 (<=7) and blue plots represent patients with Gleason score >7 (>7) (right panel).

Figure 4

EZH2 , AR and SRC3 is upregulated in prostate cancer and inversely correlated with RAR beta 2 , ER alpha , PGR and RGMA . mRNA expression of seven genes were measured using RT-qPCR in normal tissues (n = 38) and cancerous tissues (n = 34). 18S RNA was used as an internal control in PCR reactions. All genes that have high H3K27me3 levels in their promoter are consistent with the low mRNA expression levels. In contrast, genes that have low H3K27me3 levels are consistent with high mRNA expression levels. Analysis of variance, followed by a Tukey multiple comparison test, was used for statistical analysis. The statistical significant between groups was indicated by letters “a”, “b” and “c”. (N = normal; GS = Gleason score).

Figure 5

Principal component analysis (PCA) carried out to explore the difference between genes and relationships with clinicopathological parameters. The analysis of gene expression was made by PCA. On the dimension 1 (Dim 1), a clear discrimination can be noted between overexpressed genes (AR, SRC3 and EZH2) and under expressed genes (RAR beta 2, ER alpha, PGR and RGMA). The overexpression of AR, SRC3 and EZH2 gene correlated with PSA level (PSA), clinical stage (patho) and Gleason score (Gleason).

Figure 6

EZH2 Expression is inversely correlated to RAR beta 2, ER alpha, PGR and RGMA expressions. Protein expressions were analyzed using antibodies against SRC3 (155 KDa), AR (110 KDa), EZH2 (98 KDa), PGR (80 KDa), ER alphax (66KDa), RAR beta 2 (55 KDa) and RGMA (49 KDa). Anti-actin antibody (44 KDa) was used as the internal loading control. Representative data of 3 independent experiments is shown (left panel). Quantification of the western blot data is shown (right panel). The data is normalized to GAPDH (value = means ± SD, all results are statistically significant, *p < 0.05, **p < 0.001). The data is expressed as relative protein level. (N = Normal; T = Tumor).

Figure 7

Effects of DZNep and SAHA on cell viability. DU145, PC3 and LNCaP cells were treated with DZNep (2–20 μM) and SAHA (0.5-4 μM) for 24 h, 48 h and 72 h. The percentage of viable cells was determined as the ratio between treated cells and control cells. The results were expressed as the mean of triplicate independent experiments. IC50 was calculated following formula: EXP(LN(conc > 50%)-((signal > 50%-50)/(signal > 50%-signal < 50%)XLN(conc > 50%/conc < 50%))).

Figure 8

DZNep and SAHA effects on prostate cancer cell lines. Total RNAs were isolated from PC3, DU145 and LNCaP cells treated with 2 μM SAHA and 10 μM DZNep for 72 h. Results were analyzed by RT-qPCR. Relative changes in gene expressions compared to the control (the value of control was designed as 1) were calculated using comparative ΔΔCt method. The 18S RNA was used for normalization. Value = means ± SD from at least three measures, *p < 0.05.