Introduction

In Europe, as well as in the United States, 1 in 3 people is diagnosed with cancer during their lifetime [1]. Breast cancer is one of the most common cancer among women and affects one in eight women. Its rate prevalence has increased considerably by 0.3% per year. At present, the woman typical risk of developing breast cancer during her life is about 13% in the United States [2, 3]. In Iran similarly the prevalence of this cancer has increased, one out of every 10 to 15 women is at risk of developing breast cancer. Surprisingly, its prevalence is under 45 years old in Iran in comparison with Western countries which is under 56 years [4]. Since 2007, in cases younger than 50, mortality rate from breast cancer has been steady, but in older women have continued to reduce. The death decrease rate was by 1.3% per year from 2013 to 2017 (American cancer society). It is believed that these mortality reduction is due to diagnosis of breast cancer in earlier stages via screening, improved awareness and also well treatments [5,6,7]. This issue emphasizes the importance of breast cancer diagnosing in the early stages with high sensitivity and specificity methods. Different studies consider Breast cancer progress as a stepwise procedure which is associated with genetic and epigenetic changes [8] and also studies have shown the overlap between epigenetic and genetic alteration in sporadic breast tumors can provide important insights into cancer biology and may afford new strategies for breast cancer prevention and diagnosis [9]. A variation in the methylation rate is a common change in the process of cancers that occurs in the early stages of cancer spread and this epigenetic alteration causes transcription and phenotypic variation [10, 11]. Epigenetic modifications importance in tumorigenesis have described in several researches, most often it is obvious that promoter-associated CpG Island methylation alterations take place in different cancers. However, in most articles, only changes in gene methylation have been examined and not much studies assesse the DNA methylation modification effects on gene expression pattern.

According to the above explanation, by going through published data in literature to find previously reported genes which underwent methylation changes ISL1, MGMT, DMNT3b genes, with conflicting data, were selected to investigate the promoter-associated CpG Island methylation pattern of ISL1, MGMT, DMNT3b genes and the association of DNA methylation variation effects on gene expression variation [12,13,14,15,16].

The ISL1 gene encodes a member of the homeodomain LIM family of transcription factors. The protein from this gene binds to the insulin-promoting region and actually plays an important role in regulating insulin expression. This gene is a tumor suppressor gene which is in fact a natural controller over stem cells [15], Shin et al. have reported that ISL1 gene has different expression pattern based on the subtypes of tumors, they explained ISL1 expression would be elevated in TNBC(Triple negative breast cancer) and its expression was lower in other molecular types [17]. The MGMT or O6 methyl guanine DNA methyl transferase gene (also O6-alkylguanine DNA alkyl transferase) which has vital role for genome stability is a DNA repair protein and has a protective function against the toxicity and carcinogenesis of agents [18]. This protein transports methyl from O6 alkaline guanine and other methylated parts of DNA to its molecule, which eliminates toxicity and inhibit potential mistake and mismatches during DNA replication and transcription. Therefore, it seems lack of this protein increases the risk of carcinogenesis [19], Nairui et al. reported MGMT promoter hyper methylation and also declared this gene as early stage biomarker [20], Chen et al. have showed that MGMT promoter hyper methylation can elevate risk of breast and gynecologic cancer [21]. DMNT3b gene is also one of the three target genes, The DMNT3b gene or DNA cytosine-5-methyl transferase 3 beta, is methyl transferase and is supposed to be involved in de novo methylation rather than in keeping methylation, Devon et al. have assessed breast cancer cell lines with hyper methylated profile and they find that DNMT3b promoter were significantly un-methylated and this lower methylation pattern lead to hyper activation of this gene [22].

Material and methods

Patients and samples

This research was approved by the National Institute of Genetic Engineering and Biotechnology (NIGEB). Written Consent form were taken from all 40 breast cancer patients admitted to Khatam Hospital (a referral governmental hospital) in Tehran those who whom underwent surgery. Tumor and adjacent normal tissues were obtained during surgery. The tissue specimen were stored at − 70 °C for RNA extraction. All patient’s pathologic information was gained from Pathology Department. Breast issues staging was carried out as stated by the International Union against Cancer (UICC) which is based on (AJCC-TNM) classification [23, 24].

DNA extraction and bisulfite modification

DNeasy Blood and Tissue Kit (Qiagen, Germany)were used for Genomic DNA isolation from tumor and adjacent normal tissues according to the manufacturer’s guidelines and subsequently Bisulfite Treatment was performed by EpiTect Bisulfite kit (Qiagen, Germany) that previously described [25]. In all cases, bisulfite conversion of DNA was confirmed by MS-PCR (primer sequences provided in Table 1) involved two separate PCR reactions using methylated/unmethylated-specific amplifiers flanking the CpG-rich ISL1, MGMT and DMNT3b promoter regions.

MS-PCR primer design

EPD (Eukaryotic Promoter database) and Promoter 2.0 Prediction Server online software were used to determine where the genes promoter are located and the CpG island for primer design was targeted by MehtPrimer 2.0 online software. All primers specificity was examined by Primer Design and search tools (bisearch.enzim.hu) online software. Primers were made by Metabion Co, Germany, and function of primers were investigated by control methylated DNA (Qiagen Co,). Information of primers are presented in Table 1

Table 1 Primers sequences which is used for MS-PCR
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Methylated specific PCR reaction and Sanger sequencing

After bisulfite conversion of all DNA samples, by using PCR thermal cycler (Bio-rad, T100™ Thermal Cycler) PCR reaction was carried out in all samples. The reaction was done in 25 μL of solution, containing 1 μM of each primer, 2 μL of DNA (as template), 12.5 μL of Taq DNA Polymerase Mix Red-Mgcl2/2 mM (Ampliqon, Denmark), and 8.5 μL water in 0.2 vials. The thermal cycle was set as follows: 1 cycle at 95 °C for 10 min as an initial denaturation step, 45 cycles at 95 °C for 30 s, 66 °C for 35 s, 72 °C for 30sand 1 cycle at70 °C for 10 min as final elongation Each sample was amplified using methylated and un-methylated primers. After performing all reactions, in order to the results confirmation, several methylated samples were subjected to Sanger sequencing of all 3 genes.

RNA purification and cDNA synthesis

TriPure Isolation Reagent and RevertAid First Strand cDNA Synthesis Kit were used for RNA purification (Roche applied sciences) and cDNA Synthesis (Thermo Fisher Scientific, Germany), respectively.

Real-time RT-PCR

Corbett Rotor-Gene 6000 real-time PCR thermal cycler and Real-Time RT PCR using SYBR-Green master (Roche Applied Sciences) were utilized to determined mRNA level expression. The reaction was done in 10 μL of solution, containing 0.5 μM of each primers, 1 μL of cDNA (as template), 5 μL of SYBR-Green Master, 3 μL water in 0.1 vials. The thermal cycle was set as follows: 95 °C for 5 min for initial denaturation step, an amplification program (95 °C for 20, 60 °C for 15 and 72 °C for 20 s respectively) repeated for 40 cycles. Primers were designed by oligo7 software. The specificity of primers were theoretically controlled by BLAST database. The primers were made by Metabion Co, Germany. Information of primers are showed in Table 2, the relative expression levels were normalized to the level of B actin as a housekeeping gene.

Table 2 Primers sequences which is used in real time RT-PCR
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Statistical data analysis

The Real time RT-PCR raw data for each gene was evaluated by Linreg software. Subsequently, value and statistical significance of expression ratio results (tumor group difference to adjacent tissue group) analyzed by REST 2009 software as well as SPSS software V22.0 (SPSS, Inc., Chicago, IL). The normality assumption was checked by Kolmogorov–Smirnov test. The variances of the groups were determined by the analysis of variance (ANOVA) and independent sample T tests. Difference between pattern of methylation in normal and adjacent tumor tissues was evaluated by chi square. Comparison of the fold changes of the different methylation status groups was done by using One way Anova, also using LSD algorithm, and different methylation groups fold change one by one were compared.

Results

Patients’ clinical and pathological data

In total, 40 patients with breast cancer were involved in this study. Tumor grade I, grade II, and grade III were identified by pathology check in 15%, 40% and 35% of the cases, respectively. Eighteen percent, 62% and 20% were at the stage I, stage II, and stage III, respectively. Size of tumor in 55% of patient was smaller than 5 cm and in 18% was bigger than 5 cm. Forty-five percent, and 60% of patients expressed ER and PR, respectively. Additionally, in 55% of patients HER2 were positive. You can find patient clinical and pathological data in supplementary data file.

ISL1, MGMT, DMNT3b genes promoter methylation status in breast cancer patients

The methylation status of ISL1, MGMT and DMNT3b genes was determined by methylated specific PCR in tumor and adjacent normal tissues of breast cancer and confirmed by Sanger sequencing of bisulfite converted DNA. Methylation frequency of promoter-associated CpG islands of both ISL1, MGMT genes were greater in tumor tissues of breast cancer compared to adjacent normal tissues. Conversely, DMNT3b methylation frequency in adjacent normal tissues was greater than tumor tissues. Also, sensitivities and specificities for each genes were investigated (Table 3). Also, mean levels of methylation of ISL1, MGMT and DMNT3b genes were examined in tumor and normal tissues for investigation of significant level methylation. The mean level of ISL1, MGMT in tumor tissues were significantly greater in comparison to normal tissues (P ≤ 0.05). However, in tumor tissues, the mean level of methylation of DMNT3b gene was lower than normal tissues but that was not significant P (H) = (0.131) (Table 3).

Table 3 Patient characteristics
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Assessment of the predictive value of 2 or 3 genes promoter methylation variation combination

Regarding our description on previous section, none of the genes has reasonable predictive value or sensitivity and specificity. Thus, their promoter methylation variation were combined two by two and all three genes as one. As mentioned in Table 4, just MGMT and ISL1 combination has convincing sensitivity and negative predictive value, 91% and 90%, respectively.

Table 4 Methylation status in tumor and adjacent normal tissues in breast cancer patients
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Expression pattern of ISL1, MGMT and DMNT3b genes in breast cancer patients

Relative expression analysis of ISL1 showed a significant reduction in expression level of ISL1 in tumor tissues by 0.273 fold change P (H) = (0.0012). (Fig. 1A).

Fig. 1
figure 1

Mean methylation levels of ISL1, MGMT, DMNT3b genes in tumor versus normal tissues

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Investigation of association between clinico-pathological features and ISL1 expression level demonstrated a significant association between ISL1 expression and histological grade (P ≤ 0.007). Furthermore, there was a significant association between down expression of ISL1 and ER status (P ≤ 0.045). In addition, there was a significant relationship between decreased expression level of ISL1 and Her2 status (P ≤ 0.043). No association between ISL1 down expression age, PR status, Ki67 status, tumor stage, lymph node involvement and tumor size were detected.

Relative expression analysis of MGMT displayed a noticeable change between tumor groups in comparison to control group in a way that MGMT was decreased in tumor groups in contrast to normal tissues by 0.308 fold change P (H) = (0.003). (Fig. 1B).

In addition, a significant relationship between reduced expression level of MGMT and tumor size were confirmed by analysis of correlation between clinico-pathological features and MGMT down expression (P ≤ 0.05). No correlation was found between reduced expression levels of MGMT and age, ER status, PR status, Ki67 status, Her2 status, tumor stage, lymph node involvement and histological grade.

Relative expression analysis of DMNT3b demonestrated that DMNT3b expression increased significantly in tumor tissues in contrast to adjacent normal tissues by 2.001 fold change P (H) = (0.0098). (Fig. 1C).

Although increased expression was investigated in DMNT3b gene in tumor tissues, no reasonable relation was observed between DMNT3b expression level and patient’s age, ER status, PR status, Ki67 status, Her2 status, tumor stage, lymph node involvement and histological grade.

Promoter methylation and gene expression correlation

The promoter methylation status and expression of all 3 genes showed significant change in tumor tissues and normal adjacent tumor tissues. Thus, correlation between methylation status of genes promoter and their changes in expression level in all samples were analyzed. In this step all samples were separated by their methylation status in 3 groups; methylated, un-methylated and both (being mix of methylated and un-methylated). Analysis have shown significant differences in all 3 genes and two types of samples,

Also, LSD algorithm was applied for multiple comparison of each two groups for precision elucidation. As shown in Table 5, all paired groups had significant differences except ISL1 methylated and un-methylated versus both in normal adjacent tumor tissue, DMNT3b methylated versus both in tumor tissue and MGMT methylated versus both in all two types of tissues and un-methylated versus both in normal adjacent tumor tissue, This results may occurred due to the fact that one allele of these genes has normal promoter methylation status (Fig. 2).

Table 5 Predictive value of 2 or 3 genes promoter methylation variation combination
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Fig. 2
figure 2

Expression pattern of ISL1 in breast cancer tumors using the 2-∆∆Ct method P (H) = (0.0012)

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Promoter methylation correlation to ΔCt

Correlation between promoter methylation and gene expression were studied to see if we could predict methylation statue using the ΔCt (refer to Supplementary data file). Regarding ISL1 gene, if the ΔCt was between -2.67 < X < 3, the sample could be predicted as un-methylated and if the ΔCt was X > 3 the sample would be methylated. The discrimination between the samples with both methylated and un-methylated (Hetero) status was not possible (Fig. 3).

Fig. 3
figure 3

Expression pattern of MGMT in in breast cancer tumors using the 2-∆∆Ct method P (H) = (0.003)

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Regarding DMNT3b gene if the ΔCt was between -1.04 < X < 3 it could be concluded that the sample was un-methylated and if the ΔCt was X > 3 it could be concluded that the sample was methylated. Once again, the discrimination between the samples with both methylated and un-methylated (Hetero) status was not possible (Table 6).

Table 6 Comparison of promoter methylation status and fold change using one way annova
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Regarding MGMT gene the sample with − 1.32 < X < 3.2 is un-methylated and with ΔCt is X > 3.2 is methylated. Likewise, the discrimination between the samples with both methylated and un-methylated (Hetero) status is not possible.

Discussion

Breast cancer prevalence has increased considerably by 0.3% per year, and most important cause of cancer-related death among women is breast cancer [26],.At present, the woman typical risk of developing breast cancer during her life is about 13% in the United States [2, 27, 28]. In Iran similarly the prevalence of this cancer has increased, one out of every 10 to 15 women is at risk of developing breast cancer (Table 7). Surprisingly, its prevalence is under 45 years old in Iran in comparison with Western countries which is under 56 years [4, 29, 30]. Fortunately, although the incidence of this cancer has increased in recent years, its mortality rate has dropped significantly [31]. It is supposed that extensive screening, for this cancer, which leads to early detection of and also improved treatment methods caused reduction in mortality rate [32].

Table 7 Multiple comparisons of fold changes variation between different methylation status groups using one way annova by LSD algorithm
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Cancer initiation and its progression is influenced by both genetic and epigenetic events [33]. Illustrating the overlap between epigenetic and genetic alteration in sporadic breast tumors can provide important insights into cancer biology and may afford new strategies for cancer prevention and diagnosis [5, 9]. Screening and diagnostic tests based on DNA methylation are one of the new methods that have been considered in recent decades [33].Furthermore, DNA methylation is one of the crucial factor for controlling gene expression and maintaining of genomic structure [34]. It has been long time considered that DNA methylation is a significant regulator of gene expression [11, 34, 35]. By going through published data in literature to find previously reported genes which underwent methylation changes, ISL1, MGMT and DMNT3b genes were candidate to better understand the role of DNA methylation variation effects on gene expression variation [15, 19, 22].

The expression of ISL1 gene is normally inhibited in breast tissue during pregnancy, and abnormally, this gene is expressed during breast cancer [15]. For the first time, Kim et al. showed that hyper methylation of ISL1 could be an independent predictor of cancer development and recurrence in bladder cancer [36]. In other research Kitchen et al. reported an increased methylated promoter-associated island of ISL1 genes in progressive high-grade bladder tumour tissues [15].Furthermore, Convey et al. revealed hyper methylation of ISL1 genes in 517 breast tumors by using microarray analysis [37].

MGMT gene is directly involved in the DNA repair system, thus, its unusual function can lead to cancer (Table 8). In several studies it has been shown that methylation of this gene increases in breast cancer and its hyper methylation is directly related to tumor survival [38], Chen et al. have declared that down regulation of MGMT along with P16 promotes could have the anti-proliferative and pro-apoptotic effects of 5-Aza-dC and radiation on cervical cancer cells [39].

Table 8 Promoter methylation correlation to ΔCt
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The protein of DMNT3b gene is usually located in the nucleus and is well controlled during development. Devon Roll et al. demonstrated the association of overexpression DMNT3b on hyper methylation phenotype in cell lines breast cancer patients [22]. In 2013, Naghitorabi et al. measured the methylation level of DMNT3B gene in breast cancer patients and reported a hypo methylation of this gene in breast cancer patients compared to controls [40].

Up to date, methylation statues of ISL1, MGMT and DMNT3b has been studied in by numbers of researchers, but expression level of these genes have not been studied. In this regard in this paper, both methylation status and the expression level of ISL1, MGMT and DMNT3b were investigated (Fig. 4).

Fig. 4
figure 4

Expression pattern of ISL1 in in breast cancer tumors using the 2-∆∆Ct method P (H) = (0.0098)

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We found that the rate of methylation of ISL1 and MGMT genes reduced in tumor samples compared to adjacent normal samples, and relative expression analysis displayed a significant reduction in expression level of ISL1 and MGMT in breast cancer compared to adjacent normal tissues P (H) = (0.0012) and P (H) = (0.003), respectively. Furthermore, there was a significant association between down expression of ISL1 and histological grade, Her2 status and ER status (P ≤ 0.007), P ≤ 0.045), (P ≤ 0.043) respectively. Moreover, MGMT down expression was significantly associated with tumor sizes (P ≤ 0.05).

Unlike ISL1, MGMT gene it was identified that DMNT3b expression increased significantly in tumor tissues in contrast to adjacent normal tissues P (H) = (0.0098) but remarkable relation was not observed between DMNT3b expression level and clinic-pathological features [41]. Previous studies have reported that ISL1, MGMT were hyper methylated in tumor tissues whereas DMNT3b gene was hypo methylated. The hypothesis of this study was that if a gene methylation statues varies during cancer initiation and progression this change must also be seen in the expression level of genes. The gained results of this study were exactly in line with the obtained results from articles which were based on the methylation research. It was revealed that the results of the real-time and methylation methods are consistent with each other.

Conclusion

In this research, we approached to results that support previous data which were based on the relationship between methylation status of ISL1, MGMT and DMNT3b genes and tumor behavior and characteristics.

Our finding also reported DNA methylation variation effects on gene expression variation. Finally, due to the noticeable change in promoter methylation and gene expression of ISL1, MGMT and DMNT3b genes more investigating is recommend, Since methylation of these genes could be detected in normal adjacent tumor, the potential methylation statue of these genes as diagnosis biomarkers is now questionable, however, by further investigation these genes maybe be introduced as prognosis /and prediction biomarkers.