MicroRNAs as Promising Novel Biomarkers for Diagnosis of Breast Cancer

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1 Med. J. Cairo Univ., Vol. 84, No. 2, September: , MicroRNAs as Promising Novel Biomarkers for Diagnosis of Breast Cancer OLFAT G. SHAKER, M.D.*; YASSER H. NASSAR, M.D.*; AMR A. ZAHRA, M.D.**; REHAB M. ABD EL-FADEL, M.Sc.** and MOHAMED S. EL-MARZOKI, M.D.*** The Department of Medical Biochemistry & Molecular Biology, Faculty of Medicine, Cairo* & Fayoum** Universities and The Department of General Surgery, Faculty of Medicine, Cairo University*** 263 MicroRNAs (mirnas),was originally discovered by Victor Ambros, as single-stranded RNA molecules; nucleotides long [3]. Nearly 2,000 mirna are present in the human genome [4], mir- NA genes may represent 1-2% of the human genome and control the activity of nearly 50% of all protein coding gene [5]. MiRNA's unique function is precise regulation of the timing of a cellular event via synchronous posttranscriptional inhibition of a specific group of genes that are functionally interdependent [6]. Croce et al., [7] have shown that there is a connection between mirna function and several diseases, including cancer. MiRNAs can do this by either modulate oncogenic or tumor suppressor pathways or their expression can be regulated by oncogenes or tumor suppressor genes. One of the first oncomirs identified was mir-21. Overexpression of mir-21 is observed in breast carcinoma, mir-21 mediates cell survival and proliferation, stimulates invasation, extravasation and metastasis [8]. Sun et al., [9] suggested that serum mir-155 level was a potential biomarker to discriminate breast cancer patients from healthy subjects and for the first time, they observed a decrease in level of mir-155 after surgery and chemotherapy, which raises the possibility to use it as an indicator for treatment response. Subjects and Methods The present study included (75) Egyptian women with age ranged from (37-70 years). They were selected from The General Surgery Department at Faculty of Medicine, Cairo University. Collected in a period from November 2012-to May 2014.

2 264 MicroRNAs as Promising Novel Biomarkers for Diagnosis of Breast Cancer The studied subjects were divided into two groups: Group I: (n=25) healthy females served as a control who were proven to be healthy with no family history of breast cancer, were recruited during routine checkup. Group II: (n=50) patients with breast carcinoma. They were diagnosed according to history taking, clinical examination and confirmed by mammography and surgical biopsies. Written consent forms were signed by all participants in this study including controls and approval by the Ethical Committee of Kasr Al-Ainy, Cairo University was taken. All cases were subjected to history taking clinical examination, imaging, and biopsy. All cases were subjected to quantitation of mirna-21 and mirna-155 in serum. Inclusion criteria: Adult females, age ranged from (37-70) years. No previous treatment with chemotherapy or radiotherapy. Exclusion criteria: Age below 37 and above 70 years. Previous treatment with chemotherapy or radiotherapy. Other malignancy. Sample collection: Fasting blood samples were collected. Sera were used for quantitation of mirna-21 and mir- NA-155. Serum mirnas assay: RNA extraction: Total RNA with preserved mirnas was extracted from 200µL serum by mirneasy extraction kit (Qiagen, Valencia, CA, USA) using 1000µL QIAzol lysis reagent and incubated for 5min at RT. Then, 100µL chloroform were added, vortexed for 15sec, and incubated for 2-3min at RT. This was followed by centrifugation at 14000rpm at 4ºC for 15min. The upper watery phase was removed and 0.5 times of its volume (100%) ethanol was added. Seven hundred µl of this mixture were placed in RNeasy Mini spin column in 2ml collection tube and centrifuged at 10000rpm at RT for 15sec. After the mixture had completely passed the column, 700µL of buffer RWT were added to each column, and again centrifuged at 10000rpm at RT for 15sec.

3 Olfat G. Shaker, et al. 265 Statistical analysis: - Data was coded and entered using the statistical package SPSS version Data was summarized using mean, standard deviation and range for quantitative variables, and frequencies (number of cases) and relative frequencies (percentages) for categorical variables. - Comparison of quantitative variables was done using the nonparametric Kruskal-Wallis when comparing more than 2 groups and using the nonparametric Mann-Whitney U test when comparing 2 groups. - For comparing categorical data, Chi square (x 2 ) test was performed. Exact test was used instead when the expected frequency is less than 5. - Correlation was done to test for linear relations between quantitative variables by Spearman correlation coefficient. - Receiver Operator Characteristic (ROC) curves were derived and Area-Under-the Curve (AUC) analysis was performed to get the best cutoff of mirna-21 and mirna-155 in serum. <0.05 was taken as statistically significant. ER/PR (estrogen/progesterone receptors): Tumor type: Results Table (1): Description data of the breast cancer group. Family history: T (tumor size): N (nodal affection): M (metastasis): Negative Positive Invasive ductal Invasive lobular Number of patient Table (2): Comparison between breast cancer patients and controls as regards gene expression of mirna-21 and mirna 155 in serum. MiRNA-21 MiRNA-155 Mean Control No Yes T2 T3 N1 N2 N3 M1 M zero SD Table (2) shows a significant difference between patients and controls as regards gene expression of mirna-21 (p=<0.001) and mirna-155 (p= 0.016) in serum. The gene expression of mirna- 21 and mirna-155 in serum were significantly higher in breast cancer group than the control group. Table (3): Relationship between gene expression of mirna- 21 in serum and pathological data of tumor in patient group. T: T2 T3 N: N1 N2+N3 M: M1 ZERO ER/PR: T: N: Negative Positive Tumor type: Invasive ductal Invasive lobular T2 T3 N1 N2+N3 Mean Standard deviation M: M ZERO ER/PR: Negative Positive Tumor type: Invasive ductal Invasive lobular Mean Standard deviation Table (3) shows no significant difference between gene expression of mirna-21 in serum and pathological data of tumor in patient group Table (4): Relationship between gene expression of mirna- 155 in serum and pathological data of tumor in 35 patient group Breast cancer group p - Mean SD <0.001* 0.016* Table (4) no significant difference between gene expression of mirna-155 in serum and pathological data of tumor in patient group.

4 266 MicroRNAs as Promising Novel Biomarkers for Diagnosis of Breast Cancer Sensitivity ROC Curve Specificity Fig. (1): Shows the ROC curve of mirna-21 to detect breast cancer cases. As regard the ROC curve, Area Under the Curve (AUC) =.984, the best cutoff for detection of cancer for mirna-21=1.215 with sensitivity 92% and specificity 100%. Values above this cutoff is more probably cancer. Area under curve Lower Bound Asymptotic 95% confidence interval Upper Bound Sensitivity ROC Curve mirna mirna-21 Fig. (3): Positive correlation between mirna-21 and mirna There is significant positive correlation between mirna-21 and mirna-155 (r=.249, p=.031*) in patient and control. Discussion Breast cancer continues to be the most common cancer in women and represents a major issue of public health [10] with 1.38 million new cases and deaths yearly worldwide [11]. Hirko et al., [12] evaluate trends in breast cancer incidence in Egypt from 1999 to 2008 to make projections for breast cancer occurrence for the years They expected a significant increase in the breast cancer cases from 2009 to 2015 among women aged years and among women aged years. This direct the government to provide more facilities to care by this problem and research institute to make breast cancer issue as a priority in research. For this reason, our aim is searching for non-invasive markers to detect breast cancer. 0.2 Area under curve Specificity Diagonal segments are produced by ties Fig. (2): Shows the ROC curve of mirna-155 to detect breast cancer cases. As regard the ROC curve, the best cutoff for detection of cancer for mirna-155=1.365 with sensitivity 60% and specificity 100%. Values above this cutoff is more probably cancer. Lower Bound Asymptotic 95% confidence interval Upper Bound mirnas regulate genes by binding to site (s) within the 3 Untranslated Region (UTR) of multiple target messenger RNAs (mrnas), which results in either translational repression or degradation of mrnas [13]. Our study group included 50 females (37-70 year old) with breast cancer, and 25 age matched healthy females as a control group. The cancer group showed 28 with previous family history of breast cancer, (35) with T2 and (15) T3, (4) with N1, (31) with N2 and (15) N3, (20) with M1 and (40) M zero, (42) ER/PR Negative and (45) are invasive ductal and only (5) are invasive lobular. MiR-21 is implicated in different processes associated with malignant transformation, such as

5 Olfat G. Shaker, et al. 267 cell proliferation, apoptosis, invasion and metastasis [14]. In the current study we estimated the gene expression of micro RNA-21 in serum and found a significant difference between patient (mean =2.16) and control (mean =1.04), ( p= <0.001*). This is consistent with results of [15] Godfrey et al., [16] Mar-Aguilar et al., [17]. No significant difference between the gene expression of mirna-21 in serum and pathological data of tumor in our patient group was found. These results are consistent with that of Mar- Aguilar et al., [17] who found no significant difference between all stages of breast cancer and serum mirna- 21, and Godfrey et al., [16]. They concluded that there was no evidence of significant differences in serum mirna levels 21 and pathological characters of tumor. However, Si et al., [18] results are not coincided with our study; they showed that serum mirna- 21 level correlates with tumor size and lymph node metastases. At the same time, Huang et al., [19] showed that uregulated mir-21 expression was associated with lymph node positivity (p=0.01), higher proliferation index (ki67>10%) (p=0.03) and advanced breast cancer TNM clinical stage (p=0.021). So larger patient group and serum and tissue detection of micro RNA-21 level are needed to reach more accurate results. ROC curve analysis showed that Area Under the Curve (AUC)=0.984 and the best cutoff of mirna-21 for detection of cancer is with sensitivity 92% and specificity 100% and s above this cutoff is more probably cancer. These results are consistent with Si et al., [18] (AUC)= While, Mar- Aguilar et al., [17] showed the best cutoff for detection of cancer of mirna-21=6.48 with sensitivity 83.30% and specificity 100%. Micro RNA-155 found to be significantly up regulated in patient (mean =3.54) compared to control (mean =1. 10), (p=0.016*). This is consistent with Liu et al., [20] results. They profiled mirna expression in the serum of 20 BC patients and 10 healthy controls. They concluded that mir- 155 expression was uregulated in patient in comparison to control (p<0.05). Also, our results are consistent with Mar-Aguilar et al., [17] who examined 61 BC patients and 10healthy control and found significant up regulation of micro RNA- 155 level in serum of breast cancer patient ( p=.00012). In contrast, Heneghan et al., [21] did not find significant difference between cancer patients and controls regarding the level of mir-155. This difference may be due to difference in samples used for detection of mir-155, they used whole blood but we used serum samples. No significant difference was found in our study between serum mirna-155 level and tumor size, node state, metastatic state, Estrogen and Progesterone Receptor (ER/PR) state and tumor type in patient. These results are consistent with findings of Sun et al., [9] who determined the expression of mir-155 in 158 serum samples (103 from patients with breast cancer and 55 from normal control). They found that mir-155 were significantly up regulated in sera of cases compared with those of healthy controls (p<0.001). Their study failed to correlate the expression levels of mir-155 with other clinical parameters including tumor size ( p =0.0665, Kruskal-Wallis test), nodal status ( p= , Mann Whitney test), HER2 overexpression (p=0.1238, Mann Whitney test) and molecular subtype (p=0.3335, Kruskal-Wallis test). In contrast, Chen et al., [22] showed significant up regulation of mir-155 in breast cancer tissue and its correlation with stages of the disease. Zhu et al., [23] firstly reported that the expression of serum mir-155 was comparable between cancer patients and healthy women; and tumors with positive Progesterone Receptor (PR) status had higher serum mir-155 expression than those with negative progesterone receptor. Wang et al., [24] reported that increased level of serum mir-155 in subjects with invasive ductal carcinoma compared with health control; and observed that higher level of mir-155 had a negative correlation with PR status. In the present study, ROC curve analysis showed that the best cutoff for detection of cancer for mirna-155 is=1.365 with sensitivity 60% and specificity 100%. Values above this cutoff is more probably cancer. These results are consistent with the Sun et al., [9] results. Their (ROC) analysis revealed that mir- 155 had considerable diagnostic accuracy, at the cut-off =1.911 with sensitivity 65.0% and specificity 81.8%. While Mar-Aguilar et al., [17] ROC curve analysis showed the best cutoff for detection of cancer is mirna-155=7.92 with sensitivity 94.40% and specificity 100%. Conclusions: - A significant difference between patient and control as regard serum level of mirna-21 and

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